EA041350B1 - METHODS AND SYSTEMS FOR ANALYZING CHROMATOGRAPHIC DATA - Google Patents

Description

Cross-reference to related applications

This application claims the priority of US Patent Application No. 62/412563, filed October 25, 2016

the full disclosure of which is hereby incorporated by reference.

The field of technology to which the invention belongs

Aspects of the present invention relate generally to chromatography systems and methods, and in particular to embodiments of methods and systems for analyzing chromatographic data, for example, for in-process monitoring and control of chromatographic systems.

State of the art

Packed bed chromatographic processes play an important role in the production of biological medicinal products. Many active biologicals, such as proteins, are purified for use in pharmaceuticals using packed bed chromatography. Therefore, the performance of a chromatographic column can have a significant impact on critical processing parameters (CPP) and critical quality indicators (CQA). In addition, the complexity and size of biologicals compared to, for example, small molecules can make it much more difficult to analyze the quality and purity of a biological. Therefore, monitoring the quality, consistency, and integrity of chromatographic processes and equipment through in-process control is essential to ensure product quality meets any applicable standards (eg, government regulations).

Typically, column integrity can be determined by uniform flow of the plug in the mobile phase through the stationary phase of the column (eg, resin). Examples of loss of column integrity may include, for example, evidence of channeling, free space, contaminated flow regions, and the like. Channeling can occur when, among other things, the mobile phase can travel some distance from the column inlet to the column outlet without contacting the stationary phase. Headspace may refer to, inter alia, when a lateral zone is created in the column that allows flow of the mobile phase without plugs. Contaminated areas of the flow may include dirt or other debris on the surfaces of the inlet frit or outlet frit or in the pores of the resin.

There are several techniques for monitoring the efficiency and integrity of a chromatographic column. Some methods, such as the pulsed injection method used to measure the theoretical plate equivalent height (HETP), require buffer solutions that require special preparation. Pulse injection methods typically require the operation of the chromatography equipment and column outside of normal processes, resulting in increased process time and labor costs. Other methods include monitoring critical parameters (eg staging, prepool volume and maximum optical density during loading) as part of normal production. However, setting alarm limits based on these parameters is complex and inaccurate and may result in false alarms or limits that are too wide.

There is a need for methods, systems and processes to measure and monitor column efficiency and integrity with accuracy and precision and with minimal process disruption. In addition, due to the inherent differences between chromatographic columns, chromatographic column cycles, and/or production batches of any particular product that is subjected to chromatographic analysis, there is a need for methods, systems, and processes that can tailor column performance and integrity analysis to a particular column. or columns, a particular run or runs, and/or a particular batch or batches of product. Finally, there is a need for accurate in-process controls that use such analysis, as well as methods and systems for responding to deviations from such controls, so that problems with column integrity and efficiency can be identified and corrected at an early stage with minimal loss and cost. .

The essence of the invention

Embodiments of the present invention may be directed to a process control method that includes obtaining raw chromatographic data comprising a plurality of signals, where each signal of the plurality of signals is associated with one of the plurality of blocks; obtaining a subset of data by selecting a combination of the first block and the first signal from the raw chromatographic data; generating processed chromatographic data by applying a denoising technique to a subset of the data; generating transition data by performing transition analysis on the processed chromatographic data, and performing an action based on the transition data.

In some embodiments, the method may further include performing a column chromatography analysis, where raw chromatographic data can be obtained from the column chromatography analysis. In yet other embodiments, raw chromatographic data may be obtained from a chromatographic process unit. In still

- 1 041350 in some embodiments, each block of the plurality of blocks may correspond to a stage in the chromatography process. In additional embodiments, the selected combination may include a first block, a first signal, and a second signal from the plurality of signals.

In yet other embodiments, the method may also include selecting a combination of the first block and the first signal in accordance with a profile defining a plurality of selection criteria. In some embodiments, the set of selection criteria may include how many blocks appear at regular intervals in the chromatographic run; the degree to which one of the multiple signals saturates the detector; the degree to which a plurality of signals approaches the stationary phase at an individual level; the magnitude of the variation of the plurality of signals; and/or the number of inflection points shown by the plurality of signals during the transition phase.

In some embodiments, selecting a first block and first signal combination may include selecting a primary block and signal combination, and the method may further include selecting a secondary block and signal combination. In additional embodiments, the implementation of the noise reduction method may include selecting a portion of the subset of data for analysis using predetermined control points; normalizing the part to prevent the amount of deviation; using at least one smoothing filter on the part to generate smooth data; and analyzing the part for dynamic signal error. In still other embodiments, the implementation of the method may further include: selecting smooth data corresponding to the sign of the chromatographic transition, where the sign includes one of: the duration of the derivative; maximum intensity; duration from initiation; or the expected background noise of the sensor. In still other embodiments, the transition analysis may include generating a curve using the processed chromatographic data and analyzing the curve to generate performance parameters.

In some embodiments, the method may further include generating an individual value control chart, a moving range control chart, or a range control chart based on the transition data and generating performance data by applying statistical process control to the individual value control chart, the moving range control chart, or the range control chart, where performing an action based on transition data may include performing an action based on performance data. In some embodiments, applying statistical process control may include performing one of multivariate data analysis or principal component analysis. In some embodiments, performing an action based on performance data may include generating an event notification, generating an event score, or generating a rejection notification form. Some embodiments of the present disclosure may include a chromatography method that includes performing a process control method during analysis on a chromatographic column.

Some aspects of the present invention may relate to a process control method, including obtaining a sample of raw chromatographic data; generating smooth data by applying the noise reduction method to a sample of raw chromatographic data, generating processed chromatographic data by selecting smooth data corresponding to the chromatographic transition feature, and performing an action based on the processed chromatographic data. The noise reduction technique may include selecting a portion of the smoothed data for analysis using predetermined values, normalizing the portion of the data to prevent the amount of deviation, using at least one smoothing filter on the portion of the data to generate smoothed data, and analyzing the portion of the data for dynamic signal errors.

In some embodiments, obtaining a sample of raw chromatographic data may include obtaining raw chromatographic data including a plurality of signals and a plurality of blocks, where each signal from the plurality of signals may be associated with a block, and selecting a combination of the first block and the first signal from the raw chromatographic data.

In some embodiments, the method may further include applying the processed chromatographic data to generate one of an individual value control chart, a moving range control chart, or a range control chart, and generating performance data by applying statistical process control to the individual value control chart, the sliding range control chart, or the control range map by conducting multivariate data analysis or principal component analysis. In some embodiments, performing an action based on the processed chromatographic data may include performing an action based on the performance data. In some embodiments, the action may include generating an event notification, generating an event evaluation, or generating a rejection notification form.

Some aspects of the present invention may include a process control method, where the method includes obtaining processed chromatographic data, including a combination of the first

- 2 041350 blocks and the first signal, performing a transition analysis on the processed chromatographic data, generating one of the individual value control chart-sliding range control chart-range control chart (I-MR-R) based on the transition analysis, generating efficiency data by applying the method multivariate data analysis for the I-MR-R card and taking actions based on performance data. The action may include one of generating an event notification, generating an event evaluation, or generating a rejection notification form.

In some embodiments, the processed chromatographic data may include a sample of raw chromatographic data to which a noise reduction technique has been applied. In some embodiments, a sample of raw chromatographic data may be obtained from a plant for a chromatographic process.

Brief description of the figures

The accompanying figures, which are included and form part of this specification, illustrate the disclosed embodiments and, together with the description, serve to explain the principles of the disclosed embodiments.

On the figures:

In FIG. 1 is a schematic representation of an exemplary chromatographic system with which various embodiments of the present invention may be performed.

In FIG. 2 shows an exemplary chromatogram.

In FIG. 3 is an exemplary normalized up chromatographic transition graph.

In FIG. 4 is a plot of up chromatographic transitions of conductivity equilibration blocks for three batches in accordance with some aspects of the present invention.

In FIG. 5 depicts an exemplary process for analyzing chromatographic data and performing process controls in accordance with some aspects of the present invention.

In FIG. 6 shows yet another exemplary process for analyzing chromatographic data and performing process controls in accordance with some aspects of the present invention.

In FIG. 7 is an exemplary data file in accordance with some aspects of the present invention.

In FIG. 8 is an exemplary load plot of a multivariate model in accordance with some aspects of the present invention.

In FIG. 9 depicts an exemplary data smoothing process in accordance with some aspects of the present invention.

In FIG. 10 is a graph of the loading of each variable in a 27-lot main component in accordance with some aspects of the present invention.

In FIG. 11 is an exemplary graph of scores from 27 batches according to some aspects of the present invention.

In FIG. 12 is an exemplary load plot of a multivariate model in accordance with some aspects of the present invention.

In FIG. 13 is an exemplary score chart according to some aspects of the present invention.

In FIG. 14 is a control chart of individual values for the asymmetry factor during operation of a particular chromatography unit in accordance with some aspects of the present invention.

In FIG. 15 is a sliding range control chart for the asymmetry factor during operation of a particular chromatographic setup in accordance with some aspects of the present invention.

In FIG. 16 is a control chart of the ranges for the asymmetry factor during the operation of a particular chromatographic setup in accordance with some aspects of the present invention.

In FIG. 17 is a control chart of individual values for a non-Gaussian HETP distribution (NG-HETP) in accordance with some aspects of the present invention.

In FIG. 18 is a sliding range control chart for NG-HETP in accordance with some aspects of the present invention.

In FIG. 19 is a control range chart for NG-HETP according to some aspects of the present invention.

In FIG. 20 is another individual score chart for NG-HETP according to some aspects of the present invention.

In FIG. 21 is another individual score chart for NG-HETP according to some aspects of the present invention.

In FIG. 22 depicts an exemplary system with which aspects of the present disclosure may be performed.

- 3 041350

In FIG. 23 shows an exemplary user interface in accordance with some aspects of the present invention.

In FIG. 24 is an exemplary report according to some aspects of the present invention.

Detailed description of the invention

The present invention relates to improvements in drug manufacturing and laboratory methods, as well as improvements in computer functionality associated with drug manufacturing and laboratory methods. In particular, aspects of the present disclosure relate to methods and systems for chromatographic analysis, as well as methods and systems for analyzing chromatographic data, for example, to monitor and control chromatographic processes and systems.

Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the present invention pertains. The materials, methods, and examples are illustrative only and are not intended to be limiting. One of ordinary skill in the art will appreciate that normal variations of the disclosed materials, methods, and examples are possible without undue experimentation. All publications, patent applications, patents, sequences, databases and other references mentioned here are incorporated here by reference in their entirety. In the event of a conflict, the present description of the invention, including definitions, shall prevail.

As used herein, the terms contains, contains, or any derivatives thereof are intended to cover non-exclusive inclusion, such that a process, method, product, or device that contains a list of elements does not include only those elements, but may include other elements that are not explicitly listed or not inherent in such a process, method, product or device. The term exemplary is more used in the sense of exemplary rather than ideal. For such terms and for terms such as and such as and their grammatical equivalents, the expression and without limitation is to be understood as followed unless expressly stated otherwise. As used here, the term approximately and the notation ~ are intended to account for variations due to experimental error. All measurements presented here are understood to be modified by the term, whether or not the term is explicitly used, unless explicitly stated otherwise. As used here, the singular forms a, an, and the include plural references unless the context clearly dictates otherwise. Moreover, in the claims, values, limits and/or other ranges mean a value, limit and/or range of +/- 10%.

As used herein, the term antibody includes antigen-binding molecules as well as antigen-binding fragments of complete antibody molecules. The terms antigen-binding site of an antibody, antigen-binding fragment of an antibody, and the like, as used herein, include any naturally occurring, enzymatically produced, synthetic, or engineered polypeptide or glycoprotein that specifically binds to an antigen to form a complex. Antigen-binding fragments of an antibody can be obtained, for example, from complete antibody molecules using any suitable standard methods such as proteolytic cleavage or genetic engineering methods to obtain recombinant structures, including manipulation with and expression of DNA encoding variable and optionally constant domains. Such DNA is known and/or readily available, for example from commercial sources, DNA libraries (including, for example, antibody phage libraries), or can be synthesized. DNA can be sequenced and manipulated chemically or by molecular biology techniques, for example, to place one or more variable and/or constant domains in an appropriate configuration, or to introduce codons, insert cysteine residues, modify, add or delete amino acids, and so on.

Non-limiting examples of antigen-binding fragments include: (i) Fab fragments; (ii) F(ab') ₂ fragments; (iii) Fd fragments; (iv) Fv fragments; (v) single chain Fv molecules (scFv); (vi) dAb fragments; and (vii) minimal recognition units consisting of amino acid residues that mimic antibody hypervariable regions (e.g., a single complementarity determining region (CDR) such as a CDR3 peptide) or conformationalally restricted peptides FR3-CDR3-FR4 . Other engineered molecules, such as domain-specific antibodies, single-domain antibodies, domain-deleted antibodies, chimeric antibodies, CDR-grafted antibodies, diabodies, tribodies, tetrabodies, minibodies, nanobodies (e.g., monovalent nanobodies, bivalent nanobodies, etc. ), modular small molecule protein (SMIP) immunopharmaceuticals, and shark IgNAR variable domains are also included within the concept of an antigen-binding moiety, as used herein.

As used herein, the term biological may refer to a large molecule (eg, having a size greater than 30 kDa) produced in a living system, such as a cell. Biologics may include proteins (eg, antibodies), nucleic acids, large sugars, and so on. Unlike small molecules, which can have well-defined chemical structures, biological drugs can have very complex structures that cannot be

- 4 041350 is easy to quantify by laboratory methods. Thus, it may be desirable to achieve purity, integrity, and quality in the manufacture of biological products in order to ensure the quality of biological products, especially when they are intended for medical use.

As used here, the term chromatography can refer to any preparative or analytical chromatography method. While most of the present invention is presented in the context of preparative packed bed chromatography for biological purification, it is expected that the systems and methods disclosed herein can be applied to a wide variety of chromatographic processes.

As used herein, the term drug product may refer to the volume of the formulated drug substance dispensed in the primary packaging component for packaging, transport, delivery, and/or administration to a patient. Medicinal products may contain active ingredients, including, for example, biologicals.

As used here, the term raw material(s) may refer to a mixture comprising one or more biologicals that require separation or purification using a chromatographic process.

As used here, the term raw chromatographic data may refer to chromatographic data in its original data state when it was originally collected. For example, the raw chromatographic data may be in a file format, RES, other raw file format, or in a database containing values obtained directly from the measurement equipment.

As used here, the term extracted chromatographic data may refer to chromatographic data that has been extracted from the raw data without any transformation. This may be an Excel or .CSV file, or a database located on a chromatography system or computer system.

As used herein, the term noise-reduced data may refer to chromatography data, such as transition data, that has been normalized, smoothed, acquired, and/or sampled.

As discussed above, there is a need to monitor and control the chromatographic column and process quality, for example, during multiple chromatographic analyses, for many batches and over time both during and between analyses. The systems and methods disclosed herein can provide analysis of chromatographic transition data (also known as transition analysis) and use such analyzes to monitor the performance of the chromatography system, detect changes in the performance of the chromatography system, and take action on the chromatography system based on such analyzes and processes. In addition, the systems and methods disclosed herein may, in some aspects, be part of one or more in-process production or purification controls and/or may provide in-process controls using data collected during conventional chromatographic processes, thereby minimizing cost and labor costs required for the implementation of individual control processes.

Detailed reference will now be made to the exemplary embodiments of the present invention described below and illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like elements.

In FIG. 1 is a schematic representation of an exemplary chromatographic system 100 with which various embodiments of the present disclosure may be performed. System 100 includes a liquid mobile phase supply system 102, a material injection system 104, a column 106, a process controller 108, a computer device 110, and a detector 112.

System 100 may represent all or part of a chromatography system, including chromatography column 106. In some instances, system 100 may represent a chromatography unit. System 100 may include any hardware and/or software required for analysis on a chromatographic column. System 100 can be configured to perform any of various types of chromatography such as high performance liquid chromatography (HPLC), ion exchange chromatography, size exclusion chromatography, hydrophobic interaction chromatography (HIC), reverse phase chromatography, mixed mode chromatography, or affinity chromatography. System 100 can be used, for example, to separate biologicals in a crude mixture, isolate and/or purify one type of biological, and/or remove contaminants from a mixture. In some cases, system 100 may be part of a drug manufacturing system, such as a drug manufacturing system that contains a biological product, such as an antibody.

Liquid mobile phase supply system 102 may be any suitable system for supplying mobile phase to column 106 inlet. Liquid mobile phase supply system 102 may include one or more reservoirs for storing the liquid mobile phase(s) used

- 5 041350 for passing raw materials injected by the material injection system 104 through the column 106. The liquid mobile phase supply system 102 may include one or more pumps configured to pressurize the liquid mobile phase(s). In some embodiments, the pumps of the liquid mobile phase supply system 102 may be configured to mix two or more solvents (e.g., from two or more tanks) in the desired ratio prior to supplying the combined solution to the inlet of the column 106. In some embodiments, the liquid mobile phase supply system phase 102 may be configured to supply a first mobile phase to the inlet of column 106 and then supply a second mobile phase to the inlet of column 106 after the required volume of first mobile phase has been supplied. In some embodiments, the liquid mobile phase supply system may be controlled by process controller 108 or by human intervention.

Material injection system 104 may be any suitable system for supplying raw material requiring separation and/or purification to column 106. In some embodiments, for example, material injection system 104 may include one or more raw material storage tanks. Such raw material may include one or more biologicals, contaminants, solvents, or other materials.

Column 106 may represent any column suitable for separating and purifying injected raw material from material injection system 104. One of ordinary skill in the art will appreciate that column 106 may have any of a variety of different sizes (e.g., diameters ranging from about 30 to about 1500 cm) and any of a variety of stationary phases can be packaged. The size, shape, and packaging of column 106 may be selected to accommodate the raw material that requires column 106 separation.

Process controller 108 and/or computer device 110 may be suitable for controlling elements of system 100 during chromatographic analysis. Process controller 108 may be in communication with one or more elements of system 100, including liquid mobile phase supply system 102, material injection system 104, column 106, computer device 110, and detector 112. In some embodiments, process controller 108 may be a computer programmed to control elements of the system 100 in accordance with the required procedure. For example, in some embodiments, the process controller may be programmed to turn the pumps of the liquid mobile phase delivery system 102 on and off and to turn the detector 112 on and off. In some embodiments, the process controller 108 may have a display and/or other user interface elements (e.g., buttons, mouse, keyboard, touch screen, etc.) through which commands can be entered, for example, by a human operator. In still other embodiments, process controller 108 may be programmed using, for example, computer device 110.

Computing device 110 may represent any computer, such as a desktop computer, server computer, laptop, tablet, or personal portable device (eg, smartphone). In some embodiments, computing device 110 may have a display and/or other user interface elements (eg, buttons, mouse, keyboard, touch screen, etc.) through which commands may be entered, for example, by an operator. Computing device 110 may also collect data from process controller 108 and/or other elements of system 100, such as detector 112. Computing device 110 may include one or more programs configured to display or output such data, such as to a screen, hard drive, or via internet connection with remote location. The computing device 110 itself may be connected to other elements of system 100 via a wired connection, or may be wirelessly connected to other elements of system 100 (eg, process controller 108). In some embodiments, computing device 110 may be located remotely from system 100. In some embodiments, computing device 110 may be configured to display one or more user interfaces or reports. In some embodiments, process controller 108 and computing device 110 may represent a single device.

Detector 112 may be any type of detector suitable for detecting one or more indicators at the outlet of column 106. While FIG. 1 shows a single detector 112, system 100 may include more than one such detector configured to detect a plurality of measures at column outlet 106. Such measures may include, for example, column outlet conductivity, pH, absorbance, and other measures. In some embodiments, the detector 112 may be, for example, a conductivity detector, an ultraviolet (UV) detector, a fluorescent detector, a refractive detector, a pH detector, a pressure gauge, or any other type of detector.

A chromatographic run, such as an analysis performed using system 100, may typically include a sequence of steps. Such steps may include, for example, a cleaning-in-place step, an equilibration step, a loading step, a washing step, an elution step, a

- 6 041350 attraction and the stage of regeneration. The chromatographic run may be monitored and/or recorded using data collected from a detector downstream of the chromatographic column (eg, detector 112 downstream of column 106). For example, UV detection and a UV chromatogram can be used to track the chromatography process, such as the wash, elution, collection, and recovery steps. In FIG. 2 is an exemplary UV detection chromatogram showing a typical profile for collecting a single protein. As the volume of liquid passes through the column (shown on the x-axis), the UV detector detects a clearly stable rise in absorbance with a single peak near the start of the elution step. Collection can begin after a small elution peak during which absorption peaks when most of the analyte passes the UV detector.

A chromatographic transition is the response at the outlet of a column (e.g., column 106) to a step change at the column inlet (e.g., going from a wash step to an elution step, or going from an elution step to a recovery step) when one mobile phase is replaced by another. Depending on which parameters are detected at the column output (for example, by one or more detectors 112), the transition may be detected as an increase (up transition) or decrease (down transition) in one or more parameters, followed by a plateau in that parameter. after the transition. For example, in FIG. 3 shows an exemplary normalized chromatographic up-pass plot, divided into three phases. Prior to the transition, the detector detects the value of the parameter on the baseline. During the transition, the parameter rises or rises, and then after the transition it goes to a plateau. In some cases, the plateau after the boost transition is due to saturation of the detector. The data obtained during the transition is quantitative and sensitive to small changes in column efficiency.

Examples of measurable parameters that may change during transition include electrical conductivity, pH, salt concentration, light absorption, fluorescence after excitation with light of a suitable wavelength, refractive index, electrochemical response, and data obtained by mass spectrometric analysis. One skilled in the art will understand, however, that any other measurable parameters that may change during the transition may be useful in the transition assays of the present disclosure.

In order to perform a transition analysis to determine the quality and/or integrity of a chromatographic column and/or process, the chromatography data can be divided into multiple blocks, where each block corresponds to a stage in the chromatography process (e.g., CIP block, equilibration block, loading block , washing block, elution block, extraction block, regeneration block, storage block, etc.). Each block includes a plurality of signals provided by one or more detectors during the block. Any number or combination of blocks and signals can be used to perform transition analysis, such as 1 to 8 blocks (for example, 1 block, 2 blocks, 3 blocks, 4 blocks, or 5 blocks) and 1 to 8 signals (for example, 1 signal, 2 beeps, 3 beeps, 4 beeps, 5 beeps, 6 beeps, or 7 beeps). More blocks and/or signals may also be used.

In FIG. 4 is an exemplary plot of detected conductivity as a function of volume during boost transitions in equilibration blocks for three chromatographic assays. Each analysis included the same chromatography process on the same raw material on the same column, including isolation of the same protein, but different batches of raw material were used. The first peak (in all three assays) represents the backbone of the system. As can be seen, after the appearance of the peak in the three analyses, there is a variation in the transition phase. The shortest dotted line shows the transition closest to the ideal phase, since the transition is the most vertical (i.e. occurs in the shortest volume). The longer dotted line shows some indications of column failure, namely an early start of the transition phase and a narrower end. In general, this transition is taking place on a larger scale. The solid line shows more pronounced column failure rates as the transition phase starts very early and takes too long to reach saturation. Although these differences are obvious and visual, they cannot be easily quantified or given context without being compared to each other. The present disclosure provides systems and methods for performing analysis using these data and for reliably performing process control using such analysis.

In FIG. 5 and 6 depict exemplary processes for analyzing chromatographic data and using such analyzes to perform process control, in accordance with some aspects of the present disclosure. In FIG. 5 shows an exemplary process at a more general level of detail, while FIG. 6 provides more details of an exemplary process. Although they are described separately below, the details and features of the process in FIG. 6 apply to the process of FIG. 5 and vice versa.

In FIG. 5 depicts an exemplary general process 500 for analyzing chromatographic data and performing process controls in accordance with some aspects of the present disclosure. In accordance with step 510, the raw chromatographic data may be processed. At block 520, data may be obtained from raw chromatographic data. In accordance with step 530, the received data may be processed. At block 540, the processed data may be analyzed (eg, transition analysis). At 550, one or more statistical process controls may be performed. According to step 560, a report can be obtained based on the data.

According to step 510, the raw chromatographic data may be processed. Raw chromatographic data may be obtained by performing one or more chromatographic runs and receiving signals from one or more detectors (eg, detector 112 of column 106). The signals may include, for example, a UV detection signal, a conductivity signal, a pressure signal, a pH signal, and/or other signals. The data may be received, for example, in the process controller 108 and/or computing device 110, and may be stored in, for example, a database or a .RES file. The data may include, for example, a series of signal values and the corresponding volumes at which the signal values were measured. The data may also include start and end indicators for each block/stage in the chromatography run.

Data processing may include extracting data and organizing data in a data file on a computing device, such as computing device 110. Exemplary data files include, for example, a spreadsheet, a text file, a database, combinations thereof, and the like. Data files containing extracted chromatographic data may be assigned different metadata to ensure consistent storage and processing. Metadata may include, for example, titles, names, dates, times of analysis on the column, volumes of analysis on the column, mobile phases of the column, identification of crude mixtures, identification of the manufacturing processes for which the column is used, and/or any other data that can provide a consistent automatic or manual processing of data files.

At block 520, data may be obtained for analysis from data files. In some embodiments, automatic software (such as Cron, Jobber, macro, or other automation or sequencing software) can keep track of one or more possible data file storage locations for one or more data files that match one or more profiles. The data files may be assigned a profile based on, for example, the metadata associated with the data files. The profile may be, for example, a pre-compiled series of selection criteria so that one or more combinations of blocks and signals suitable for performing a transition analysis can be selected. A profile may be assigned based on, for example, the type of column being analyzed, the characteristics of the mobile phase, the volume of mobile phase in the assay, the time of analysis on the column, or any other characteristics of the data files.

Obtaining data for analysis may include selecting one or more combinations of blocks and signals based on an assigned profile, where the block corresponds to a step in the chromatography process and the signal corresponds to the type of data being collected (e.g. UV absorbance data, conductivity, pH, etc.). d.). In some embodiments, a primary combination of blocks and signals may be selected. In additional embodiments, a primary combination of blocks and signals and one or more secondary combinations of blocks and signals may be selected. The transition analysis may be performed first with the primary combination of blocks and signals, and optionally then with one or more secondary combinations of blocks and signals. Profiles, selection criteria, and combinations of blocks and signals are described in more detail with reference to process 600.

At block 530, the received data can be processed again to obtain denoised data. Processing the received data may include applying one or more smoothing and/or noise reduction techniques to a set of data in the received data, such as data associated with the primary combination of blocks and signals, and optionally data associated with the secondary combination of blocks and signals. In some embodiments, the data processing may include standardizing the size of the data set to ensure that the smoothing windows are consistently affected. In some embodiments, data processing may include normalizing the data to eliminate variation based on the amount of transitions. This variation may be due to the unique formulations of mobile phase buffers, which have inherent variability in the final value for base phase or saturation phase.

Noise reduction techniques may include removing inherent errors introduced by measurement instruments (eg, detector 112 in system 100) and random errors introduced by batch processes when data is collected (eg, earlier stages of method 500). Denoising may include deduplication of records in a data set, detection and removal of outliers, and/or any other technique for increasing the signal to noise ratio in a data set. The noise reduction may also include data smoothing and signal deflection, which are described in more detail below for process 600.

The processed data may include, for example, step yields and/or measurements of other mobile phase parameters, which may be in the form of one or more smooth curves corresponding to one or more chromatographic transition steps. One or more

- 8 041350 curves may be an array of normalized enable signal data.

At block 540, the denoised data may be analyzed. Such an analysis may represent a transition analysis. Transition analysis may involve performing one or more mathematical operations on the processed data. For example, one or more curves may be generated from the processed data, for example by taking the first derivative of the curve, to form another curve having a peak. This curve can be analyzed to generate performance parameters such as number of inflection points, maximum rate of change, column breakthrough volume, total error, NG-HETP, curve skewness, and Gaussian distribution of HETP, for example. These performance parameters, alone or in combination with previous data, can help determine column integrity.

For example, an increase in the number of inflection points may indicate that there is a small amount of early breakthrough through the column, which may be due to a violation of the integrity. A decrease in the maximum rate of change when using multiple columns may indicate that the transition is occurring in a larger volume, which may indicate a breach of integrity. A decrease in the amount of passage through the column may also be indicative of a breach of integrity. An increase in NGHETP or a Gaussian distribution of HETP may indicate a decrease in column integrity. Other conversion metrics can be derived based on modification of data set variation, skewness, kurtosis factors, peak skewness, column breakthrough or washout volume, and total error. The transition analysis is described in more detail below. Systems and methods for performing transition analysis are also described, for example, in Larson et al., Use of Process Data To Assess Chromatographic Performance in Production-Scale Protein Purification Columns, 2003, 19, 485-492, which is incorporated by reference in its entirety by pltcm.

The results of the transition analysis may be stored, for example, in a memory element of the computing device 110 or in another computing device along with other data. For example, all raw data, raw data sets, smoothed data sets, and transition analysis data may be stored.

At block 550, one or more statistical process controls may be performed using the results of the transition analysis. In some embodiments, statistical process control may include performing methods in one of several categories, including: 1) unusual control chart analysis (e.g., individual value control chart, sliding range control chart, and/or range control chart analysis), 2) multivariate analysis (MVA); or 3) a combination of unusual control chart analysis and MVA. These processes may include, for example, analyzing transition analysis results as part of a larger set of data, including transition analysis results from previous chromatographic analyses, such as analyzes in the same production run, analyzes of the same batch of product, or analyzes of the same and that raw mix. These processes are described with additional specifics below for process 600.

The result of performing one or more statistical process controls may be related to performance evaluation data. Performance evaluation data can refer to any process data, including the results of transition analysis, that are relevant in assessing the reproducibility and effectiveness of the process.

According to block 560, the data may be converted into reports. In some embodiments, one or more reports may be received. For example, the disclosed methods and systems may generate a tabular report of any results analyzed using a given profile. Reports can be generated based on the desired number of previous chromatographic analyses, for a specific period of time, for specific analyzes and/or for specific batches. An example of a report is shown in Fig. 24 and described in more detail below.

In FIG. 6 in more detail than FIG. 5, an exemplary process 600 for analyzing chromatographic data and performing a process control is shown in accordance with some aspects of the present disclosure. According to step 610, raw chromatographic data may be obtained. At 620, the raw chromatographic data may be processed according to the profile. At 630, a noise reduction technique may be applied. At 640, a transition analysis can be performed on the raw chromatographic data to generate transition data representative of column integrity. At 650, at least one of an individual value control chart (I), a sliding range control (MR) chart, or a range control chart (R) can be generated based on the transition data. At 660, one or more multivariate statistical analysis methods can be applied to at least one I-map, MR-map, or R-map to generate performance data. At 660, an action may be taken based on the performance data.

According to step 610, raw chromatographic data may be obtained. As with process 500, raw chromatographic data may be obtained from, for example, a chromatographic system such as system 100. Raw chromatographic data can be

- 9 041350 may include a plurality of signals associated with a plurality of blocks. Obtaining raw chromatographic data may include directly retrieving raw chromatographic data from one or more detectors (eg, detector 112 of system 100) or from a computing device (eg, computing device 110), and/or may include monitoring the network location of the raw chromatographic data file. The raw chromatographic data may, in some embodiments, be processed as described above for step 510 in process 500.

An exemplary extracted chromatographic data file 1000 is shown in FIG. 7. The data file 1000 may include, for example, the name of the data file, which may aid in the identification of the data file by the automated system.

As shown, the extracted chromatographic data in data file 1000 may be in the form of a spreadsheet (eg, Microsoft Excel). The data file 1000 may include volume measurements on the first column 1002, which may correspond to periodic measurements of the total volume that has passed through the chromatographic system. The second column 1004 may include signal measurements (eg, UV absorbance, electrical conductivity, pH, etc.) corresponding to each of the volume measurements on the column 1002. In this case, the second column 1004 contains electrical conductivity data expressed in mS/ cm. Other columns may provide additional data. Here, for example, the third column 1006 includes volume measurements corresponding to the logs in the fourth column 1008. This may allow identification of various chromatographic analysis metrics such as block/stage start and end points (CG002_START, CG002_END, CG003_START), flow rate, and points at which the elements of the chromatographic system were initiated (for example, pump 1 may correspond to the time when the pump, for example, associated with the liquid mobile phase supply system 102) is activated). One skilled in the art will appreciate that many variations of data file 1000 are possible. For example, while volume measurements are shown in data file 1000 as markers of the progress of a chromatographic analysis, other measurements such as time can be used. Additional columns for other signal data may be included, and additional log data may be included (eg, mobile phase identification, analyte identification, etc.).

Referring to FIG. 6, according to step 620, the chromatographic data can be processed according to the profile. As briefly described at step 520, a profile may be selected for the chromatographic data file according to the characteristics of the chromatographic data in the file. For example, profiles may have been created previously for a specific type of chromatographic assay, a specific chromatographic column, and/or a specific analyte. Thus, such profiles can be matched to a chromatographic data file for the respective assay, column, and/or analyte.

In some aspects, a user may create a profile. A profile may be associated with a particular drug or drug product. In one aspect, the drug is a small molecule. In other aspects, the drug is a peptide or polypeptide.

In some aspects, the drug is a vascular endothelial growth factor (VEGF) derivative. В еще одних аспектах лекарственное средство представляет собой афлиберцепт, который описан в одном или нескольких патентах США № 7070959, 7303746, 7303747, 7306799, 7374757, 7374758, 7531173, 7608261, 7972598, 8029791, 8092803, 8343737 и 8647842, каждый из которых в полном volume is incorporated here by reference.

In still other aspects, the drug is an antigen-binding molecule. In some aspects, the antigen-binding molecule is an antibody or antigen-binding fragment. In some aspects, the drug is alirocumab, as described in US Patent Application Publication Nos. 2014/0356371 and 2014/035670, each of which is incorporated herein by reference in its entirety. In yet another aspect, the drug is sarilumab as described in US Patent Application Publication Nos. 2016/0152717, 2014/0302053, and 2013/0149310, each of which is incorporated herein by reference in its entirety. In yet another aspect, the drug is dupilumab as described in US Patent Application Publication No. 2014/0356372, which is incorporated herein by reference in its entirety. In yet another aspect, the drug is selected from the group consisting of evolocumab, bevacizumab, ranibizumab, tocilizumab, certolizumab, etanercept, adalimumab, abatacept, infliximab, rituximab, anakinra, trastuzumab, pegfilgrastim, interferon beta-1a, interferon beta-1, interferon beta -1, insulin glargine (rDNA) injectable, epoetin alfa, darbepoetin, filigrastim and golimumab.

In some embodiments, a profile may be configured to direct Sentinel software (eg, macro, Jobber, Cron, or other software depending on the programming task) to periodically scan a designated network location for chromatographic data files. A profile can direct data to be retrieved from a file when the file name matches the file name identifier in the profile.

- 10 041350

Once a profile has been selected or has been selected on or associated with a data file, the data file can be scanned. For example, with regard to the exemplary data file 1000 shown in FIG. 7, the fourth column 1008 containing the log can be scanned for block start time, block end time, flow rates, and the like. For example, with respect to data file 1000, the volume measurements corresponding to CG002_START and CG002_END include volume measurements that correspond to the chromatographic operation and the signal transition of interest. The first column 1002 and the second column 1004 can then be used to extract the complete set of signal data and volume measurements for the operation.

The values in the profile may also define one or more selection criteria for selecting one or more combinations of blocks and/or signals in the chromatographic data file for which transition analysis is to be performed. Thus, profiles may represent tools for obtaining preferred subsets of data from a chromatographic data file. The selection criteria in the profile can be determined in advance, for example from empirical experience, structured optimization and/or process documentation. Such selection criteria may allow combinations of blocks and signals to be identified that provide a more precise, accurate, or otherwise more useful analysis. Such selection criteria may include, for example, how available transition materials are. This includes blocks that move in or out of product solutions. This allows for additional column evaluations between production steps, if needed. Such selection criteria may also or alternatively include how much the blocks appear at regular intervals in the cycle. This includes blocks that are not executed after the completion of the final collection cycle of the production lot. Such selection criteria may also or alternatively include whether the signals reach detector saturation before or after the transition. Such selection criteria may also or alternatively include the extent to which the signals approach stationary phase at a discernible and identifiable level, and do not drift continuously. Such selection criteria may also or alternatively include how much the signals in a particular block have a large difference between the minimum and maximum values. Such selection criteria may also or alternatively include how many signals have inflection points during the transition. Fewer inflection points may indicate more reliable data collection.

In some cases, the results of previous chromatographic analyzes can help determine suitable selection criteria for selecting combinations of blocks and signals in future chromatographic analyzes. In FIG. 8, for example, is a plot of NG-HETP calculations for two different combinations of blocks and signals (combination of elution step-UV absorption signal and combination of re-equilibration step-conductivity signal) for six different chromatography lots (lots A-F). Solid columns representing three standard deviations for each set are given as references. As can be seen from this graph, the NG-HETP calculations for the combination of elution step-UV absorption signal show much greater variability than the combination of the re-equilibration step-conductivity signal. Obviously, both the scale of the trends and the standard deviations differ. When monitoring performance shifts, it may be desirable to have less variability across batches that are considered representative.

This allows increased sensitivity in monitoring shifts in efficiency. Thus, selection criteria for chromatographic analyzes of batches like batches A-F may include a preference for a combination of reequilibration step and conductivity signal over a combination of elution step and UV absorbance signal. One skilled in the art will appreciate that analyzing the results of previous chromatographic analyzes in a similar manner may reveal other potential criteria for selecting combinations of blocks and signals.

In some embodiments, the profile may include instructions for applying one or more selection criteria to a data file containing the corresponding chromatographic data. Thus, processing the chromatographic data according to the profile may include identifying and extracting a preferred (eg, primary) combination of blocks and signals for transition analysis and/or one or more additional (eg, secondary) combinations of blocks and signals for transition analysis. In some embodiments, the primary combination of blocks and signals will match the majority of the selection criteria in the profile from all possible combinations of blocks and signals in the chromatographic data file. In some embodiments, the secondary combination of blocks and signals will match the second selection criterion in the profile from all possible combinations of blocks and signals in the chromatographic data file. While the primary combination of blocks and signals may include data that is likely to provide useful transition analysis for evaluating column and process integrity, the secondary combination of blocks and signals may provide a secondary measurement and cross-check of column integrity.

In some embodiments, the implementation of the profile according to block 620 may be a data file in or by itself, which may contain instructions for retrieving certain

- 11 041350 data from the chromatographic data file or changing the chromatographic data file with the corresponding metadata. In some embodiments, such instructions in the profile may be executed by a computer program.

Referring to FIG. 6, after the chromatographic data has been processed according to the profile, a denoising technique can be applied to the processed data according to step 630. As with step 530 of process 500, this step may include applying one or more smoothing and/or denoising techniques to the processed data (eg, data associated with selected combinations of blocks and signals). In some embodiments, this step includes standardizing the size of the dataset to ensure that the smoothing windows are consistently affected. In some embodiments, this step may include normalizing the data to eliminate variation based on the magnitude of the transitions. This variation may be due to the unique compositions of the mobile phase buffers, which have their own variability in the final value for the base phase or saturation phase.

Noise reduction techniques may include removing inherent errors introduced by measurement instruments (eg, detector 112 in system 100) and random errors introduced by batch processes when data is collected (eg, earlier stages of method 500). Denoising may include deduplication of reports in the dataset, detection and removal of outliers, and/or any other technique for increasing the signal to noise ratio in the dataset.

The denoising may also or alternatively include applying a data smoothing algorithm and signal error rejection. In this regard, in FIG. 9 shows an exemplary algorithm 900 in block diagram form. According to steps 902 and 904 of the algorithm 900, the algorithm can be run and the corresponding signal data (eg, data that has been processed according to step 620) is retrieved. At block 906, the extracted data may be normalized to remove the amount of variance.

A multilevel smoothing algorithm 950 may then be applied. This may include applying one or more initial antialiasing filters (steps 908, 910) in accordance with the desired antialiasing filter settings (909, 911). According to block 912, derivation can optionally be performed. One or more additional anti-aliasing filters may then be applied (steps 914, 916) in accordance with the desired additional anti-aliasing filter settings (913, 915). The number of anti-aliasing filters applied (stages 908, 910, 914, 916), as well as the number and performance of settings 909, 911, 913, 915 may vary depending on, for example, the state of the data, expected results, type of signals, and other factors. Data will be derivated, may also depend on these factors.

The process may then continue with the dynamic signal error rejection algorithm 980 . This algorithm can be configured to remove data from the extracted data that is not associated with a chromatographic transition. For example, errors that must be corrected to provide a meaningful transition analysis include alarms, machine stalls, plant sensor failures, or gaps in data. This can be achieved by determining the expected indicators of the chromatographic transition, such as the duration of the derivative; maximum intensity; duration from initiation; or expected background noise. For example, starting point deviation 918 may be made based on expected transition position 919, initial dead zone deviation 920 may be made based on expected background noise level 921, height and width derivative deviation may be made based on expected signal error rates, and the final deviation a dead zone may be made based on expected background noise levels 925. Expected transition rates may be generated, for example, based on previously accumulated transition data. After completion of algorithm 900, in accordance with block 990, the data may be ready for use in transition analysis.

While algorithm 900 is one exemplary model of the signal error smoothing and rejection algorithm, one skilled in the art will appreciate that variations of this algorithm are possible. For example, only the smoothing algorithm 950 may be performed, or only the signal error rejection algorithm 980 may be performed. Additionally or alternatively, more or fewer smoothing filters may be applied and/or more or fewer points may be subject to rejection.

After applying a denoising and/or smoothing technique to the data, the data may include, for example, stage exits and measurements of other mobile phase parameters in the form of a breakthrough or washout curve corresponding to the stage transition.

Referring to FIG. 6, at step 640, a transition analysis can be performed on the processed chromatographic data to obtain transition data representative of column integrity. Transition analysis may include performing one or more mathematical operations on the processed data to derive variance parameters from the stage transition. For example, one or more curves may be generated from the processed data, for example by taking the first derivative of the curve to form another curve having a peak. This curve can be used to generate performance parameters such as, for example, number of inflection points, maximum rate of change, column breakthrough volume, total error, NG-NETP, curve skewness, and Gaussian distribution of HETP. As described for step 540, these parameters can be used as indicators of column integrity or lack thereof (eg, when compared to transition analysis parameters from previous representative chromatographic data).

For example, an increase in the number of inflection points may indicate that a small amount of early breakthrough through the column is occurring, which may be due to a breach of integrity. The number of inflection points can be determined from the number of peaks when plotting the derivative curve against the total volume data.

As another example, a decrease in the maximum rate of change when using multiple columns may indicate that the transition is taking place in a larger volume, which may be an indication of integrity violations. The maximum rate of change is equivalent to the maximum value of the curve derivative.

As yet another example, a decrease in the amount of passage through the column can also be indicative of a breach of integrity. The column breakthrough volume can be determined by finding the first volume value at which the signal is less than 95% of its highest value (for a high-to-low transition) or greater than 5% of its lowest value (for a low-to-high transition). .

As another example, an increase in NG-HETP or a Gaussian distribution of HETP may indicate a decrease in column integrity. Other conversion metrics can be derived based on modification of data set variation, skewness, kurtosis, peak skewness, column breakthrough or washout volume, and total error. Systems and methods for performing transition analysis are also described, for example, in Larson et al., Use of Process Data To Assess Chromatographic Performance in Production-Scale Protein Purification Columns, Biotechnol. Prog., 2003, 19, 485-492 2003, 19, 485-492, which is hereby incorporated by reference in its entirety.

The results of the transition analysis may be stored, for example, in a memory element of the computing device 110 or in another computing device along with other data. For example, all raw data, raw data sets, smoothed data sets, and transition analysis data may be stored.

Referring to FIG. 6, at 650, at least one of an individual value control chart (I), a sliding range control (MR) chart, or a range control chart (R) can be derived from the transition data. For purposes of simplicity, this disclosure will refer to all of them collectively as an I-MR-R card; however, an I-MR-R card should be understood to mean only a reference to an I card, an MR card only, an R card only, or any combination and number of such cards. The I-MR-R Control Chart is a personalized visualization of the results of the conversion analysis and can assist in the interpretation of trends in the conversion analysis data across multiple column or batch analyzes in the form of NG-HETP, skewness, kurtosis, or other parameters. The advantage of I-MR-R maps is that the data can be quickly viewed and easily interpreted from a visual point of view. This makes small trends or immediate data changes recognizable at an early stage.

Control chart I, for example, can display a plot of the values for each batch analyzed (eg, skewness factor). The MR Control Chart can display a graph of values for the difference between the value for each analyzed lot and the previously analyzed lot. The control chart R can display a plot of values for the difference between values within a lot (eg, the skewness coefficient for two transition analyzes performed on the same lot, for the primary combination of blocks and signals and the secondary combination of blocks and signals). Each control chart may include a median line, upper control limits (UCL), and lower control limits (LCL), which can be calculated using available data that have been determined to be representative of a typical process and are equidistant from the median line at each control card.

Some of the parameters presented on the I-MR-R control charts, such as NG-HETP and skewness in transition analyses, may show significant dynamics over the lifetime of certain boundaries. In such cases, using an I-MR-R chart with control limits set using a short-term standard may result in excessive out-of-trend signals, even after resetting the control chart after repacking the column. One solution to this problem is to use the Levy-Jennings control chart, which uses long-term standard deviation calculations from representative batches of columns that take into account the special variations associated with the launch of a new column pack. The extent to which the data is considered representative can be determined by the absence of anomalous readings for the various batch performance evaluation datasets. These sets can be used to calculate standard deviation, sometimes with special attention to +/- 3 standard deviation (SD) lines. Multiple batches can be analyzed on the same column to determine if the main

- 13 041350 or the full period of use of the dispenser. In one aspect, the Levey-Jennings control chart can be fully simulated for viable column dynamics, resulting in a regression model that accounts for specific causes of column repackaging variability. However, the Levy-Jennings control chart requires more long-term data and will therefore be limited by the rate of data aggregation.

In addition, since transition analysis is known to have variation due to column repacking events, I-MR-R control charts can account for column packing and repacking - for example, the first batch analysis after a column repack will not have an MR value based on the last change batch analysis prior to column repackaging. In some aspects, control strategies can be configured to only consider certain violations that eliminate known variation due to repacking events when monitoring deviations in trends.

The generation of I-MR-R control charts may be performed, for example, by an analysis module in computer device 110 or by another analysis module elsewhere. The generation of the I-MR-R map can also be performed in the computing device 110, such as by a control chart module. For example, in FIG. 14-21 show I-MR-R data for chromatographic lots 21-100 and are discussed below.

Referring again to FIG. 6, at 660, one or more multivariate statistical analysis methods may be applied to the I-MR-R data. Alternatively, one or more multivariate statistical analysis methods may be applied to the transition analysis data. This step can be performed in addition to or as an alternative to step 650 and similar to generating maps in accordance with step 650, taking into account the transition analysis of previous chromatographic data. Multivariate statistical analysis takes multiple variables and simplifies them into component vectors. This allows for a holistic view of large datasets. Advantages include that multiple, subtle changes in multiple implementations that would not be apparent when looking at individual datasets can become apparent when their component vectors are displayed. Fluctuations in these data may be caused by differences in materials, equipment, environmental conditions, etc. and may be insignificant to the perception of the operator or human observer. Examples of multivariate statistical analysis methods may include principal component analysis (PCA), partial least squares (PLS), orthogonal partial least squares (OPLS), multivariate regression, canonical correlation analysis, factor analysis, cluster analysis, graphical procedures, and the like. Such multivariate statistical analyzes can be performed using, for example, specialized computer software.

The general purpose of applying multivariate analysis is to transform large amounts of data into interpretable information. With the ability to search for correlations and patterns among multivariate variables and extract statistically significant values from large volumes of raw data, multivariate analysis allows for the interpretation of, for example, any statistical significance for variation between transition analyzes of similar batches of chromatographic data.

For example, PCA is a multivariate statistical method in which a dataset containing many variables (eg, transition analysis involving multiple parameters) is reduced to a few variables, called t-scores (t-score). For example, a dataset containing many variables can be reduced to a dataset where each observation (eg, each transition analysis) is represented by two t-scores. T-scores contain information about the variation of each variable in the data set and the correlation of each variable with every other variable in the data set. Essentially, t-scores describe the pattern of variation and correlation of each observation (eg, each transition analysis) in the dataset to each other observation in the dataset. The graphical representation of the SAR results is usually the SAR plot. The PCA plot plots one t-score against another for each observation. Typically, an SAR plot is a distribution showing how the pattern of variation and correlation compares across all observations in a dataset. Thus, the plot can serve to combine similar observations.

As another example, PLS regression analysis is a technique for analyzing systems of explanatory variables and response variables. PLS introduces a predictive method with which many independent variables can be handled, even if the variables are multicollinear. The PLS may also associate a set of independent variables with a set of multiple dependent variables (response variables). Often in PLS, one set of latent variables may be extracted for the set of explicit independent variables, and another set of latent variables may be extracted for the set of explicit response variables (or dependent variables). This extraction process can be based on the decomposition of a matrix of cross products including both independent variables and response variables. The scores or x-values of the latent explanatory variables are used to predict the scores or y-values of the latent response variables.

- 14 041350

The predicted y-values are then used to predict additional response variables. The x and y scores are chosen so that the relationship of successive pairs of x and y variables is as close as possible. The benefits of PLS include the ability to model multiple independent and dependent variables, the ability to control for multicollinearity between the independent variables, robustness to data noise, and (depending on the software used) skip data and generate independent latent variables directly from cross-products involving the response variable(s). , which makes it possible to obtain more accurate forecasts.

In some embodiments, multivariate statistical analysis may be performed on the I-MR-R control chart to determine additional statistical significance of the variation shown on the I-MR-R chart.

In addition to the above analyses, trends in transition analysis can be created by computing non-stationary ranges that allow slow variations to remain within control limits while abrupt shifts in column efficiency can be flagged as out-of-trend potentials. The main methods for defining control limits include moving average, weighted moving average, and various degrees of exponential smoothing. One such method for calculating trend boundaries, which is known as the Holt-Winters method or the triple exponential smoothing method, can be used with high efficiency. The Holt-Winters method uses seasonality to predict the appropriate boundaries, which are defined as individual column packing events for direct application to chromatographic monitoring. Regression modeling (such as used in the Levy Jennings control chart) is an additional way to delineate trends. Once sufficient empirical data is available, regression modeling of column integrity with respect to total column packing usage can be performed. This can provide accurate, consistent column efficiency ranges based on historical column efficiency data included in the model.

Referring again to FIG. 6, at 670, an action can be taken based on the performance data. In some embodiments, this may be because branch analysis is identified as in-process control (IPC). In general, the act of step 670 may include generating a report, generating and/or transmitting an alarm to an operator or display, such as the display of computer device 110, or terminating the chromatography process. The act of step 660 may also include, for example, storing all data obtained from the systems and methods disclosed herein in a database for further analysis.

The result of performing multivariate analysis and/or I-MR-R control chart analysis for these transitions may be referred to as performance evaluation data. Performance evaluation data may refer to any process data, including transition analysis results, that may be relevant in evaluating the reproducibility and performance of a process (eg, a chromatography process).

In one aspect, step 670 may include generating one or more reports. For example, the disclosed methods and systems may generate tabular format reports of any results analyzed using a given profile. Reports can be generated based on an analysis of the desired number of previous batches, for a specific time period and/or for specific batches. Datasets can be fully extracted in numerous formats and can be entered into external applications if further analysis is required.

In FIG. 24 is an exemplary report 2400 in accordance with some aspects of the present disclosure. The exemplary report 2400 includes a summary report table that includes the results of four chromatographic runs from a single production batch. Each of the four cycles is identified by lot and cycle number and includes a list with the dates and times the analysis was performed. The results of the transition analysis are presented in columns including NG-HETP, Gaussian HETP distribution, skewness, skewness, kurtosis, non-Gaussian N, and Gaussian N. A snapshot of the data source is also provided with the name of the chromatographic system from which the data was obtained, a log in which they were written, and the blocks for which the data was received. Below the data for each of the cycles are the trend data for each of the analysis results. It should be understood that this report is exemplary and many variations are possible. For example, the desired number of chromatographic cycles may be given and/or included in one or more trend data plots.

In some aspects, the systems and methods disclosed herein can be used to continuously monitor column and process integrity. As such, using the systems and methods disclosed here, you can analyze data in relation to a particular column and/or process. In one aspect, one or more alarms may be generated based on data analysis. In yet another aspect, the chromatographic process can be stopped based on data analysis. For example, one or more notices (e.g., notice of

- 15 041350 event, event evaluation or rejection notification form) to take corrective action. For example, one or more overlays of the object may be displayed on the screen, such as on the screen of the computing device 110, and/or a message may be sent to the operator at the time the analysis is completed, advising whether to continue or stop the chromatography process.

In one aspect, the results of the disclosed methods and systems may be directed to conveying information about current trends in column packaging quality prior to using the column in production. In yet another aspect, the results of the disclosed methods and systems can be used to evaluate column performance in real time (or offline) and column integrity can be confirmed until the next cycle of product use (for example, if the acceptable range and control limits in the trend map are set).

In a further aspect, the results can be used with statistical information to predict process outcomes based on process simulation using multivariate statistical analysis prior to costly and time consuming research and testing.

For example, one evaluation criterion for statistical analysis plots may include that when generating a score plot for a data set using PCA, a lot that is below a threshold number of standard deviations from the mean may be identified as having a column integrity problem, and may result in the generation of an alarm or instructions for changing the batch.

In particular, one evaluation criterion for I-MR-R control charts may include that any points beyond the upper or lower control limits for one or more chart types may be the basis for an alarm. Thus, the action taken according to step 670 may be to issue an alarm, for example, from the computer device 110, if the batch shows points outside the control boundaries. Such alerts may include, for example, an event notification, an event evaluation, and/or a rejection notification form to be presented to an operator or to a database.

In some aspects, the systems and methods disclosed herein may be implemented as part of an in-process control system that may operate within an organization's quality control system to ensure consistency and compliance with security requirements. As part of such a program, data from the systems and methods disclosed herein can be used to determine critical process variables (CPPs) and critical quality indicators (CQAs) that should be tracked in an in-process control program. In addition, as part of such a program, signal transition and column integrity shifts can be detected in real time or near real time (for example, during or simultaneously with column operation), allowing preventive and corrective actions to be taken in response to performance data. .

In FIG. 23 depicts an exemplary user interface 2300 in accordance with some aspects of the present disclosure. The user interface 2300 depicts a create/edit transition analysis profile screen with which a user can generate or edit a new transition analysis profile. The parameters selected when creating a profile can be used to customize the transition analysis based on the unique characteristics of the chromatographic process and to optimize the robustness of the output for each column and program. The parameters provided in the exemplary user interface 2300 include, for example, profile name, comments, historical data and/or test position, file template, end value, key indicator, hard reload, moving average window size, values for the first filter (e.g. , SG filter), the value for the second filter, the percentage of the first Vmax below which the signal should register as zero, the percentage of the maximum width to retain the peak, the height of the chromatographic column, the start date, end date, and database name.

The methods and systems disclosed herein can be used to monitor column integrity relatively continuously. For example, using the methods and systems disclosed herein, column integrity can be monitored without the need to interrupt regular chromatographic processes in order to perform diagnostics on the chromatographic system. In addition, using the methods and systems disclosed here, you can analyze data for a particular column and a particular process. As discussed, one or more alarms may be generated based on an analysis of the data over time. In yet another aspect, based on data analysis, the chromatographic process can be stopped. For example, one or more notifications may be displayed to the operator to take corrective action in the event that the integrity of the dispenser is compromised. For example, one or more object overlays may be displayed on the screen, and a message box may be displayed to the operator at the time the analysis is completed, a recommendation to continue or stop the chromatography process, or a recommendation for other actions.

In some aspects, the results of the disclosed methods and systems can be used to communicate

- 16 041350 information on current trends in evaluating the quality of column packaging before using the column in production. In still other aspects, the results of the described methods and systems can be used to evaluate column performance in real time (or offline) and column integrity can be confirmed until the next cycle of product use (for example, if the acceptable range and control limits on the trend map are established). In some aspects, the results can be used with statistical information to predict process outcomes based on process simulation using MVA prior to costly and time consuming research and testing.

Examples

Example 1

The primary combination of blocks and signals was selected based on protein A capture affinity chromatography data as follows. The affinity-capture data includes eight blocks and two signals (UV absorbance and electrical conductivity) in each block, giving a total of 16 possible combinations of blocks and signals. The data is assigned a profile that includes a series of block and signal selection criteria that are applied in the following order to select the primary combination of blocks and signals:

Given the selection criteria that blocks should appear at regular intervals among production cycles, two blocks and their corresponding signals can be removed, leaving 12 potential combinations.

Given the selection criteria that the signal must saturate the UV absorbance indicator, the UV signal for three blocks can be removed as candidates, leaving nine potential combinations.

Given the selection criteria that signals approach stationary phase at a discernible and identifiable level, the UV signal can be removed for three blocks as candidates, leaving six potential combinations (all with conductivity as the selection signal).

Given the selection criteria, according to which the signals should have a large difference between the minimum and maximum values in a given block, it is possible to remove the conductivity for four blocks, leaving two potential combinations.

Given the selection criteria that signals that display the fewest inflection points are preferred, it is possible to remove the conductivity for one block, leaving only one combination of block and signal.

The final selection of the remaining block and conductivity signal is the primary combination of block and signal on which transition analysis can be performed. The last block-signal combination to be removed becomes the secondary block-signal combination.

Example 2

I-MR-R control chart data for skewness and NG-HETP trends were plotted for 100 chromatographic batches in this chromatographic program B as follows. In FIG. 14-16 are control charts I, MR and R, respectively, showing the skewness factor. In FIG. 17-19 are control charts I, MR and R, respectively, showing NG-HETP. UCL and LCL indicate 3 standard deviations, as determined by previously accepted data. Breaks in the mean, UCL, and LCL lines indicate column repackaging. The continuous shifts on these lines indicate the point where the boundaries have been recalculated.

In FIG. 14 shows the skewness factor for all 100 batches prepared in program B. It can be seen that the first and second column packs exhibit different behavior during their use. As shown, for package 1 there is a shift in the boundaries after the analysis of the first four batches and maintaining the values of the coefficient of asymmetry from 0.055 to 0.855. Pack 2 is out of this trend, but eventually reaches a steady state on batch #67. This may be because the new column pack takes longer to shift and settle than pack 1.

In FIG. 15 is a control MR chart for the asymmetry factor for all batches made in program B. For package 2, outliers can be observed indicating large shifts between batches based on individual values.

In FIG. Figure 16 is a control R-map for the skewness factor for all batches prepared in program B. Some outliers are noted for package 1. This increased the boundaries for package 2. There are three packages on the map, and the batches are mapped sequentially, so package 1 represents the leftmost solid line and packing 3 represents the rightmost solid line. Notice that there are no trend points in the second half of pack 1. This may indicate that the column has experienced variation in batch cycles.

In FIG. Figure 17 shows the I-Chart for NG-HETP for all batches prepared in Program B. For package 1, there is a decrease in NG-HETP, indicating an improvement in the column behavior pattern. Pack 2 shows a steady increase in NG-HETP, which could correlate with a decrease in column efficiency.

In FIG. 18 is a control chart of the NG-HETP sliding ranges for all batches prepared in program B. Outliers can be noted for both package 1 and package 2. This revealed several points showing significant shifts from one individual value to another individual value.

In FIG. 19 shows the R-chart for NG-HETP for all batches made in program B. Package 2 shows persistently elevated peak-to-peak values that were investigated and determined to be the root cause of the change in flow direction in the third cycle of the batch. This resulted in the third cycle showing a different value than the other cycles.

Example 3

Control charts with individual values (I) were plotted on the transition analyzes of two groups of chromatographic batches for a given program A.

In FIG. 20 shows Control Chart I for NG-HETP for 46 batches prepared in Program A. The data shows that the column is efficient within the established limits for process consistency.

In FIG. 21 shows Control Chart I for NG-HETP for 21 additional batches prepared in Program A. The data shows that two batches (56 and 58) exceeded the upper control limits.

Example 4

Multivariate analysis was performed using transition analysis data from 27 chromatographic lots, including the three lots shown in FIG. 4. Load values were calculated for seven parameters from 27 lots, including the three lots shown in FIG. 4. The seven parameters included NG-HETP for each of the control I-card, control MR-card and control R-card for parties, the skewness coefficient for each of the control I-card, control MR-card and control R-card for lots and kurtosis coefficient for I-chart. In FIG. 10 shows a loading schedule for each of the seven parameters. The value of each of the columns corresponds to the influence of the parameter on the main component. The error columns indicate the relative error in the load value.

In FIG. 11 shows an exemplary score chart for 27 games. A score plot was calculated for seven parameters for 27 batches, including load values calculated for the batches shown in FIG. 4 (main component 1), as well as the main component 2. Batches with similar parameter values were grouped. An ellipse around most of the plot points excludes outliers with a 95% probability.

Example 5

Multivariate analysis was performed on I-MR-R control chart data for transition analysis for 46 chromatographic batches as follows. I-MR-R card data was collected for each of 46 batches. Lots that were found to be atypical or unsuitable based on the data in the I-MR-R cards were removed from the analysis and the data for the remaining lots were summarized in Table 1. 1 below. Lots containing values for multiple transitions were averaged and presented as individual measurements. The range values were calculated as the maximum values minus the minimum values of the transitions within the batch.

Table 1

Batch number Individual NGHETP Values Individual Skewness Coefficient Values NGHETP scope The range of the asymmetry coefficient 1 0.0824 0.73 0.01 0.09 2 0.0951667 0.84 0.007 0.07 3 0.0994 0.826 0.007 0.07 4 0.206167 -0.263333 0.204 1.62 5 0.96625 0.135 2.037 1.75 6 1.91875 -0.10625 3.622 1.83 7 0.107 0.925 0.015 0.06 8 0.55075 0.25625 3.355 2.16 9 0.738625 -0.06375 3.418 2.32 10 0.565714 -0.05 1.302 2.72 eleven 0.107667 0.715 0.012 0.14 12 0.0745714 0.595714 0.009 0.11 13 0.0651429 0.56 0.006 0.07 14 0.0575714 0.472857 0.002 0.07 15 0.0575714 0.5 0.007 0.17 16 0.054 0.395714 0.008 0.16 17 0.0701429 0.302857 0.115 0.77 18 0.0628571 0.545714 0.019 0.26 19 0.0702857 0.452857 0.018 0.47 20 0.0671429 0.491429 0.033 0.45 21 0.111429 0.172857 0.06 1.19 22 0.167429 -0.101429 0.163 1.41 27 0.1274 0.544 0.009 0.08 28 0.131833 0.575 0.009 0.06 29 0.136667 0.605 0.009 0.05 31 0.133833 0.6 0.011 0.04 32 0.134833 0.595 0.005 0.03

- 18 041350

33 0.1368 0.626 0.003 0.04 34 0.135 0.613333 0.003 0, About 35 0.1344 0.632 0.005 0.05 41 0.137667 0.638333 0.014 0.07 42 0.134833 0.62 0.007 0.05 43 0.1352 0.642 0.004 0.03 44 0.1316 0.638 0.008 0.07 45 0.131833 0.641667 0.003 0.07 46 0.135167 0.675 0.009 0.03

Using the data in Table. 1, the principal component was calculated by constructing load plots showing the coefficients for each input parameter. Each data series was converted to a single value. An estimate of the accuracy and relevance of the model to the physical system was shown by the R2 and Q2 values of the PCA model, where R2 is a statistic of how close the test dataset is to the fitted regression line, and Q2 is a statistic of how close the test dataset should be to regression lines. Together, R2 and Q2 indicate how well the model describes the system being analyzed, where 1 represents a perfect simulation and 0 represents no correlation at all.

In FIG. 12 shows the model loading schedule. The R ² value for the model was 0.798 and the Q ² value was 0.591, indicating that the model was acceptable for application and that all input values affected the principal component of the model because they were not located near the center line. In FIG. 12, the value of the y-coordinate of each point corresponds to the effect of the parameter (eg, the effect of NG-HETP mean, NG-HETP range, skewness range, and skewness average) on the principal component. The coordinate at each point corresponds to the number of entries per point.

The principal component values were changed and displayed linearly with respect to the respective batches. In FIG. 13 is a graph of the scores for the data set. The scoring graph shows the PC1 value (the value entered in the direction of greatest variation) for each lot used. As follows from FIG. 13, one batch (batch 6) was outside three standard deviations and several points were close to exceeding two standard deviations, indicating that the system has variation in these batches.

As will be appreciated by one of skill in the art, the methods and systems disclosed herein may take the form of entirely hardware-based embodiments, entirely software-based embodiments, or embodiments combining software and hardware aspects. In addition, the systems and methods of the present invention may take the form of computer program products on a computer-readable storage medium having computer-readable instructions (eg, computer software) embodied in the storage medium. Suitable computer-readable media may include hard disks, CD-ROMs, optical storage devices, or magnetic storage devices. More specifically, the present methods and systems may take the form of Internet-based computer software.

Embodiments of the present invention are described with reference to block diagrams and process flow diagrams of methods, systems, devices, and computer program products. It will be appreciated that one or more of the blocks in the block diagrams and flow diagrams, respectively, may be implemented by computer program instructions. These computer program instructions can be downloaded to a general purpose computer, special purpose computer, or other programmable data processing device to obtain a machine, such that the instructions that are executed on the computer or other programmable data processing device create a means to implement the functions indicated on the technological diagram or block.

Such computer program instructions may also be stored in computer readable memory, which may direct a computer or other programmable data processing device to operate in a particular manner such that the instructions stored in computer readable memory produce a product of manufacture, including computer readable instructions for implementing a function specified by a flow chart or blocks. Computer program instructions may also be downloaded to a computer or other programmable processing device to cause a sequence of operating steps to be performed on the computer or other programmable device to create a computer-implemented process such that instructions that are executed on the computer or other programmable device provide steps for implementation of the functions indicated on the technological scheme or blocks.

Accordingly, blocks in block diagrams and flow diagrams support combinations of means to perform said functions, combinations of steps to perform said functions, and program instructions to perform said functions. It should also be understood that each block in the block diagrams and flow diagrams and combinations of blocks in the block diagrams and flow diagrams may be implemented by computer system hardware that performs the specified functions or steps, or by combinations of hardware (e.g., dedicated hardware for chromatography) and computer instructions.

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Claims (15)

In FIG. 22 shows an operating environment 2200 in which some of the systems and methods of the present invention may be implemented. As an example, process controller 108 and computing device 110 (or a component thereof) in FIG. 1 may represent a computer 2201 as shown in FIG. 22. Computer 2201 may include one or more components, such as one or more processors 2203, system memory 2212, and a bus 2213 that connects various components of computer 2201, including one or more processors 2203, to system memory 2212. In the case of multiple processors 2203 the system can use parallel computing. Bus 2213 may include one or more of several possible types of bus structures such as a memory bus, a memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus structures. Bus 2213 and all buses mentioned herein can also be implemented via a wired or wireless network connection. Computer 2201 typically includes various computer readable media. An exemplary computer-readable media can be any available media that is accessible by the computer 2201 and includes, for example, and is not intended to be limited to, both volatile and nonvolatile media, removable and non-removable media. System memory 2212 may include a computer-readable medium in the form of volatile memory, such as random access memory (RAM), and/or non-volatile memory, such as read only memory (ROM). System memory 2212 may typically contain data, such as chromatography data 2207 and/or software modules, such as operating system 2205 and chromatography software 2206, that are accessible and/or controlled by one or more processors 2203. Numerous features and advantages of the present disclosure are apparent from detailed description, and thus the appended claims are intended to cover all such features and advantages of the present invention as fall within the true spirit and scope of the invention. In addition, since numerous modifications and changes can easily occur to those skilled in the art, it is undesirable to limit the present invention to the exact construction and operation that are illustrated and described, and accordingly, all suitable modifications and equivalents can be resorted to without departing from the scope. of the present invention. In yet another aspect, computer 2201 may also include other removable/non-removable, volatile/nonvolatile computer storage media. Storage device 2204 may provide non-volatile storage of computer code, computer readable instructions, data structures, program modules, and other data for computer 2201. For example, storage device 2204 may be a hard drive, removable magnetic disk, removable optical disk, magnetic cassettes, or other magnetic storage devices. , flash memory cards, CD-ROM, digital versatile disks (DVD) or other optical storage, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and the like . Those skilled in the art will appreciate that the concept on which the present invention is based can easily be used as a basis for developing other structures, methods, and systems to accomplish several of the objectives of the present disclosure. Accordingly, the claims are not to be construed as being limited by the foregoing description. CLAIM 1. Method for monitoring the efficiency of a chromatographic column; wherein the method comprises obtaining raw chromatographic data collected during the execution of the chromatographic process and containing a plurality of signals provided by one or more detectors, each signal from the plurality of signals is associated with one of the plurality of blocks, and each block corresponds to a step in the chromatographic process; obtaining a subset of data by selecting a combination of one of the blocks and a first signal from the raw chromatographic data that is suitable for performing a transition analysis, where the transition is a chromatographic transition, which is the response at the exit of the column to the stage change at the entrance of the column when one mobile phase is replaced by another ; generating processed chromatographic data by applying a denoising method to a subset of data, wherein applying the denoising method includes selecting a portion of the data subset for analysis using predetermined set values; normalizing said portion to prevent deviation due to variation based on the transition amount; applying at least one smoothing filter on said part to generate smoothed data and analyzing said part for dynamic signal errors; - 20 041350 generating transition data by performing a transition analysis on the processed chromatographic data, where performing the transition analysis includes generating a curve using the processed chromatographic data; parsing the curve to generate performance parameters and performing an action based on the transition data and previous representative chromatographic data, the action including generating an event notification; generating an event score or generating a rejection notification form. 2. The method of claim 1 wherein the raw chromatographic data is obtained from a chromatographic process unit. 3. The method of claim 1, further comprising performing a chromatographic column analysis, wherein raw chromatographic data is obtained from the chromatographic column. 4. The method of claim 1, wherein each block of the plurality of blocks corresponds to a step in the chromatographic process. 5. The method of claim 1, wherein the selected combination includes a first block, a first signal, and a second signal from the plurality of signals. 6. The method of claim 1, further comprising selecting a combination of the first block and the first signal in accordance with a profile defining a plurality of selection criteria. 7. The method according to claim 6, where the set of selection criteria includes the following: how much blocks appear at regular intervals in a chromatographic run; the degree to which one of the multiple signals saturates the detector; the extent to which a plurality of signals approaches stationary phase at a discernible level; the amount of variation in the plurality of signals; or the number of inflection points shown by the plurality of signals during the transition phase. 8. The method of claim 1, wherein selecting the combination of the first block and the first signal comprises selecting a primary combination of block and signal and further comprising selecting a secondary combination of block and signal. 9. The method according to claim 1, where the noise reduction method includes selecting a portion of a subset of data for analysis using predetermined set values; normalizing said part to prevent deviation of the value; applying at least one smoothing filter on said part to generate smoothed data; and analyzing said part for dynamic signal errors. 10. The method of claim 9, further comprising selecting smooth data corresponding to a chromatographic transition feature, where the feature includes one of: the duration of the derivative; maximum intensity; duration from initiation or expected sensor background noise. 11. The method of claim 1, wherein the transition analysis comprises generating a curve using the processed chromatographic data and analyzing the curve to generate performance parameters. 12. The method of claim 1, further comprising generating an individual value control chart, a moving range control chart, or a range control chart based on the transition data and generating performance data by applying statistical process control to the individual value control chart, the moving range control chart, or a range control chart, where performing an action based on transition data includes performing an action based on performance data. 13. The method of claim 12, wherein applying the statistical process control to an individual value control chart, a moving range control chart, or a range control chart comprises performing one of multivariate data analysis or principal component analysis. 14. The method of claim 12, wherein performing an action based on performance data includes generating an event notification; generating an event score or generating a rejection notification form. 15. Chromatographic method, where the method includes performing the method according to claim 1 during analysis on a chromatographic column. -