Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
  • A61K47/00—Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additives; Targeting or modifying agents chemically bound to the active ingredient
  • A61K47/06—Organic compounds, e.g. natural or synthetic hydrocarbons, polyolefins, mineral oil, petrolatum or ozokerite
  • A61K47/26—Carbohydrates, e.g. sugar alcohols, amino sugars, nucleic acids, mono-, di- or oligo-saccharides; Derivatives thereof, e.g. polysorbates, sorbitan fatty acid esters or glycyrrhizin
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
  • A61K9/00—Medicinal preparations characterised by special physical form
  • A61K9/08—Solutions
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
  • B01D—SEPARATION
  • B01D15/00—Separating processes involving the treatment of liquids with solid sorbents; Apparatus therefor
  • B01D15/08—Selective adsorption, e.g. chromatography
• • C—CHEMISTRY; METALLURGY
  • C07—ORGANIC CHEMISTRY
  • C07K—PEPTIDES
  • C07K1/00—General methods for the preparation of peptides, i.e. processes for the organic chemical preparation of peptides or proteins of any length
  • C07K1/14—Extraction; Separation; Purification
  • C07K1/16—Extraction; Separation; Purification by chromatography
• • C—CHEMISTRY; METALLURGY
  • C07—ORGANIC CHEMISTRY
  • C07K—PEPTIDES
  • C07K1/00—General methods for the preparation of peptides, i.e. processes for the organic chemical preparation of peptides or proteins of any length
  • C07K1/14—Extraction; Separation; Purification
  • C07K1/16—Extraction; Separation; Purification by chromatography
  • C07K1/20—Partition-, reverse-phase or hydrophobic interaction chromatography
• • C—CHEMISTRY; METALLURGY
  • C07—ORGANIC CHEMISTRY
  • C07K—PEPTIDES
  • C07K1/00—General methods for the preparation of peptides, i.e. processes for the organic chemical preparation of peptides or proteins of any length
  • C07K1/14—Extraction; Separation; Purification
  • C07K1/34—Extraction; Separation; Purification by filtration, ultrafiltration or reverse osmosis
• • C—CHEMISTRY; METALLURGY
  • C07—ORGANIC CHEMISTRY
  • C07K—PEPTIDES
  • C07K16/00—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies
• • C—CHEMISTRY; METALLURGY
  • C07—ORGANIC CHEMISTRY
  • C07K—PEPTIDES
  • C07K16/00—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies
  • C07K16/06—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies from serum
  • C07K16/065—Purification, fragmentation
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
  • G01N30/02—Column chromatography
  • G01N30/80—Fraction collectors
  • G01N30/82—Automatic means therefor
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
  • G01N30/02—Column chromatography
  • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
  • G01N30/02—Column chromatography
  • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
  • G01N2030/8886—Analysis of industrial production processes
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
  • G01N30/02—Column chromatography
  • G01N30/62—Detectors specially adapted therefor
  • G01N30/74—Optical detectors

Definitions

• Biologic products are a rapidly developing and increasingly important class of drug products obtained (e.g., isolated) from natural sources, such as humans, animals, or microorganisms. They may include a range of products, such as vaccines, blood and blood components, allergenics, somatic cells, gene therapy components, tissues, and proteins.
• vaccines e.g., blood and blood components
• allergenics e.g., somatic cells
• gene therapy components e.g., somatic cells
• tissues, and proteins e.g., BACKGROUND
• Gene therapy has immense potential to transform lives of patients with genetic diseases.
• biologics are manufactured via highly-complex biological processes, and are – either as end products or at various steps along the production line – complex mixtures that are challenging to identify and/or characterize. Multi-step purification processes are typically required to obtain a consistent, pure, and effective end product.
• Difficulties associated with accurate identification and characterization of biological products and their ingredients along the manufacturing cycle thus present an obstacle to development and testing of manufacturing processes for new biologics, scaling up production capacity following approval and/or to address variations in demand, and refinement and control of existing procedures to ensure consistent product quality, avoid adverse events, and reduce downtime. Improved technologies for monitoring inputs and outputs of biological product processing steps and control of manufacturing are thus needed.
• bio-production monitoring and control technologies described herein utilize mid-infrared (mid-IR) analyzers capable of obtaining mid-IR spectral data from aqueous samples in substantially real-time. Technologies described herein may leverage this mid-IR spectral data to measure sample quality metrics, such as protein content, titer, secondary structure, aggregation, etc.
• mid-IR mid-infrared
• Sample quality metrics may, accordingly, be measured in substantially real-time and/or on a continuous basis to assess production quality for, e.g., protein therapeutics and gene-therapy agents on an ongoing basis.
• Control systems and methods may, in turn, use this real-time data to control and/or adjust process parameters, such as collection windows for collecting target fractions during a chromatography elution process, flow rates, salt gradients, etc., to improve target recovery, sample purity, potency, stability, and the like.
• the present disclosure is directed to methods for obtaining a purified sample of a target protein species via real-time monitoring of protein heterogeneity and (e.g., automated; e.g., semi-automated) control of purification processing, the method comprising: (a) measuring, via one or more mid-infrared (MIR) analyzer(s), at each of one or more time points, a corresponding infrared (IR) absorbance signal from an aqueous sample exiting from a purification unit (e.g., a chromatography column), the aqueous sample comprising one or more protein species including the target protein species; (b) receiving, by - 2 - 11677372v1 Attorney Docket No.
• MIR mid-infrared
• IR infrared
• a processor of a computing device IR absorbance data corresponding to the IR absorbance signal(s) at each of the one or more time points; (c) determining, by the processor, values of one or more sample quality metrics based on the IR absorbance data, the one or more sample quality metrics comprising a protein aggregation metric indicative of a level of protein aggregation within the aqueous sample; and (d) using the one or more sample quality metrics to control collection of a target fraction of the aqueous sample (e.g., during a particular collection window), thereby obtaining the purified sample of the target protein species.
• a purification unit is or comprises a chromatography column.
• the chromatography column is a member selected from the group consisting of an affinity chromatography (AC) column, a hydrophobic interaction chromatography (HIC) column, an ion exchange chromatograph (IEX) column, a size exclusion chromatography (SEC) column, and a mixed-mode chromatography column [e.g., any combination of the foregoing (e.g., IEX and HIC; e.g., IEX and SEC)].
• AC affinity chromatography
• HIC hydrophobic interaction chromatography
• IEX ion exchange chromatograph
• SEC size exclusion chromatography
• mixed-mode chromatography column e.g., any combination of the foregoing (e.g., IEX and HIC; e.g., IEX and SEC)].
• a target protein species is or comprises one or more members selected from the group consisting of a monoclonal antibody (mAb), a fusion protein, a viral capsid protein, an antibody-drug conjugate, a recombinant protein, and a plasmatic protein.
• the target protein species is or comprises a peptide chain and/or a protein fragment.
• an aqueous sample comprises a plurality of different protein species. In certain embodiments, wherein the aqueous sample comprises a heterogeneous population of the target protein species, comprising a monomeric portion and an aggregated portion.
• the target protein species is a sub- species of a particular protein species
• the target protein species having a particular desired level and/or type of molecular conjugation (e.g. glycan, small-molecule drug, polyethylene glycol, etc.).
• At least one of the one or more MIR analyzers is or comprises a MIR spectrometer comprising: a MIR source aligned and operable to emit a beam of MIR light [e.g., comprising a range of wavelengths substantially within the MIR spectral range (e.g., ranging from about 5000 cm -1 to about 500 cm -1 (e.g., about 2 to 20 microns))]; one or more sampling optics, aligned to direct and/or allow passage of the beam of MIR light, and/or at least a portion thereof, through and/or into contact with at least a portion of the aqueous sample [e.g., wherein the beam of MIR light contacts the portion of - 3 - 11677372v1 Attorney Docket No.
• aqueous sample via reflection at an interface between a solid material (e.g., an ATR crystal and/or optical fiber) and the aqueous sample (e.g., wherein the beam of MIR light undergoes total internal reflection, and contacts / probes the portion of the aqueous sample via an evanescent wave extending into the aqueous sample)] and, following passage through or contact with the portion of the aqueous sample, towards one or more detectors; and the one or more detectors, aligned and operable to detect the beam of MIR light following its passage through and/or contact with the aqueous sample.
• a solid material e.g., an ATR crystal and/or optical fiber
• the aqueous sample e.g., wherein the beam of MIR light undergoes total internal reflection, and contacts / probes the portion of the aqueous sample via an evanescent wave extending into the aqueous sample
• one or more sampling optics comprise a high- refractive index material (e.g., an ATR crystal; e.g., an optical fiber) aligned such that the beam of MIR light is directed towards, incident upon, and reflected internally (e.g., back within the high-refractive index material) by an interface between the high-refractive index material and the aqueous sample (e.g., such that the beam of MIR light is incident upon the interface at an angle above a critical angle for total internal reflection); and the one or more detectors are aligned and operable to detect the beam of MIR light exiting from, following its internal reflection by, the high-refractive index material.
• a high- refractive index material e.g., an ATR crystal; e.g., an optical fiber
• the high- refractive index material is an ATR crystal. In certain embodiments, the high-refractive index material is an optical fiber. [0014] In certain embodiments, one or more sampling optics comprise a flow cell comprising a detection channel through which the aqueous sample flows; and the one or more detectors are aligned and operable to detect the beam of MIR light exiting from, following its transmission through, the detection channel.
• a path length (e.g., followed by the beam of MIR light upon transmission) through the detection channel is about 10 ⁇ m or greater (e.g., about or at least 15 ⁇ m or greater; e.g., about 25 ⁇ m or greater; e.g., about 30 ⁇ m or greater; e.g., about 40 ⁇ m or greater; e.g., about 50 ⁇ m or greater).
• one or more MIR analyzer(s) is or comprise a quantum cascade laser (QCL)-based MIR spectrometer comprising a QCL source operable to emit a beam of MIR light [e.g., comprising a range of wavelengths substantially within the MIR spectral range (e.g., ranging from about 5000 cm -1 to about 500 cm -1 (e.g., about 2 to 20 microns))].
• QCL quantum cascade laser
• the MIR source is a laser and a beam of MIR light has spectral line width of about 4 cm -1 or less (e.g., about 2 cm -1 or less; e.g., about 1 cm -1 or less; e.g., about 0.5 cm -1 or less).
• - 4 - 11677372v1 Attorney Docket No.
• a power of the beam of MIR light is about 1 mW or greater (e.g., about 10 mW; e.g., about 50 mW or greater; e.g., about 100 mW or greater; e.g., about 500 mW or greater; e.g., about 1000 mW or greater).
• a spectral resolution of the MIR spectrometer is about 4 cm -1 or better (e.g., less) [e.g., about 2 cm -1 or better (e.g., less); e.g., about 1 cm -1 or better (e.g., less); e.g., about 0.5 cm -1 or better (e.g., less); e.g., about 0.25 cm -1 or better (e.g., less); e.g., about 0.1 cm -1 or better (e.g., less); e.g., about 0.05 cm -1 or better (e.g., less)].
• an (e.g., frequency / wavelength) accuracy of the MIR spectrometer is about 2 cm -1 or better (e.g., less) [e.g.; about 1 cm -1 or better (e.g., less); e.g., about 0.5 cm -1 or better (e.g., less); e.g., about 0.25 cm -1 or better (e.g., less); e.g., about 0.1 cm -1 or better (e.g., less); e.g., about 0.01 cm -1 or better (e.g., less)].
• a (e.g., frequency / wavelength) repeatability of the MIR spectrometer is about 0.5 cm -1 or better (e.g., less) [e.g., about 0.25 cm -1 or better (e.g., less); e.g., about 0.1 cm -1 or better (e.g., less); e.g., about 0.05 cm -1 or better (e.g., less); e.g., about 0.001 cm -1 or better (e.g., less)].
• an MIR source is a tunable laser (e.g., a tunable QCL) (e.g., operable sweep an emission frequency of the beam of MIR light through a plurality of frequencies across a scan range), and the method comprises, at each of the one or more time points: sweeping an emission frequency of the beam MIR light across a scan range of the tunable laser, thereby illuminating the aqueous sample with a plurality of emission frequencies; and detecting, with the one or more detectors, the beam of MIR light (e.g., having been (i) internally reflected by an interface between the high-index material and the aqueous sample and/or (ii) transmitted through the detection channel through which the aqueous sample flows) at each of the plurality of emission frequencies, thereby measuring, as the corresponding infrared (IR) absorbance signal from the aqueous sample, a corresponding IR spectrum comprising a plurality of values, each associated with and representing and/or
• a tunable laser
• a plurality of emission wavelengths comprises one or more wavelengths within a spectral band ranging from about 1800 to about 800 cm -1 (e.g., from about 1725 to about 1025 cm -1 ; e.g., from about 1750 to about 1350 cm -1 ; e.g., from about 1725 to about 1375 cm -1 ; e.g., from about 1700 to about 1500 cm -1 ; e.g., from about 1700 to about 1600 cm -1 ; e.g., from about 1700 to about 1000 cm -1 ).
• - 5 - 11677372v1 Attorney Docket No.
• an MIR analyzer is an on-line sensor (e.g., as opposed to an off-line or at-line sensor) that measures the IR absorbance signal in substantially real- time as the aqueous solution exits from the purification unit.
• IR absorbance data comprises, for each of the one or more time points, a corresponding Amide band spectrum [e.g., the Amide band spectrum comprising, for each particular wavelength of a plurality of sampled (e.g., emission) wavelengths within a range from about 1800 to about 800 cm -1 , an associated absorption value representing a level of absorption, by a portion of the aqueous sample, at the particular wavelength].
• step (d) comprises determining, for each particular time point of at least a portion of the one or more time points, a corresponding value of the protein aggregation metric.
• determining a corresponding value of the protein aggregation metric comprises: computing, from the Amide band spectrum corresponding to the particular time point, a value of an Amide II peak metric that quantifies one or more properties of an Amide II band (e.g., a frequency position, a linewidth, an intensity) at the particular time point; and using the Amide II peak metric value to determine the corresponding value of the protein aggregation metric (e.g., wherein the protein aggregation metric is or is a function of the Amide II peak metric).
• an Amide II peak metric is a frequency position metric that quantifies a frequency about which the Amide II band is substantially centered at the particular time point [e.g., a center of mass frequency, a frequency of a maximal height of the Amide II band, a center frequency of a fitted peak function (e.g., a Gaussian, a Lorentz, etc.), etc.].
• the frequency position metric is a center of mass frequency for the Amide II band.
• determining a corresponding value of the protein aggregation metric comprises: computing, from the Amide band spectrum corresponding to the particular time point, a value of an Amide I peak metric that quantifies one or more properties of an Amide I band (e.g., frequency position, a linewidth, an intensity) at the particular time point; and using both the Amide I peak metric - 6 - 11677372v1 Attorney Docket No. 2017297-0013 value and the Amide II peak metric value to determine the corresponding value of the protein aggregation metric (e.g., wherein the protein aggregation metric is a function of the Amide I peak metric and the Amide II peak metric).
• an Amide I peak metric is a peak intensity metric that quantifies an intensity of the Amide I band at the particular time point (e.g., a peak height of the Amide I band, an area under the curve (AUC) for the Amide I band);
• an Amide II peak metric is a peak intensity metric that quantifies an intensity of the Amide II band at the particular time point (e.g., a peak height of the Amide I band, an area under the curve (AUC) for the Amide I band); and determining the value of the protein aggregation metric comprises computing (i) a ratio of the Amide I peak metric value to the Amide II peak metric value and/or (ii) a ratio of the Amide II peak metric value to the Amide I peak metric value.
• one or more sample quality metrics further comprise a total protein content metric indicative of a level of protein content within the aqueous sample.
• step (d) comprises causing, by the processor, transmission of one or more trigger signals (e.g., voltages) to a controller unit of the purification unit.
• the one or more trigger signals comprise an analog voltage signal having a time varying amplitude based on (e.g., substantially proportional to) a value of the protein aggregation metric.
• the one or more trigger signals comprise an analog voltage signal having a time varying amplitude based on (e.g., substantially proportional to) a value of a total protein content metric.
• step (d) comprises one or both of: initiating, by the controller unit, based on the one or more trigger signals, collection of the target fraction of the aqueous sample [e.g., wherein a particular one of the one or more trigger signals is an analog signal and the controller unit initiates collection of the target fraction based on an amplitude of the analog signal (e.g., when it exceeds a particular threshold; e.g., when it drops below a particular threshold); e.g., wherein a particular one of the one or more trigger signals is a digital signal that triggers (e.g., by transitioning from a 0 voltage level to 1 voltage level or vice-a-versa) initiating collection of the target fraction] and stopping, by the controller unit, based the one or more trigger signals, collection of the target fraction of the aqueous sample [e.g., wherein a particular one of the one or more trigger signals is an analog signal and the controller unit stops collection of the target fraction based on an amplitude of the analog signal (e.
• the analog signal e.g., when it exceeds a particular threshold; e.g., when it drops below a particular threshold); e.g., wherein a particular one of the one or more trigger signals is a digital signal that triggers (e.g., by transitioning from a 0 voltage level to 1 voltage level or vice-a-versa) stopping collection of the target fraction].
• the present disclosure is directed to methods for real-time monitoring of protein aggregation in a sample, the method comprising: (a) repeatedly receiving, by a processor of a computing device, infrared (IR) absorbance data corresponding to IR absorbance signals measured at each of a plurality of time points, the IR absorbance data comprising, for each particular time point of the plurality of time points, a corresponding IR absorbance spectrum measured from the sample at the particular time point and comprising a plurality of absorbance values, each associated with a particular wavenumber; (b) analyzing (e.g., automatically), by the processor, the IR absorbance data to obtain a real- time protein aggregation signal providing a measure of protein aggregation in the sample as a function of time, by, for each particular time point of the plurality of time points: determining, using the IR absorbance spectrum corresponding to the particular time point, values of one or more peak metrics for one or both of an Amide I band and an Amide II
• step (b) comprises, for each particular time point, determining, as the value of the protein aggregation metric indicative of the level of protein aggregation within the sample at the particular time point, a value of a frequency position metric that quantifies a frequency about which the Amide II band is substantially centered at the particular time point [e.g., a center of mass frequency, a frequency of a maximal height of the Amide II band, a center frequency of a fitted peak function (e.g., a Gaussian, a Lorentz, etc.), etc.].
• the frequency position metric is a center of mass frequency for the Amide II band.
• step (b) comprises, for each particular time point: determining a value of an Amide I peak intensity metric that quantifies an intensity of the - 8 - 11677372v1 Attorney Docket No. 2017297-0013 Amide I band at the particular time point (e.g., a peak height of the Amide I band, an area under the curve (AUC) for the Amide I band); determining a value of an Amide II peak intensity metric that quantifies an intensity of the Amide II band at the particular time point (e.g., a peak height of the Amide I band, an area under the curve (AUC) for the Amide I band); and determining, as the value of the protein aggregation metric, (i) a ratio of the Amide I peak metric value to the Amide II peak metric value and/or (ii) a ratio of the Amide II peak metric value to the Amide I peak metric value.
• an Amide I peak intensity metric that quantifies an intensity of the - 8 - 11
• the present disclosure is directed to methods for mid-IR (MIR)-spectroscopy-based monitoring and control of a production unit for manufacture of a biological product (e.g., a protein; e.g., a nucleic acid; e.g., a virus) the method comprising: (a) measuring, via one or more (e.g., integrated) mid-infrared (MIR) analyzer(s) [e.g., MIR analyzer(s) as described in one or more aspects and/or embodiments herein (e.g., in paragraphs above)], at each of one or more time points, a corresponding infrared (IR) absorbance signal from an aqueous sample flowing to and/or from the production unit (e.g., and which comprises one or more inputs, output products, waste products, or in-progress products of the production unit); (b) receiving, by a processor of a computing device, IR absorbance data corresponding to the IR absorbance signal(s
• MIR mid-IR
• a production unit is or comprises a purification unit.
• the purification unit is a member selected from the group consisting of an alternating tangential flow filtration (ATF) system, tangential flow depth filtration (TFDF) system, tangential flow filtration (TFF) system, a chromatography column, a direct, or normal, flow filtration unit, an ultra-filtration unit, and a dia-filtration unit.
• the purification unit is a chromatography column ⁇ e.g., and wherein the chromatography column is a member selected from the group consisting of an affinity chromatography (AC) column, a hydrophobic interaction chromatography (HIC) column, an ion exchange chromatograph (IEX) column, a size exclusion chromatography (SEC) column, and a mixed-mode chromatography column [e.g., any combination of the foregoing (e.g., IEX and HIC; e.g., IEX and SEC)] ⁇ .
• AC affinity chromatography
• HIC hydrophobic interaction chromatography
• IEX ion exchange chromatograph
• SEC size exclusion chromatography
• mixed-mode chromatography column e.g., any combination of the foregoing (e.g., IEX and HIC; e.g., IEX and SEC)] ⁇ .
• a production unit is or comprises a bioreactor (e.g., a seed bioreactor; e.g., a production bioreactor).
• a bioreactor e.g., a seed bioreactor; e.g., a production bioreactor.
• a production bioreactor e.g., a seed bioreactor; e.g., a production bioreactor.
• a aqueous sample comprises one or more protein species selected from the group consisting of a monoclonal antibody (mAb), a fusion protein, a viral capsid protein, an antibody-drug conjugate, a recombinant protein, and a plasmatic protein.
• the aqueous sample comprises a peptide chain and/or a protein fragment.
• an aqueous sample comprises a plurality of different protein species.
• the aqueous sample comprises a heterogeneous population of a target protein species, comprising a monomeric portion and an aggregated portion.
• the aqueous sample comprises one or more sub-species of a particular protein species, having a particular desired level and/or type of molecular conjugation (e.g. glycan, small-molecule drug, polyethylene glycol, etc.).
• an aqueous sample comprises nucleic acid (e.g., DNA, RNA, mRNA, etc.).
• an aqueous sample comprises one or more species of virus and/or virus-like particles [e.g., adeno-associated viral vectors (AAV); e.g., lentiviral vectors].
• step (c) comprises using the IR absorbance data to determine values one or more sample quality metrics at each of the one or more time points (e.g., and adjusting the one or more process parameters based thereon).
• one or more sample quality metrics comprise(s) a total protein content metric that quantifies a quantity and/or concentration of protein within the aqueous sample.
• the one or more sample quality metrics comprise(s) a protein aggregation metric indicative of a level of protein aggregation within the aqueous sample.
• the one or more sample quality metrics comprise(s) one or more protein species metrics that identify presence and/or quantify content (e.g., absolute content; e.g., relative content) of one or more particular protein species within the aqueous sample.
• the one or more sample quality metrics comprise a protein conjugation metric that quantifies a level and/or type of molecular conjugation (e.g. glycan, small-molecule drug, polyethylene glycol, etc.).
• the one or more sample quality metrics comprise one or more protein secondary structure metrics that quantify presence and or content of one or more protein - 10 - 11677372v1 Attorney Docket No. 2017297-0013 secondary structure motifs (e.g., alpha-helical content, beta-sheet content, turn content, disordered content).
• one or more sample quality metrics comprise one or more nucleic acid content metrics that quantify a content of nucleic acid [e.g., a total content of nucleic acid (e.g., a concentration (e.g., titer), mass per volume (e.g., mg/mL, number of particles per volume, number of viral genome copies, etc.); e.g., a total and/or relative content of one or more particular types of nucleic acid (e.g., DNA, RNA, ssDNA, dsDNA), e.g., independent and/or distinguishable content metrics measuring viral nucleic acid and host cell nucleic acid; e.g., the total amount of particular nucleotide bases in a nucleic acid sample, for example, GC content] within the aqueous sample.
• a content of nucleic acid e.g., a concentration (e.g., titer), mass per volume (e.g., mg/mL, number of particles per volume
• one or more sample quality metrics comprise one or more viral content metrics that quantify a content of viral and/or virus like particles within the aqueous sample [e.g., a concentration (e.g., titer), mass per volume (e.g. mg/mL), number of (viral) particles per volume, etc.].
• the one or more sample quality metrics comprise one or more empty/full capsid ratios that quantify an content and/or relative fraction of empty and/or full viral vectors (e.g., percent, ratio, etc. of full viral vectors) within the aqueous sample.
• one or more sample quality metrics comprise a capsid aggregation metric indicative of a level of capsid aggregation within the viral vector sample.
• one or more sample quality metrics comprise a viral nucleic acid (e.g., viral DNA, RNA, etc.) content metric that differentiates the viral nucleic acid from the host cell proteins and host cell nucleic acid content.
• At least a portion (e.g., one or more) of the sample quality metrics are computed based on one or more peak metrics that measure features of one or more absorption bands in IR spectral data ⁇ e.g., wherein each peak metric is associated with one or more particular spectral bands [e.g., a continuous range of wavelengths / wavenumbers (e.g., an Amide-I band, an Amide-II band, an Amide-III band, e.g.; an Amide region, spanning two or more of the Amide bands; e.g., an antisymmetric PO4 band; e.g.; a symmetric PO4 band)] and quantifies a particular structural feature [e.g., an intensity (e.g., a peak amplitude; e.g., an Area Under the Curve (AUC)); e.g., a linewidth; e.g., a frequency position (e.g., peak frequency;
• AUC Area Under the Cur
• IR absorbance data comprises: (i) one or more (e.g., a plurality of) Amide II absorbance value(s), associated with and measuring IR absorption (of the viral sample) at wavenumbers within an Amide II spectral band (e.g., ranging from about 1500 cm -1 to about 1600 cm -1 , e.g., ranging from about 1500 cm -1 to about 1575 cm -1 ; e.g., ranging from about 1500 cm -1 to about 1550 cm -1 ; e.g., ranging from about 1540 cm -1 to about 1560 cm -1 ); and/or (ii) one or more (e.g., a plurality of) Amide III absorbance value(s), associated with and measuring IR absorption (of the viral sample) at wavenumbers within an Amide II spectral band (e.g., ranging from about
• determining one or more sample quality metrics comprises determining values of a protein content metric [e.g., concentration (e.g., titer)] that quantifies protein content within the sample based at least in part on the Amide II and/or Amide III absorbance value(s).
• methods comprise determining a value of an Amide II peak metric based on the Amide II absorbance values and/or a value of an Amide III peak metric based on the Amide III absorbance values; and using the Amide II peak metric value and/or the Amide III peak metric value to determine the protein content metric value.
• an Amide II peak metric and/or the Amide III peak metric is a peak intensity metric that quantifies an intensity of the Amide II band and/or Amide III band, respectively [e.g., a peak height, an area under the curve (AUC), etc.].
• IR absorbance data comprises: (i) one or more (e.g., a plurality of) antisymmetric Phosphate Stretch (antisymmetric-PO4) absorbance value(s), associated with and measuring IR absorption (of the viral sample) at wavenumbers within an antisymmetric-PO4 spectral band (e.g., ranging from about 1150 cm -1 to about 1250 cm -1 , e.g., ranging from about 1175 cm -1 to about 1250 cm -1 ; e.g., ranging from about 1200 cm -1 to about 1250 cm -1 ; e.g., ranging from about 1210 cm -1 to about 1230 cm -1 ); and/or (ii) one or more (e.g., a plurality of) symmetric Phosphate Stretch (symmetric-PO 4 ) absorbance value(s), associated with and measuring IR absorption (of the viral sample) at wavenumbers within a symmetric-PO 4 spectral band (
• determining one or more sample quality metric values comprises determining values of a nucleic acid (e.g., DNA, RNA, etc.) content metric that - 12 - 11677372v1 Attorney Docket No. 2017297-0013 quantifies nucleic acid content [e.g., concentration (e.g., titer)] within the sample based at least in part on the antisymmetric-PO4 and/or symmetric-PO4 absorbance value(s).
• a nucleic acid e.g., DNA, RNA, etc.
• concentration e.g., titer
• methods comprise determining a value of an antisymmetric-PO 4 peak metric based on the antisymmetric-PO4 absorbance values and/or a value of an symmetric-PO4 peak metric based on the symmetric-PO 4 absorbance values; and using the antisymmetric-PO 4 peak metric value and/or the symmetric-PO4 peak metric value to determine the nucleic acid content metric value.
• an antisymmetric-PO 4 peak metric and/or the symmetric-PO4 peak metric is a peak intensity metric that quantifies an intensity of the antisymmetric-PO 4 band and/or symmetric-PO 4 band, respectively [e.g., a peak height, an area under the curve (AUC), etc.].
• determining one or more sample quality metric values comprises determining (i) value(s) of a protein content metric [e.g., concentration (e.g., titer)] that quantifies protein content within the sample and (ii) values of a nucleic acid (e.g., DNA, RNA, etc.) content metric that quantifies nucleic acid content [e.g., concentration (e.g., titer)] within the sample, thereby independently quantifying total protein and nucleic acid content within the sample.
• a protein content metric e.g., concentration (e.g., titer)
• a nucleic acid e.g., DNA, RNA, etc.
• determining one or more sample quality metric values comprises determining a total capsid content based at least in part on (e.g., as a function of) the value(s) of the protein content metric. In certain embodiments, determining one or more sample quality metric values comprises determining a full capsid fraction based at least in part on (e.g., as a function of) (i) the value(s) of the protein content metric and/or the total capsid content and (ii) the value(s) of the nucleic acid content metric.
• IR absorbance data is or comprises one or more IR absorbance spectra, each IR absorbance spectra comprising, for each particular wavenumber of a plurality of wavenumber spanning a measured spectral band, a corresponding IR absorbance value representing a measure of absorption of IR light, by the aqueous sample, at the particular wavenumber.
• measured spectral bands span one or more bands selected from the group consisting of an Amide II band, an Amide III band, an asymmetric- PO4 band, and a symmetric-PO4 band.
• provided methods comprise determining values of the one or more sample quality metrics for each of the one or more time points, thereby monitoring the one or more sample quality metrics over time.
• - 13 - 11677372v1 Attorney Docket No. 2017297-0013 methods comprise determining values of one or more sample quality metrics in substantially real-time.
• at least one particular sample quality metric of the one or more sample quality metrics is computed using a machine learning model that receives, as input, one or more IR spectra and generates the particular sample quality metric as output.
• IR absorbance data comprises an IR absorbance spectrum and step (c) comprises: receiving (e.g., and/or accessing) one or more reference spectra, each measured from a corresponding (e.g., high-quality) reference sample [e.g., comprising a target viral vector species at high purity and/or concentration and/or one or more model constituents thereof (e.g., a model protein solution; e.g., a model ssDNA solution)]; and determining (e.g., automatically), values of at least a portion of one or more sample quality metrics using the IR absorbance spectrum and the one or more reference spectra [e.g., determining, as the values of the portion of the one or more viral vector sample quality metrics, one or more (e.g., a plurality) measure(s
• one or more reference spectra comprises a high- quality viral vector spectrum measured from a reference sample having a full capsid fraction (e.g., a-priori known; e.g., determined to be) at or above a particular threshold fraction.
• a threshold fraction is about 75% [e.g., about 80% (e.g., about 90%)].
• step (c) comprises computing a difference spectrum based on at least one of the one or more reference spectra and the IR absorbance spectrum (e.g., by subtracting an IR absorbance spectrum, and/or a scaled or otherwise pre-processed version thereof, from a reference spectrum, and/or a scaled or otherwise pre-processed version thereof, or vice-versa).
• step (c) comprises computing one or more derivative spectra (e.g., a first derivative; e.g., a second derivative) of at least one of the one or more reference spectra and/or the IR absorbance spectrum.
• step (c) comprises computing (e.g., as values of one or more of the sample quality metrics) one or more members selected from the group consisting of: a correlation value based on a correlation of (i) a particular one of the one or more reference spectra and/or one or more - 14 - 11677372v1 Attorney Docket No.
• provided methods comprise determining values of a first sample quality metric at each of the plurality of time points and determining a value of a second (e.g., time differential; e.g., time aggregated) sample quality metric using values of the first sample quality metric corresponding to two or more of the plurality of time points.
• a second e.g., time differential; e.g., time aggregated
• step (c) comprises using a machine learning model to adjust the one or more process parameters [e.g., wherein the machine learning model receives one or more sample quality metrics as input and generates an adjustment to and/or a target process parameter as output; e.g., wherein the machine learning model receives one or more IR spectra as input and generates an adjustment to and/or a target process parameter as output].
• one or more process parameters comprise one or more members selected from the group consisting of a flow rate, a flow direction a pressure, a temperature, and a pH.
• the one or more process parameters comprise an amount (e.g., absolute and/or relative) of one or more raw materials (e.g., used as input to the production unit). In certain embodiments, the one or more process parameters comprise a time to initiate and/or halt a sub-process (e.g., heating, collection of an eluted fraction, growth, etc.).
• a sub-process e.g., heating, collection of an eluted fraction, growth, etc.
• one or more IR absorbance signal(s), to which the IR absorbance data received at step (b) corresponds, is/are measured from the aqueous sample, at each of one or more time points, as it (the aqueous sample) exits from a purification unit (e.g., a chromatography column); and methods comprise: using the IR absorption data to determine one or both of (i) a total capsid content and (ii) a full capsid fraction; and using the determined total capsid content and/or full capsid fraction to control collection of a target - 16 - 11677372v1 Attorney Docket No.
• one or more IR absorbance signal(s), to which the IR absorbance data received at step (b) corresponds, is/are measured from the aqueous sample, at each of one or more time points, as it (the aqueous sample) exits from a purification unit (e.g., a chromatography column); and the method comprises: using the IR absorption data to determine a capsid aggregation metric that measures a level of aggregation between capsids in the viral vector sample; and using the capsid aggregation metric to control collection of a target fraction of the aqueous sample (e.g., during a particular collection window), thereby obtaining a purified sample of viral vector material.
• a purification unit e.g., a chromatography column
• the present disclosure is directed to systems for obtaining a purified sample of a target protein species via real-time monitoring of protein heterogeneity and (e.g., automated; e.g., semi-automated) control of purification processing, the system comprising: (a) one or more mid-infrared (MIR) analyzer(s), aligned and operable to measure, at each of one or more time points, a corresponding infrared (IR) absorbance signal from an aqueous sample exiting from a purification unit (e.g., a chromatography column), the aqueous sample comprising one or more protein species including the target protein species; (b) a processor of a computing device; and (c) memory having instructions stored thereon, wherein the instructions, when executed by the processor cause the processor to: receive IR absorbance data corresponding to the IR absorbance signal(s) at each of the one or more time points; determine values of one or more sample quality metrics based on the IR absorbance
• MIR mid-in
• provided systems further comprise the purification unit and/or a controller unit thereof.
• the present disclosure is directed to systems for real-time monitoring of protein aggregation in a sample, the system comprising: a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, - 17 - 11677372v1 Attorney Docket No.
• the present disclosure is directed to systems for mid-IR (MIR)-spectroscopy-based monitoring and control of a production unit for manufacture of a biological product (e.g., a protein; e.g., a virus) the method comprising: (a) one or more (e.g., integrated) mid-infrared (MIR) analyzer(s) [e.g., MIR analyzer(s) as recited in one or more aspects and/or embodiments herein (e.g., in paragraphs above)] aligned and operable to measure, at each of one or more time points, a corresponding infrared (IR) absorbance signal from an aqueous sample flowing to and/or from the production unit (e.g., and which comprises one or more inputs, output products, waste products, or in-progress products of the production unit); (b) a processor of a computing device; and (c) memory having instructions stored thereon, wherein the instructions, when executed by the processor cause the processor
• MIR mid-IR
• provided systems further comprise a production unit and/or a controller unit thereof.
• - 18 - 11677372v1 Attorney Docket No. 2017297-0013
• the present disclosure is directed to methods for quantifying and/or monitoring (e.g., in real-time) viral vector quality within an aqueous sample comprising one or more species of virus and/or virus-like particles, the methods comprising: (a) receiving (e.g., repeatedly), by a processor of a computing device, infrared (IR) absorbance data corresponding to one or more IR absorbance signal(s) measured from the sample; (b) determining (e.g., automatically), by the processor, using the IR absorbance data, values of one or more viral vector sample quality metrics; and (c) storing and/or providing for display and/or further processing, the one or more viral vector sample quality metric value(s).
• IR infrared
• one or more viral vector quality metrics comprise a total capsid content that quantifies a content of viral capsids within the sample [e.g., a concentration (e.g., titer), mass per volume (e.g. mg/mL), number of (viral) particles per volume, etc.].
• one or more viral vector sample quality metrics comprise a full capsid fraction (e.g., percent, ratio, etc. of full viral vectors).
• one or more viral vector sample quality metrics comprise a capsid aggregation metric indicative of a level of capsid aggregation within the viral vector sample.
• one or more viral vector sample quality metrics comprise a protein content metric that quantifies protein content within the sample [e.g., a concentration (e.g., titer), mass per volume (e.g. mg/mL), number of particles per volume, etc.].
• a protein content metric that quantifies protein content within the sample [e.g., a concentration (e.g., titer), mass per volume (e.g. mg/mL), number of particles per volume, etc.].
• a nucleic acid e.g., DNA, RNA, etc.
• a concentration e.g., titer
• mass per volume e.g. mg/mL
• one or more viral vector sample quality metrics comprise a viral nucleic acid (e.g., viral DNA, RNA, etc.) content metric that differentiates the viral nucleic acid from the host cell proteins and host cell nucleic acid content.
• a viral nucleic acid e.g., viral DNA, RNA, etc.
• content metric that differentiates the viral nucleic acid from the host cell proteins and host cell nucleic acid content.
• step (b) comprises: determining, by the processor, a value for each of one or more peak metrics for the IR absorption data, wherein each peak metric is associated with one or more particular spectral bands [e.g., a continuous range of wavelengths / wavenumbers (e.g., an Amide-I band, an Amide-II band, an Amide-III band, e.g.; an Amide region, spanning two or more of the Amide bands; e.g., an antisymmetric PO4 band; e.g.; a symmetric PO4 band)] and quantifies a particular structural feature [e.g., an - 19 - 11677372v1 Attorney Docket No.
• spectral bands e.g., a continuous range of wavelengths / wavenumbers (e.g., an Amide-I band, an Amide-II band, an Amide-III band, e.g.; an Amide region, spanning two or more of the Amide bands;
• intensity e.g., a peak amplitude; e.g., an Area Under the Curve (AUC)); e.g., a linewidth; e.g., a frequency position (e.g., peak frequency; e.g., center of mass frequency)] of one or more absorption peaks within the particular spectral band; and using the determined values of the one or more peak metrics to determine the values of at least a portion of the viral vector sample quality metrics.
• AUC Area Under the Curve
• IR absorbance data comprises: (i) one or more (e.g., a plurality of) Amide II absorbance value(s), associated with and measuring IR absorption (of the viral sample) at wavenumbers within an Amide II spectral band (e.g., ranging from about 1500 cm -1 to about 1600 cm -1 , e.g., ranging from about 1500 cm -1 to about 1575 cm -1 ; e.g., ranging from about 1500 cm -1 to about 1550 cm -1 ; e.g., ranging from about 1540 cm -1 to about 1560 cm -1 ); and/or (ii) one or more (e.g., a plurality of) Amide III absorbance value(s), associated with and measuring IR absorption (of the viral sample) at wavenumbers within an Amide II spectral band (e.g., ranging from about 1250 cm -1 to about 1350 cm -1 , e.g., ranging from
• step (b) comprises determining values of a protein content metric [e.g., concentration (e.g., titer)] that quantifies protein content within the sample based at least in part on the Amide II and/or Amide III absorbance value(s).
• a protein content metric e.g., concentration (e.g., titer)
• provided methods comprise: determining a value of an Amide II peak metric based on the Amide II absorbance values and/or a value of an Amide III peak metric based on the Amide III absorbance values; and using the Amide II peak metric value and/or the Amide III peak metric value to determine the protein content metric value.
• an Amide II peak metric and/or an Amide III peak metric is a peak intensity metric that quantifies an intensity of the Amide II band and/or Amide III band, respectively [e.g., a peak height, an area under the curve (AUC), etc.].
• one or more viral vector sample quality metrics comprise one or more protein structure metrics (e.g., protein structure metrics; e.g., protein tertiary and/or quaternary structure metrics) indicative of presence and/or content (e.g., absolute content; e.g., relative content) of one or more particular protein structural forms (e.g., particular secondary structure motifs; e.g., particular tertiary and/or quaternary structure motifs/forms) within the sample (e.g., thereby providing for monitoring variation in capsid protein secondary/tertiary/quaternary structure).
• protein structure metrics e.g., protein structure metrics; e.g., protein tertiary and/or quaternary structure metrics
• content e.g., absolute content; e.g., relative content
• particular protein structural forms e.g., particular secondary structure motifs; e.g., particular tertiary and/or quaternary structure motifs/forms
• IR absorbance data comprises: (i) one or more (e.g., a plurality of) antisymmetric Phosphate Stretch (antisymmetric-PO4) absorbance value(s), associated with and measuring IR absorption (of the viral sample) at wavenumbers within an antisymmetric-PO4 spectral band (e.g., ranging from about 1150 cm -1 to about 1250 cm -1 , e.g., ranging from about 1175 cm -1 to about 1250 cm -1 ; e.g., ranging from about 1200 cm -1 to about 1250 cm -1 ; e.g., ranging from about 1210 cm -1 to about 1230 cm -1 ); and/or (ii) one or more (e.g., a plurality of) symmetric Phosphate Stretch (symmetric-PO 4 ) absorbance value(s), associated with and measuring IR absorption (of the viral sample) at wavenumbers within a symmetric-PO
• an antisymmetric-PO4 spectral band e
• step (b) comprises determining values of a nucleic acid (e.g., DNA, RNA, etc.) content metric that quantifies nucleic acid content [e.g., concentration (e.g., titer)] within the sample based at least in part on the antisymmetric-PO 4 and/or symmetric-PO4 absorbance value(s).
• a nucleic acid e.g., DNA, RNA, etc.
• concentration e.g., titer
• provided methods comprise determining a value of an antisymmetric-PO 4 peak metric based on the antisymmetric-PO4 absorbance values and/or a value of an symmetric-PO4 peak metric based on the symmetric-PO 4 absorbance values; and using the antisymmetric-PO 4 peak metric value and/or the symmetric-PO4 peak metric value to determine the nucleic acid content metric value.
• an antisymmetric-PO 4 peak metric and/or the symmetric-PO4 peak metric is a peak intensity metric that quantifies an intensity of the antisymmetric-PO 4 band and/or symmetric-PO 4 band, respectively [e.g., a peak height, an area under the curve (AUC), etc.].
• step (b) comprises determining (i) value(s) of a protein content metric [e.g., concentration (e.g., titer)] that quantifies protein content within the sample and (ii) values of a nucleic acid (e.g., DNA, RNA, etc.) content metric that quantifies nucleic acid content [e.g., concentration (e.g., titer)] within the sample, thereby independently quantifying total protein and nucleic acid content within the sample.
• a protein content metric e.g., concentration (e.g., titer)
• a nucleic acid e.g., DNA, RNA, etc.
• step (b) comprises: determining as one of the viral vector sample quality metrics, a total capsid content based at least in part on (e.g., as a function of) the value(s) of the protein content metric. In certain embodiments, step (b) comprises determining, as one of the viral vector sample quality metrics, a full capsid fraction based at least in part on (e.g., as - 21 - 11677372v1 Attorney Docket No. 2017297-0013 a function of) (i) the value(s) of the protein content metric and/or the total capsid content and (ii) the value(s) of the nucleic acid content metric.
• IR absorbance data is or comprises one or more IR absorbance spectra, each IR absorbance spectra comprising, for each particular wavenumber of a plurality of wavenumber spanning a measured spectral band, a corresponding IR absorbance value representing a measure of absorption of IR light, by the aqueous sample, at the particular wavenumber.
• measured spectral band spans one or more bands selected from the group consisting of an Amide II band, an Amide III band, an asymmetric-PO4 band, and a symmetric-PO4 band.
• step (a) comprises repeatedly receiving the IR absorbance data at a plurality of time points, thereby obtaining, for each of the plurality of time points, a corresponding set of IR absorbance data; and provided methods comprise performing steps (b) through (c) for each set of IR absorbance data, thereby monitoring the total capsid content and/or full capsid fraction over time.
• provided methods comprise performing steps (a) through (c) in substantially real-time to obtain (i) a real-time capsid content signal providing a measure of capsid content in the sample as a function of time and/or full capsid fraction signal providing a measure of a fraction of capsids within the sample that are full, as a function of time.
• provided methods comprise measuring, via one or more (e.g., integrated) mid-infrared (MIR) analyzer(s) [e.g., MIR analyzer(s) as described in various aspects and embodiments herein (e.g., in paragraphs above)], the one or more IR absorbance signal(s).
• MIR mid-infrared
• provided methods comprise measuring, at each of one or more time points, a corresponding one of the one or more infrared (IR) absorbance signal.
• provided methods comprise measuring the one or more IR absorbance signal(s) from the aqueous sample as it (the aqueous sample) flows to (e.g., into) and/or from a production unit (e.g., the aqueous sample comprising one or more inputs, output products, waste products, or in-progress products of the production unit).
• one or more species of virus and/or virus-like particles comprise one or more species of adeno-associated virus (AAV).
• AAV adeno-associated virus
• one or more species of virus comprises adeno viruses and/or retroviruses (e.g., - 22 - 11677372v1 Attorney Docket No. 2017297-0013 lentiviruses).
• one or more species of virus comprises plant-based viruses (e.g., tobacco mosaic viruses).
• one or more IR absorbance signal(s), to which the IR absorbance data correspond is/are measured from the aqueous sample as it (the aqueous sample) flows to (e.g., into) and/or from a production unit (e.g., the aqueous sample comprising one or more inputs, output products, waste products, or in-progress products of the production unit).
• a production unit is a purification unit.
• a purification unit is a member selected from the group consisting of an alternating tangential flow filtration (ATF) system, tangential flow depth filtration (TFDF) system, tangential flow filtration (TFF) system, a chromatography column, a direct, or normal, flow filtration unit, an ultra-filtration unit, and a dia-filtration unit.
• ATF alternating tangential flow filtration
• TFDF tangential flow depth filtration
• TFF tangential flow filtration
• a purification unit is a chromatography column ⁇ e.g., and wherein the chromatography column is a member selected from the group consisting of an affinity chromatography (AC) column, a hydrophobic interaction chromatography (HIC) column, an ion exchange chromatograph (IEX) column, a size exclusion chromatography (SEC) column, and a mixed-mode chromatography column [e.g., any combination of the foregoing (e.g., IEX and HIC; e.g., IEX and SEC)] ⁇ .
• AC affinity chromatography
• HIC hydrophobic interaction chromatography
• IEX ion exchange chromatograph
• SEC size exclusion chromatography
• mixed-mode chromatography column e.g., any combination of the foregoing (e.g., IEX and HIC; e.g., IEX and SEC)] ⁇ .
• a production unit is or comprises a bioreactor (e.g., a seed bioreactor; e.g., a production bioreactor).
• step (c) comprises causing, by the processor, generation and/or transmission of one or more trigger signals (e.g., voltages) to a controller unit of the production unit based at least in part on (e.g., a value of) one or more of the determined viral vector sample quality metrics [e.g., a value of a determined capsid content and/or (e.g., a value of) a determined full capsid fraction].
• trigger signals e.g., voltages
• step (c) comprises causing, by the processor, generation of a trigger signal (e.g., an analog signal) having a value based at least in part on a determined viral vector quality metric [e.g., a capsid content; e.g., a full capsid fraction; e.g., a capsid aggregation metric].
• a trigger signal e.g., an analog signal
• one or more IR absorbance signal(s), to which the IR absorbance data received at step (a) corresponds, is/are measured from the aqueous sample, at each of one or more time points, as it (the aqueous sample) exits from a purification unit (e.g., a chromatography column); and provided methods comprise: using the IR absorption - 23 - 11677372v1 Attorney Docket No.
• 2017297-0013 data to determine one or both of (i) a total capsid content and (ii) a full capsid fraction; and using the determined total capsid content and/or full capsid fraction to control collection of a target fraction of the aqueous sample (e.g., during a particular collection window), thereby obtaining a purified sample of viral vector material.
• one or more IR absorbance signal(s), to which the IR absorbance data received at step (a) corresponds, is/are measured from the aqueous sample, at each of one or more time points, as it (the aqueous sample) exits from a purification unit (e.g., a chromatography column); and provided methods comprise: using the IR absorption data to determine a capsid aggregation metric that measures a level of aggregation between capsids in the viral vector sample; and using the capsid aggregation metric to control collection of a target fraction of the aqueous sample (e.g., during a particular collection window), thereby obtaining a purified sample of viral vector material.
• a purification unit e.g., a chromatography column
• IR absorbance data comprises an IR absorbance spectrum and wherein step (b) comprises: receiving (e.g., and/or accessing) one or more reference spectra, each measured from a corresponding (e.g., high-quality) reference sample comprising the target viral vector species at high purity and/or concentration and/or one or more model constituents thereof (e.g., a model protein solution; e.g., a model ssDNA solution); and determining (e.g., automatically), the values of at least a portion of the one or more viral vector sample quality metrics using the IR absorbance spectrum and the one or more reference spectra [e.g., determining, as the values of the portion of the one or more viral vector sample quality metrics, one or more (e.g., a plurality) measure(s) of deviation between the reference spectra and the IR absorbance spectrum].
• a corresponding (e.g., high-quality) reference sample comprising the target viral vector species at high purity and/or concentration and/or one or
• one or more reference spectra comprises a high- quality viral vector spectrum measured from a reference sample having a full capsid fraction (e.g., a-priori known; e.g., determined to be) at or above a particular threshold fraction.
• a threshold fraction is about 75% [e.g., about 80% (e.g., about 90%)].
• step (b) comprises computing a difference spectrum based on at least one of the one or more reference spectra and the IR absorbance spectrum (e.g., by subtracting an IR absorbance spectrum, and/or a scaled or otherwise pre-processed version thereof, from a reference spectrum, and/or a scaled or otherwise pre-processed version thereof, or vice-versa).
• - 24 11677372v1 Attorney Docket No.
• step (b) comprises computing one or more derivative spectra (e.g., a first derivative; e.g., a second derivative) of at least one of the one or more reference spectra and/or the IR absorbance spectrum.
• one or more derivative spectra e.g., a first derivative; e.g., a second derivative
• step (b) comprises computing (e.g., as the measure of deviation) one or more members selected from the group consisting of: a correlation value based on a correlation of (i) a particular one of the one or more reference spectra and/or one or more derivatives thereof, and (ii) the IR absorbance spectrum and/or one or more derivatives thereof; a covariance value based on a covariance of (i) a particular one of the one or more reference spectra and/or one or more derivatives thereof, and (ii) the IR absorbance spectrum and/or one or more derivatives thereof; a Pearson’s correlation value between (i) a particular one of the one or more reference spectra and/or one or more derivatives thereof, and (ii) the IR absorbance spectrum and/or one or more derivatives thereof; and an overlap integral value based on an overlap integral of (i) a particular one of the one or more reference spectra and/or one or more derivatives thereof, and (i
• step (b) comprises: determining values for a set of one or more particular peak metrics from the IR absorbance spectrum, thereby obtaining a set of sample peak metric values; and determining the measure of deviation based the set of sample peak metric values and a set of reference peak metric values having been determined for the one or more particular peak metrics from the one or more reference spectra.
• provided methods comprise determining, as the measure of deviation, a similarity score that measures a similarity between the one or more reference spectra and the IR absorbance spectrum.
• provided methods comprise performing steps (a) – (c) repeatedly, in substantially real-time, thereby monitoring deviation from the one or more reference spectra in real-time.
• the present disclosure is directed to certain methods for evaluating and/or monitoring (e.g., in real-time) quality of viral vector content within an aqueous sample comprising a target viral vector species, the method comprising: (a) receiving (e.g., repeatedly), by a processor of a computing device, infrared (IR) absorbance data corresponding to one or more IR absorbance signal(s) measured from the sample, the IR absorbance data comprising an (e.g., at least one) IR absorbance spectrum measured from the - 25 - 11677372v1 Attorney Docket No.
• IR infrared
• a corresponding (e.g., high-quality) reference sample comprising the target viral vector species at high purity and/or concentration and/or one or more model constituents thereof (e.g.,
• the present disclosure is directed to systems for quantifying and/or monitoring (e.g., in real-time) viral vector quality within an aqueous sample comprising one or more species of virus and/or virus-like particles, the systems comprising: a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to perform provided methods as described in certain aspects and embodiments herein (e.g., in paragraphs above).
• the present disclosure is directed to methods for (e.g., real- time) monitoring of compositional changes of a sample via infrared (IR) absorption spectroscopy, the methods comprising: (a) repeatedly receiving, by a processor of a computing device, infrared (IR) absorbance data corresponding to IR absorbance signals measured at each of a plurality of time points, the IR absorbance data comprising, for each particular time point of the plurality of time points, a corresponding IR absorbance spectrum measured from the sample at the particular time point and comprising a plurality of absorbance values, each associated with a particular wavenumber; (b) analyzing (e.g., automatically), by the processor, the IR absorbance data to obtain a (e.g., real-time) normalized spectral difference signal that measures a change normalized spectral absorbance between consecutive time points, by, for each particular time point of the plurality of time points: normalizing a current IR absorbance spectrum that corresponds to the particular
• 2017297-0013 absorption spectrum e.g., is an average of a plurality of previously obtained IR absorption spectra), each corresponding to and having been measured at a particular previous time point (e.g., preceding the current particular time point by a particular time interval and/or multiples thereof) and each particular previously obtained IR absorption spectrum having been normalized using a reference value determined from values of the that particular previously obtained IR absorbance spectrum at the one or more reference wavenumbers; and updating the real-time normalized spectral difference signal according to the current value of the normalized spectral difference metric; and (c) storing and/or providing, by the processor, the real-time normalized spectral difference signal for one or more of (i) further processing, (ii) display, and (iii) use as a control signal for adjustment of one or more process parameters of a production unit (e.g., a chromatography unit; e.g., a filtration unit).
• a production unit e.g.,
• provided methods comprise identifying (e.g., by the processor) a change in composition of the sample (e.g., at a particular time) based on the real- time normalized spectral difference signal.
• provided methods comprise detecting a changepoint [e.g., a change statistical properties (e.g., mean, median, mode, variance, etc.); e.g., a step- change] in the real-time normalized spectral difference signal and identifying the change in composition of the sample based on the detected changepoint.
• provided methods comprise determining (e.g., as a sample quality metric) (e.g., at each time point, e.g., in real-time) values of one or more statistical parameter(s) of the real-time normalized spectral difference signal.
• one or more statistical parameter(s) comprise one or more members selected from the group consisting of: a mean [e.g., running (e.g., backward looking) mean, e.g., computed as a mean of the real-time normalized spectral difference signal over a time window comprising (e.g., ending at) the current time point and one or more previous time point(s)]; a variance [e.g., running (e.g., backward looking) variance, e.g., computed as a variance of the real-time normalized spectral difference signal over a time window comprising (e.g., ending at) the current time point and one or more previous time point(s)]; a mode; and a standard deviation.
• a mean e.g., running (e.g., backward looking) mean, e.g., computed as a mean of the real-time normalized spectral difference signal over a time window comprising (e.g., ending at) the current time
• provided methods comprise identifying the change in composition based on a value of at least one of the one or more statistical parameter(s) (i) exceeding one or more threshold values and/or (ii) varying outside a particular range [e.g., - 27 - 11677372v1 Attorney Docket No. 2017297-0013 predetermined threshold values and/or ranges; e.g., threshold values and/or ranges that are determined on-the-fly, e.g., during an initial phase of a process run (e.g., during an initial time-window of a chromatography run, e.g., during an initial ramp-up of a salt gradient, e.g., before protein elutes)].
• a particular range e.g., - 27 - 11677372v1 Attorney Docket No. 2017297-0013 predetermined threshold values and/or ranges; e.g., threshold values and/or ranges that are determined on-the-fly, e.g
• a sample is or comprises an aqueous sample.
• sample comprises one or more protein species [e.g., a target protein species, such as a monoclonal antibody; e.g., as recited in certain embodiments herein].
• provided methods comprise identifying (e.g., by the processor) a change in composition of the sample (e.g., at a particular time) corresponding to a change in purity [e.g., presence of protein species other than the target; e.g., presence of undesired forms (e.g., non-monomeric) of the target protein species] and/or properties (e.g., secondary structure composition) of a target protein species.
• an identified change in composition is or comprises (e.g., is indicative of) a change in level and/or presence of protein aggregation within the aqueous sample.
• an identified change in composition is or comprises (e.g., is indicative of) a one or more members selected from the group consisting of: a change in content (e.g., relative content) of one or more particular protein species within the (e.g., aqueous) sample; a change in a level and/or type of molecular conjugation (e.g.
• a sample comprises nucleic acid (e.g., DNA, RNA, mRNA, etc.).
• provided methods comprise identifying (e.g., by the processor) a change in composition of the sample (e.g., at a particular time) corresponding to a change in purity and/or properties of the nucleic acid within the sample (e.g., a change in relative content of one or more particular types of nucleic acid (e.g., DNA, RNA, ssDNA, dsDNA)] within the (e.g., aqueous) sample.
• an aqueous sample comprises one or more species of virus and/or virus-like particles [e.g., adeno-associated viral vectors (AAV); e.g., lentiviral vectors].
• AAV adeno-associated viral vectors
• provided methods comprise identifying (e.g., by the processor) a change in composition of the sample (e.g., at a particular time) corresponding to a change in purity and/or properties of the virus and/or virus like particles within the sample.
• a change in composition corresponds to (e.g., is indicative of) a change in a relative fraction of empty and/or full viral vectors within the aqueous sample (e.g., percent, ratio, etc. of full viral vectors).
• change in composition corresponds to (e.g., is indicative of) a level of capsid aggregation within the sample.
• a change in composition corresponds to (e.g., is indicative of) a change in a relative content between viral nucleic acid from host cell proteins and host cell nucleic acid content.
• provided methods comprise triggering a response of a production unit (e.g., any of the parameters discussed herein) [e.g., wherein the production unit is a first production unit and the sample is associated with (e.g., is an input, an output, or component processed by) a second (e.g., upstream or downstream) production unit].
• a production unit e.g., any of the parameters discussed herein
• the production unit is a first production unit and the sample is associated with (e.g., is an input, an output, or component processed by) a second (e.g., upstream or downstream) production unit.
• a sample is an aqueous sample and the method comprises causing adjustment a collection window to control collection of a target fraction of the aqueous sample (e.g., a monomeric species of protein), thereby obtaining a purified sample.
• a production unit is or comprises a filtration unit (e.g., an ultrafiltration and/or diafiltration unit) (e.g., and wherein the method comprises causing - 29 - 11677372v1 Attorney Docket No. 2017297-0013 adjustment to a flow rates, transmembrane pressure, processing time, etc., to control a composition of retentate and/or permeate).
• a method comprises monitoring progress of a chemical reaction [e.g., within the production unit (e.g., a bioreactor, a transfection unit, a pegylation unit, an antibody drug conjugation unit]based on (e.g., identification of a compositional change using) the real-time normalized spectral difference signal.
• a method comprises causing adjustment to one or more members selected from the group consisting of an inline buffer preparation, a mixing process (e.g., in mixing tanks), a temperature controller.
• a reference absorbance value is determined from a value of the current IR absorbance spectrum at a single reference wavenumber and the prior reference value is determined from a value of the prior IR absorbance spectrum at the single reference wavenumber.
• determining a current value of the spectral difference metric comprises computing, for each of the current normalized spectrum and the prior normalized spectrum, an integrated absorbance over one or more particular spectral bands (e.g., and subtracting the integrated absorbance values).
• one or more particular spectral bands comprise one or more members selected from the group consisting of: an Amide I spectral band (e.g., ranging from about 1600 cm -1 to about 1700 cm -1 or about 1800 cm -1 (e.g., ranging from about 1600 cm -1 to about 1725 cm -1 ; e.g., ranging from about 1625cm -1 to about 1725 cm -1 ; e.g., ranging from about 1630 cm -1 to about 1650 cm -1 ), and/or an Amide II region, ranging from about 1500 to about 1600 cm -1 (e.g., ranging from about 1500 cm -1 to about 1575 cm -1 ; e.g., ranging from about 1500 cm -1 to about 1550 cm -1 ; e.g., ranging from about 1540 cm -1 to about 1560 cm -1 )); an Amide II spectral band (e.g., ranging from about 1500 cm -1 to about 1600 cm -1 or about 1800 cm -1 (
• one or more particular spectral bands comprise one or more members selected from the group consisting of: an antisymmetric-PO 4 spectral band - 30 - 11677372v1 Attorney Docket No. 2017297-0013 (e.g., ranging from about 1150 cm -1 to about 1250 cm -1 , e.g., ranging from about 1175 cm -1 to about 1250 cm -1 ; e.g., ranging from about 1200 cm -1 to about 1250 cm -1 ; e.g., ranging from about 1210 cm -1 to about 1230 cm -1 ); and a symmetric-PO 4 spectral band (e.g., ranging from about 1000 cm -1 to about 1100 cm -1 , e.g., ranging from about 1050 cm -1 to about 1100 cm -1 ; e.g., ranging from about 1075 cm -1 to about 1100 cm -1 ; e.g., ranging from about 1075 cm
• the present disclosure provides for methods for (e.g., real- time) monitoring of temporal changes of a sample via infrared (IR) absorption spectroscopy, the method comprising: (a) repeatedly receiving, by a processor of a computing device, infrared (IR) absorbance data corresponding to IR absorbance signals measured at each of a plurality of time points, the IR absorbance data comprising, for each particular time point of the plurality of time points, a corresponding IR absorbance spectrum measured from the sample at the particular time point and comprising a plurality of absorbance values, each associated with a particular wavenumber; (b) analyzing (e.g., automatically), by the processor, the IR absorbance data to obtain one or both of: a (e.g., real-time) a time
• a time differential signal and/or the time-aggregated signal for one or more of (i) further processing, (ii) display, and (iii) use as a control signal for adjustment of one or more process parameters of a production unit (e.g., a chromatography unit; e.g., a filtration unit).
• a production unit e.g., a chromatography unit; e.g., a filtration unit.
• the present disclosure provides systems for (e.g., real-time) monitoring of temporal (e.g., compositional) changes of a sample via infrared (IR) absorption spectroscopy, the system comprising: a processor of a computing device; and memory having - 31 - 11677372v1 Attorney Docket No. 2017297-0013 instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to perform various methods described herein (e.g., in paragraphs above).
• provided systems further comprise one or more MIR analyzer(s) (e.g., as described in paragraphs above).
• the present disclosure provides methods for obtaining a purified sample of a target protein species via mid-infrared (IR) spectroscopy-based bioprocess monitoring and control, said provided methods comprising: (a) measuring, via one or more mid-infrared (MIR) analyzer(s), at each of a plurality of time points, a corresponding mid-IR absorbance spectrum from an aqueous sample exiting from a purification unit, the aqueous sample comprising one or more protein species including the target protein species, thereby measuring a plurality of mid-IR absorbance spectra over time; (b) receiving, by a processor of a computing device, spectral data corresponding to the plurality of measured mid-IR absorbance spectra; (c) determining, by the processor, for each of at least a portion of the plurality of time points, corresponding values of one or more sample quality metrics based on the spectral data, the one or more sample quality metrics comprising a measure of concentration and/or purity of the
• a purification unit is or comprises a chromatography column ⁇ e.g., wherein the chromatography column is a member selected from the group consisting of an affinity chromatography (AC) column, a hydrophobic interaction chromatography (HIC) column, an ion exchange chromatograph (IEX) column, a size exclusion chromatography (SEC) column, and a mixed-mode chromatography column [e.g., any combination of the foregoing (e.g., IEX and HIC; e.g., IEX and SEC)] ⁇ .
• AC affinity chromatography
• HIC hydrophobic interaction chromatography
• IEX ion exchange chromatograph
• SEC size exclusion chromatography
• mixed-mode chromatography column e.g., any combination of the foregoing (e.g., IEX and HIC; e.g., IEX and SEC)] ⁇ .
• a purification unit is or comprises an ultrafiltration and diafiltration system (UF/DF) [e.g., a tangential flow filtration (TFF) system].
• UF/DF ultrafiltration and diafiltration system
• a target protein species is selected from the group consisting of a monoclonal antibody (mAb), a fusion protein, a viral capsid protein, an antibody-drug conjugate, a recombinant protein, and a plasmatic protein.
• mAb monoclonal antibody
• a fusion protein e.g., a viral capsid protein, an antibody-drug conjugate, a recombinant protein, and a plasmatic protein.
• an aqueous sample comprises a plurality of different molecular forms of a particular protein [e.g., a therapeutic protein (e.g., a mAb)], including a - 32 - 11677372v1 Attorney Docket No.
• an aqueous sample comprises one or more sub- species of a particular protein, each having a particular desired level and/or type of molecular conjugation (e.g., glycan, small-molecule drug, polyethylene glycol, etc.), and wherein the target protein species is a particular one of the one or more sub-species.
• a particular desired level and/or type of molecular conjugation e.g., glycan, small-molecule drug, polyethylene glycol, etc.
• one or more MIR analyzer(s) comprise a quantum- cascade laser (QCL)-based mid-IR spectrometer comprising: a QCL-based source, aligned and operable to emit a beam of MIR light [e.g., comprising one or more wavelengths substantially within the MIR spectral range (e.g., ranging from about 5000 cm-1 to about 500 cm-1 (e.g., about 2 to 20 microns))]; one or more sampling optics, aligned to direct and/or allow passage of the beam of MIR light, and/or at least a portion thereof, through and/or into contact with at least a portion of the aqueous sample [e.g., wherein the beam of MIR light contacts the portion of the aqueous sample via reflection at an interface between a solid material (e.g., an ATR crystal and/or optical fiber) and the aqueous sample (e.g., wherein the beam of MIR light undergoes total internal reflection
• QCL quantum- cascade
• one or more sampling optics comprise a flow cell comprising a detection channel through which the aqueous sample flows; and one or more detectors are aligned and operable to detect the beam of MIR light exiting from, following its transmission through, the detection channel.
• a QCL-based source is a tunable QCL operable sweep an emission frequency of the beam of MIR light through a plurality of frequencies across a scan range (e.g., wherein the scan range comprises a range from about 1700 cm-1 to 1400 cm-1; e.g., wherein the scan range comprises a range from 1300 cm-1 to 1050 cm-1; e.g., wherein the scan range comprises a range from at least 1200 cm-1 to 1000 cm-1) and the method comprises, at each of the one or more time points: sweeping the emission frequency of the beam MIR light across the scan range of the tunable laser, thereby illuminating the aqueous sample with a plurality of emission frequencies; and detecting, with the one or more - 33 - 11677372v1 Attorney Docket No.
• the beam of MIR light (e.g., having been (i) internally reflected by an interface between the high-index material and the aqueous sample and/or (ii) transmitted through the detection channel through which the aqueous sample flows) at each of the plurality of emission frequencies, thereby measuring, as the corresponding infrared (IR) absorbance signal from the aqueous sample, a corresponding IR spectrum comprising a plurality of values, each associated with and representing and/or based on a detected power at a particular one of the plurality of emission frequencies.
• IR infrared
• a MIR analyzer is an on-line sensor (e.g., as opposed to an off-line or at-line sensor) and wherein step (a) comprises repeatedly measuring IR absorbance spectra over time ⁇ e.g., every 20s or less [e.g., every 10s or less (e.g., every 5s or less; (e.g., every second or less))] ⁇ , as the aqueous solution exits from the purification unit (e.g., thereby measuring IR absorbance spectra from the aqueous sample in substantially real time).
• spectral data comprises, for each of the one or more time points, a corresponding Amide band spectrum [e.g., the Amide band spectrum comprising, for each particular wavelength of a plurality of sampled (e.g., emission) wavelengths within a range from about 1800 to about 800 cm-1 (e.g., with a range from about 1700 to 1400 cm-1), an associated absorption value representing a level of absorption, by a portion of the aqueous sample, at the particular wavelength].
• a corresponding Amide band spectrum e.g., the Amide band spectrum comprising, for each particular wavelength of a plurality of sampled (e.g., emission) wavelengths within a range from about 1800 to about 800 cm-1 (e.g., with a range from about 1700 to 1400 cm-1), an associated absorption value representing a level of absorption, by a portion of the aqueous sample, at the particular wavelength.
• step (c) comprises: receiving (e.g., and or accessing), by the processor, a reference spectrum for the target protein species, said reference having been measured from a particular corresponding reference sample comprising the target protein species substantially in isolation and/or at high purity [e.g., 75% purity or better (e.g., 90% purity or better (e.g., 95% purity or better))]; and repeatedly, at each of the plurality of time points, using the reference spectrum to determine a concentration of the target protein species within the aqueous sample at each time point, thereby tracking a concentration of the target protein species over time.
• high purity e.g., 75% purity or better (e.g., 90% purity or better (e.g., 95% purity or better)
• step (c) comprises: receiving (e.g., and or accessing), by the processor, one or more impurity reference spectra, each associated with a particular impurity of interest and having been measured from a particular corresponding reference sample comprising the impurity of interest substantially in isolation and/or at high purity [e.g., 75% purity or better (e.g., 90% purity or better (e.g., 95% purity or better))]; and - 34 - 11677372v1 Attorney Docket No. 2017297-0013 repeatedly, at each of the plurality of time points, using the one or more impurity reference spectra reference spectrum to determine a concentration of each of the impurities of interest within the aqueous sample.
• high purity e.g., 75% purity or better (e.g., 90% purity or better (e.g., 95% purity or better)
• spectral data comprises, for each of the one or more time points, a corresponding Amide band spectrum and wherein step (c) comprises determining, as the measure of sample purity, a ratio of absorbance at at least two wavenumbers within the Amide band spectrum.
• step (d) comprises causing, by the processor, transmission of one or more trigger signals (e.g., voltages) to a controller unit of the purification unit and/or a downstream (from the purification unit) valve.
• trigger signals e.g., voltages
• step (d) comprises one or both of: initiating, by the controller unit, based on the one or more trigger signals, collection of the target fraction of the aqueous sample [e.g., wherein a particular one of the one or more trigger signals is an analog signal and the controller unit initiates collection of the target fraction based on an amplitude of the analog signal (e.g., when it exceeds a particular threshold; e.g., when it drops below a particular threshold); e.g., wherein a particular one of the one or more trigger signals is a digital signal that triggers (e.g., by transitioning from a 0 voltage level to 1 voltage level or vice-a-versa) initiating collection of the target fraction]; and stopping, by the controller unit, based the one or more trigger signals, collection of the target fraction of the aqueous sample [e.g., wherein a particular one of the one or more trigger signals is an analog signal and the controller unit stops collection of the target fraction based on an amplitude of the analog signal (e
• a target protein species is a monomeric form of a particular protein (e.g., a monoclonal antibody) and the method comprises: at step (c), determining, over time, values of (i) a concentration of the monomeric form of the particular protein and/or (ii) a cumulative purity [e.g., a relative fraction (e.g., mass) of the monomeric form of the particular protein collected relative to total protein collected] of the monomeric form of the particular protein within a total collected volume of sample exiting from the purification unit; and at step (d), stopping collection of aqueous sample exiting from the - 35 - 11677372v1 Attorney Docket No.
• an aqueous sample comprises (i) one or more high aggregated forms of the particular protein and/or (ii) one or more fragmented species of the particular protein and wherein step (c) comprises determining concentrations of the one or more aggregated forms and/or concentrations of the one or more fragmented species of the particular protein over time.
• an aqueous sample comprises one or more excipients and the method comprises, at step (c): determining, based on the spectral data, values of concentrations and/or quantities of the one or more excipients within the aqueous sample exiting the purification unit at one or more time points; and at step (d), using the determined values of excipient concentrations and/or quantities to control collection of the target fraction of the aqueous sample.
• the present disclosure provides methods for preparing a biologic drug formulation comprising one or more excipients, said provided methods comprising: (a) receiving a solution comprising a purified drug substance comprising a protein species (e.g., a monoclonal antibody); (b) injecting and/or mixing, into the solution of the purified drug substance, one or more excipients, over a period of time, thereby creating an in-process drug substance solution comprising the purified drug substance and the one or more excipients at relative concentrations that vary over the period of time, as the one or more excipients are injected and/or mixed; (c) measuring, via one or more mid-infrared (MIR) analyzer(s), at each of one or more of time points, one or both of: (i) a corresponding mid-IR absorbance spectrum from the in-process drug substance solution; and (ii) a corresponding mid-IR absorbance spectrum from a stock solution comprising at least one of the one or more excip
• MIR mid-in
• step (b) comprises using an ultra- filtration/diafiltration (UF/DF) system (e.g., to perform buffer exchange).
• UF/DF ultra- filtration/diafiltration
• a protein species is or comprises a monoclonal antibody.
• one or more excipients are or comprise one or more surfactants ⁇ e.g., detergents; e.g., wetting and/or solubilizing agents [e.g., Polysorbate 20 (Tween 20), Polysorbate 80 (Tween 80), Poloxamer (Pluronic F68 and F127), Triton X-100, Brij 30, Brij 35, etc.] ⁇ .
• surfactants e.g., detergents; e.g., wetting and/or solubilizing agents [e.g., Polysorbate 20 (Tween 20), Polysorbate 80 (Tween 80), Poloxamer (Pluronic F68 and F127), Triton X-100, Brij 30, Brij 35, etc.] ⁇ .
• one or more excipients are or comprise one or more bulking agents ⁇ e.g., sugars and/or polyols [e.g., Sucrose, Trehalose, Glucose, Lactose, Sorbitol, Mannitol, Glycerol, etc.]; e.g., amino acids [e.g., Arginine, Aspartic Acid, Glutamic acid, Lysine, Proline, Glycine, Histidine, Methionine, Alanine, etc.]; e.g., polymers and proteins [e.g., Gelatin, PVP, PLGA, PEG, dextran, cyclodextrin and derivatives, starch derivatives, HSA, BSA] ⁇ .
• bulking agents e.g., sugars and/or polyols [e.g., Sucrose, Trehalose, Glucose, Lactose, Sorbitol, Mannitol, Glycerol, etc.]
• one or more MIR analyzer(s) comprise a quantum- cascade laser (QCL)-based mid-IR spectrometer comprising: a QCL-based source, aligned and operable to emit a beam of MIR light [e.g., comprising one or more wavelengths substantially within the MIR spectral range (e.g., ranging from about 5000 cm-1 to about 500 cm-1 (e.g., about 2 to 20 microns))]; one or more sampling optics, aligned to direct and/or allow passage of the beam of MIR light, and/or at least a portion thereof, through and/or into contact with at least a portion of the stock solution and/or a portion of the in-process drug substance solution [e.g., wherein the beam of MIR light contacts the portion of the stock solution and/or the portion of the in-process drug substance solution via reflection at an interface between a solid material (e.g., an ATR crystal and/or optical fiber) and the portion of the stock
• QCL quantum- cascade
• one or more sampling optics comprise a flow cell comprising a detection channel through which the portion of the stock solution and/or the portion of the in-process drug substance solution flow; and one or more detectors are aligned and operable to detect the beam of MIR light exiting from, following its transmission through, the detection channel.
• a QCL-based source is a tunable QCL operable sweep an emission frequency of the beam of MIR light through a plurality of frequencies across a scan range (e.g., wherein the scan range comprises a range from about 1700 cm-1 to 1400 cm-1; e.g., wherein the scan range comprises a range from 1300 cm-1 to 1050 cm-1; e.g., wherein the scan range comprises a range from at least 1200 cm-1 to 1000 cm-1) and the method comprises, at each of the one or more time points: sweeping the emission frequency of the beam MIR light across the scan range of the tunable laser, thereby illuminating the portion of the stock solution and/or the portion of the in-process drug substance solution with a plurality of emission frequencies; and detecting, with the one or more detectors, the beam of MIR light (e.g., having been (i) internally reflected by an interface between the high-index material and the portion of the stock solution and/or the portion of the
• a MIR analyzer is an on-line sensor (e.g., as opposed to an off-line or at-line sensor) and wherein step (c) comprises repeatedly measuring IR absorbance spectra over time ⁇ e.g., every 20s or less [e.g., every 10s or less (e.g., every 5s or less; (e.g., every second or less))] ⁇ , as one or more excipients are injected and/or mixed (e.g., thereby measuring IR absorbance spectra from the in-process drug substance solution in substantially real time).
• step (c) comprises repeatedly measuring IR absorbance spectra over time ⁇ e.g., every 20s or less [e.g., every 10s or less (e.g., every 5s or less; (e.g., every second or less))] ⁇ , as one or more excipients are injected and/or mixed (e.g., thereby measuring IR absorbance spectra from the in-process drug substance solution in substantially real
• spectral data comprises, for each of the one or more time points, a corresponding Amide band spectrum [e.g., the Amide band spectrum comprising, for each particular wavelength of a plurality of sampled (e.g., emission) wavelengths within a range from about 1800 to about 800 cm-1 (e.g., with a range from about 1700 to 1400 cm-1), an associated absorption value representing a level of absorption, by a portion of the aqueous sample, at the particular wavelength].
• a corresponding Amide band spectrum e.g., the Amide band spectrum comprising, for each particular wavelength of a plurality of sampled (e.g., emission) wavelengths within a range from about 1800 to about 800 cm-1 (e.g., with a range from about 1700 to 1400 cm-1), an associated absorption value representing a level of absorption, by a portion of the aqueous sample, at the particular wavelength.
• spectral data comprises, for each of the one or more time points, a corresponding sugar band spectrum [e.g., the sugar band spectrum comprising, for each particular wavelength of a plurality of sampled (e.g., emission) wavelengths within a range from about 1400 to about 800 cm-1 (e.g., with a range from about 1200 to 1000 cm-1), an associated absorption value representing a level of absorption, by a portion of the aqueous sample, at the particular wavelength].
• sampled e.g., emission
• step (e) comprises: receiving (e.g., and or accessing), by the processor, a reference spectrum for the protein species, said reference having been measured from a particular corresponding reference sample comprising the protein species substantially in isolation and/or at high purity [e.g., 75% purity or better (e.g., 90% purity or better (e.g., 95% purity or better))]; and repeatedly, at each of the plurality of time points, using the reference spectrum to determine a concentration of the protein species within the in- process drug substance solution at each time point, thereby tracking a concentration of the protein species over time.
• high purity e.g., 75% purity or better (e.g., 90% purity or better (e.g., 95% purity or better)
• step (e) comprises: receiving (e.g., and or accessing), by the processor, one or more excipient reference spectra, each associated with a particular excipient of interest (of the one or more excipients) and having been measured from particular corresponding reference sample comprising the particular excipient of interest substantially in isolation and/or at high purity [e.g., 75% purity or better (e.g., 90% purity or better (e.g., 95% purity or better))]; and repeatedly, at each of the plurality of time points, using the one or more excipient reference spectra reference spectrum to determine a concentration of each of the one or more excipients of interest within the stock solution and/or in-process drug substance solution.
• high purity e.g., 75% purity or better (e.g., 90% purity or better (e.g., 95% purity or better)
• step (f) comprises causing, by the processor, transmission of one or more trigger signals (e.g., voltages) to a controller unit (e.g., of a UF/DF system; e.g., of one or more valves).
• a controller unit e.g., of a UF/DF system; e.g., of one or more valves.
• step (f) comprises one or both of: initiating, by the controller unit, injection and/or mixing of the one or more excipients [e.g., wherein a particular one of the one or more trigger signals is an analog signal and the controller unit initiates the injection and/or mixing based on an amplitude of the analog signal (e.g., when it exceeds a particular threshold; e.g., when it drops below a particular threshold); e.g., wherein a particular one of the one or more trigger signals is a digital signal that triggers (e.g., by transitioning from a 0 voltage level to 1 voltage level or vice-a-versa) initiating the injection and/or mixing] and stopping, by the controller unit, based the one or more trigger signals, injection and/or mixing of the one or more excipients [e.g., wherein a particular one of the one or more trigger signals is an analog signal and the controller unit stops injection and/or mixing of the one or
• the present disclosure provides systems for obtaining a purified sample of a target protein species via mid-infrared (IR) spectroscopy-based bioprocess monitoring and control, said provided systems comprising: one or more mid- infrared (MIR) analyzer(s) [e.g., each operable to (e.g., based on one or more signals / communication with a processor) measure, at each of a plurality of time points, a corresponding mid-IR absorbance spectrum from an aqueous sample exiting from a purification unit, the aqueous sample comprising one or more protein species including the target protein species, thereby measuring a plurality of mid-IR absorbance spectra over time]; a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive spectral data corresponding to a plurality of measured mid-IR absorbance spectra, each having been measured, by the one or more MIR analyzer
• MIR mid- in
• the present disclosure provides systems for preparing a biologic drug formulation comprising one or more excipients, said provided systems comprising: one or more mid-infrared (MIR) analyzer(s); a processor of a computing device; and memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receiving spectral data corresponding to one or more mid-IR absorbance spectra having been measured, via the one or more MIR analyzer(s) at each of one or more time points, from one or both of: (i) an in-process drug substance solution comprising a purified drug substance and one or more excipients being injected and/or mixed therein/therewith, over time; and (ii) a stock solution comprising at least one of the one or more excipients
• MIR mid-infrared
• FIG.1A is a graph showing an absorption spectrum of biological material, according to an illustrative embodiment.
• FIG.1B is a schematic illustrating certain vibrational modes, according to an illustrative embodiment.
• FIG.2 is a graph and a schematic showing nucleic acid absorption in the mid- IR, according to an illustrative embodiment.
• FIG.3 is a schematic showing protein amide-band vibrations and relation to secondary structure conformation, according to an illustrative embodiment.
• FIG.4 is a diagram comparing mid-IR absorption spectroscopy with UV absorption, according to an illustrative embodiment.
• FIG.5 shows two graphs of UV absorption, according to an illustrative embodiment.
• FIG.6A is a schematic of an FT-IR spectrometer, according to an illustrative embodiment.
• FIG.6B is a schematic of a tunable QCL-based spectrometer, according to an illustrative embodiment.
• FIG.6C is a schematic illustrating operation of a FT-IR spectrometer and a tunable-QCL spectrometer, according to an illustrative embodiment.
• FIG.6D is a schematic illustrating operation of a FT-IR spectrometer and a tunable-QCL spectrometer, according to an illustrative embodiment.
• FIG.6E is a schematic illustrating operation of a FT-IR spectrometer and a tunable-QCL spectrometer, according to an illustrative embodiment.
• FIG.6F is a schematic illustrating operation of a FT-IR spectrometer and a tunable-QCL spectrometer, according to an illustrative embodiment.
• FIG.7 is a graph showing spectral brightness of certain mid-IR sources, according to an illustrative embodiment.
• FIG.8 is a graph showing water absorption in the mid-IR, according to an illustrative embodiment.
• FIG.9A is a schematic of a tunable QCL-based spectrometer, according to an illustrative embodiment.
• FIG.9B is a schematic illustrating tuning ranges of two QCL-based spectrometers, according to an illustrative embodiment.
• FIG.10A is a schematic illustrating certain steps and mathematical treatments used to obtain absorbance spectra of analytes present in a mobile phase, according to one or more illustrative embodiments.
• - 42 11677372v1 Attorney Docket No. 2017297-0013
• FIG.10B is a schematic illustrating certain steps and mathematical treatments used to obtain absorbance spectra of analytes present in a mobile phase, according to one or more illustrative embodiments.
• FIG.10C is a schematic illustrating certain steps and mathematical treatments used to obtain absorbance spectra of analytes present in a mobile phase, according to one or more illustrative embodiments.
• FIG.10D is a schematic illustrating certain steps and mathematical treatments used to obtain absorbance spectra of analytes present in a mobile phase, according to one or more illustrative embodiments.
• FIG.10E is a schematic illustrating certain steps and mathematical treatments used to obtain absorbance spectra of analytes present in a mobile phase, according to one or more illustrative embodiments.
• FIG.10F is a schematic illustrating certain steps and mathematical treatments used to obtain absorbance spectra of analytes present in a mobile phase, according to one or more illustrative embodiments.
• FIG.10G is a schematic illustrating certain steps and mathematical treatments used to obtain absorbance spectra of analytes present in a mobile phase, according to one or more illustrative embodiments.
• FIG.10H is a schematic illustrating certain steps and mathematical treatments used to obtain absorbance spectra of analytes present in a mobile phase, according to one or more illustrative embodiments.
• FIG.11 is an illustrative graph of three IR absorbance spectra showing absorbance of a protein-nucleic acid mixture, along with individual protein and nucleic acid component spectra, according to an illustrative embodiment.
• FIG.12 is a plot of a IR absorbance spectra measured from a high-quality viral vector sample (e.g., having a high fraction of full capsids) and lower quality viral vector sample (e.g., comprising a large fraction of empty capsids), shown along with a difference spectrum, according to an illustrative embodiment.
• FIG.13 is a schematic showing use of an example mid-IR spectrometer for measurements at various stages and with various production units in a biologic manufacturing process, according to an illustrative embodiment. - 43 - 11677372v1 Attorney Docket No.
• FIG.14 is a schematic showing an empty adeno-associated virus (AAV) capsid along with one (AAV capsid) loaded with a genetic cassette, according to an illustrative embodiment.
• FIG.15 is a block flow diagram of an example process for using IR absorption data measured from a viral vector sample to determine sample quality metrics and/or control bioproduction processes, according to an illustrative embodiment.
• FIG.16A is a diagram showing steps in an AAV vector manufacturing process, according to an illustrative embodiment.
• FIG.16B is a diagram showing steps in a lentiviral vector manufacturing process, according to an illustrative embodiment.
• FIG.17 is a block flow diagram of an example process for using a reference spectrum to determine sample quality metrics and/or control bioproduction processes, according to an illustrative embodiment.
• FIG.18 is a block diagram of an exemplary cloud computing environment, used in certain embodiments.
• FIG.19 is a block diagram of an example computing device and an example mobile computing device, used in certain embodiments.
• FIG.20A is a graph showing measurement of bovine serum albumin (BSA) with mid-IR spectroscopy, according to an illustrative embodiment.
• FIG.20B is a graph showing measurement of bovine serum albumin (BSA) with mid-IR spectroscopy, according to an illustrative embodiment.
• FIG.20C is a graph showing measurement of bovine serum albumin (BSA) with mid-IR spectroscopy, according to an illustrative embodiment.
• FIG.20D is a graph showing measurement of bovine serum albumin (BSA) with mid-IR spectroscopy, according to an illustrative embodiment.
• FIG.21 is a graph showing use of absorption spectroscopy for chromatography process monitoring, according to an illustrative embodiment.
• FIG.22A is a graph showing use of absorption spectroscopy for chromatography process monitoring, according to an illustrative embodiment.
• - 44 - 11677372v1 Attorney Docket No.
• FIG.23D is a graph demonstrating use of mid-IR spectroscopy to measure protein secondary structure content, according to an illustrative embodiment.
• FIG.24 is a set of graphs demonstrating use of mid-IR spectroscopy to measure protein secondary structure content, according to an illustrative embodiment.
• FIG.25 illustrates use of mid-IR absorption spectroscopy to compute protein relative density, according to an illustrative embodiment.
• FIG.26A is a graph showing variation in mid-IR baseline with conductivity during elution, according to an illustrative embodiment.
• FIG.26B is a graph showing variation in mid-IR baseline with conductivity during elution, according to an illustrative embodiment.
• FIG.27 is a graph illustrating calculation of certain sample quality metrics described herein, according to certain illustrative embodiments.
• FIG.28A is a graph illustrating calculation of certain sample quality metrics described herein, according to certain illustrative embodiments.
• FIG.28B is a graph illustrating calculation of certain sample quality metrics described herein, according to certain illustrative embodiments.
• FIG.29A is a graph illustrating calculation of certain sample quality metrics described herein, according to certain illustrative embodiments.
• FIG.29B is a graph illustrating calculation of certain sample quality metrics described herein, according to certain illustrative embodiments. - 45 - 11677372v1 Attorney Docket No. 2017297-0013 [0233]
• FIG.30A is a graph illustrating calculation of certain sample quality metrics described herein, according to certain illustrative embodiments.
• FIG.30B is a graph illustrating calculation of certain sample quality metrics described herein, according to certain illustrative embodiments.
• FIG.31 is a graph illustrating calculation of certain sample quality metrics described herein, according to certain illustrative embodiments.
• FIG.32A is a block flow diagraph for a process for control of a production process based on IR absorption spectroscopy, according to an illustrative embodiment.
• FIG.32B is an illustrative sketch showing anticipated (hypothetical) variation in sample quality metrics that measure total protein content and protein aggregation, as described herein, over time, during elution from an IEX chromatography column, according to an illustrative embodiment.
• FIG.33A is a diagram illustrating certain workflows and data processing approaches for performing mid-IR spectroscopic measurements in accordance with various illustrative embodiments.
• FIG.33B is a diagram illustrating certain workflows and data processing approaches for performing mid-IR spectroscopic measurements in accordance with various illustrative embodiments.
• FIG.33C is a diagram illustrating certain workflows and data processing approaches for performing mid-IR spectroscopic measurements in accordance with various illustrative embodiments.
• FIG.34A is a schematic illustrating control of elution, according to an illustrative embodiment.
• FIG.34B is a schematic illustrating control of elution, according to an illustrative embodiment.
• FIG.35 is a set of graphs demonstrating protein secondary structure monitoring during TFF injections, according to an illustrative embodiment.
• FIG.36 is a graph showing spectral coverage of a QCL system, according to an illustrative embodiment.
• - 46 - 11677372v1 Attorney Docket No. 2017297-0013
• FIG.37 is a graph demonstrating sensitivity improvements in a mid-IR QCL system.
• FIG.38A is a graph showing measurement of multiple analytes, according to an illustrative embodiment.
• FIG.38B is a graph showing measurement of multiple analytes, according to an illustrative embodiment.
• FIG.39A is a graph showing measurement of multiple analytes, according to an illustrative embodiment.
• FIG.39B is a graph showing measurement of multiple analytes, according to an illustrative embodiment.
• FIG.40A is a graph showing measurement of multiple analytes, according to an illustrative embodiment.
• FIG.40B is a graph showing measurement of multiple analytes, according to an illustrative embodiment.
• FIG.41A is a graph showing protein secondary structure measurements, according to an illustrative embodiment.
• FIG.41B is a graph showing protein secondary structure measurements, according to an illustrative embodiment.
• FIG.41C is a graph showing protein secondary structure measurements, according to an illustrative embodiment.
• FIG.42 is a graph showing protein secondary structure measurements, according to an illustrative embodiment.
• FIG.43A is a graph showing protein secondary structure measurements, according to an illustrative embodiment.
• FIG.43B is a graph showing protein secondary structure measurements, according to an illustrative embodiment.
• FIG.44 is a schematic of an example system comprising multiple mid-IR analyzers and an external computer, according to an illustrative embodiment. - 47 - 11677372v1 Attorney Docket No. 2017297-0013
• FIG.45A is a graph plotting results of certain tests of reproducibility, according to an illustrative embodiment.
• FIG.45B is a graph plotting results of certain tests of reproducibility, according to an illustrative embodiment.
• FIG.45C is a graph plotting results of certain tests of reproducibility, according to an illustrative embodiment.
• FIG.45D is a graph plotting results of certain tests of reproducibility, according to an illustrative embodiment.
• FIG.45E is a graph plotting results of certain tests of reproducibility, according to an illustrative embodiment.
• FIG.46A is a graph showing IR absorbance spectra for a DNA and a protein (BSA) solution, according to an illustrative embodiment.
• FIG.46B is a graph showing IR absorbance spectra for various DNA-protein mixtures, according to an illustrative embodiment.
• FIG.46C is a diagram showing molecular structure of nucleotide bases.
• FIG.46D is a plot of three IR absorbance spectra showing absorbance of a protein-nucleic acid mixture, along with individual protein and nucleic acid component spectra, according to an illustrative embodiment
• FIG.47A is a graph of IR absorbance spectra measured for a sample comprising a monoclonal antibody at various concentrations, according to an illustrative embodiment.
• FIG.47B is a graph of IR absorbance spectra measured for a sample comprising a monoclonal antibody at various concentrations, according to an illustrative embodiment.
• FIG.48 is a graph showing an absorbance-based chromatogram along with variation in a normalized spectral difference signal over time, according to an illustrative embodiment.
• FIG.49 shows a set of graphs showing an absorbance-based chromatogram along with variation in different versions of normalized spectral difference signal over time, according to an illustrative embodiment.
• - 48 - 11677372v1 Attorney Docket No.
• FIG.50A is a schematic illustrating a system for protein purification, according to an illustrative embodiment.
• FIG.50B is a block flow diagram of a process for monitoring protein purification via mid-IR spectroscopy, according to an illustrative embodiment.
• FIG.50C is a block flow diagram of a process for determining component concentrations and/or sample quality metrics from measured IR spectra, according to an illustrative embodiment.
• FIG.51A is a graph of three reference spectra, according to an illustrative embodiment.
• FIG.51B is a graph of simulated variation in integrated absorbance over time over the course of an ion exchange column run, according to an illustrative embodiment.
• FIG.51C is a graph showing extracted protein components from a simulated experiment, according to an illustrative embodiment.
• FIG.51D is a graph showing variation in purity and yield determined from a simulated chromatography run, according to an illustrative embodiment.
• FIG.51E is a set of three graphs showing (i) extracted protein components, (ii) integrated absorbance, and (iii) purity and yield as determined for a simulated chromatography run, according to an illustrative embodiment.
• FIG.51F is a set of three graphs showing (i) extracted protein components, (ii) integrated absorbance, and (iii) purity and yield as determined for a simulated chromatography run, according to an illustrative embodiment.
• FIG.51G is a set of three graphs showing (i) extracted protein components, (ii) integrated absorbance, and (iii) purity and yield as determined for a simulated chromatography run, according to an illustrative embodiment.
• FIG.51H is a set of three graphs showing (i) extracted protein components, (ii) integrated absorbance, and (iii) purity and yield as determined for a simulated chromatography run, according to an illustrative embodiment.
• FIG.51I is a graph showing integrated absorbance measured over the course of a size exclusion chromatography run, according to an illustrative embodiment. - 49 - 11677372v1 Attorney Docket No. 2017297-0013 [0284]
• FIG.51J is a graph showing extracted dimer, monomer, and mixed dimer/monomer spectra, according to an illustrative embodiment.
• FIG.51K is a graph showing a fragment reference spectrum, according to an illustrative embodiment.
• FIG.52A is a graph showing mid-IR spectra of certain buffers.
• FIG.52B is a graph showing mid-IR spectra of certain sugars.
• FIG.53 is a schematic illustrating UF/DF process monitoring via an inline mid-IR analyzer and an inline UV spectrometer, according to an illustrative embodiment.
• FIG.54A is a graph showing measured BSA concentration over time as determined via mid-IR spectroscopy and UV absorbance.
• FIG.54B is a graph showing sucrose concentration over time as measured via mid-IR spectroscopy and a Cedex assay.
• FIG.55A is a graph showing mid-IR spectra of polysorbate 80 (PS80) in water at various concentrations.
• FIG.55B is a graph showing mid-IR spectra of PS80 spiked into a formulation buffer at various concentrations.
• FIG.56A is a graph showing mid-IR spectra of two sugars.
• FIG.56B is a graph showing mid-IR spectra of two proteins within a sugar absorption band.
• FIG.57A is a graph showing mid-IR spectra of a high molecular weight form of a monoclonal antibody spiked into a pure monomer solution at varying concentrations.
• FIG.57B is a graph showing detail around the amide band region for the spectra shown in FIG.57A.
• FIG.57C is a graph comparing an IR peak metric correlation with monomer purity as measured by size exclusion chromatograph.
• A an: As used herein, “a” or “an” with reference to a claim feature means “one or more,” or “at least one.”
• Absorption data, absorption spectra, absorbance data, absorbance spectra As used herein, the terms “absorption data”, “absorption spectra”, “absorbance data”, “absorbance spectra” as in, e.g., “IR absorption data”, “IR absorption spectra”, “IR absorbance data”, “IR absorbance spectra”, etc., are used to refer to data, such as spectral data, that represents signal produced by and/or indicative of optical absorption by a sample, whether the data and/or underlying signal is obtained via a transmission measurement, an attenuated total internal (ATR) measurement, a reflection measurement, or other measurement.
• ATR attenuated total internal
• an absorption spectrum may be represented as a transmission spectrum, in which absorption bands appear as negative peaks, or as an absorption spectrum, in which absorption peaks are positive, pointing upwards.
• An absorption spectrum may be represented in linear units or logarithmic units. While the term “absorbance” is, in certain cases, used in IR spectroscopy to refer to a unit that is directly proportional to concentration and path length (e.g., a logarithm of transmittance), its use herein is not intended to limit any method, system, processing approach, computation, etc.
• Administration typically refers to the administration of a composition to a subject or system.
• routes that may, in appropriate circumstances, be utilized for administration to a subject, for example a human.
• administration may be ocular, oral, parenteral, topical, etc.
• administration may be bronchial (e.g., by bronchial instillation), buccal, dermal (which may - 51 - 11677372v1 Attorney Docket No.
• 2017297-0013 be or comprise, for example, one or more of topical to the dermis, intradermal, interdermal, transdermal, etc.), enteral, intra-arterial, intradermal, intragastric, intramedullary, intramuscular, intranasal, intraperitoneal, intrathecal, intravenous, intraventricular, within a specific organ (e.g., intrahepatic), mucosal, nasal, oral, rectal, subcutaneous, sublingual, topical, tracheal (e.g., by intratracheal instillation), vaginal, vitreal, etc.
• a specific organ e.g., intrahepatic
• mucosal nasal, oral, rectal, subcutaneous, sublingual, topical, tracheal (e.g., by intratracheal instillation), vaginal, vitreal, etc.
• administration may involve dosing that is intermittent (e.g., a plurality of doses separated in time) and/or periodic (e.g., individual doses separated by a common period of time) dosing. In some embodiments, administration may involve continuous dosing (e.g., perfusion) for at least a selected period of time.
• Affinity As is known in the art, “affinity” is a measure of the tightness with which two or more binding partners associate with one another. Those skilled in the art are aware of a variety of assays that can be used to assess affinity, and will furthermore be aware of appropriate controls for such assays. In some embodiments, affinity is assessed in a quantitative assay.
• affinity is assessed over a plurality of concentrations (e.g., of one binding partner at a time). In some embodiments, affinity is assessed in the presence of one or more potential competitor entities (e.g., that might be present in a relevant – e.g., physiological – setting). In some embodiments, affinity is assessed relative to a reference (e.g., that has a known affinity above a particular threshold [a “positive control” reference] or that has a known affinity below a particular threshold [ a “negative control” reference”]. In some embodiments, affinity may be assessed relative to a contemporaneous reference; in some embodiments, affinity may be assessed relative to a historical reference. Typically, when affinity is assessed relative to a reference, it is assessed under comparable conditions.
• Amino acid in its broadest sense, as used herein, refers to any compound and/or substance that can be incorporated into a polypeptide chain, e.g., through formation of one or more peptide bonds.
• an amino acid has the general structure H 2 N–C(H)(R)–COOH.
• an amino acid is a naturally-occurring amino acid.
• an amino acid is a non-natural amino acid; in some embodiments, an amino acid is a D-amino acid; in some embodiments, an amino acid is an L- amino acid.
• Standard amino acid refers to any of the twenty standard L-amino acids commonly found in naturally occurring peptides.
• Nonstandard amino acid refers to any amino acid, other than the standard amino acids, regardless of whether it is prepared synthetically or obtained from a natural source.
• an amino acid, - 52 - 11677372v1 Attorney Docket No. 2017297-0013 including a carboxy- and/or amino-terminal amino acid in a polypeptide can contain a structural modification as compared with the general structure above.
• an amino acid may be modified by methylation, amidation, acetylation, pegylation, glycosylation, phosphorylation, and/or substitution (e.g., of the amino group, the carboxylic acid group, one or more protons, and/or the hydroxyl group) as compared with the general structure.
• such modification may, for example, alter the circulating half-life of a polypeptide containing the modified amino acid as compared with one containing an otherwise identical unmodified amino acid.
• such modification does not significantly alter a relevant activity of a polypeptide containing the modified amino acid, as compared with one containing an otherwise identical unmodified amino acid.
• an antibody polypeptide may be used to refer to a free amino acid; in some embodiments it may be used to refer to an amino acid residue of a polypeptide.
• antibody polypeptide As used herein, the terms “antibody polypeptide” or “antibody”, or “antigen-binding fragment thereof”, which may be used interchangeably, refer to polypeptide(s) capable of binding to an epitope.
• an antibody polypeptide is a full-length antibody, and in some embodiments, is less than full length but includes at least one binding site (comprising at least one, and preferably at least two sequences with structure of antibody “variable regions”).
• antibody polypeptide encompasses any protein having a binding domain which is homologous or largely homologous to an immunoglobulin-binding domain.
• antibody polypeptides encompasses polypeptides having a binding domain that shows at least 99% identity with an immunoglobulin binding domain.
• antibody polypeptide is any protein having a binding domain that shows at least 70%, 80%, 85%, 90%, or 95% identity with an immuglobulin binding domain, for example a reference immunoglobulin binding domain.
• An included “antibody polypeptide” may have an amino acid sequence identical to that of an antibody that is found in a natural source.
• Antibody polypeptides in accordance with the present invention may be prepared by any available means including, for example, isolation from a natural source or antibody library, recombinant production in or with a host system, chemical synthesis, etc., or combinations thereof.
• An antibody polypeptide may be monoclonal or polyclonal.
• An antibody polypeptide may be a member of any immunoglobulin class, including any of the human classes: IgG, IgM, IgA, IgD, and IgE.
• an antibody may be a - 53 - 11677372v1 Attorney Docket No. 2017297-0013 member of the IgG immunoglobulin class.
• antibody polypeptide or “characteristic portion of an antibody” are used interchangeably and refer to any derivative of an antibody that possesses the ability to bind to an epitope of interest.
• the “antibody polypeptide” is an antibody fragment that retains at least a significant portion of the full-length antibody’s specific binding ability. Examples of antibody fragments include, but are not limited to, Fab, Fab’, F(ab’)2, scFv, Fv, dsFv diabody, and Fd fragments.
• an antibody fragment may comprise multiple chains that are linked together, for example, by disulfide linkages.
• an antibody polypeptide may be a human antibody.
• the antibody polypeptides may be a humanized.
• Humanized antibody polypeptides include may be chimeric immunoglobulins, immunoglobulin chains or antibody polypeptides (such as Fv, Fab, Fab', F(ab')2 or other antigen-binding subsequences of antibodies) that contain minimal sequence derived from non-human immunoglobulin.
• humanized antibodies are human immunoglobulins (recipient antibody) in which residues from a complementary-determining region (CDR) of the recipient are replaced by residues from a CDR of a non-human species (donor antibody) such as mouse, rat or rabbit having the desired specificity, affinity, and capacity.
• CDR complementary-determining region
• Backbone refers to the portion of the peptide or polypeptide chain that comprises the links between amino acids of the chain but excludes side chains.
• a backbone refers to the part of a peptide or polypeptide that would remain if side chains were removed.
• the backbone is a chain comprising a carboxyl group of one amino acid bound via a peptide bond to an amino group of a next amino acid, and so on. Backbone may also be referred to as “peptide backbone”.
• peptide backbone As used herein, the term “biologic” refers to a composition that is or may be produced by recombinant DNA technologies, chemical synthesis, peptide synthesis, or purified and/or isolated from natural sources (such as human, animal, or microorganisms) and that has a desired biological activity.
• a biologic can be, for example, a protein, peptide, glycoprotein, polysaccharide, nucleic acid, phospholipids, a mixture of proteins or peptides, a mixture of glycoproteins, a mixture of polysaccharides, a mixture of nucleic acids, a mixture of one or more of a protein, peptide, glycoprotein, polysaccharide or nucleic acid, or a derivatized form and/or an assembly of any of the foregoing entities.
• biologics may be or comprise living entities, such as cells or tissues.
• biologics can vary widely, from about 1000 Da for small peptides such as peptide hormones to one thousand kDa or more for complex polysaccharides, mucins, and other heavily glycosylated proteins.
• biologics include, without limitation vaccines, blood and blood components, allergenics, somatic cells, gene therapy, tissues, and recombinant therapeutic proteins.
• a biologic is a drug used for treatment of diseases and/or medical conditions.
• biologic drugs include, without limitation, native or engineered antibodies or antigen binding fragments thereof, and antibody-drug conjugates, which comprise an antibody or antigen binding fragments thereof conjugated directly or indirectly (e.g., via a linker) to a drug of interest, such as a cytotoxic drug or toxin.
• biologic drugs such as gene therapy drugs, comprise vectors, such as adeno-associated viral (AAV), adenoviral, lentiviral, and retroviral vectors, together with (e.g., loaded with) nucleic acid, such as DNA or RNA.
• AAV adeno-associated viral
• adenoviral adenoviral
• lentiviral lentiviral
• retroviral vectors together with (e.g., loaded with) nucleic acid, such as DNA or RNA.
• a biologic is a diagnostic, used to diagnose diseases and/or medical conditions.
• allergen patch tests utilize biologics (e.g., biologics manufactured from natural substances) that are known to cause contact dermatitis. Diagnostic biologics may also include medical imaging agents, such as proteins that are labelled with agents that provide a detectable signal that facilitates imaging such as fluorescent markers, dyes, radionuclides, and the like.
• biologics e.g., biologics manufactured from natural substances
• Diagnostic biologics may also include medical imaging agents, such as proteins that are labelled with agents that provide a detectable signal that facilitates imaging such as fluorescent markers, dyes, radionuclides, and the like.
• In vitro refers to events that occur in an artificial environment, e.g., in a test tube or reaction vessel, in cell culture, etc., rather than within a multi-cellular organism.
• In vivo refers to events that occur within a multi-cellular organism, such as a human and a non-human animal. In the context of cell- based systems, the term may be used to refer to events that occur within a living cell (as opposed to, for example, in vitro systems).
• Peptide The term “peptide” as used herein refers to a polypeptide that is typically relatively short, for example having a length of less than about 100 amino acids, less than about 50 amino acids, less than about 40 amino acids less than about 30 amino acids, less than about 25 amino acids, less than about 20 amino acids, less than about 15 amino acids, or less than 10 amino acids.
• Polypeptide As used herein refers to a polymeric chain of amino acids.
• a polypeptide has an amino acid sequence that occurs in nature.
• a polypeptide has an amino acid sequence that does not occur in nature.
• a polypeptide has an amino acid sequence that is engineered in that it is designed and/or produced through action of the hand of man.
• a polypeptide may comprise or consist of natural amino acids, non-natural amino acids, or both.
• a polypeptide may comprise or consist of only natural amino acids or only non-natural amino acids.
• a polypeptide may comprise D-amino acids, L-amino acids, or both.
• a polypeptide may comprise only D-amino acids. In some embodiments, a polypeptide may comprise only L-amino acids. In some embodiments, a polypeptide may include one or more pendant groups or other modifications, e.g., modifying or attached to one or more amino acid side chains, at the polypeptide’s N-terminus, at the polypeptide’s C-terminus, or any combination thereof. In some embodiments, such pendant groups or modifications may be selected from the group consisting of acetylation, amidation, lipidation, methylation, pegylation, etc., including combinations thereof. In some embodiments, a polypeptide may be cyclic, and/or may comprise a cyclic portion.
• a polypeptide is not cyclic and/or does not comprise any cyclic portion.
• a polypeptide is linear.
• a polypeptide may be or comprise a stapled polypeptide.
• the term “polypeptide” may be appended to a name of a reference polypeptide, activity, or structure; in such instances it is used herein to refer to polypeptides that share the relevant activity or structure and thus can be considered to be members of the same class or family of polypeptides. For each such class, the present specification provides and/or those skilled in the art will be aware of exemplary polypeptides within the class whose amino acid - 56 - 11677372v1 Attorney Docket No.
• polypeptides are reference polypeptides for the polypeptide class or family.
• a member of a polypeptide class or family shows significant sequence homology or identity with, shares a common sequence motif (e.g., a characteristic sequence element) with, and/or shares a common activity (in some embodiments at a comparable level or within a designated range) with a reference polypeptide of the class; in some embodiments with all polypeptides within the class).
• a member polypeptide shows an overall degree of sequence homology or identity with a reference polypeptide that is at least about 30-40%, and is often greater than about 50%, 60%, 70%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more and/or includes at least one region (e.g., a conserved region that may in some embodiments be or comprise a characteristic sequence element) that shows very high sequence identity, often greater than 90% or even 95%, 96%, 97%, 98%, or 99%.
• a conserved region that may in some embodiments be or comprise a characteristic sequence element
• Such a conserved region usually encompasses at least 3-4 and often up to 20 or more amino acids; in some embodiments, a conserved region encompasses at least one stretch of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or more contiguous amino acids.
• a relevant polypeptide may comprise or consist of a fragment of a parent polypeptide.
• a useful polypeptide as may comprise or consist of a plurality of fragments, each of which is found in the same parent polypeptide in a different spatial arrangement relative to one another than is found in the polypeptide of interest (e.g., fragments that are directly linked in the parent may be spatially separated in the polypeptide of interest or vice versa, and/or fragments may be present in a different order in the polypeptide of interest than in the parent), so that the polypeptide of interest is a derivative of its parent polypeptide.
• Protein refers to a polypeptide (i.e., a string of at least two amino acids linked to one another by peptide bonds).
• Proteins may include moieties other than amino acids (e.g., may be glycoproteins, proteoglycans, etc.) and/or may be otherwise processed or modified.
• a “protein” can be a complete polypeptide chain as produced by a cell (with or without a signal sequence), or can be a characteristic portion thereof.
• a protein can sometimes include more than one polypeptide chain, for example linked by one or more disulfide bonds or associated by other means.
• Polypeptides may contain L-amino acids, D-amino acids, or both and may contain any of a variety of amino acid modifications or analogs known in the art.
• proteins may comprise natural amino acids, non-natural amino acids, synthetic amino acids, and combinations thereof.
• the term “peptide” is generally used to refer to a polypeptide having a length of less than about 100 amino acids, less than about 50 amino acids, less than 20 amino acids, or less than 10 amino acids.
• proteins are antibodies, antibody fragments, biologically active portions thereof, and/or characteristic portions thereof.
• Machine learning module As used herein, the terms “machine learning module” and “machine learning model” are used interchangeably and refer to a computer implemented process (e.g., a software function) that implements one or more particular machine learning algorithms, such as an artificial neural networks (ANN), convolutional neural networks (CNNs), random forest, decision trees, support vector machines, and the like, in order to determine, for a given input, one or more output values.
• machine learning modules implementing machine learning techniques are trained, for example using curated and/or manually annotated datasets. Such training may be used to determine various parameters of machine learning algorithms implemented by a machine learning module, such as weights associated with layers in neural networks.
• machine learning module may receive feedback, e.g., based on user review of accuracy, and such feedback may be used as additional training data, for example to dynamically update the machine learning module.
• a trained machine learning module is a classification algorithm with adjustable and/or fixed (e.g., locked) parameters, e.g., a random forest classifier.
• two or more machine learning modules may be combined and implemented as a single module and/or a single software application.
• two or more machine learning modules may also be implemented separately, e.g., as separate software applications.
• a machine learning module may be software and/or hardware.
• a machine learning module may be implemented entirely as software, or certain functions of a ANN module may be carried out via specialized hardware (e.g., via an application specific integrated circuit (ASIC), field programmable gate arrays (FPGAs), and the like).
• ASIC application specific integrated circuit
• FPGAs field programmable gate arrays
• Mid-Infrared, Mid-IR, MIR As used herein, the terms mid-infrared, mid-IR, and MIR are used interchangeably to refer to the portion of the electromagnetic spectrum ranging from about 5000 cm -1 to about 500 cm -1 (corresponding to a wavelength range from about 2 ⁇ m to about 20 ⁇ m) and/or, in certain embodiments, from about 3,000 cm -1 to about 800 cm -1 (corresponding to wavelengths ranging from about 3 ⁇ m to about 12 ⁇ m).
• biologic production monitoring and/or control technologies described herein utilize mid-infrared (MIR) analyzers to measure infrared (IR) absorption signals from liquid samples and generate IR absorption data, such as IR spectra, in substantially real-time. Liquid samples measured in this manner may serve as inputs to and/or outputs of one or more production units used in manufacture of biologics.
• MIR mid-infrared
• IR infrared
• IR spectral data is used by biologic production monitoring and control technologies described herein to determine sample quality metrics that provide, among other things, measures of sample characteristics such as content of one or more desired target molecules, presence of impurities, molecular structural information, and the like.
• sample quality attributes e.g., critical quality attributes
• One or more sample quality attributes may be monitored in real-time, and used, individually and/or in combination with each other and/or data from other sensors, to control and/or refine operation of one or more production units, thereby facilitating compliance with demanding quality tolerances, which may be required by regulation and/or produce increasingly effective and/or safe product, scaling up production capacity, improving efficiency, and the like.
• Mid-Infrared (MIR) Spectroscopy can be used to obtain detailed information about vibrational transitions of biological molecules, such as carbohydrates, lipids, nucleic acids, proteins, and the like.
• mid-IR spectroscopy - 60 - 11677372v1 Attorney Docket No. 2017297-0013 measures IR absorption signals resulting from vibrational modes of molecules.
• mid-IR light comprising a range of MIR frequencies/wavelengths
• Molecules have characteristic sets of vibrational modes, which are dependent, among other things, on their molecular structure as well as local environment. Accordingly, as shown in FIG.1A, various molecules, such as lipids, proteins, nucleic acids, carbohydrates, and the like, absorb light within (one or more) characteristic bands. Absorption at these characteristic bands may be observed, for example, as a series of peaks in an IR absorption spectrum. As described in further detail herein, features of these peaks, such as their amplitude, linewidth, center frequenc(ies), area, etc., can be used to determine metrics that measure sample properties such as total protein content, molecular identity and/or heterogeneity, etc.
• one or more mid-IR spectral bands are associated with nucleic acid molecules.
• one or more peaks in a mid- IR spectrum may be associated with a antisymmetric PO4 stretch and/or an symmetric PO4 stretch bands, which, for the sample shown in FIG.2, present at around 1220 - 1250 cm -1 and 1075 – 1100 cm -1 , respectively and, accordingly, used to detect presence of and/or characterize nucleic acid, such as DNA and/or RNA, within a sample.
• an IR absorption band associated with an antisymmetric PO4 stretch vibrational mode may occur/range from about 1150 cm -1 to about 1250 cm -1 (e.g., ranging from about 1175 cm -1 to about 1250 cm -1 ; e.g., ranging from about 1200 cm -1 to about 1250 cm -1 ; e.g., ranging from about 1210 cm -1 to about 1230 cm -1 ).
• IR absorption associated with a symmetric PO4 stretch vibrational mode may occur/range from about 1000 cm -1 to about 1100 cm -1 (e.g., ranging from about 1050 cm -1 to about 1100 cm -1 ; e.g., ranging from about 1075 cm -1 to about 1100 cm -1 ; e.g., ranging from about 1075 cm -1 to about 1085 cm -1 ).
• features such as central frequencies, shapes, individual and/or relative amplitudes, of peaks associated with these two (symmetric and antisymmetric) PO 4 stretch bands may be used to characterize a type and/or particular conformation of nucleic acid molecules.
• FIG.2 shows variations in these spectral peaks for several nucleic acid molecules, including single-stranded and double stranded DNA and RNA.
• - 61 - 11677372v1 Attorney Docket No. 2017297-0013 [0327]
• one or more mid-IR spectral bands may be associated with, and used to evaluate properties of, proteins.
• Amide I and Amide II bands may be associated with spectral peaks within a range from about 1500 to about 1700 cm -1 and may be used to detect and/or characterize protein content and/or structure within a sample.
• Amide-I and Amide-II bands are believed to correspond to vibrational modes associated with protein peptide backbone atoms. Accordingly, in certain embodiments, presence and strength of these (Amide-I and Amide-II) bands can be used to determine presence and/or content of protein in a sample. In certain embodiments, additionally or alternatively, other bands associated with protein (e.g., backbone) vibrations, such as an Amide-III band, which ranges from about 1250 cm -1 to about 1350 cm -1 , may be used to determine presence and/or content of protein in a sample.
• other bands associated with protein (e.g., backbone) vibrations such as an Amide-III band, which ranges from about 1250 cm -1 to about 1350 cm -1 , may be used to determine presence and/or content of protein in a sample.
• Amide-I band measurements may be used to characterize secondary structure and/or changes therein of one or more proteins in a sample.
• protein secondary structure motifs such as a random coil, alpha-helix, and beta-sheet each produce characteristic Amide-I absorption peaks, with characteristic center frequencies, linewidths, and splitting.
• UV absorption measures absorption at or near 280 nm, which originates from three particular amino acids – Tyrosine (Tyr), Tryptophan (Trp), and Phenylalanine (Phe). Accordingly, not all amino acids of a protein produce measurable UV absorption signal. In contrast, all amino acids (side chains and/or peptide bonds linking them) may contribute to detectable absorption in the IR, that can be measured via mid-IR spectroscopy.
• Amide-I, Amide-II, and Amide-III absorption features are associated with vibrational modes of protein backbone atoms and, accordingly, are produced by all proteins and scale (in strength – i.e., level of absorption) roughly with amino acid count.
• the Amide-II and III are believed to result from (out of phase and in phase combinations, respectively, of) - 62 - 11677372v1 Attorney Docket No. 2017297-0013 NH and CN bond vibrations. Accordingly, mid-IR-based measurements are not restricted to particular molecular weights and/or types of proteins.
• UV absorption suffers from poor linearity and cannot be used to assess (e.g., de-convolve) heterogeneity of a protein mixture.
• mid-IR absorption spectroscopy can, among other things, be used to measure total protein concentration as well as, additionally or alternatively, to quantify heterogeneity of protein mixtures, thereby offering functionality and insight into protein samples that is not achievable with UV absorption measurements.
• A.ii Mid-IR Analyzers utilize one or more MIR analyzer(s) to measure infrared (IR) absorbance signal from a sample mixture and produce IR absorbance data that can, for example, be analyzed to identify and/or characterize spectral peaks and/or peak features thereof that are associated with characteristic molecular absorption bands.
• Mid-IR analyzers may be or comprise systems that comprise one or more components such as mid-IR light sources, detectors, and associated optics, such as (but not limited to) sampling optics to direct light to and/or from a sample in a particular fashion, in order to interrogate it via a particular sampling geometry.
• a MIR analyzer comprises one or more MIR light sources, operable to emit MIR light.
• a MIR light source is or comprises a thermal source that emits light comprising a broad range of wavelengths, having a spectral profile corresponding approximately to a blackbody spectrum at a particular temperature, selected, for example, to place a substantial fraction of its emitted power within the MIR.
• one or more light sources of a MIR analyzer comprise one or more lasers, which emit light at substantially a single frequency (e.g., within a narrow frequency band about a central wavelength) within the MIR.
• a MIR laser may be a tunable laser, such that its emission frequency can be tuned over a particular spectral range.
• tunable MIR lasers include, but are not limited to, quantum cascade lasers (QCLs).
• Other laser-based sources may include, without limitation, Interband Cascade - 63 - 11677372v1 Attorney Docket No. 2017297-0013 Lasers (ICLs), difference-frequency generation-based sources, frequency combs (e.g., dual comb sources), etc.
• ICLs Interband Cascade - 63 - 11677372v1 Attorney Docket No. 2017297-0013 Lasers
• difference-frequency generation-based sources e.g., dual comb sources
• frequency combs e.g., dual comb sources
• a MIR analyzer may measure IR absorption signals at a plurality of wavelengths and/or times in order to create IR spectral data, which provides a measure of detected signal and/or absorbance at a plurality of wavelengths within a particular spectral range.
• FIGs.6A-F illustrate and compare two approaches for performing IR spectral measurements.
• One approach is an interferometric technique, referred to as Fourier Transform Infrared (FT-IR) spectroscopy.
• FT-IR spectroscopy uses a split-beam interferometer with a moving mirror to record an interference pattern as a function of temporal delay between two beams.
• a MIR analyzer uses a spectral scanning technique to record an IR spectrum.
• a spectral scanning approach may be implemented using a tunable IR laser, such as a tunable QCL.
• a tunable laser emits light within a narrow frequency band, at a substantially single particular emission frequency/wavelength.
• the emission frequency of a tunable laser may be scanned, to illuminate a sample at plurality of wavelengths, one at a time, within a tuning range of the tunable laser. Signal may then be detected at each of the plurality of wavelengths scanned, to build up an IR spectrum, one wavelength at a time.
• laser sources such as QCLs emit intense beams of MIR light, with spectral brightness (units W ⁇ sr -1 ⁇ m -2 ⁇ m -1 , e.g., Watts per square-meter per steradian per - 64 - 11677372v1 Attorney Docket No. 2017297-0013 unit wavelength) several orders of magnitude (about 10 4 – 10 6 - fold) greater than that of a thermal source.
• FIG.7 shows three QCLs, each having a tuning range of about 2-3 um.
• high spectral brightness provided by laser sources such as QCLs obviates several significant shortcomings that historically have limited application of conventional IR spectroscopy instruments, such as FTIRs, which relied on thermal sources, for measurement of biological samples and processes, particularly in aqueous environments.
• FTIRs IR spectroscopy instruments
• liquid water has two strong and wide absorption bands in the MIR, one of which – a H-O-H bending mode centered at approximately 1638 cm -1 – overlaps substantially with Amide-I and Amide II bands used to measure protein content and to characterize structure.
• thermal sources typically are used with cryogenically cooled (e.g., with liquid nitrogen) detectors (e.g., MCT detectors) in order to obtain adequate sensitivity.
• cryogenically cooled detectors e.g., MCT detectors
• Such cryogenically cooled detectors are cumbersome to operate, have poor linearity, long-term stability, and reproducibility.
• low spectral brightness thermal sources require long data acquisition times, in order to achieve adequate sensitivity. These (long acquisition times) are incompatible to PAT needs.
• a MIR analyzer comprises one or more detectors.
• a variety of detectors, operable to detect light within a MIR range, may be used to detect MIR light, for example as part of a MIR analyzer.
• a detector may be a single element detector or a multi-element detector, such as a linear array or a focal plane array (FPA).
• FPA focal plane array
• a MIR detector is cryogenically cooled, for example via liquid nitrogen.
• a MIR detector is thermoelectrically cooled or uncooled.
• a MIR detector is a quantum detector, such as a mercuric cadmium telluride (MCT) detector.
• MCT mercuric cadmium telluride
• a MIR detector is a thermal detector, such as a - 65 - 11677372v1 Attorney Docket No. 2017297-0013 deuterated, L-alanine doped triglycine sulfate (DLaTGS) or deuterated triglycine sulfate (DTGS) detector.
• a MIR detector is a bolometer or a micro- bolometer.
• a MIR analyzer comprises a single detector.
• a MIR analyzer comprises two or more detectors.
• a MIR analyzer such as the MIR analyzer shown in FIG.6B, may comprise a sample detector, that detects signal from light having passed through a sample, and a reference detector, that detects signal from a portion of an illumination beam split off (e.g., via a beam-splitter) before the sample.
• a sample detector and a reference detector may be used in this fashion to compensate for laser power fluctuations.
• a MIR analyzer comprises sampling optics that are used to direct a beam of MIR light, from a MIR source, on and/or into a particular region of a sample, and then to a detector for detecting signal from the region of the sample.
• Sampling optics may be used to interrogate a sample in a particular fashion, for example, and without limitation, by providing a transparent window through which a beam of light can pass, by directing light on/into a region of a sample at particular angles, for example via reflective elements such as mirrors and high refractive index materials, and by focusing a beam of light, using lenses or curved (e.g., parabolic) reflectors.
• sampling optics may be used to interrogate a sample using a particular type of sampling geometry, such as a transmission or attenuated total internal reflection (ATR) geometry.
• a transmission geometry uses sampling optics to direct a beam of infrared light along a substantially straight path through a sample, and onto a detector after having passed through the sample. In this manner, a transmission geometry measures absorption of IR light resulting from its propagation through a sample.
• an ATR geometry uses sampling optics comprising a high-refractive index material, such as an ATR crystal, a surface of which is in contact with a sample.
• Sampling optics direct a beam of infrared light into the high-refractive index material, such that it is incident on the surface in contact with the sample at an angle above that required for total-internal-reflection (TIR angle).
• TIR angle total-internal-reflection
• Light is, accordingly, reflected by the high-refractive index material-sample interface, and back to a detector, probing the sample - 66 - 11677372v1 Attorney Docket No. 2017297-0013 with an evanescent wave, rather than propagating through it.
• an ATR geometry uses an ATR crystal having a shape comprising one or more angled surfaces through which light may enter and/or exit the ATR crystal and a flat surface that makes contact with a sample.
• an ATR crystal is a multi-bounce ATR crystal, that is shaped to cause light to reflect within the crystal multiple times, so as to increase an effective path-length used to probe a sample.
• an ATR crystal is situated at an end of a fiber probe.
• a fiber probe itself may be used as a high-refractive index material to implement an ATR sampling geometry.
• a MIR analyzer comprises a flow cell.
• any application-appropriate flow cell may be used.
• Non-limiting example MIR analyzers with flow cells are described in detail, for example, in U.S. Patent No.10,753,856, Issued August 25, 2020, in U.S. Patent Publication No.2021/0405001 A1, published December 30, 2021, and in U.S. Patent No.11,119,079, issued September 14, 2021, the content of each of which is hereby incorporated by reference in its entirety.
• Example QCL-Based MIR Spectrometer A variety of MIR analyzers based on QCL sources are described in detail, for example, in U.S. Patent No.10,753,856, Issued August 25, 2020, in U.S. Patent Publication No.2021/0405001 A1, published December 30, 2021, and in U.S. Patent No.11,119,079, issued September 14, 2021, the content of each of which is hereby incorporated by reference in its entirety.
• - 67 - 11677372v1 Attorney Docket No. 2017297-0013
• FIG.9A shows an example QCL-based MIR analyzer for performing absorption measurements in solution.
• QCL-based MIR analyzer comprises a tunable QCL laser source.
• QCL source is a scanning source that repeatedly sweeps its emission wavelength through a particular tuning range, completing a full spectral scan (i.e., across the entire tuning range) approximately each second.
• different QCL sources may have different tuning windows and, accordingly, may be used to probe different portions of the MIR spectral range.
• a commercial QCL-based IR spectrometer – Daylight Solutions’ Culpeo-LA-P – has a spectral window ranging from about 1725 cm -1 to about 1375 cm -1 , which can, for example, be used to measure Amide bands associated with proteins and, accordingly, can be used for protein detection and characterization.
• Another QCL may have a different spectral scan window, for example from about 1375 cm -1 to about 1025 cm -1 or from about 1225 cm -1 to about 1000 cm -1 , which among other things, includes bands associates with sugars, polysaccharides, and nucleic acids.
• multiple QCL sources may be used to cover a desired spectral range. For example, two QCL sources may be combined to cover a range from about 1725 cm -1 to about 1025 cm -1 .
• two or more QCL sources may be included in a single MIR analyzer, such that they share at least a portion of the sampling optics and/or detectors.
• two separate MIR analyzers may be used to provide a desired spectral coverage.
• a commercial implementation of example QCL-based IR spectrometer offers a variety of performance features advantageous to mid-IR spectroscopy-based measurements of biological production processes.
• a QCL-based IR spectrometer may allow for quantitative measurements to be performed over a wide dynamic range (e.g., from below 0.1 to above 300 mg/mL; e.g., from about 0.001 to above 300 g/L), in substantially real-time, for example at rates of about 1 Hz.
• a QCL-based IR spectrometer is compatible with flow rates from up to about 10 L/min and/or may probe sample volumes as small as picoliters.
• a QCL-based IR spectrometer is a modular instrument and/or suitable for in-line and/or at-line measurements. [0349] Table 1 below shows advantages of a QCL-IR spectrometer system in comparison with other techniques for measuring biological production processes: - 68 - 11677372v1 Attorney Docket No.
• IR absorption signals measured from a sample are combined and/or pre-processed mathematically to create IR absorbance spectra indicative of sample absorbance.
• FIGs.10A-H illustrate various steps and mathematical treatments used to obtain absorbance spectra of analytes present in a mobile phase.
• one or more reference spectra, indicative of a mobile phase without analyte present may be measured and divided/subtracted out to yield spectra of analytes present in a sample comprising a mobile phase.
• bioproduction monitoring and/or control technologies described herein leverage analytical technologies – in particular mid-IR spectroscopy as described herein - for the analysis of raw materials, in process monitoring and control, and also final product analysis.
• technologies described herein are implemented as part of a Process Analytical Technologies (PAT) framework, for example utilizing mid-IR spectroscopy as an integrated component within bioprocessing workflow to, among other things, identify sources of variability, monitor and facilitate management (e.g., via system control and feedback) of these sources of variability, and ensure product quality - 69 - 11677372v1 Attorney Docket No.
• PAT Process Analytical Technologies
• attributes e.g., critical quality attributes
• technologies described herein may, for example, be used as a process fingerprinting tool in bioprocess unit operations.
• technologies described herein may, additionally or alternatively, be used e.g., in biopharma forensic labs, to call out counterfeit drugs and biosimilars.
• one or more mid-IR analyzers may be used as inline sensors, embedded within process streams, in order to monitor sample quality attributes in substantially real-time. Mid-IR-based monitoring may be used alone and/or in combination with other, other measurement modalities.
• IR spectral data may be processed via a variety of methods, including methods for determining particular metrics associated with certain absorption bands, as well as, advanced in machine learning techniques. Sample quality metrics may, accordingly, be monitored and/or used to control bioproduction processes. In certain embodiments, automated and/or semi-automated decision support and/or control systems including, for example artificial intelligence (AI)-based systems may be used for process control in real-time and/or refinement.
• AI artificial intelligence
• IR absorption data obtained from liquid samples may be used to determine one or more sample quality metrics that characterize properties of one or more target analytes within a sample.
• a sample quality metric is or comprises a value and/or a set of (multiple) values that characterize one or more properties (e.g., physical, chemical, biological, or microbiological properties) of one or more target analytes in a sample.
• sample quality metrics characterize properties of one or more biological analytes, such as proteins, virus and/or virus-like particles, nucleic acids, and the like.
• a target analyte is a desired species biologic, to be purified and retained, such as a particular protein, viral vector, nucleic acid, or form thereof.
• a target analyte is an undesired impurity, such as a portion of a mixture to be removed.
• Sample quality metrics may include, but are not limited to, particular attributes that should be within an appropriate limit, range, or distribution to ensure a desired product quality (referred to as “Critical Quality Attributes (CQAs)”).
• CQAs Cosmetic Quality Attributes
• a sample quality metric may be a value, such as a numerical value, that provides a measure of content of a particular target analyte, such as a particular molecular species or form thereof, in a sample.
• a numerical value may, for example, be a direct measurement of physical content, such as a total mass, number, concentration, etc., or may be a value that is proportional, indicates a relative change, or correlates with, physical content of a particular target analyte.
• a sample quality metric may be a metric that is indicative of a particular species or state based on whether its value lies within one or more particular (e.g., pre-specified) ranges and/or above or below one or more threshold values.
• a sample quality metric is or comprises a measure of content of a particular target analyte, such as concentration, total mass, etc.
• Sample quality metrics may include, for example, real-time measurements of total protein concentration (e.g., titer), total protein mass, etc. in a sample. Additionally or alternatively, sample quality metrics may include, for example, real-time measurements of total nucleic acid concentration (e.g., titer), total nucleic acid mass, etc., in a sample. In certain embodiments, sample quality metrics include (e.g., real-time) content measures of target analytes such as a total concentration, mass, etc. of one or more of the following: lipids, polysaccharides, etc.
• sample quality metrics include (e.g., real-time) content measures of assemblies of multiple molecules, such as a total concentration (e.g., titer), total mass, number (e.g., discrete number) of viral and/or virus-like particles, such as viral vector assemblies, including, but not limited to, adeno-virus, adeno-associated viruses (AAV), retroviruses (e.g., lentivirus), plant-based viruses (e.g., tobacco mosaic virus), and the like.
• a total concentration e.g., titer
• total mass e.g., total mass
• number e.g., discrete number
• viral vector assemblies including, but not limited to, adeno-virus, adeno-associated viruses (AAV), retroviruses (e.g., lentivirus), plant-based viruses (e.g., tobacco mosaic virus), and the like.
• AAV adeno-associated viruses
• retroviruses e.g.,
• a sample quality metric may be or comprise a measure of absolute and/or relative content of particular species or forms of a molecules, for example biomolecules such as proteins, nucleic acids, lipids, polysaccharides, etc.
• one or more sample quality metrics may be or comprise measurements of molecular conformation and/or heterogeneity, such as protein secondary structure, aggregation, identity and relative concentration of various protein species, conjugation (e.g., glycosylation), anti-body drug conjugate ratios, etc.
• a sample quality metric may be a measure of absolute or relative content of a particular protein secondary structure motif.
• IR spectra can be used to identify content of protein secondary structure motifs, such as alpha-helix, beta-sheet, beta-turn, and disordered secondary structures.
• a sample quality metric may be a measure, such as a numerical value that is proportional to or correlates with, content of a particular secondary structure motif.
• a sample quality metric may be a measure of relative content, for example between two secondary structure motifs.
• a sample quality metric may be or comprise a measure of absolute and/or relative content of particular forms of proteins resulting from one or more post-translational modifications, such as covalent addition of functional groups or proteins, proteolytic cleavage of regulatory subunits, or degradation of entire proteins, for example phosphorylation, glycosylation, ubiquitination, nitrosylation, lipidation and proteolysis, and the like.
• a sample quality metric may be or comprise a measure of absolute and/or relative content of particular nucleic acid conformations, such as an concentration (e.g., titer), total mass, etc.
• a sample quality metric may be or comprise a measure of absolute and/or relative content of one or more particular types of nucleic acid bases (e.g., guanine (G), cytosine (C), thymine (T), adenine (A), uracil (U), etc.) and/or combinations thereof.
• G guanine
• C cytosine
• T thymine
• A uracil
• U uracil
• a sample quality metric may be or comprise a GC content metric, providing a measure of total and/or relative content of GC bases within a sample.
• a GC content metric may be or comprise a measure of total GC content with in a sample, such as a concentration, total mass, etc. of GC.
• a GC content metric may be or comprise a measure of relative GC content, for example scaled relative to total nucleic acid content or, for example, incorporating known, intended, or assumed values such as strand length to provide an average measure of GC content per nucleic acid molecule / strand (e.g., a percentage of GC bases in each nucleic acid molecule, on average, in a sample).
• a sample quality metric may be or comprise a value that indicates an extent of aggregation, or absence and/or presences thereof, in a sample.
• a sample quality metric may be a value (e.g., a numerical value) that provides a measure of content a particular species of aggregate, such as monomer, dimer, trimer, multi- - 72 - 11677372v1 Attorney Docket No. 2017297-0013 mer, etc., in a sample.
• one sample quality metric may be a value that provides a measure – e.g., is proportional to and/or correlates (e.g., increases or decreases) with – content of a particular species of a protein aggregate within a sample.
• one sample quality metric may measure monomer content, another may measure dimer and/or higher molecular weight species (e.g., dimer, trimer, etc.) content, within a sample.
• a sample quality metric may be indicative of a particular aggregation species or state based on its value in comparison with one or more ranges and/or thresholds.
• a sample quality metric may be indicative of monomeric protein species when its value falls within a particular range, and indicative of presence of aggregation (e.g., dimers and/or multimers) when its value moves outside of the particular range.
• the particular range may be a pre-specified numerical range, may be calibrated for a particular sample or protein species, or may be determined in real-time, for example, based on measurements during a particular sample processing run, such as a chromatography elution.
• Identification of Analytes may be or comprise a value that identifies presence, or absence of a particular target analyte (e.g., or sufficient content thereof) within a sample.
• a sample quality metric may be or comprise a Boolean value, having two states (e.g., 1 or 0, True or False, etc.), indicative of whether a particular target analyte, such as a desired protein or protein species and/or an undesired impurity, is present within a sample.
• a sample quality metric is or comprises a value that identifies one or more particular analytes within a sample.
• a sample quality metric may be or comprise a value or set of values that encodes an identity of one or more components within a sample, such as an alphanumeric string, a set of strings and/or alphanumeric characters, a numerical or Boolean array, etc.
• sample quality metrics may relate to gene therapy products.
• a sample quality metric may characterize a content of viral particles, nucleic acid content, and/or mixtures or assemblies thereof.
• a sample quality metric may be or include measurements of viral and/or capsid titer as described herein.
• a sample quality metric may be or comprise a measure of capsid content, such as a total content of empty capsids, a total content of full capsids, or a relative measure, such as a fraction, percentage, etc. of empty versus full capsids, etc. - 73 - 11677372v1 Attorney Docket No.
• sample quality metrics are determined using mid-IR absorbance data, such as mid-IR spectral data.
• a sample quality metric is determined using mid-IR spectral data that includes an Amide I region, ranging from about 1600 cm -1 to about 1700 cm -1 or about 1800 cm -1 (e.g., ranging from about 1600 cm -1 to about 1725 cm -1 ; e.g., ranging from about 1625cm -1 to about 1725 cm -1 ; e.g., ranging from about 1630 cm -1 to about 1650 cm -1 ), and/or an Amide II region, ranging from about 1500 to about 1600 cm -1 (e.g., ranging from about 1500 cm -1 to about 1575 cm -1 ; e.g., ranging from about 1500 cm -1 to about 1550 cm -1 ; e.g., ranging from about 1540 cm -1 to about
• mid-IR spectral data including one or both of an Amide I and Amide II spectral region may be used to determine one or more sample quality metrics indicative of and/or characterizing one or more protein species within a sample.
• a sample quality metric is determined using mid-IR spectral data that includes a spectral range from about 1000 cm -1 to about 1350 cm -1 , which, for example, may be used to determine one or more sample quality metrics indicative of and/or characterizing viral and/or nucleic acid species (e.g., DNA, mRNA, etc.) within a sample.
• a sample quality metric is determined using mid-IR spectral data that includes a spectral range from about 1000 cm -1 to about 1700 cm -1 (e.g., up to about 1800 cm -1 ).
• systems capable of measuring from about 1000 cm -1 and up to 1800 cm -1 may be used to monitor combined or multi-component process streams that include both viral and/or protein (e.g., monoclonal antibody) species.
• a sample quality metric is determined using multiple bands, for example, to quantify both protein and nucleic acid content within a sample.
• measures of protein and nucleic acid content within a sample may be combined to determine, additionally or alternatively, sample quality metrics that measure viral capsid content and/or full / empty capsid content and/or relative fractions.
• FIG.11 shows an illustrative schematic of an IR absorption spectra of a sample comprising ssDNA and protein, including a composite spectrum 1102 corresponding to a raw spectrum acquired from a sample comprising both ssDNA and protein, along with spectra corresponding to individual protein 1104 and ssDNA 1106 components.
• protein content may be measured using and Amide I 1112 and/or Amide II 4014 spectral band.
• protein content may be measured using IR absorption data within an Amide-III spectral - 74 - 11677372v1 Attorney Docket No. 2017297-0013 region 1116, ranging from about ranging from about 1250 cm -1 to about 1350 cm -1 (e.g., ranging from about 1250 cm -1 to about 1325 cm -1 ; e.g., ranging from about 1275 cm -1 to about 1325 cm -1 ; e.g., ranging from about 1280 cm -1 to about 1300 cm -1 ).
• Nucleic acid content may be quantified using IR absorption data within spectral ranges associated with antisymmetric and/or symmetric phosphate stretch (PO4) vibrations.
• a sample quality metric measuring nucleic acid content e.g., a nucleic acid content metric
• a sample quality metric measuring nucleic acid content may be determined using IR absorption data an antisymmetric-PO 4 spectral band 1118 (e.g., ranging from about 1150 cm -1 to about 1250 cm -1 , e.g., ranging from about 1175 cm -1 to about 1250 cm -1 ; e.g., ranging from about 1200 cm -1 to about 1250 cm -1 ; e.g., ranging from about 1210 cm -1 to about 1230 cm -1 ) (shown, in FIG.11, to peak at around 1220 cm -1 ).
• a sample quality metric measuring nucleic acid content may be determined using IR absorption data an symmetric-PO4 spectral band 1120 (e.g., ranging from about 1000 cm -1 to about 1100 cm -1 , e.g., ranging from about 1050 cm -1 to about 1100 cm -1 ; e.g., ranging from about 1075 cm -1 to about 1100 cm -1 ; e.g., ranging from about 1075 cm -1 to about 1085 cm -1 ) (shown to peak at around 1080 cm -1 in FIG.11).
• a symmetric-PO4 spectral band 1120 e.g., ranging from about 1000 cm -1 to about 1100 cm -1 , e.g., ranging from about 1050 cm -1 to about 1100 cm -1 ; e.g., ranging from about 1075 cm -1 to about 1100 cm -1 ; e.g., ranging from about 1075 cm -1 to about 1085 cm -1
• particular spectral bands may be used / selected to allow for independent quantification of protein and nucleic acid content in composite sample comprising a mixture of the two.
• absorption within an Amide-I spectral region 1112 of a spectrum taken from a mixture 1102 may be contributed to by both protein 1104 and nucleic acid 1106 components, whereas Amide-II 4014 and Amide- III 1116 absorption is due mainly to protein 1104, ssDNA spectrum 4006 shown in FIG.11 being relatively flat / minimal in both these (Amide-II and Amide-III) regions.
• an Amide-I and/or Amide-III band may be used to quantify protein content in samples where nucleic acid is or may be present.
• Antisymmetric and symmetric PO4 regions 1118 and 1120, where absorption results primarily from nucleic acid content can be used to quantify nucleic acid content, e.g., independent of variations in protein content.
• Peak Metrics values of one or more sample quality metrics as described herein may be determined and monitored by analyzing on or more absorption bands within mid-IR spectral data.
• values of one or - 75 - 11677372v1 Attorney Docket No. 2017297-0013 more peak metrics are determined for each of one or more particular absorption bands.
• Peak metrics aim to quantify features of absorption bands, such as a frequency position (e.g., a center frequency), a linewidth, an intensity of one or more absorption bands, and may be calculated via a variety of approaches.
• a peak metric is or comprises a measure of frequency position of a particular absorption band.
• a frequency position (e.g., a center frequency) of a particular absorption band may be or comprise a peak frequency ( ⁇ Peak ), which may be determined directly from an absorption spectra, by determining a frequency at which an amplitude of the particular absorption band peaks (e.g., reaches a maximum), by fitting a pre-defined function, such as a Gaussian or Lorentz, and obtaining a center frequency from the fitted function, or by other methods.
• a frequency position (e.g., a center frequency) of a particular absorption band may be or comprise a center-of-mass frequency ( ⁇ COM).
• ⁇ COM for a particular absorption band may be computed from an absorbance spectrum (e.g., a mid-IR absorbance spectrum) A( ⁇ ) according to Equation 1 below.
• one or more peak metrics may be or comprise a linewidth measurement, such as a full-width at half maximum (FWHM), computed, for example, by various techniques such as descending along one or two sides from a peak, or via a functional (e.g., Gaussian, Lorentzian, etc.) fit.
• FWHM full-width at half maximum
• a peak metric may be or comprise a measure of intensity of one or more particular absorption bands, such as peak amplitude or area under the curve (AUC).
• a measure of strength of one or more particular absorption bands is determined by computing an AUC.
• an AUC is computed for two or more bands, such as two or more neighboring bands or a region comprising a plurality of bands believed to be associated with a particular target analyte of interest.
• an AUC for a collection of two or more bands or a particular overall region may be computed according to Equation (2) above, with ⁇ min and ⁇ max specifying bounds of the two or more bands or particular overall region.
• a sample quality metric may be, or be computed from (e.g., as a function of) a particular peak metric.
• a sample quality metric may be computed as a function of two or more peak metrics, together with other values or constants, such as scaling factors, normalization constants, and the like, which may be a-priori known and/or assumed, and/or determined, e.g., via molecular structure or other known properties and/or from other (e.g., orthogonal) measurement approaches, such as other spectroscopy techniques, prior measurements, etc., as described herein.
• other values or constants such as scaling factors, normalization constants, and the like, which may be a-priori known and/or assumed, and/or determined, e.g., via molecular structure or other known properties and/or from other (e.g., orthogonal) measurement approaches, such as other spectroscopy techniques, prior measurements, etc., as described herein.
• sample quality metrics may be determined based on and/or using one or more reference spectra.
• Reference spectra may be or comprise one or more spectra that are obtained from IR absorption measurements on reference samples.
• Reference spectra may be measured previously and stored, e.g., in a proprietary database, accessed from public databases, or measured in parallel with various processing steps, e.g., substantially simultaneously.
• reference samples from which reference spectra may be obtained, may include, but are not limited to, samples for which one or more particular sample quality metrics are known (e.g., having been determined by other, orthogonal, e.g., more expensive and/or time consuming methods, not suitable for real-time and/or in-line analysis), having a known and/or desired purity, having known individual constituents, etc.
• Reference samples may be prepared so as to match with samples or constructs being screened for better stability, expression or a binding attribute to its cognate substrate.
• one or more reference samples having a known purity e.g., of a particular molecular species (e.g., protein, nucleic acid, viral particle) and/or form thereof (e.g., a particular secondary structure, glycosylation, monomeric purity) may be obtained and their IR spectra measured.
• reference samples may be (e.g., intentionally) spiked with one or more impurities, such as waste products, undesired molecular forms, (e.g., known) bioprocess inputs that may be incompletely converted and/or filtered out by upstream processing, etc., and corresponding IR spectra measured.
• reference spectra may be obtained and/or modified in-silico, via various computational processes.
• reference spectra may be constructed via ab-initio calculation methods for particular molecular structures.
• Initial, e.g., library, reference spectra may be combined, for example according to Beer’s law, scaled, or otherwise pre-processed to create new, tailored reference spectra that, for example capture particular variations in sample parameters that may be of interest, remove baselines, reflect sub-band deconvolution, show second derivative spectra, etc., and accordingly are tailored for a particular sample quality metric and/or sample.
• sample quality metrics e.g., reflecting sample purity
• a reference spectra approach as described herein may be applied to measurement of gene therapy products, such as a viral vector sample.
• one or more reference spectra may be obtained from a high- quality reference sample, for example comprising a desired purity in terms of a fraction of full capsids (e.g., a full capsid fraction above a particular threshold), monomeric particles, lack of certain impurities (e.g., host cell proteins and/or host cell nucleic acid; e.g., aggregates; e.g., fragments), and the like.
• a high quality viral vector sample may have a full capsid fraction above a particular threshold, such as 70%, 80%, 90%, etc.
• An aqueous sample comprising one or more viral vector species may, e.g., subsequently or in parallel, be interrogated by a mid-IR analyzer as described herein to obtain one or more target IR absorption spectra.
• One or more target IR absorption spectra may then be compared with the high-quality reference spectra to determine a measure of sample quality.
• FIG.12 shows an illustrative absorbance plot 1200 of an example (scaled) high quality reference spectrum 1202 of a high quality viral vector sample, having a high content of full capsids (e.g., about or better than 80% full capsids), in comparison with a lower quality viral vector sample spectrum 1204, having a low content of full capsids. Differences between the two spectra are observable in the absorbance plot 1200. In certain embodiments, difference spectra may be determined by subtracting a target spectrum from a high quality reference spectrum (e.g., or vice versa) to show a change in absorbance as a function of wavenumber.
• FIG.12 shows a corresponding difference spectrum 1220, determined by subtracting high quality reference spectrum 1202 from low quality viral vector sample spectrum 1204.
• differences spectrum 1220 effects such as a frequency shift in an Amide-II band – in particular, a redshift (e.g., shift to lower frequency) - and a reduction in absorption at an Amide-III region are apparent as an asymmetric line-shape 1222 and a pair of negative peaks 1224, respectively.
• a redshift e.g., shift to lower frequency
• a reduction in absorption at an Amide-III region are apparent as an asymmetric line-shape 1222 and a pair of negative peaks 1224, respectively.
• One or both of these features, and/or various peak metrics computed therefrom, could be used to determine sample quality metrics indicative of various attributes of viral vector sample quality.
• Comparison of target IR absorption spectra with one or more reference spectra may be accomplished in a variety of manners, such as computing a difference spectrum, to obtain comparison spectra.
• numerical measures of similarity may be computed using target spectra and reference spectra, pre-processed versions thereof (e.g., derivative spectra, scaled and/or baseline corrected spectra, etc.), and/or comparison spectra computed therefrom.
• Numerical similarity measures may include, without limitation, correlation values, covariance values, Pearson’s correlation values, overlap integrals, etc.
• one or more machine learning models may be used to determine one or more sample quality metrics from IR absorbance data.
• Machine learning models may, among other things, be trained using various reference spectra, as examples, in order to adjust and/or optimize variable (learnable) parameter weights in one or more network layers. Once trained, a machine learning model may then be used for inference – i.e., to determine metrics from new, unknown sample spectra. For example, in certain embodiments, for example, a machine learning model may receive, as input, an IR spectrum and generate, as output (e.g., via inference) determined values of one or more sample quality metrics. In certain embodiments, a machine learning model receives a single IR spectrum (e.g., corresponding to a single time point) as input.
• a single IR spectrum e.g., corresponding to a single time point
• a machine learning model receives multiple IR spectra (e.g., collected at different time points) as input.
• a similarity score may be determined, for example based on correlation values, covariance values, Pearson’s correlation values, overlap integrals, etc., as well as machine learning-based techniques described herein.
• similarity scores may be generated and updated in substantially real time. For example, a reference spectra of full and empty capsid samples may obtained (e.g., loaded, received, or otherwise accessed) by a mid-IR analyzer and/or processor in communication therewith. As mid-IR absorption data is repeatedly obtained over time, for example to monitor viral vector production, purification, etc.
• a similarity score may be generated in real time, and displayed (e.g., to provide a real-time view of quality), stored (e.g., as a quality log) and/or provided for further processing, such as to control various parameters of production units as described herein.
• - 80 - 11677372v1 Attorney Docket No. 2017297-0013 Data Pre-Processing
• IR absorbance data such as mid-IR spectral data, used for determining one or more sample quality metrics is preprocessed data.
• one or more pre-processing steps may be performed on mid-IR spectral data prior to it being used to compute a particular sample quality metric and/or being used as input to a machine learning model.
• mid-IR spectral data may be pre- processed via a baseline correction approach that removes background signal, such as background absorption from a mobile phase (e.g., water’s H-O-H bending mode), in order to obtain mid-IR absorption spectra indicative of one or more analytes in the mobile phase.
• a mobile phase e.g., water’s H-O-H bending mode
• a reference spectrum may be computed and/or selected based on a model, such as a mixture model, in order to reflect presence and/or variations in amounts of one or more background components in a mobile phase.
• one or more background components are or comprise water molecules, such that a reference spectrum is selected or computed to reflect an appropriate strength / relative amplitude of absorption due to water molecules present in a sample.
• a reference spectrum may be computed to reflect, for example, displacement of water molecules by analyte molecules, for example as described herein.
• multiple reference spectra may be computed, for example, to reflect variation in certain background molecules over time, during a particular biological and/or sample processing step.
• parameters such as salt content, pH, etc., may be varied, for example in a step-wise or gradient fashion, such that, when IR spectral data is measured from an aqueous sample exiting from a chromatography column, desired, time-varying protein absorption spectra is superimposed on a time-varying background spectrum.
• Variations in background spectrum may be due, for example in the case of ion-exchange chromatography, where salt content is varied, to displacement of water molecules by salt. Accordingly, in certain embodiments, multiple and/or a continuously - 81 - 11677372v1 Attorney Docket No. 2017297-0013 scaled reference spectra are used to reflect variation in background absorption as salt concentration increases.
• selecting and/or computing a particular reference spectra may comprise using one or more template reference spectra together with a value of an input parameter (e.g., such as a salt concentration curve used by a chromatography column) and/or a measured sensor value, such as a conductivity or pH value, measured by a sensor.
• an input parameter e.g., such as a salt concentration curve used by a chromatography column
• a measured sensor value such as a conductivity or pH value
• conductivity may be measured with a conductivity sensor as mid-IR spectral data is obtained during an IEX chromatography column elution. Conductivity measured by a conductivity sensor may then be used to select or compute a particular reference spectrum that reflects a particular water molecule concentration as salt concentration is increased and, accordingly, salt displaces water molecules.
• Conductivity measured by a conductivity sensor may then be used to select or compute a particular reference spectrum that reflects a particular water molecule concentration as salt concentration is increased and, accordingly, salt displaces water molecules.
• IR spectra may be averaged, for example, to improve their quality (e.g., signal to noise ratio). For example, to obtain an IR spectrum at a particular time point, t1, multiple IR absorption spectra may be acquired in succession about t1, e.g., within a window t 1 + ⁇ and averaged to create a single, signal averaged, spectrum. Accordingly, in certain embodiments, an IR spectrum corresponding to a particular time point is itself a function of (e.g., an average) of a plurality of IR spectra collected about the particular time point.
• sample quality metrics may be determined based on values of IR absorption at multiple (e.g., not necessarily just a current) time points.
• a sample quality metric may measure a temporal change or aggregated value of one or more features of IR absorption spectra.
• a differential or time-aggregated sample quality metric may be determined using values computed from multiple IR absorption spectra, each corresponding to a different time - 82 - 11677372v1 Attorney Docket No. 2017297-0013 point.
• individual values of a particular, single time-point, sample quality metric may be determined at multiple time points (e.g., each value corresponding to a particular time point) and then combined, for example via computing a difference, average, median, variance, etc.
• a differential sample quality metric is computed as a difference between values of a particular (e.g., other) sample quality metric at two time- points, for example as a difference between consecutive times.
• a time-aggregated sample quality metric may be computed based on a running sum, mean, median, mode, variance, standard deviation, etc. over a particular time window, such as a backward looking window of a particular number of seconds and/or measurements, and/or in a cumulative fashion, aggregating measurements from an initial time point to a current one.
• Various differential and/or aggregated sample quality metrics may be determined, for example based on temporal differences, cumulative (over time) sums, time-averages, etc.
• spectra may be manipulated in order to emphasize particular features and or changes of interest when computing differential and/or time- aggregated metrics. For example, in certain embodiments, a normalization approach may be used that allows for creation of normalized spectral difference metric that facilitates identification of compositional changes in a sample. [0395] Turning to equations (4-6), below, spectra may be normalized by a reference in order to produce a normalized spectra for a sample that does not depend on concentration of a particular composition (e.g., a single particular analyte or composition of one or more analytes).
• concentration of a particular composition e.g., a single particular analyte or composition of one or more analytes.
• a normalized absorbance spectra, ⁇ 0 ( ⁇ ) may be determined in accordance with Eq.6, by dividing by a reference value taken at a particular wavenumber. For un-normalized absorption spectra taken at multiple time points, if composition and concentration stay constant, there will be no difference between spectra taken at multiple, e.g., consecutive, time points.
• a change in concentration will cause an overall increase in absorption at each wavelength, such that at each wavenumber a difference in absorption will be approximately proportional to a change in concentration. If a normalization, in accordance with Eq.6, is carried out, to obtain, at each time point, a normalized spectra, then changes in concentration will not influence a spectral difference at one or more wavelengths.
• a composition may be a single analyte, or a mixture of different analytes and/or species, forms etc. of analytes.
• a change in composition may occur, accordingly, due to addition of new, different analytes, as well as due to a difference in relative fractions of particular analytes (e.g., ratios between) and/or species thereof.
• a change in concentration e.g., as opposed to composition refers to an overall concentration of the mixture, holding ratios between its components constant. As explained above, such changes in concentration will not impact normalized absorbance spectra.
• a normalized spectral difference, ⁇ may be determined in a variety of manners. For example, Eq.7a shows a difference at a particular wavelength. In certain - 84 - 11677372v1 Attorney Docket No. 2017297-0013 embodiments, a normalized spectral difference, ⁇ may be computed by taking an integral over one or more particular bands, as shown in Eq.7b, below.
• the one or more particular bands may include any bands described herein, for example an Amide I, Amide II, Amide III, asymmetric and/or symmetric PO4 stretch bands, as well as other bands of interest, not necessarily described herein.
• a normalized spectral difference may, accordingly, be determined and monitored in real-time, and used to identify if and/or when a change in composition occurs.
• identifying a change in composition may comprise analyzing the normalized spectral difference signal to detect occurrence of a change point, for example using various approaches including, but not limited to, those described in Killick R., P. Fearnhead, and I.A.
• a step change may be detected to identify a change in composition.
• a variation in one or more statistical properties of a normalized spectral difference signal may be used to identify a change in composition, for example based on whether they exceed a particular threshold value and/or move outside a particular window (e.g., acceptable range).
• multiple changes in composition may be identified, for example, among other things, due to multiple variations in analyte compositions, addition of different analytes at different times, as well as aggregation, temperature and/or other buffer (e.g., salt gradient) induced conformational changes, etc. (e.g., any alteration in spectral line-shape).
• These changes in concentration may occur on different time-scales and/or alter various statistical properties in distinctive manners and, accordingly, may be used to distinguish between various distinct mechanisms that change sample composition.
• Equations 7a and 7b show subtraction of an absorbance at time t + ⁇ t from an earlier measurement of absorbance, at time t.
• IR absorbance spectra may be base-line corrected spectra.
• absolute value, squared, shifted version, etc. of IR absorption spectra may be used, for example to ensure a particular (e.g., positive) sign of a differential signal, such as those shown and/or time-aggregated signal.
• process monitoring and/or control technologies described herein may include and/or use data generated by, one or more additional sensors (e.g., other than mid-IR analyzers).
• one or more additional sensors comprise sensors for measuring parameters such as measure parameters such as temperature, pressure, pH, conductivity, flow rate etc.
• Such sensors may include, without limitation, one or more temperature sensors, one or more pressure sensors, one or more pH sensors, one or more conductivity sensors, one or more flow rate sensors, optical sensors, etc.
• one or more additional sensors may comprise sensors that are also capable of measuring one or more physical, chemical, biological, or - 86 - 11677372v1 Attorney Docket No. 2017297-0013 microbiological properties of one or more analytes within a sample.
• one or more additional sensors may be used, for example together with, one or more mid-IR analyzer(s) to generate data used for determining one or more sample quality metrics.
• mid-IR spectral data may be used together with data from one or more additional sensors to determine one or more sample quality metrics.
• an additional sensor is or comprises a UV absorption sensor.
• a UV absorption sensor may be or comprise any sensor operable to measure absorption of a sample in a UV (e.g., from about 200 nm to about 300 nm) spectral range.
• a UV absorption sensor may be or comprise a fixed path- length sensor, that measures UV absorption of a sample using a fixed path-length cell.
• a UV absorption sensor may be or comprise a slope spectroscopy sensor, such as the CTech TM SoloVPE ® and/or a variation/embodiment thereof, which measures UV absorption while varying path-length through a sample.
• UV absorption measurements may be used together with mid-IR absorption measurements as, for example, an internal check and/or a complementary technology.
• a combined and/or expanded range of data acquisition may provide, among other things, intrinsic verification and the quantification of different types of data.
• total protein concentration may be measured with one or both of UV absorption and mid-IR absorption spectral data.
• label and tagging solutions in the mid-IR would add value for the UV based methods as well for species identification, combatting counterfeit products and traceability of product.
• B.iii Real-Time Control of PAT Systems data analytics, machine learning and artificial intelligence, and the like can be leveraged with IR spectroscopic measurements and/or sample quality metrics determined as described herein not only provide real time monitoring and reporting of sample quality metrics, but can, additionally or alternatively, be used for predictive analytics whereby data collected are analyzed in real time and, additionally or alternatively, together with historical data sets and/or empirical models to predict future states of processes and/or the qualities of the materials in the process streams.
• IR measurement and data analysis tools described herein may provide detailed information about process parameters, process performance and process stability. They may be used to control process parameters, for example to improve performance in various growth and purification (filtration) steps, including, but not limited to cell culture, virus production, clarification, concentration, diafiltration, chromatography, purification, direct flow filtration, tangential flow filtration, tangential flow depth filtration. Additionally or alternatively, predictive analytics may be leveraged during analytical development, process development, and formulation to design experiments to develop extremely detailed process understanding, potential failure mode and effect analysis and sensitivity analysis in modeled processes.
• FIG.13 shows various upstream and downstream bioproduction process steps together with properties that may be, among other things, monitored via MIR analyzer(s) 1302 and/or used to control process parameters via systems and methods described herein.
• raw materials used in biologic e.g., protein, nucleic acid, viral vector, etc.
• production may be tested initially and/or monitored as they are provided, e.g., to various processing steps and production units to evaluate, among other things, sample quality metrics pertaining to material purity, identity, and presence of impurities.
• a bioreactor production unit such as a seed bioreactor and/or production bioreactor
• a bioreactor production unit may be monitored using techniques described herein, for example to confirm identity of desired biomolecules produced, determine titer, and ensure adequate nutrients are present in a bioreactor and/or being provided.
• one or more MIR analyzers as described herein may be used in connection with purification (filtration) units, such as alternating tangential flow filtration (ATF) systems, tangential flow depth filtration (TFDF) systems, tangential flow filtration (TFF) systems, chromatography columns, direct, or normal, flow filtration, ultra-filtration, dia-filtration, and the like.
• purification filtration
• one or more sample quality metrics and/or IR spectral data may be provided to control software and/or hardware systems and components such as Supervisory Control and Data Acquisition (SCADA), Manufacturing Execution System (MES) systems, and the like, for use in making decisions on changes to process control parameters and produce release.
• SCADA Supervisory Control and Data Acquisition
• MES Manufacturing Execution System
• data such as empty/full ratios (e.g., in the context of viral vector production processes) or the presence of high weight molecules such as dimers, trimers and multimers when processing proteins may be used to adjust process parameters such as flow rate, flow direction, pressures, temperatures, pH, etc.
• Process parameters controlled and/or adjusted as described herein may include, but are not limited to, process parameters that impact certain attributes which should be within an appropriate limit, range, or distribution to ensure a desired product quality (i.e., CQAs) (e.g., process parameters that impact CQAs, referred to as “Critical Process Parameters (CPPs)”).
• CQAs process parameters that impact certain attributes which should be within an appropriate limit, range, or distribution to ensure a desired product quality
• CCPPs Critical Process Parameters
• data such as particular sample quality metrics characterizing protein content, secondary structure, protein aggregation, viral vector content, empty-full ratio, capsid aggregation, etc.
• decisions on column loading can be made by monitoring breakthrough from the chromatography columns.
• a continuous production system upon detection of breakthrough a continuous production system can be issued commands to open/close values redirecting process flow to a next chromatography column inline and/or transitioning a fully loaded column into elute and/or wash stages of the process.
• A affinity chromatography
• HIC hydrophobic interaction chromatography
• IEX ion exchange chromatograph
• SEC size exclusion chromatography
• mixed-mode chromatography column e.g., which implements any combination of the foregoing (e.g., IEX and HIC; e.g., IEX and SEC)].
• approaches described herein can be used in connection with a TFF concentration production unit, for example during viral vector and/or - 89 - 11677372v1 Attorney Docket No. 2017297-0013 antibody production process(es) to monitor aggregation and continue and/or halt concentration, e.g., based on a level of measured aggregation.
• sample quality metrics computed from IR absorption spectra as described herein may be used to measure different types of molecular heterogeneity.
• a mixture may be heterogeneous in terms of having different species of free (unbound) proteins.
• a heterogeneity may result from aggregation, with a mixture comprising a population of identical proteins, each protein being either free (unbound) or chemically bound to one (dimer) or more (trimer, tetramer, pentamer, etc.) identical proteins, thus forming varying degrees of aggregation.
• a desirable form of molecular heterogeneity to measure may include a population of identical proteins having different types or degrees of molecular conjugation, e.g. polysaccharides, glycans, polyethylene glycol, or small molecules used for therapeutic means.
• approaches described herein may quantify protein heterogeneity can be quantified in a number of different ways. For example, in certain embodiments (e.g., as described in further detail in certain Examples, below), a protein aggregation metric that quantifies a total or percent level of aggregation may be determined.
• a conjugation-based metric that quantifies a total or percent level of glycation or small molecule conjugation may be computed from IR spectra.
• a molecular weight may be determined and a histogram of molecular weight displayed.
• quantifying a level of aggregation is of particular importance because aggregates can degrade the efficacy and safety of the drug product.
• IEX ion exchange chromatography
• a heterogenous aqueous mixture of proteins is intentionally separated into its individual protein constituents in time by using a chromatographic column and an ionic salt gradient.
• optimal results e.g. highest purity of monomer.
• Optimizing a collection window can maximize the protein target quality and yield while also maximizing life of a column.
• columns are increasingly loaded with higher concentrations of total protein in order to maximize the target protein yield and extend a useful life of a column.
• Such practices tend to lead to more aggregates being formed, as well as the reduction of a separation time between a target protein (monomer) peak and a first protein aggregate peak (dimer). That is, monomer and dimer peaks become increasingly overlapped and therefore more ambiguous – creating challenges for determining an appropriate (e.g., optimal) target protein capture window.
• approaches as described herein may be used for other types of separation (e.g., not limited to aggregate removal) where a product (e.g., or an impurity) breakthrough may occur, for example, removal of HCPs and/or DNA onto a direct flow filter in flow-through mode during clarification before chromatography.
• systems and methods described herein may be used to monitor protein secondary/tertiary/quaternary structure. Secondary/tertiary/quaternary structure metrics may be used to control processes in production of antibodies, as well as in production of viral vector samples (e.g., by monitoring capsid protein secondary/tertiary/quaternary structure).
• changes in a secondary/tertiary/quaternary structure of a protein molecule may be detected and used to e.g., shunt flow differently as an action (e.g., based on an electronic trigger signal).
• - 91 - 11677372v1 Attorney Docket No. 2017297-0013
• Sample quality metrics indicative of protein aggregation, secondary structure motif, and other properties may, for example, as described in examples below, be monitored by calculating one or more peak metrics from IR absorption spectra.
• a frequency position such as a center frequency and/or a center of mass frequency as described herein may be determined for one or more of an Amide-I band, an Amide-II band, and Amide-III band and monitored to track levels of protein aggregation.
• a sample quality metric is a protein aggregation metric indicative of a level of protein aggregation within a sample.
• the protein aggregation metric is determined based on (e.g., as) a frequency position (e.g., center frequency; e.g., center of mass frequency) of an Amide I band.
• the protein aggregation metric is determined based on (e.g., as) a frequency position (e.g., center frequency; e.g., center of mass frequency) of an Amide II band. In certain embodiments the protein aggregation metric is determined based on (e.g., as) a frequency position (e.g., center frequency; e.g., center of mass frequency) of an Amide III band.
• a frequency position e.g., center frequency; e.g., center of mass frequency
• approaches described herein may be used to monitor and control viral vector production control.
• technologies described herein are suitable for use with various viral vector production approaches, including, for example, transfection-based techniques as well as those that utilize stable producer cell lines (e.g., for producing viral vectors or other products, for example, antibodies).
• viral vectors are a class of large molecules, having molecular weights, in certain embodiments, above 1 MDa.
• Viral vectors may be used to infect a targeted host cell with genetic material, for example for purposes such as editing a genome of an infected cell or directly translating specific protein(s) within an infected cell.
• a viral vector used to edit an infected cell genome may, accordingly, be used as a gene therapy device.
• a viral vector may be used to trigger an immune response, e.g., accomplishing vaccine functionality.
• Viral vectors include, without limitation, adeno viruses, adeno associated viruses (AAV), retroviruses (e. g. lentiviruses), and plant-based viruses (e. g. tobacco mosaic viruses).
• viral vector capsids comprise (e.g., are loaded with) a gene cassette. During production, certain viral vector particles may be empty – lacking the - 92 - 11677372v1 Attorney Docket No. 2017297-0013 desired genetic payload to be delivered to an infected cell.
• viral vectors comprise a (e.g., approximately spherical) protein shell, about 100 nm or less in diameter, and comprising (e.g., encapsulating) one or more nucleic acid strands, such as DNA or RNA (e.g., mRNA) of varying lengths in terms of number of nucleotide bases.
• a nucleic acid strands such as DNA or RNA (e.g., mRNA) of varying lengths in terms of number of nucleotide bases.
• mid-IR organic fingerprint band spanning approximately one thousand (1,000) to eighteen hundred (1,800) wavenumbers comprises multiple spectral sub-regions (sub-bands) which can be assigned to either a capsid, a genetic payload (cassette) contained within the capsid, or to a combination of the capsid and the genetic payload.
• Methods described herein may exploit an entire range of a mid-IR fingerprint spectral region (from about 1,000 cm-1 to about 1800 cm- 1 ), and/or various sub-bands thereof. Such methods may quantify a concentrations of capsid, genetic material and/or their volumetric ratio, as well as, additionally or alternatively, quantify differences between the composite spectrum of the sample and that of a purified, high-quality sample.
• various spectral bands can be used to measure content of protein and/or nucleic acid in a sample, including in mixtures, where certain bands, such as an Amide-II and/or Amide-III may be used to quantify protein content independently with respect to nucleic acid concentration.
• a protein capsid encasing nucleic acid material spectral absorption within these bands can be used (e.g., together with Beer Lambert law) to determine protein content metrics and nucleic acid metrics that quantitate protein and nucleic acid concentration, respectively, which, in turn, may be used to quantify (protein) capsid and (nucleic acid) payload metrics.
• a total capsid concentration may be computed based on a (e.g., as a scaled version of) a protein content metric, which may be determined based on absorption in an Amide-I, Amide-II, and/or Amide-III region.
• use of Amide-II and/or Amide-III spectral regions is desirable, and facilitates - 93 - 11677372v1 Attorney Docket No. 2017297-0013 independent quantification of protein content, since nucleic acid spectra has relatively little absorption in those (Amide-II and Amide-III) regions.
• Absorption strength in Amide-II and/or Amide-III regions may be determined using a peak intensity metric that measures intensity of an Amide-II and/or Amide-III band, such as peak amplitude or area under the curve (AUC) measure.
• a protein content metric and/or total capsid content may, accordingly, be determined based on peak intensity measures for one or both (e.g., a linear combination) of the Amide-II and Amide-III bands.
• a nucleic acid content metric such as a total nucleic acid concentration, may be computed via one or more peak metrics computed based on antisymmetric and/or symmetric PO 4 bands.
• Absorption strength in asymmetric and/or symmetric PO4 regions may be determined using a peak intensity metric that measures intensity of an antisymmetric PO 4 and/or symmetric PO 4 band, such as peak amplitude (“peak”) or area under the curve (“AUC”) measure.
• a nucleic acid metric may be determined based on peak intensity measures for one or both (e.g., a linear combination) of the antisymmetric PO4 and/or symmetric PO4 bands [0433]
• a protein content metric and/or nucleic acid metric may be used to compute a full capsid fraction that provides a measure of a fraction (e.g., a ratio, a percentage, etc.) of capsids that are full – i.e., successfully loaded with desired genetic payload.
• a full capsid fraction may be determined based on a ratio of (i) a nucleic acid content metric to (ii) a protein content metric and/or a total capsid content determined therefrom.
• a frequency position (e.g., center frequency, center of mass frequency) may be determined for one or more of an Amide-I band, an Amide-II band, and Amide-III band, an antisymmetric PO 4 band, and a symmetric PO 4 band.
• a frequency position may be indicative of a particular percentage content of full versus empty capsids.
• one or more peak metrics may be used to determine a level of aggregation of capsids in a sample, for example, based on frequency position variations.
• one or more protein secondary structure metrics may be determined, as described herein, for a viral vector sample.
• combining ion exchange chromatography (charge separation of viral capsids) - 94 - 11677372v1 Attorney Docket No. 2017297-0013 with mid-IR measurement (change in protein secondary structure) could lead to higher purity by selectively isolating full capsids
• Sample quality metrics pertaining to viral vector production as described herein may be stored, displayed, or provided, for example as trigger signals, in order to monitor, interactively adjust, and/or automatically tune process parameters.
• FIG.15 shows an example process 1500 for real-time evaluation and monitoring of viral process production via mid-IR spectroscopy.
• a raw IR absorption spectra from an aqueous sample comprising a viral vector species is collected 4302.
• various pre-processing steps such as smoothing, decimation, baseline correction (e.g., to account for water temperature drift, displacement of water, etc.), and the like, may be performed 1504.
• IR absorption spectra may then be used to determine sample quality metrics, such as protein content, nucleic acid content, viral capsid content and full capsid fraction 1506.
• sample quality metrics such as these may be CQA’s, and/or used to determine one or more CQA’s and/or CPP’s.
• sample quality metrics and/or parameters determined therefrom may be displayed 1508a and/or stored in memory 1508c.
• a determined sample quality metric such as a viral capsid content and/or full capsid fraction may be used to produce digital and/or analog electronic triggers signals 1508b, that can be used for, for example, feed-forward and/or feedback process control.
• processes such as flow rate, flow direction, pressures, temperatures, pH, etc. may be adjusted.
• trigger signals may be used to direct decisions such as when to stop a processing, open or close valves direct sample collection and fraction collection, e.g., to collect a particular fraction of sample eluting from a chromatography column comprising, e.g., high titer and/or high full capsid fraction.
• trigger signals may be used to various processing steps in production of viral vectors.
• FIGs.16A and 16B illustrate process flow for AAV and lentiviral vector production, respectively. Viral capsid content and/or full fraction may, accordingly, be monitored from aqueous sample, for example during the and/or various steps including production and subsequent steps.
• in-line measurements of viral vector full fraction can be used to monitor separation of empty and full capsids during a chromatography polishing step.
• Chromatography polishing may, for example, utilize anion exchange (AEX) chromatography to separate empty capsids from full - 95 - 11677372v1 Attorney Docket No. 2017297-0013 capsids.
• AEX anion exchange
• chromatography techniques such as affinity chromatography (AC) column, a hydrophobic interaction chromatography (HIC) column, an ion exchange chromatograph (IEX) column, a size exclusion chromatography (SEC) column, and a mixed-mode chromatography column [e.g., which implements any combination of the foregoing (e.g., IEX and HIC; e.g., IEX and SEC)] may be used.
• AC affinity chromatography
• HIC hydrophobic interaction chromatography
• IEX ion exchange chromatograph
• SEC size exclusion chromatography
• mixed-mode chromatography column e.g., which implements any combination of the foregoing (e.g., IEX and HIC; e.g., IEX and SEC)] may be used.
• in- line monitoring of aqueous sample as it elutes from a chromatography column can allow observation and/or control based on measured viral capsid content and/or full fraction to select particular target fractions of the eluate to retain or discard depending on desired target purity and/or yield.
• reference spectra as described herein, may be used to determine sample quality metrics.
• raw spectral data may be converted into quantitative sample quality metrics, such as CQA’s, and then displayed, stored, or used to trigger process control.
• a raw IR absorption spectra from an aqueous sample comprising a viral vector species is collected 1702.
• various pre-processing steps such as smoothing, decimation, baseline correction (e.g., to account for water temperature drift, displacement of water, etc.), and the like, may be performed 1704.
• raw spectra 1702, with or without preprocessing 1704 may be scaled 1706, for example to adjust overall amplitude.
• raw spectra may be scaled by a constant equal to and/or based on (e.g., determined using) one or more peak metrics, such as a peak amplitude, area under the curve, etc., computed for one or more particular absorption bands.
• a target spectrum may be compared to one or more reference spectra, such as high quality viral vector spectra, as described herein, to determine one or more sample quality metrics 1708.
• Comparison between target spectrum and reference spectrum may comprise computing a difference spectrum, a difference first derivative, a difference second derivative, a correlation, a covariance, a Pearson’s correlation, an overlap integral, etc.
• comparison between target spectra and reference spectra yields a comparison spectrum, such as a difference spectrum, which may in term be used to compute peak metrics and, ultimately, sample quality metrics.
• sample quality metrics may be computed based on target and reference spectra, without necessarily computing a comparison spectra, for example by computing a covariance, overlap integral, etc.
• sample quality metrics such as these may be CQA’s, and/or used to determine one or more CQA’s and/or CPP’s.
• sample quality metrics and/or - 96 - 11677372v1 Attorney Docket No. 2017297-0013 parameters determined therefrom may be displayed 1710a and/or stored in memory 1710c.
• a determined sample quality metric such as a viral capsid content and/or full capsid fraction may be used to produce digital and/or analog electronic triggers signals 1710b, that can be used for, for example, feed-forward and/or feedback process control.
• processes such as flow rate, flow direction, pressures, temperatures, pH, etc. may be adjusted.
• trigger signals may be used to direct decisions such as when to stop a processing, open or close valves direct sample collection and fraction collection, e.g., to collect a particular fraction of sample eluting from a chromatography column comprising, e.g., high titer and/or high full capsid fraction.
• MIR Analyzers into Production Units and/or Process Streams
• one or more mid-IR analyzer(s) are incorporated into a production unit and/or the process stream, e.g., measuring liquid input and/or output from production units.
• one or more mid-IR analyzer(s) are incorporated into in-line, for example via a split stream whereby a portion of input/output stream is sampled through an analyzer and then returned to the main process stream for continued processing.
• ATR sampling geometries offer advantages for integration with pilot and/or commercial scale manufacturing production units which rely on in/out-flow through large diameter channels, ranging from a millimeter or more to 1 ⁇ 4 inch to an inch in diameter.
• transmission through more than 40 microns may be impractical and, accordingly, ability of ATR geometries to provide a fixed, limited path-length via an evanescent wave, which is independent of the size of a channel through which liquid flows, dramatically facilitates integration with production scale systems.
• processing, communication, instrument control, and the like as described herein may be implemented in whole or in part via a variety of components associated with production units, central processing systems, or remote devices. For example, various processing, communication, and control steps may be carried out by - 97 - 11677372v1 Attorney Docket No.
• a connected computer may control process parameters directly or may transmit and/or the data, process commands, control signals and the like one or more communication channels.
• a variety of communication channels may be used, including, but not limited to, data packets shared over a communication network and/or communication port, an analog signal transmitted as a voltage or a current to a device that can decode the signal such as a PLC or another computer, a standardized communication protocol/system such as OPC-UA, Profibus, ModBus etc., or a dedicated proprietary communication channel.
• Communication channels may be wired or use wireless protocols such as Bluetooth, WiFI, RF, or other technologies.
• data and/or commands are encoded prior to being transmitted and/or shared and decrypted and used (e.g., to adjust process parameters) by a receiving device.
• the cloud computing environment 1800 may include one or more resource providers 1802a, 1802b, 1802c (collectively, 1802). Each resource provider 1802 may include computing resources.
• computing resources may include any hardware and/or software used to process data.
• computing resources may include hardware and/or software capable of executing algorithms, computer programs, and/or computer applications.
• exemplary computing resources may include application servers and/or databases with storage and retrieval capabilities.
• Each resource provider 1802 may be connected to any other resource provider 1802 in the cloud computing environment 1800.
• the resource providers 1802 may be connected over a computer network 1808.
• Each resource provider 1802 may be connected to one or more computing device 1804a, 1804b, 1804c (collectively, 1804), over the computer network 1808.
• - 98 - 11677372v1 Attorney Docket No. 2017297-0013 [0445]
• the cloud computing environment 1800 may include a resource manager 1806.
• the resource manager 1806 may be connected to the resource providers 1802 and the computing devices 1804 over the computer network 1808.
• the resource manager 1806 may facilitate the provision of computing resources by one or more resource providers 1802 to one or more computing devices 1804.
• the resource manager 1806 may receive a request for a computing resource from a particular computing device 1804.
• the resource manager 1806 may identify one or more resource providers 1802 capable of providing the computing resource requested by the computing device 1804.
• the resource manager 1806 may select a resource provider 1802 to provide the computing resource.
• the resource manager 1806 may facilitate a connection between the resource provider 1802 and a particular computing device 1804.
• the resource manager 1806 may establish a connection between a particular resource provider 1802 and a particular computing device 1804.
• the resource manager 1806 may redirect a particular computing device 1804 to a particular resource provider 1802 with the requested computing resource.
• FIG.19 shows an example of a computing device 1900 and a mobile computing device 1950 that can be used to implement the techniques described in this disclosure.
• the computing device 1900 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers.
• the mobile computing device 1950 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart-phones, and other similar computing devices.
• the components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to be limiting.
• the computing device 1900 includes a processor 1902, a memory 1904, a storage device 1906, a high-speed interface 1908 connecting to the memory 1904 and multiple high-speed expansion ports 1910, and a low-speed interface 1912 connecting to a low-speed expansion port 1914 and the storage device 1906.
• Each of the processor 1902, the memory 1904, the storage device 1906, the high-speed interface 1908, the high-speed expansion ports 1910, and the low-speed interface 1912 are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate.
• the processor 1902 can process instructions for execution within the computing device 1900, including instructions stored in the memory 1904 or on the storage device 1906 to display - 99 - 11677372v1 Attorney Docket No.
• the memory 1904 stores information within the computing device 1900.
• the memory 1904 is a volatile memory unit or units.
• the memory 1904 is a non-volatile memory unit or units.
• the memory 1904 may also be another form of computer-readable medium, such as a magnetic or optical disk.
• the storage device 1906 is capable of providing mass storage for the computing device 1900.
• the storage device 1906 may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. Instructions can be stored in an information carrier. The instructions, when executed by one or more processing devices (for example, processor 1902), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices such as computer- or machine-readable mediums (for example, the memory 1904, the storage device 1906, or memory on the processor 1902).
• a computer-readable medium such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. Instructions can be stored in an information carrier. The instructions, when executed by one or more processing devices (for example, processor 1902), perform
• the high-speed interface 1908 manages bandwidth-intensive operations for the computing device 1900, while the low-speed interface 1912 manages lower bandwidth- intensive operations. Such allocation of functions is an example only.
• the high-speed interface 1908 is coupled to the memory 1904, the display 1916 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 1910, which may accept various expansion cards (not shown).
• the low-speed interface 1912 is coupled to the storage device 1906 and the low-speed expansion port 1914.
• the low-speed expansion port 1914 which may include various communication ports (e.g., USB, Bluetooth®, Ethernet, wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
• the computing device 1900 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 1920, or multiple times in a group of such servers. In addition, it may be implemented in a personal computer such as a laptop computer 1922. It may also be implemented as part of a rack server system 1924.
• components from the computing device 1900 may be combined with other components in a mobile device (not shown), such as a mobile computing device 1950.
• a mobile computing device 1950 may contain one or more of the computing device 1900 and the mobile computing device 1950, and an entire system may be made up of multiple computing devices communicating with each other.
• the mobile computing device 1950 includes a processor 1952, a memory 1964, an input/output device such as a display 1954, a communication interface 1966, and a transceiver 1968, among other components.
• the mobile computing device 1950 may also be provided with a storage device, such as a micro-drive or other device, to provide additional storage.
• the processor 1952 can execute instructions within the mobile computing device 1950, including instructions stored in the memory 1964.
• the processor 1952 may be implemented as a chipset of chips that include separate and multiple analog and digital processors.
• the processor 1952 may provide, for example, for coordination of the other components of the mobile computing device 1950, such as control of user interfaces, applications run by the mobile computing device 1950, and wireless communication by the mobile computing device 1950.
• the processor 1952 may communicate with a user through a control interface 1958 and a display interface 1956 coupled to the display 1954.
• the display 1954 may be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic - 101 - 11677372v1 Attorney Docket No. 2017297-0013 Light Emitting Diode) display, or other appropriate display technology.
• the display interface 1956 may comprise appropriate circuitry for driving the display 1954 to present graphical and other information to a user.
• the control interface 1958 may receive commands from a user and convert them for submission to the processor 1952.
• an external interface 1962 may provide communication with the processor 1952, so as to enable near area communication of the mobile computing device 1950 with other devices.
• the external interface 1962 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.
• the memory 1964 stores information within the mobile computing device 1950.
• the memory 1964 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units.
• An expansion memory 1974 may also be provided and connected to the mobile computing device 1950 through an expansion interface 1972, which may include, for example, a SIMM (Single In Line Memory Module) card interface.
• SIMM Single In Line Memory Module
• the expansion memory 1974 may include instructions to carry out or supplement the processes described above, and may include secure information also.
• the expansion memory 1974 may be provide as a security module for the mobile computing device 1950, and may be programmed with instructions that permit secure use of the mobile computing device 1950.
• secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.
• the memory may include, for example, flash memory and/or NVRAM memory (non-volatile random access memory), as discussed below.
• instructions are stored in an information carrier. The instructions, when executed by one or more processing devices (for example, processor 1952), perform one or more methods, such as those described above.
• the instructions can also be stored by one or more storage devices, such as one or more computer- or machine-readable mediums (for example, the memory 1964, the expansion memory 1974, or memory on the processor 1952).
• the instructions can be received in a propagated signal, for example, over the transceiver 1968 or the external interface 1962.
• - 102 - 11677372v1 Attorney Docket No. 2017297-0013 [0457]
• the mobile computing device 1950 may communicate wirelessly through the communication interface 1966, which may include digital signal processing circuitry where necessary.
• the communication interface 1966 may provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), among others.
• GSM voice calls Global System for Mobile communications
• SMS Short Message Service
• EMS Enhanced Messaging Service
• MMS Multimedia Messaging Service
• CDMA code division multiple access
• TDMA time division multiple access
• PDC Personal Digital Cellular
• WCDMA Wideband Code Division Multiple Access
• CDMA2000 Code Division Multiple Access
• GPRS General Packet Radio Service
• a GPS (Global Positioning System) receiver module 1970 may provide additional navigation- and location-related wireless data to the mobile computing device 1950, which may be used as appropriate by applications running on the mobile computing device 1950.
• the mobile computing device 1950 may also communicate audibly using an audio codec 1960, which may receive spoken information from a user and convert it to usable digital information.
• the audio codec 1960 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile computing device 1950.
• Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on the mobile computing device 1950.
• the mobile computing device 1950 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 1980. It may also be implemented as part of a smart-phone 1982, personal digital assistant, or other similar mobile device. [0460] Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof.
• ASICs application specific integrated circuits
• implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
• a programmable processor which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
• Actions associated with implementing the systems may be performed by one or more programmable processors executing one or more computer programs. All or part of the systems may be implemented as special purpose logic circuitry, for example, a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC), or both.
• FPGA field programmable gate array
• ASIC application-specific integrated circuit
• All or part of the systems may also be implemented as special purpose logic circuitry, for example, a specially designed (or configured) central processing unit (CPU), conventional central processing units (CPU) a graphics processing unit (GPU), and/or a tensor processing unit (TPU).
• CPU central processing unit
• CPU central processing unit
• GPU graphics processing unit
• TPU tensor processing unit
• machine-readable medium and computer-readable medium refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine- readable medium that receives machine instructions as a machine-readable signal.
• machine-readable signal refers to any signal used to provide machine instructions and/or data to a programmable processor.
• the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer.
• a display device e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor
• a keyboard and a pointing device e.g., a mouse or a trackball
• Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
• the systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components.
• the components of the system can be interconnected by any form or medium of digital data communication - 104 - 11677372v1 Attorney Docket No. 2017297-0013 (e.g., a communication network).
• Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.
• the computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
• modules described herein can be separated, combined or incorporated into single or combined modules. The modules depicted in the figures are not intended to limit the systems described herein to the software architectures shown therein.
• Elements of different implementations described herein may be combined to form other implementations not specifically set forth above. Elements may be left out of the processes, computer programs, databases, etc.
• FIG. 20A-D demonstrate measurement of IR spectra from several different protein mixtures, each comprising bovine serum albumin (BSA) at a particular concentration, along with graphs comparing QCL-measured concentration/related peak metrics with a-priori known concentration.
• FIG.20A shows several IR absorbance spectra measured for samples having different BSA concentrations.
• FIG.20B shows a value of measured concentration, determined by computing an AUC of a protein absorption region, by integrating each spectrum from a minimum frequency (around 1360 cm -1 ) to about 1700cm -1 . The measured concentration was determined by scaling the determined AUC and displayed against nominal reference concentration in FIG.20B.
• FIGs.20C and 20D show similar measurements, with - 105 - 11677372v1 Attorney Docket No. 2017297-0013 FIG.20D displaying AUC directly against nominal BSA concentration and showing parameters of a linear fit, along with an R 2 value, indicating highly linear correlation. Accordingly, As shown in the graphs of FIG.20B and 20D, measured concentration closely matches the nominal concentration of the prepared samples and is highly linear, even at high concentrations.
• Example 2 Analytical Chromatography Measurements and Comparison with UV Absorption
• FIG.21 shows recording of mid-IR spectra over time, during an elution.
• FIGs.22A and 22B compare protein content as measured by UV absorbance (FIG.22A) with protein content measured via mid-IR absorbance (FIG.22B, showing AUC, computed as an integrated absorbance over Amide-I and Amide-II bands, variation over time).
• Example 3 Amide Sub-Band Deconvolution
• This example demonstrates use of a mid-IR QCL-based spectrometer to record amide-band spectra of protein mixtures in aqueous solution and monitor changes in protein secondary structure as a function of temperature.
• FIG.23A shows IR spectra of a 10mg/mL BSA solution measured while flowing at a rate of 400 ⁇ L/min, as temperature is varied (each curve obtained at a different time/temperature).
• FIG. 23B shows second derivative spectra determined from each of the spectra shown in FIG.23A. Second derivative spectra may be used to emphasize/monitor variations in protein secondary structure, which is reflected in changes in sub-peaks that make up the Amide band region.
• FIGs.23C and 23D show variations in secondary structure components such as alpha-helical, turn, beta-sheet, and disordered content as temperature is varied.
• content of a particular secondary structure motif was measured by determining absorbance at a particular, nominal, frequency associated with a particular secondary structure motif. For example, as shown in FIG.23C, absorbance at 1618 - 106 - 11677372v1 Attorney Docket No. 2017297-0013 cm -1 was used to measure beta sheet content, absorbance at 1692 cm -1 was used to measure turn content, absorbance at 1656 cm -1 was used to measure alpha-helix content, and absorbance at 1645 cm -1 was used as a measure of unordered content.
• FIG.24 shows heat maps that display Amide sub-band intensities as they vary with temperature increases from room temperature to about 75oC for five different proteins.
• Amide band spectra for each of the five proteins were de-convolved to allow particular peak frequencies and intensities of constituent sub-bands (e.g., indicative of various secondary structure motifs) to be determined directly from spectra recorded at each temperature, for each of the five proteins shown in FIG.24.
• tracking position of sub-bands associated with secondary structure motifs allows stability of proteins in different solutions/formulations to be observed and characterized.
• HEWL hen egg-white lysosome
• PBS phosphate buffer solution
• the HEWL in PBS shows a single relatively stable dominant peak over a wide range of lower temperatures
• the HEWL in acetate map shows meandering bands even at lower temperatures, indicative of lower stability.
• this example demonstrates capability to monitor variations in protein secondary structure in substantially real time.
• Example 4 Protein Biophysical Characteristics Based on Isobestic Point
• FIG.25 shows spectra of BSA at various concentrations.
• amide band spectra can be used to determine a variety of information about a protein under study.
• the inset shows an expanded view of the spectra in the vicinity of 1700 cm -1 .
• absorbance at a particular wavenumber of a protein in water sample, relative to (i.e., having been normalized to) a background spectrum of pure water may be computed as indicated in FIG.25, where A is the absorbance - 107 - 11677372v1 Attorney Docket No.
• ⁇ p is the absorption cross section for protein
• ⁇ is the relative density of protein to water
• ⁇ w is the absorption cross section for water
• Cp(t1) is the concentration of protein (at the time measured)
• L is the path length.
• Example 5 Baseline Correction During Elution Using Conductivity Sensors [0479] This example shows baseline variations due to increased presence of ions in measurements recorded during elution of a chromatography column. FIG.26A shows variation in spectra before and after elution.
• FIG.26B shows variation in absorption with conductivity.
• the relatively flat shape of the water absorption curves shown in FIG.26A allow the position of the amide-II band, for example as computed by a center of mass frequency, to be insensitive to baseline fluctuations during an elution. Accordingly, this allows the Amide-II band to be used to measure protein structural changes without, in certain embodiments, having to precisely correct for baseline variations during elution.
• Example 6 Protein Aggregation Metrics from IR Absorption Spectra
• An absorption spectrum of a static or flowing aqueous mixture of proteins may be collected approximately over a range of about 800 cm -1 to about 1800 cm -1 , or any sub- - 108 - 11677372v1 Attorney Docket No. 2017297-0013 divided continuous or discontinuous range thereof.
• Such absorption spectra may be obtained using a variety of infrared absorption spectrometers, including, for example, Fourier transform infrared (FT-IR), quantum cascade laser infrared (QCL-IR) analyzers, etc.
• FIG.27 shows a typical IR absorption spectrum of a protein measured in an aqueous buffer.
• the absorption spectrum of FIG.27 was computed using a reference background taken from a nominal zero-protein aqueous buffer. Protein infrared absorption spectra such as that shown in FIG.27 may be analyzed via one or more of the techniques described in this example to determine sample quality metrics indicative of protein heterogeneity, in particular, aggregation (protein aggregation metrics) as well as total protein content. These sample quality metrics may then be used to create a digital or analog signal that can be used for dynamic process control and/or process optimization.
• sample quality metrics relating to protein content, heterogeneity, aggregation, and the like may be computed based on properties of absorption peaks associated with an Amide-I and/or an Amide-II band in IR spectral data. Table 2, below, shows values of approximate ranges in the mid-IR for Amide-I and/or Amide-II spectral bands.
• FIGs.28A and 28B illustrate a first protein aggregation metric computed as a particular peak metric – namely, a “center of mass” of an Amide II band approximately corresponding to a region from about 1500 to about 1600 cm -1 ( ⁇ COM Amide-II).
• FIG.28A shows an example spectrum of a zero aggregation state (e.g., all monomer).
• FIG.28B shows a spectrum of a 5% by mass aggregation state, showing that a center of mass of an Amide II band changes in proportion to a relative concentration of aggregates.
• a nominal Amide II center of mass for a high-purity protein e.g. monoclonal antibody
• protein aggregates or fragments could be higher or lower than this nominal center-of-mass value by plus or minus 0.5 cm -1 (FIG.28B).
• Small frequency shifts such as this can readily be observed using high-performance QCL-based infrared based mid- IR analyzers.
• FIGs.29A and 29B shows a second protein aggregation metric computed as a ratio of two peak metrics.
• a first, Amide-I, peak metric characterizes an Amide-I band and is computed as an area under the curve (AUC) for the Amide-I band (corresponds to the region of 1600 to 1700 wavenumbers)
• Amide-II, peak metric characterizes an Amide-II band and is computed as an area under the curve (AUC) for the Amide-II band (approximately corresponds to the region of 1500 to 1600 wavenumbers).
• Second protein aggregation metric RAmide-I to Amide-II is then computed as the ratio of the Amide-I AUC to the Amide II AUC (alternatively, Amide II to Amide I).
• Tables 3A and 3B Typical values for the ratio are provided in Tables 3A and 3B, below.
• High-purity protein monomers typically have ratios in the range of 1.2 to 1.6.
• Aggregates typically have ratios between 0.8 and 1.0 and fragments have ratios between 0.4 and 0.7.
• Table 3A Ranges and typical values for four peak metrics Meas. Units Approximate Approximate Approximate Typical - 110 - 11677372v1 Attorney Docket No. 2017297-0013 RAmideI to Ratio (a.u.) 0.0 – 10.0 0.0 – 3.0 0.0 – 2.0 1.3 – 1.6 Amide I
• Table 3B Examples of possible values for four peak metrics Meas.
• a third embodiment involves computing the ratio of the peak heights of Amide I to Amide II (alternatively, Amide II to Amide I) where the Amide I band approximately corresponds to the region of 1600 to 1700 wavenumbers and Amide II band approximately corresponds to the region of 1500 to 1600 wavenumbers. Typical values for the ratio are provided in FIG.26A. High-purity protein monomers typically have ratios in the range of 1.2 to 1.6.
• Aggregates typically have ratios between 0.8 and 1.0 and fragments have ratios between 0.4 and 0.7.
• first aggregation metric – ⁇ COM Amide-II provided highest correlation with aggregation level and performance as a quantitative measure of % aggregation.
• FIG.31 total protein content was measured using a total area under the curve for the Amide-I and Amide-II bands.
• FIGs.32A and 32B these sample quality metrics may then be used to create a digital or analog signal that can be used for dynamic process control and/or process optimization.
• FIG.32B is an illustrative sketch showing anticipated (hypothetical) variation in sample quality metrics that measure total protein content and protein aggregation, as described herein, over time, during elution from an IEX - 111 - 11677372v1 Attorney Docket No. 2017297-0013 chromatography column.
• the solid (black) curve shows anticipated variation in value of a total protein content metric, computed by integrating over an Amide-I and Amide-II band region, as described in FIG.31. Total protein content is expected to peak as monomeric protein begins to elute from a column. Dashed (black) curve shows protein content as would be measured via UV absorption, which mirrors variation in the mid-IR measurement.
• Dash- dot (red) curve shows anticipated variation in a protein aggregation metric, such as ⁇ COM Amide- II .
• Protein aggregation metric curve peaks initially 3202 as low molecular weight species, such as fragments, are eluted first, and then reaches and stays at a relatively stable value over a period of time 3204 as high purity monomer elutes, after which its value shifts to reflect elution of higher molecular weight species, such as dimers, trimers, etc.3208.
• FIG.32B it is expected that use of a protein aggregation metric such as ⁇ COM Amide-II, may allow for collection over an additional time period 3206 during which a total protein content metric, measured either by IR absorption spectroscopy or UV absorption, begins to decrease, but high purity monomer continues to elute, thereby improving yield. Additional discussion of process control based on protein aggregation metrics is provided in Example 10, below.
• Example 7 Protein Identification and Metric Development Workflows [0492] This example shows illustrative embodiments of example workflows that can be used to create sample quality metrics and identification reference spectra. [0493] FIGs.33A and 33B show an example methodology for creating a protein identification method.
• FIG.33C provides a schematic showing a non-limiting list of functional modules and sample quality metrics that can be used to evaluate bioproduction process control in accordance with various embodiments described herein.
• Example 8 Downstream Purification Process Monitoring and Control
• This example provides an example control strategy based on measurement of multiple sample quality metrics in order to provide real time control of collection start/stop based on protein purity.
• FIG.34A in one example control strategy, two sample quality metrics are monitored in real-time – a total protein content metric and a protein aggregation metric, for example a total Amide band AUC and an Amide-II center of mass as described in Example 6. These two metrics may be used to compute an output analog - 112 - 11677372v1 Attorney Docket No.
• FIG.34A shows variation of voltage signals used to control a chromatography elution in an IEX column over time.
• a first, top-most trace shows a signal that initiates a conductivity ramp to begin elution.
• a second trace from the top shows a signal that triggers a mid-IR analyzer to record a reference background spectrum, e.g., of water absorbance. As indicated in the figure, the background spectrum is recorded just before the conductivity ramp begins.
• the figure shows a short time period 3402 (shaded light blue region) before protein begins to elute, while conductivity is increased. This time period may be used to calibrate a conductivity dependent baseline removal function (e.g., depending one amount of water displaced for a given conductivity level), if desired.
• a third trace from the top shows variation in an analog voltage output that measures total protein content. This voltage output may, for example, be used to trigger a start of collection, as it rises and stabilizes once pure monomer begins to elute. Below the total protein content voltage trace is an analog voltage trace proportional to an Amide-II center of mass frequency (computed as described in Example 6, above).
• sample quality metrics may be computed and used to further refine and/or optimize collection windows.
• an “other species” protein metric may be computed.
• sample quality metrics that measure content of low molecular weight (e.g., fragments) and high molecular weight species may be computed and used to generate analog and/or digital control signals.
• Example 9 Downstream Filtration Process Monitoring
• This example demonstrates use of systems and methods as described herein to perform accurate real-time quantitation of protein from 1 to 300+ g/L, provide real-time quantitation and control of buffer stoichiometry, and to provide real-time monitoring of protein aggregation and/or structural changes (e.g., denaturation).
• - 113 - 11677372v1 Attorney Docket No. 2017297-0013
• FIG.35 shows data created via several sequential injections in a TFF system, including Amide-I sub-band deconvolution heatmaps that can be used to visualize secondary structure variations and a total protein concentration.
• Example 10 Broad Spectral Coverage and Low Noise QCL Systems
• FIG.36 shows use of multiple QCL’s to obtain continuous coverage from about 1025 cm -1 to about 1725 cm -1 .
• FIG.37 shows capabilities for > 10X sensitivity improvements.
• Example 11 Simultaneous Measurement of Multiple Analytes
• This example demonstrates simultaneous measurement of multiple analyzes in solution via a QCL-based mid-IR spectrometer in accordance with certain embodiments described herein.
• FIGs.38A and 38B show measured absorbance spectra for several example analytes in solution
• FIGs.39A and 39B demonstrate excellent linearity for glucose in water (FIG.39A) and ammonia in water (FIG.39B).
• FIG.40A and 40B shows ability to identify and measure, at high degree of linearity (RMS concentration error ⁇ 10 micrograms/mL), target analytes in complex mixtures (e.g., in presence of other analytes).
• RMS concentration error ⁇ 10 micrograms/mL
• Example 12 Formulation Development and Forced Degradation
• This example demonstrates use of mid-IR absorption spectroscopy to observe variations in protein secondary structure over time, for example relevant to formulation development.
• FIG.41A shows variations in protein Amide band spectra as temperature is varied.
• IR spectra e.g., when measured at sufficient sensitivity and/or spectral resolution
• Attorney Docket No. 2017297-0013 range provide rich information about intermolecular interactions with side chains and NH bending from Amide-II peaks.
• FIG.41B shows a view of the Amide-I band region of the spectra shown in FIG.41A
• FIG.41C shows second derivative spectra over the Amide-I band region, illustrating, among other things, changes in secondary structure such as relative decreases in alpha helical content and increasing turn content.
• FIG.42 shows a spectrum of a 10 mg/ml mAb in a particular formulation buffer (blue) together with a deconvolution of constituent sub bands (red curves).
• FIG.43A shows variation in Amide-I band structure for a mAb with a high beta sheet content as temperature is increased from 25C to 80C.
• the central panel is a heat map showing changes in sub-band intensities with temperature.
• FIG.43B summarizes changes in secondary structure motifs and content as temperature is increased, as indicated via overlaid trajectories on the heat map.
• Example 13 Data Automation and Stability
• This example demonstrates automation capability and reproducibility and applicability of mid-IR analyzers as described in certain embodiments herein, relevant, for example, for GMP-compliant bioproduction.
• FIG.44 shows an example analysis system with multiple mid-IR analyzers stacked and linked together and connected to a central computer.
• the mid-IR system is connected to a processor that allows workflow management of multiple instruments and fluid handlers.
• the system provides for automated self-check for wavelength accuracy and optical power.
• the systems comprise an alarm system for leaks and/or volatile compounds.
• the systems comprise OPC-UA secure communication and instrument control interface, allowing for 21 CFR P11 compliance.
• FIG.45A-45E show results of several tests of reproducibility, including, among other things, high reproducibility over sequential injections, and repeated sets of injections over multiple days.
• - 115 - 11677372v1 Attorney Docket No. 2017297-0013
• Example 14 Independent Protein and Nucleic Acid Quantification and Viral Capsid Titer and Full Fraction Metrics
• This example demonstrates use of a QCL-based mid-IR analyzer to independently quantify protein and nucleic acid content, and describes several example approaches whereby protein and nucleic acid content metrics can be used to determine viral capsid concentration and full fraction.
• BSA solution spectrum includes Amide-I and Amide-II spectral features.
• FIG.46B shows absorption spectra for several mixtures of BSA and ssDNA with varying relative concentration of BSA and ssDNA. Since BSA portions of the spectra were measured using a Culpeo LA-P unit with a 20 um path length and ssDNA portions of the spectra measured using a Culpeo LA-S unit with a 50 um path length flow cell, data were normalized to account for difference in path length. As shown in FIG.46B, intensity of the asymmetric PO4 and symmetric PO4 peaks tracks with ssDNA content, while intensity of the Amide-II peak tracks with protein (BSA) content.
• BSA protein
• FIG.46A demonstrates potential of Amide band spectral features for quantification of protein content and asymmetric and symmetric PO4 features for quantification of nucleic acid content.
• the particular example spectra shown in FIGs.46A and 46B do not cover the Amide-III band region, but, as shown, for example, in FIG.30 and described in Example 10, other embodiments of mid-IR analyzers described herein may supply substantially continuous coverage across an entire spectral range from about 1025 cm- 1 to about 1725 cm -1 , including an Amide-III region.
• intensities of various Amide and PO4 absorption bands can be used to quantify content of protein and nucleic acid content in a mixture, including not only free protein and nucleic acid, but also structures such as viral capsids comprising protein and which may or may not enclose nucleic acid material, which may be used to characterize viral vector samples.
• adeno associated viruses AAVs
• AAVs are an example of actively investigated non-enveloped viruses that can be engineered to deliver DNA to target cells. AAVs are attractive for therapeutic purposes and have attracted significant attention in their design due to, among other things, their safety, efficacy, and long-term gene-expression capabilities.
• Certain product-related CQAs of rAAV include virus titer, a fraction of capsids successfully loaded with a complete genome (content ratio), and an amount of aggregated capsids.
• Content ratios that quantify ratios of empty to full to partially filled capsids capture a common impurity in rAAV production. Aggregates of capsids can also have a detrimental impact on safety and stability of vector product.
• Previous analytical methods such as Transmission Electron Microscopy (TEM), Analytical Ultra Centrifuge (AUC), Anion Exchange Chromatography (AEC), Mass photometry (MassP), Dynamic Light Scattering (DLS), and Size Exclusion Chromatography- Multi Angle Light Scattering (SEC-MALS) may be used for quantification of empty, partially filled, and full rAAV capsids.
• TEM Transmission Electron Microscopy
• AUC Analytical Ultra Centrifuge
• AEC Anion Exchange Chromatography
• MassP Mass photometry
• DLS Dynamic Light Scattering
• SEC-MALS Size Exclusion Chromatography- Multi Angle Light Scattering
• AAV-2 has a capsid comprising 60 total proteins made up of a combination of three different viral proteins (VP1, VP2, and VP3) with a stoichiometric ratio of 1:1:10. That is, an AAV-2 capsid comprises of five VP1, five VP2, and fifty VP3 proteins and has a total molecular weight of approximately 3.7-3.8 MDa.
• An AAV capsid has an effective diameter of about twenty-five (25) allowing it to hold genetic material of about five thousand nucleotide bases or less.
• Range 1 Range 2 3 Range 4 0 0 0 0 5 [0524]
• peak intensity metrics – such as peak amplitude and/or AUC, may be used to measure protein content and/or nucleic acid content in a sample. Assuming all protein content in a viral vector sample originates from viral capsid, and all nucleic acid content originates from genetic payload, a variety of linear combinations of peak metrics, as shown in Equations 8a-f and 9a-f, below, can be used to determine capsid content and amount of genetic material in a sample.
• Equations shown in 8a-f and 9a-f include embodiments whereby peak intensity of a particular band is determined using peak amplitude (the function “peak()”), as well as area-under the curve (the function “AUC()”).
• Equations 8a-f and 9a-f include (collectively) a total of sixteen linear coefficients (a1, a2, ..., a16) that can be created through a combination of theoretical calculations or empirical measurements. For example, a total capsid concentration or titer can be computed by first measuring a peak absorbance value within the spectral range B (See Table 5) and then scaling that absorbance value by a linear coefficient (a1) to convert the absorbance value into a number of capsids per unit volume.
• Coefficient used for equations 8a-f and 9a-f may vary significantly between viral vector types (e.g., AAV vs. lentivirus), but are expected vary by a very small degree within a family of viral vectors (e.g. AAV-2 vs. AAV-3). Liner coefficients may also depend on instrument settings including spectral data spacing and resolution. Example values for each of the sixteen linear coefficients are shown in Table 6, below. Table 6: Approximate ranges and values of linear coefficients used in equations 8a-f and 9a- f. - 121 - 11677372v1 Attorney Docket No.
• Example 15 Computation of Sample Quality Metrics from Temporal Changes in IR Spectra and Monitoring Compositional Changes
• This example demonstrates an approach for monitoring composition changes in a sample by monitoring temporal changes in infrared (e.g., absorption) spectra collected over a particular range of wavenumbers.
• this example describes how changes between consecutive scans can be determined and evaluated to determine compositional changes in a sample.
• spectra are collected and averaged within a QCL spectrometer (Culpeo) (e.g., processed and saved on local memory).
• QCL spectrometer Culpeo
• FIGs.47A and 47B demonstrate use of normalization in accordance with Eqs. 4-6 to remove dependence of IR absorption spectra on sample concentration.
• FIGs.47A and 47B show un-normalized and normalized spectra, respectively, for samples comprising a monoclonal antibody (in monomeric form) at different concentrations.
• Normalization is performed by dividing each spectrum by its absorbance value at a reference wavenumber of 1550cm -1 (other reference wavenumbers could be used). As shown in the figures, without normalization, spectra shown in FIG.47A vary in amplitude as concentration is changes. Normalized spectra, plotted in FIG.47B, lie on top of each other, substantially overlapping. Small imperfections in overlap between normalized spectra are due to IR data noise. Protein Heterogeneity and Chromatography Collection Window Control [0532] This approach may be used to, among other things, evaluate protein heterogeneity in a sample. Quality of drug products, such as monoclonal antibodies, is impacted by presence of aggregates, and aggregation / monomeric purity is typically considered a critical product quality attributes.
• FIG.48 shows an overlay of an absorbance-based chromatograph and a ⁇ value computed over the course of the chromatography run. Temporal changes in the value of ⁇ were computed at 3.5 second time intervals.
• FIG.49 shows different versions of the ⁇ , computed in accordance with Eq. 7b described herein. The different panels show versions integrated over different combinations of the Amide I, Amide II, and Amide III bands.
• Calculate a mean and the variance of the normalized spectral difference signal calculated from a time when a change in column effluent composition was detected for example, based on a change in non-normalized absorbance – e.g., by detecting a non-zero value of ⁇ / ⁇ g, as calculated at step 4) and/or a particular otherwise determined (e.g., based on a column start time and various chromatography parameters; e.g., based on additional sensors), or pre-determined, start time; 8.
• Monitor for a step change in the normalized spectral difference signal using a technique for step change detection (e.g., comparing the latest value of delta to the current mean delta value plus minus a standard deviation, or comparing a change in variance between calculated with a long and a short time interval) (e.g., various methods as described in Killick R., P. Fearnhead, and I.A. Eckley. "Optimal detection of changepoints with a linear computational cost.” Journal of the American Statistical Association. Vol.107, Number 500, 2012, pp.1590-1598); 9.
• the identified step change will represent a detection/presence of a new component in the column effluent; 10. Start a new segment for calculation of mean and variance of the normalized spectral difference signal; and 11.
• Example 16 Real-Time Concentration Measurement in Multi-Component Mixtures - 125 - 11677372v1 Attorney Docket No. 2017297-0013 [0538]
• This example demonstrates an approach for quantifying concentrations of components in heterogeneous mixtures.
• This particular example shows an in-silico simulation of an approach for monitoring and determining concentrations of protein forms during a purification process, but it should be understood that the approach described herein can be utilized for monitoring mixture components and/or purity of other molecular species and/or during other purification processes.
• FIGs.50A and 50B this example simulates data that would be obtained during a protein purification process 5050 using a system 5000 in which a mid-IR analyzer 5010 and data processing unit 5014 are combined in line with a purification column 5004 and associated flow-control to purify a sample solution comprising a protein solution 5002, such as a monoclonal antibody (mAb).
• Sample solution 5002 is a heterogeneous protein solution, comprising a particular desired mAb in multiple forms – such as pure monomer, as well as aggregates (e.g., dimer, trimer, etc.) and fragments.
• sample solution 5002 is flowed through a purification column 5004.
• sample is eluted 5008 and monitored via a mid-IR analyzer 5010.
• Mid-IR analyzer 5010 measures mid-IR spectral data 5054 and communicates with processing unit 5014 to analyze measured mid-IR spectral data to determine and monitor concentrations of various sample components of interest 5056, in substantially real-time.
• FIG.50A shows mid- IR analyzer 5010 and processing unit 5014 as spatially separate, distinct components (e.g., a separate mid-IR analyzer and a computer), but, in certain embodiments, may be integrated (e.g., in a single housing, with processing unit 5014 dedicated to mid-IR analyzer 5010). These concentration measurements may, in turn, be used to compute various sample quality metrics 5058, such as measures of purity, yield, etc. Once these sample quality metrics have reached a desired target, crossed a threshold, etc., processing unit 5014 may communicate (e.g., provide a signal 5010) to a valve 5006 to adjust or halt collection 5012 of eluted sample, thereby obtaining a desired purity and/or yield of collected sample.
• concentration measurements may, in turn, be used to compute various sample quality metrics 5058, such as measures of purity, yield, etc.
• processing unit 5014 may communicate (e.g., provide a signal 5010) to a valve 5006 to adjust or halt collection 5012 of eluted
• FIG.50C shows an example process 5070 for determining individual component concentrations of a heterogeneous sample and computing sample quality metrics in real-time.
• Process 5070 utilizes one or more reference spectra 5072, each of which corresponds to a particular (e.g., target) sample component. That is, each individual reference spectra is a representative mid-IR spectrum of a substantially pure sample of the - 126 - 11677372v1 Attorney Docket No. 2017297-0013 particular component to which it corresponds.
• mAb purification processes may utilize a reference spectrum measured from a substantially pure monomer solution – a pure monomer reference, as well as reference spectra corresponding to impurities, such as aggregate and fragment references.
• Reference spectra 5072 may be compared with a measured mid-IR spectrum 5074 to extract relative contributions of individual components 5076 and, in turn, individual component concentrations 5078.
• mid-IR absorbance (A) is a linear combination of individual component absorbances
• a linear least squares approach can be used to extract the relative weights of each reference spectra 5076, which can, in turn, be used to determine component concentrations.
• a measured mid-IR absorbance spectrum, Am( ⁇ ) can be written as a weighted sum of one or more reference spectra, Ai( ⁇ ), plus a residual term, ⁇ ( ⁇ ), as follows: E q.
• a single reference spectrum corresponding to the desired, target component may be used to extract the target component’s concentration, with remaining impurities reflected in a residual term.
• equation 11a can be solved to determine the weights, wi, of each reference spectrum component, A i ( ⁇ ). Beer’s law can then be used relate equation 11b to the concentration, Ci, and absorption cross section, ⁇ i( ⁇ ), of each individual sample component, along with the path length, L, of the IR measurement, as shown below, E q.
• FIGs.51A-H show a simulated process run whereby reference spectra are used to extract and monitor concentrations of pure monomer, aggregated (dimer), and fragments during a mAb purification run and, in turn, determine and monitor sample purity and yield to collect an optimal fraction of sample.
• FIG.51A shows three reference spectra used in the simulated process run.
• Reference spectra shown in FIG.51A include a pure monomer reference, a dimer reference, and a fragment reference.
• Monomer and dimer references were obtained from experimental data, during a size exclusion chromatography run, as shown in FIGs.51I and 51J.
• a test solution comprising 93% pure monomer and 7% dimer form of a mAb in a phosphate buffer solution (PBS) was purified using a Superdex 200 increase (10 ⁇ 20 cm; 24 ml) column (1.5 mL sample volume, with a flow rate of 0.8 mL/min).
• PBS phosphate buffer solution
• FIG.51I shows a chromatogram obtained by measuring mid-IR spectra from the eluted sample and computing the integrated absorbance as a measure of total protein content. Dimer eluted first, followed by monomer, then fragment (e.g., in SEC compounds elute from a column in order of their size, largest first and smallest last).
• 2017297-0013 spectra (shown in FIG.51K) was obtained from measurements taken on a difference SEC run, using a sample obtained via digestion of a monomer mAb. Noise was added to each reference spectrum based on wavelength dependent signal standard deviations, in order mimic real experimental noise conditions.
• simulated concentration profiles for monomer, dimer, and fragment sample components were created to simulate measurements during an ion exchange chromatography (IEX) purification step by solving a general rate model describing ion exchange gradient separations.
• IEX ion exchange chromatography
• the model parameters were chosen so the model predictions describe a typical order of elution and a peak shape of obtained when a sample with a similar composition as the one used in the simulations is separated on a cation exchange resin using gradient elution. .
• Relative fractions of the monomer, dimer, and fragment components were used to compute, at each time point, t, a simulated sample absorbance spectrum, A m ( ⁇ ) as a weighted sum of the reference spectra shown in FIG.51A.
• FIG.51B shows integrated absorbance measured over time, computed from simulated absorbance spectra, A m (v; t).
• FIG.51C reference spectra shown in FIG.51A were used to extract, from each simulated absorbance spectrum, a monomer, dimer, and fragment concentration. Concentrations of each of these components at each time point are graphed in FIG.51C as a function of time. Noise (e.g., jitter) observed in extracted time-dependent concentrations results from noise in underlying spectral data, which caused fluctuations in extracted concentration profiles from the least-squares algorithm.
• Noise e.g., jitter
• Example 17 Real-time Monitoring of Ultra-Filtration/Diafiltration (UF/DF) Bioprocessing
• UF/DF Ultra-Filtration/Diafiltration
• FIG.52B Spectra shown in FIG.52B were also obtained using a Culpeo® LA-S MIR analyzer. Glucose and sucrose spectra are for 100mM solutions, and the glycerine spectrum is for a 1% solution.
• FIG.53 experiments were performed using a sample solution comprising bovine serum albumin (BSA) in buffer (10mM sodium phosphate, pH 6.5, 30 g/L) concentrated to 55 g/L. After reaching a target concentration (here 55 g/L), BSA was - 130 - 11677372v1 Attorney Docket No.
• BSA bovine serum albumin
• a peristaltic pump 5302 delivered diafiltration buffer solution 5304 to a receptacle placed on a mass balance and stir plate 5306, from which solution was fed to a UF/DF cassette 5312 via a quattro flow pump 5310.
• Permeate 5314 from UF/DF cassette 5312 flowed to another receptacle on a mass balance 5316, while retentate 5318 flowed through pinch valve 5320 back to receptacle 5306.
• a Culpeo LA-S MIR analyzer 5322 was used to repeatedly measure mid-IR spectra from a fraction of retentate extracted via a pump 5320 and sample components were determined using a least square fitting procedure as described in Example 16, above. In this manner, a single mid-IR analyzer was used to perform in-line measurements of protein and sucrose concentration.
• Table 7A Comparison of Mid-IR and UV A280 Measurements of Protein Concentration Time (min.) FlowVPX® UV A280 Culpeo Mid-IR % M t /L M t /L Diff
• Table 7B Comparison of Mid-IR and Cedex Bio Analyzer Measurement of Sucrose Concentration Sucrose Time Cedex Bio Analyzer Culpeo Mid-IR % Difference - 131 - 11677372v1 Attorney Docket No.
• this example demonstrates how mid-IR analysis systems and methods described herein can be used to provide real-time in-line monitoring of multiple sample components, such as protein and excipients, via a single analyzer.
• This combination of analytes would ordinarily require a combination of conventional methods such as UV/Vis absorbance and/or Cedex-style assays, some of which – such as a Cedex assay – are performed off-line rather than in-line.
• the Cedex assay is commonly done with bioreactor samples, to measure analytes. It requires a kit from Roche for the standards, which it runs before the sample.
• Example 18 Polysorbate 80 (PS80) Formulation Measurements
• This example demonstrates mid-IR measurement of detergent components used in drug substance formulations, in particular polysorbate 80 (PS80), which is commonly used in DS formulations for biologics.
• FIG.55A shows absorbance measurements for solutions of PS80 in water at varying concentrations
• FIG.55B shows absorbance measurements for PS80 spiked into a 30mM histidine formulation buffer with 25mM sucrose. Spectra were obtained by measuring baseline spectra of purified drug substance (PDS) comprising protein, sugar, and formulation buffer, prior to injection of PS80 to create a final DS.
• PDS purified drug substance
• Table 8A PS80 Concentration Measurements in a PDS Solution Comprising 146 g/L BSA, 20mM his, and 50mM sucrose Actual PS80 Concentration (g/L) Measured PS80 Concentration Extracted from Mid-IR Spectral Data
• Table 8B PS80 Concentration Measurements in a PDS Solution Comprising 90 g/L BSA, 10mM his, and 250mM sucrose Actual PS80 Concentration (g/L) Measured PS80 Concentration Extracted - 133 - 11677372v1 Attorney Docket No. 2017297-0013 [0561] Accurate measurements of PS80 demonstrated herein can be used during formulation of drug substances to measure detergent stock before addition, and afterwards in the DS mixture.
• PS80 is a key quality control release assay performed during formulation of drug substances and products. Conventional methods typically take over 24 hours to obtain results, requiring complex handling and review at a quality control lab, multiple sample dilutions, standard curves and system suitability injections. In contrast, mid-IR techniques described herein can be used in an at-line and/or in-line fashion, obtaining results in under ten minutes, and/or real-time, respectively.
• Example 19 Mid-IR Measurement of Glycosylation
• This example demonstrates mid-IR based glycosylation measurements using a scanning QCL-based mid-IR analyzer, providing opportunity for fast, real-time, measurement of protein glycosylation during bioprocessing steps.
• Sugars have distinct spectra. Accordingly, without wishing to be bound to any particular theory, it is believed that relative amounts of common N-glycans can be calculated from absorbance measurements in a mid-IR spectral band ranging from 1000 to 1100 cm -1 and compared to a total protein amount.
• FIGs.56A and 56B demonstrate potential for a scanning QCL-based mid-IR analyzer – in particular, a Culpeo LA-S, to obtain accurate spectra for use in determining glycan content of proteins in solution, on rapid time scales (e.g., 1s).
• FIG.56A shows individual spectra of sugar solutions, namely glucose and sucrose, each in solution at 100mM.
• FIG.56B shows IR spectra for two proteins with different glycosylation profiles – - 134 - 11677372v1 Attorney Docket No. 2017297-0013 BSA and the mAb Herceptin.
• BSA has no glycosylation
• Herceptin has two N- glycan sites.
• glycosylation can be observed from the increased IR absorption exhibited by Herceptin within the 1000 to 1100 cm -1 spectral band.
• this example shows that a QCL-based spectrometer, capable of recording a high-quality absorbance spectrum every second, can obtain absorbance spectra of proteins in solution that can be used to compare glycosylation. Such measurements could, accordingly, be used a real-time in-line and/or at-line assays, during bioprocessing as a quality check to ensure correct relative fractions of various N-glycan species and total amount of glycosylation per protein molecule in a biologic drug being manufactured.
• Example 20 Mid-IR Measurement of mAb Aggregation Based on Peak Ratios
• This example demonstrates measurement of aggregation content and sample purity in a mAb sample via mid-IR measurements and use of a peak metric as a proxy for sample purity.
• FIG.57A shows spectra of various protein solutions corresponding to a high molecular weight rich mAb sample spiked into a 97% pure mAb solution to create sample solutions with varying monomer content (samples were 97, 95, 93, 90, 86, 82, 75, 67, and 58% monomer).
• FIG.57B shows a graph showing the Amide region (e.g., between ⁇ 1450 and 1800 cm -1 ) in detail.
• FIG.57C shows that, accordingly a ration of absorbance at 1640 and 1620 cm -1 correlates with sample purity, as measured by size exclusion chromatography (SEC).
• SEC size exclusion chromatography

Landscapes

• Chemical & Material Sciences (AREA)
• Health & Medical Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• General Health & Medical Sciences (AREA)
• Organic Chemistry (AREA)
• Biochemistry (AREA)
• Analytical Chemistry (AREA)
• Medicinal Chemistry (AREA)
• Molecular Biology (AREA)
• Immunology (AREA)
• Genetics & Genomics (AREA)
• Proteomics, Peptides & Aminoacids (AREA)
• Biophysics (AREA)
• Physics & Mathematics (AREA)
• Pathology (AREA)
• General Physics & Mathematics (AREA)
• Public Health (AREA)
• Pharmacology & Pharmacy (AREA)
• Epidemiology (AREA)
• Animal Behavior & Ethology (AREA)
• Veterinary Medicine (AREA)
• Engineering & Computer Science (AREA)
• Chemical Kinetics & Catalysis (AREA)
• Oil, Petroleum & Natural Gas (AREA)
• General Chemical & Material Sciences (AREA)
• Water Supply & Treatment (AREA)
• Spectroscopy & Molecular Physics (AREA)
• Investigating Or Analysing Materials By Optical Means (AREA)

Applications Claiming Priority (4)

Publications (1)

Family

ID=89321943

Family Applications (1)

Country Status (5)

Families Citing this family (3)

Family Cites Families (6)

• 2023
  • 2023-11-15 EP EP23828565.4A patent/EP4619746A2/de active Pending
  • 2023-11-15 CN CN202380078683.XA patent/CN120188039A/zh active Pending
  • 2023-11-15 WO PCT/US2023/079780 patent/WO2024107814A2/en not_active Ceased
  • 2023-11-15 KR KR1020257016707A patent/KR20250110237A/ko active Pending
  • 2023-11-15 JP JP2025526254A patent/JP2025539014A/ja active Pending

Also Published As

Similar Documents

Legal Events