EP4732292A1

EP4732292A1 - Product quality attribute target ranges

Info

Publication number: EP4732292A1
Application number: EP24742399.9A
Authority: EP
Inventors: Siddharth PRABHU; Joshua TOKUDA; Ahmed ELBARADEI; Marisa JOUBERT
Original assignee: Amgen Inc
Current assignee: Amgen Inc
Priority date: 2023-06-22
Filing date: 2024-06-20
Publication date: 2026-04-29
Also published as: MX2025015278A; CN121399692A; KR20260026538A; AU2024314454A1; WO2024263774A1

Abstract

Methods of selecting or rejecting a specification target range for a product quality attribute of a pharmaceutical product are described herein. Methods of manufacturing a pharmaceutical product are described herein. Methods of assessing an impact of a product quality attribute of a pharmaceutical product are described herein.

Description

PRODUCT QUALITY ATTRIBUTE TARGET RANGES

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Patent Application No. 63/509,688, filed June 22, 2023, which is incorporated herein by reference in its entirety.

FIELD

Embodiments herein relate to target ranges for product quality attributes of pharmaceutical products, and implementing target ranges of product quality attributes in the manufacturing of pharmaceutical products.

BACKGROUND

The native structure or chemistry of biological molecules (such as therapeutic proteins) adapts or alters in response to changes within the molecules' environment. Other biological therapies, including nucleic acid and cell-based therapies can also undergo changes within their environment. Although this flexibility in structure or chemistry is required for the biological function of most, if not all, biological molecules and cells, it also presents many challenges during the development and manufacture of biological therapies for pharmaceutical applications. For example, therapeutic proteins endure various conditions during the many process steps that lead up to being administered to a patient. The many process steps include, for instance, one or more of protein production (e.g., recombinant production), harvest, purification, formulation, filling, packaging, storage, delivery, and final preparation immediately prior to administration to the patient. During each of these steps, a therapeutic protein is placed in one or more environments that may or may not lead to a change in its structure or chemistry. The change in structure or chemistry can lead to the formation of different species of the biological therapies that results in a heterogeneous product. While some species retain their ability to bind to their targets and therefore maintain therapeutic efficacy, others lose target binding ability and thus become functionally inactive. In order to maximize and maintain quality control of these biological therapies, the biopharmaceutical industry has focused much effort to understand why some species lose activity while others retain activity

Product quality attributes (which for conciseness may be generally referred to herein as "attributes") define the physicochemical property of pharmaceutical products such as therapeutic biological molecules, and can therefore impact the drug safety and efficacy. The levels of product quality attributes critical to the drug quality, or critical quality attributes (CQAs), are explicitly defined by the product purity specifications subject to extensive regulatory reviews. SUMMARY

In accordance with embodiments herein, the following items are described:

1. In some embodiments, a method of selecting or rejecting a specification target range for a product quality attribute of a pharmaceutical product is provided. The method comprises: a) providing a candidate target range for the product quality attribute of the pharmaceutical product; b) obtaining clinical outcomes data for subjects that have received an administration of the pharmaceutical product; c) determining a level of exposure of the product quality attribute upon said administration; d) partitioning the clinical outcomes data according to the level of exposure of the product quality attribute, wherein clinical outcomes data for exposure of the product quality attribute outside the candidate target range are apportioned to a first partition, and clinical outcomes data for exposure of the product quality attribute within the candidate target range are apportioned to a second partition; and e) determining an incidence rate of a specified clinical outcome in the first partition and an incidence rate of the specified clinical outcome in the second partition; and f) either:

(i) selecting the specification target range for the product quality attribute based on the candidate target range if there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions; or

(ii) rejecting basing the specification target range upon the candidate target range if there is a clinical difference between the incidence rate of the specified clinical outcome in said partitions.

2. In the method of some embodiments, with reference to the method of item 1, the method further comprises: repeating a) - e) for at least one additional candidate target range of the product quality attribute, and either:

(iii) selecting the specification target range based upon the greatest magnitude candidate target range value for which there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions; or (iv) rejecting basing the specification target range upon any of the candidate target ranges if there is a clinical difference between the incidence rate of the specified clinical outcome in said partitions for each of the candidate target ranges.

3. In the method of some embodiments, with reference to the method of item 1, if the method comprises (iv), the method further comprises repeating the method with an additional candidate target range that is of lower magnitude than the candidate target range and the at least one additional candidate target range.

4. In the method of some embodiments, the method of any one of items 1-3 further comprises: g) manufacturing a lot of the pharmaceutical product; h) determining a level of the product quality attribute in the lot of the pharmaceutical product; and i) either:

(i) rejecting the lot if the determined level of the product quality attribute is outside the specification target range for the product quality attribute; or

(ii) applying an acceptance justification to the lot if the determined level of the product quality attribute is within the specification target range for the product quality attribute.

5. In the method of some embodiments, regarding the method of item 4, if the lot is rejected, the method further comprises marking the lot for investigation.

6. In the method of some embodiments, for the method of any one of items 1-3, the method furhter comprising developing a manufacturing process for the pharmaceutical product based upon a quality target product profile comprising the specification target range.

7. In some embodiments, a method of manufacturing a pharmaceutical product is provided. The method comprises: a) providing a candidate target range for a product quality attribute of the pharmaceutical product; b) obtaining clinical outcomes data for subjects that have received an administration of the pharmaceutical product; c) determining a level of exposure of the product quality attribute upon said administration; d) partitioning the clinical outcomes data according to the level of exposure of the product quality attribute, wherein clinical outcomes data for exposure of the product quality attribute outside the candidate target range are apportioned to a first partition, and clinical outcomes data for exposure of the product quality attribute within the candidate target range are apportioned to a second partition; e) determining that there is no clinical difference between the incidence rate of the specified clinical outcome among the partitions; f) selecting the specification target range for the product quality attribute based upon the candidate target range as for the pharmaceutical product; and g) either: i) applying an acceptance justification to a lot of the pharmaceutical product comprising a level of the product quality attribute within the specification target range; or ii) rejecting a lot of the pharmaceutical product comprising a level of the product quality attribute outside of the specification target range.

8. In the method of some embodiments, the method of item 7, further comprise: h) repeating a) - e) for at least one additional candidate target range of the product quality attribute, wherein f) selecting the specification target range for the product quality attribute, is based upon the greatest magnitude candidate target range value for which there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions.

9. In some embodiments, a method of assessing an impact of a product quality attribute of a pharmaceutical product is provided. The method comprises: a) providing a candidate target range for the product quality attribute of the pharmaceutical product; b) obtaining clinical outcomes data for subjects that have received an administration of the pharmaceutical product; c) determining a level of exposure of the product quality attribute upon said administration; d) partitioning the clinical outcomes data according to the level of exposure of the product quality attribute, wherein clinical outcomes date for exposure of the product quality attribute outside the candidate target range are apportioned to a first partition, and clinical outcomes data for exposure of the product quality attribute within the candidate target range are apportioned to a second partition; e) determining an incidence rate of a specified clinical outcome in the first partition and an incidence rate of the specified clinical outcome in the second partition; and f) either: i) if there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions, determining that the product quality attribute does not have an impact on the specified clinical outcome within the candidate target range; or ii) if there is a clinical difference between the incidence rate of the specified clinical outcome in said partitions, determining that the product quality attribute has a possible impact on the specified clinical outcome within the target range.

10. In the method of some embodiments, the method of item 9 is repeated for two or more different candidate target ranges.

11. In some embodiments, a method of assessing an impact of an injection site for a pharmaceutical product is provided. The method comprises: a) providing two or more candidate injection sites for the pharmaceutical product; b) obtaining clinical outcomes data for subjects that have received an administration of the pharmaceutical product in at least one of the two or more candidate injection sites; c) partitioning the clinical outcomes data according to the injection site, wherein clinical outcomes data for a first injection site are apportioned to a first partition, and clinical outcomes data for exposure for a second injection site to a second partition; e) determining an incidence rate of a specified clinical outcome in the first partition and an incidence rate of the specified clinical outcome in the second partition; and f) either: i) if there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions, determining that the injection site does not have an impact on the specified clinical outcome within the candidate target range; or ii) if there is a clinical difference between the incidence rate of the specified clinical outcome in said partitions, determining that the injection site has a possible impact on the specified clinical outcome within the target range.

In some embodiments, the specified clinical outcome is an adverse event, such as injection site reaction.

12. In some embodiments, for any of the methods described herein, the clinical outcomes data comprise at least one of efficacy data, or adverse events incidence data.

13. In some embodiments, for any of the methods described herein, the specified clinical outcome comprises at least one of efficacy outcome, or adverse events incidence.

14. In some embodiments, for any of the methods described herein, the specified clinical outcome comprises adverse events incidence.

15. In the method of some embodiments, the method of claim 14 comprises, prior to said partitioning, filtering from the adverse event incidence data any adverse event that happened before the administration.

16. In some embodiments, for any of the methods described herein, wherein the clinical difference comprises at least one of a qualitative difference, a statistically significant difference, or a quantifiable trend.

17. In some embodiments, for any of the methods described herein, apportioning the clinical outcomes data for exposure of the product quality attribute outside the candidate target range to the second partition comprises apportioning said clinical outcomes data into two or more different partitions.

18. In some embodiments, for any of the methods described herein, the specified clinical outcomes data comprise two or more different categories or types of clinical outcomes, such as two or more different categories or types of adverse events. 19. In some embodiments, for any of the methods of items 9-18, the method further comprises determining an association or absence of an association between the product quality attribute exposure level and two or more different categories or types of clinical outcomes data.

20. In some embodiments, for any of the methods of items 9-18, the method further comprises determining an association or absence of an association between exposure levels of two or more different product quality attributes and at least one category or type of clinical outcomes data.

21. In some embodiments, for any of the methods of items 19-20, the method further comprising, the association or absence of an association between the product quality attribute exposure level and only some of the two or more different categories of clinical outcomes is determined automatically, and/or wherein the association or absence of an association between exposure levels of two or more different product quality attributions and at least one category or type of clinical outcomes data is determined automatically.

22. In some embodiments, for any of the methods described herein, the method further comprises determining an association or lack of association between at least one clinical characteristic of the subjects of the first partition and the specified clinical outcome.

23. In some embodiments, regarding the method of item 22, the at least one clinical characteristic comprises one or more of: preexisting condition, biomarker, laboratory result, or demographic information.

24. In some embodiments, for any of the methods described herein, the clinical outcomes data for the pharmaceutical product comprise data for two or more different manufacturing lots of the pharmaceutical product.

25. In some embodiments, regarding the method of item 24, the two or more different manufacturing lots of the pharmaceutical product comprise manufacturing lots from different manufacturers. 26. In some embodiments, for any of the methods described herein, the method is performed for two or more different sets of clinical outcomes data, wherein each of the two or more different sets comprises clinical outcomes data for a different manufacturing lot of the pharmaceutical product.

27. In some embodiments, the method of item 26, further comprises comparing the specification target range for each of the manufacturing lots of the two or more different sets.

28. In some embodiments, regarding the method of any one of items 2-8, 8, or 11-27, the greatest magnitude candidate target range value comprises a highest and/or lowest value of the product quality attribute.

29. In some embodiments, for any of the methods described herein, determining the level of exposure of the product quality attribute comprises the calculation: wherein A_t is the estimated level of product quality attribute exposure at the time of administration, summed from the contributions of each lot used at the time of administration, n is total number manufactured lots used at the time of administration, and for each manufactured lot /, %Ao,i is the percentage of the product quality attribute at time of lot release or analytical testing, %A_a,i is the percent rate of change of the product quality attribute level over time at a given storage condition, t, is the time of storage at the given condition, and Di is a dose strength of the administration.

30. In some embodiments, for any of the methods described herein, determining the level of exposure of the product quality attribute comprises the calculation: wherein % ei is percentage of relative level of the product quality attribute exposure with respect to dosage, wherein is level of the product quality attribute exposure computed using the equation 1 or 2, and wherein D_re/ is dose strength in the dimension of mass of active pharmaceutical ingredient of the pharmaceutical product associated with each administration.

31. In some embodiments, for any of the methods described herein, the product quality attribute comprises a molecular attribute, , endotoxin, color, clarity, polysorbate, nitrosamines, process related impurities, a process related impurity, or a drug product characteristic, or two or more of the listed items.

32. In the method of some embodiments, such as the method of item 31, the molecular attribute comprises at least one of: acidic species, basic species, high molecular weight species, subvisible particle number, visible particles, aggregation, low molecular weight, middle molecular weight, glycosylation (such as non-glycosylated heavy chain or high mannose), glycation, sialylation, non-heavy chain and light chain, deamidation, deamination, cyclization, oxidation, sulfation, hydroxylysine, isomerization, fragmentation/clipping, N-terminal and C-terminal variants, signal peptide, reduced and partial species, misfolding, disulfide scrambling, domain swapping, folded structure, surface hydrophobicity, chemical modification, covalent bond, mutations/misincorporations, a C-terminal amino acid motif PARG, a C-terminal amino acid motif PAR-Amide, drug antibody ratio (DAR), or peptide antibody ratio (PAR).

33. In the method of some embodiments, such as the method of item 31 or 32, the process related impurities comprise at least one of CHOP, HCP, residual host cell DNA, residual ProA, or a process reagent.

34. In the method of some embodiments, such as the method of any one of items 31- 33, wherein the drug product characteristic comprises an impurity, a particle, an excipient, as nondrug product feature, a process reagent, an extractable, a leachable, and/or a component feature.

35. In some embodiments, for any of the methods described herein, a level of the product quality attribute is determined by one or more of mass spectrometry, chromatography, electrophoresis, spectroscopy, light obscuration, a particle method (such as nanoparticle/visible/micron-sized resonant mass or Brownian motion), analytical centrifugation, imaging or imaging characterization, or immunoassay. 36. In some embodiments, for any of the methods described herein, the specification target range comprises an action limit, acceptance criteria, or quality target.

37. In some embodiments, for any of the methods described herein, a product quality attribute level outside the candidate or specification target range is a level (i) that exceeds the maximum value of the candidate or specification target range, (ii) that is below the minimum of the candidate or specification target range, or (iii) is either (i) or (ii).

38. In some embodiments, for any of the methods described herein, the pharmaceutical product comprises a biological therapy, a synthetic molecule, a small molecule, or a nucleic acid.

39. In the method of some embodiments, for example in the method of item 38, the biological therapy is selected from the group consisting of: an antibody, an antigen-binding antibody fragment, an antibody protein product, a Bi-specific T cell engager (BiTE®) molecule, a bispecific antibody, a trispecific antibody, an Fc fusion protein, a recombinant protein, a recombinant virus, a recombinant T cell, a synthetic peptide, and an active fragment of a recombinant protein.

40. In the method of some embodiments, for example in the method of item 38, the pharmaceutical product comprises a synthetic small molecule.

41. In the method of some embodiments, for example in the method of item 38, the nucleic acid comprises an siRNA, an mRNA or a DNA.

42. In the method of some embodiments, for example method of any one of items 4-8 or 11-39, the pharmaceutical product comprises a biological therapy, and manufacturing the pharmaceutical product comprises culturing a genetically engineered mammalian host cell comprising one or more nucleic acids encoding the biological therapy.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating aspects of a method in accordance with methods of some embodiments herein.

FIG. 2 is a histogram illustrating incidence of adverse events in accordance with methods of some embodiments herein. DETAILED DESCRIPTION

Described herein are methods of assessing the clinical impact of product quality attributes, establishing target ranges for product quality attributes, and manufacturing pharmaceutical products. It is contemplated herein that acceptable ranges of product quality attributes in pharmaceutical products can be determined based on whether or not a relationship exists between the greatest magnitude of product quality attributes exposed to subjects in vivo and the development of individual clinical outcomes, such as adverse events. A candidate target range for a product quality attribute can be provided for evaluation. Subjects from a clinical study can be binned according to whether or not their product quality attribute exposure at the time of pharmaceutical product administration is within or outside (e.g., in excess of) that candidate target range. Then, an association (or absence of association) between the attribute exposure outside the candidate target range and specified clinical outcomes such as adverse events can be determined. If no association exists between the clinical outcome (e.g., adverse event incidence) and product quality attribute levels outside the limits of the candidate target range, it can be concluded that the clinical exposure of the product quality attribute does not impact the clinical outcome, and the candidate target range can be considered to be acceptable. A product quality attribute specification may be based upon the acceptable target range. If there is no clinical difference between the clinical outcomes in the bins of subjects that did and did not receive product quality attribute outside the target range, it can be concluded that there is no association. Alternatively, if there is an association between the clinical outcome (e.g., adverse event incidence) and product quality attribute levels exceeding the limits of the target range, it may not be concluded that the product quality attribute has no impact on the clinical outcome. A clinical difference between the bins of subjects that did and did not receive product quality attribute outside the target range can indicate such an association. In such circumstances, a different target range may be selected and tested to identify a specification in which the ranges of product quality attribute do not impact the clinical outcome.

Advantageously, the methods described herein use a binned approach to assess narrow patient populations that have the highest magnitude level of exposure of a particular attribute, so that higher-risk patients (in terms of attribute exposure) will get more visibility. The methods described herein comprise a targeted analysis to only the subpopulation of subjects who have been exposed to product quality attributes beyond a specified threshold. In contrast to methods that analyze an entire patient population (regardless of attribute exposure level), the methods described herein bin subjects into small groups based on attribute exposure. The methods described herein are contemplated to enhance the sensitivity and likelihood of detecting clinical impacts of product quality attributes, since each subject in the high-magnitude exposure group is individually examined. The methods described herein have advantages over other methodologies that analyze the entire subject population, for which statistical averaging may dilute the response of a few individual subjects (which might be responding to product quality attributes), but which would not be detectable when combined with the response of all subjects in the population. Such other methodologies, which example population level dynamics, may be diluted by the number of data points that are being used, in contrast to methods described herein, which target top subjects individually to avoid dilution of data.

In other words, the methods described herein can focus the analysis on only the group of subjects with the highest-magnitude exposure to attributes, and provide a "worst case scenario" for what high-magnitude levels of product quality attributes might do. On the other hand, analyzing the entire subject population, regardless of attribute exposure level may dilute any impacts of the greatest magnitude of product quality attribute exposure.

Accordingly, the methods described herein may avoid false negatives for analysis of the impact of product quality attributes of a pharmaceutical product on clinical outcomes such as adverse events. Furthermore, by performing a targeted analysis of a narrow subpopulation, rather than looking for patterns across an entire subject population, the methods described herein can be performed rapidly, with minimal time investment, and, if automated, with minimal use of computing resources. The methods described herein can be used to demonstrate product safety to regulators using clinical data. They provide a fit-for-purpose approach, and can rapidly focus on clinical study subjects who were exposed to high-magnitudes of attributes outside of (e.g., in excess of) a candidate target range. Aspects of methods of some embodiments are illustrated schematically in FIG. 1.

The methods described herein may be used to analyze the impact of a candidate target product quality attribute range (for a pharmaceutical product) on clinical outcomes such as adverse events, or changes in potency. A specification product quality attribute target range accepted by the methods described herein may be considered to define a safe and effective level of product quality attributes, as justified by clinical data. Such a specification product quality attribute target range may be used for a quality target product profile to guide the development of a manufacturing process for the pharmaceutical product. Additionally, such a specification product quality attribute target range may be used as a pass/fail specification for manufacturing lots of the pharmaceutical product. The lots may be evaluated at a suitable phase of manufacturing, for example at the drug substance phase or the drug product phase. Additionally, the methods described herein may combine results for two or more manufacturing lots to enhance their analytical power. For example, if a particular active pharmaceutical ingredient is manufactured according to two or more different processes, locations, and/or manufacturers, which results in a greater range of product quality attribute levels, only the highest-magnitude levels of product quality attribute exposure can be evaluated, increasing the likelihood that any impact of the product quality attribute will be detected.

Moreover, the patient bins may further be divided or sub-binned to identify additional ways in which the product quality attributes may or may not have a clinical impact. For example, methods described herein may assess the impact of product quality attributes on subpopulations of patients with particular clinical characteristics, such as preexisting condition, biomarker, laboratory result, or demographic information. If there is a different impact of the product quality attribute, a different dose of the pharmaceutical product (and thus a different level of product quality attribute exposure) may thus be provided for subpopulations that are differently impacted by a product quality attribute.

It will be understood that pharmaceutical products may be dosed according to flat dosing (e.g., mg of active pharmaceutical ingredient (API)), or body mass-based dosing (e.g., mg of API per kg of patient body mass). Alternatively, pharmaceutical products may be dosed via a step-dosing regimen in which the dose depends on the number of administrations the subject receives (e.g., the first injection may lower dose than subsequent injections). However, product quality attribute specification target ranges may be assessed at the manufacturing lot level, in which each lot contains many potential doses of the pharmaceutical product. Specification target ranges for a product quality attribute in manufacturing lots may be based upon candidate target ranges for the product quality attribute at the level of individual patient attribute exposure, using gravimetric calculation and the clinical dosing to ensure that the target ranges are expressed in appropriate units in the correct context. For example, if a monoclonal antibody is dosed at a flat dose of 100 mg, and a candidate target range of up to 5 mg of HMW species is not associated with any adverse events (or any loss of potency), it may be determined that a specification target range for HMW species of up to 5% (5 mg/100 mg) is justifiable and acceptable in manufacturing lots. It will be further understood, however, that some product quality attributes, such as subvisible particles are not typically expressed in mass units. The skilled person, in view of this disclosure, will readily be able to apply and convert (as necessary) applicable units between specification product quality attribute target ranges, candidate product quality attribute target ranges, and levels of attribute exposure in clinical studies. By performing applicable unit conversions (between units of clinical attribute exposure, and acceptable attribute ranges in lots of a pharmaceutical product, including drug substance or drug product), the skilled person may arrive at a specification target range of the product quality attribute "based on" the candidate target range. It will further be appreciated that a specification target range of the product quality attribute "based on" the candidate target range may include adjustments based on practical factors, such as significant digits based on the sensitivities of the relevant analytical assays, and practical considerations. For example, negative values of a product quality attribute parameter may not have a real-world meaning, and thus zero (0) would be understood to be a real-world lower bound for a range of "less than X" or "less than or equal to X."

Typically, the "high-magnitude" or "highest-magnitude" levels of product quality attributes will refer to levels that numerically exceed the maximum of a candidate target range for the product quality attributes. For example, high molecular weight (HMW) species of biological therapies can be associated with immunogenicity and/or loss of potency. Accordingly, for an analysis of an impact of HMW species on potency, the skilled person would appreciate that HMW species exceeding the maximum level of a candidate target range would be informative of whether the HMW species outside that range may have an impact on clinical outcomes comprising adverse events and/or changes in efficacy. However, it will further be appreciated that for some product quality attributes, such as mid-molecular weight species (MMW) a value that is below the minimum of a candidate target range may also have a potential clinical impact. For each product quality attribute, the skilled person in view of this disclosure and available prior knowledge can readily ascertain whether the relevant analysis of a given product quality attribute outside of a candidate target range would refer to product quality attribute levels that exceed a maximum level, are below a minimum, or both. Thus, in some embodiments, a product quality attribute level outside the specification target range is a level (i) that exceeds the maximum value of the specification target range. In some embodiments a product quality attribute level outside the specification target range is a level (ii) that is below the minimum of the specification target range. In some embodiments, a product quality attribute level outside the specification target range is a level (i) that exceeds the maximum value of the specification target range, or (ii) that is below the minimum of the specification target range.

Product quality attributes

"Product quality attribute" and variations of this root term has its ordinary and customary meaning as would be understood by one of ordinary skill in the art in view of this disclosure. It refers to a physical or chemical characteristic of a pharmaceutical product in addition to the backbone or core structure of the active pharmaceutical ingredient itself. Examples of product quality attributes include molecular attributes, endotoxin, color, clarity, polysorbate, nitrosamines, process related impurities, drug substance characteristics, drug product characteristics, or a combination of two or more of any of the listed items.

"Molecular attribute" and variations of this root term has its ordinary and customary meaning as would be understood by one of ordinary skill in the art in view of this disclosure. It refers to a chemically or physically changed structure on a macromolecule, such as a protein or nucleic acid, and may be characterized in terms of its physicochemical identity or attribute type and location within the sequence of the macromolecule, e.g., the position of the amino acid on which the attribute is present. For example, asparagine and glutamine residues are susceptible to deamidation. A deamidated asparagine at position 10 of a therapeutic protein amino acid sequence is an example of an attribute. Exemplary molecular attribute types are described herein. For conciseness, molecular attributes may be referred to herein simply as "attributes." The levels of attributes critical to the drug quality, or critical quality attributes (CQAs), may be explicitly defined by the product purity specifications. These specifications are typically subject to extensive regulatory reviews. In some embodiments, a specification may set the permissible levels of one or more molecular attributes in the manufacture of a biological therapy.

In some embodiments, the molecular attribute comprises or consists of one or more of acidic species, basic species, high molecular weight species, particle number (visible and/or subvisible particles), low molecular weight, middle molecular weight, glycosylation (such as non-glycosylated heavy chain or high mannose), non-heavy chain and light chain, deamidation, deamination, glycation, sialylation, sulfation, hydroxylation, misassembled molecules, mutations, cyclization, oxidation, isomerization, fragmentation/clipping, N-terminal and C-terminal variants, reduced and partial species, folded structure, surface hydrophobicity, chemical modification, covalent bonds a C-terminal amino acid motif PARG, a C-terminal amino acid motif PAR-Amide, drug antibody ratio (DAR), or peptide antibody ratio (PAR). In some embodiments, the molecular attribute comprises or consists of at least one of: acidic species, basic species, high molecular weight species, amino acid isomers, or subvisible particle number. PARG is an alternative C-terminal variant of antibodies that may occur as a result of alternative splicing. It represents 4 amino acids (Proline, Alanine, Arginine, Glycine) where the "AR" was genetically inserted in the canonical lgG2 C-terminal sequence. PAR-amide is another C-terminal variant that results from further processing of PARG. It refers to the C-terminal glycine getting cleaved off of antibodies ending in PARG, leaving behind an amide group on the C-terminal Arginine.

"Process related impurity" and variations of this root term has its ordinary and customary meaning as would be understood by one of ordinary skill in the art in view of this disclosure. It refers to substances in addition to the intact active pharmaceutical ingredient and any specified excipients that may be present in a pharmaceutical product. Examples of process related impurities include host cell protein (HCP), unfiltered process materials, Chinese hamster ovary protein (CHOP), residual host cell DNA, residual Protein A (ProA) and/or Protein L (ProL), a process reagent, or a combination of two or more of any of the listed items.

"Drug product characteristic" and variations of this root term has its ordinary and customary meaning as would be understood by one of ordinary skill in the art in view of this disclosure. Drug product characteristics may include impurities, particle (visible and/or subvisible), excipients, as well as non-drug product features. Examples of non-drug product features include materials used in a type of administration device such as a syringe (e.g., metal components), components used for an administration device, and/or components used in plastics, etc. Drug product characteristics may also include process reagents, extractables and leachables (such as plastics and/or metal ions from reaction vessels), and component features (such as silicone oil and/or excipients).

Techniques for detecting levels of product quality attributes

Any suitable analytical technique for detecting levels of a product quality attribute may be used with the methods described herein. It will be appreciated that a pharmaceutical product has a corresponding drug substance, and that drug product characteristics may be assessed in such a corresponding drug substance. As such, product quality attributes as described herein may be determined in a drug product, or drug substance as applicable. The skilled person will appreciate approaches for properly qualifying analytical techniques, utilizing appropriate standards and controls when applicable, and suitable limits of detection for each analytical technique. Techniques for detecting a product quality attribute include, but are not limited to mass spectrometry, chromatography, electrophoresis, spectroscopy, light obscuration, particle methods (nanoparticle/visible/micron-sized resonant mass or Brownian motion), analytical centrifugation, imaging and imaging characterizations, and immunoassays.

Example techniques for detecting product quality attributes include reduced and non-reduced peptide mapping (which may detect chemical modifications), chromatography (such as size exclusion chromatography (SEC), ion exchange chromatography (IEX) such as cation exchange chromatography (CEX), hydrophobic interaction chromatography (HIC), affinity chromatography such as Protein A- column chromatography, or reverse phase (RP) chromatography), capillary isoelectric focusing (clEF), capillary zone electrophoresis (CZE), field flow fractionation (FFF), or ultracentrifugation (UC), HIAC (such as for detecting subvisible particle count), MFI (such as for detecting subvisible particle count and morphology), visual inspection (visible particles), SDS-PAGE (such as for detecting fragments, covalent aggregates), color analysis (Trp Ox), rCE-SDS and nrCE-SDS (such as for detecting fragments that are partial molecules), nanoparticle sizing methods, spectroscopy methods (such as FTIR, CD, intrinsic fluorescence, or ANS dye binding), an Ellman's assay (free sulfhydryl's), SEC-MALS, hydrophilic interaction liquid chromatography (HILIC) (glycan map), ELISA (such as for detecting HCP), or mass spectrometry.

Pharmaceutical

As used herein "pharmaceutical product" and variations of this root term has its ordinary and customary meaning as would be understood by one of ordinary skill in the art in view of this disclosure. It refers to a therapeutic product comprising an active pharmaceutical ingredient (API). A pharmaceutical product may further comprise additional substances such as carriers or excipients. In some embodiments, a pharmaceutical product is subject to regulation and premarket approval by a government regulatory agency, such as the Food and Drug Administration (FDA) or the European Medicines Agency (EMA). In some embodiments, a pharmaceutical product is authorized for administration to a human subject by such a government regulatory agency. Examples of pharmaceutical products include biological therapies, small molecules, synthetic molecules, and nucleic acids such as small interfering RNA (siRNA) and DNA. In some embodiments, the pharmaceutical product is for medical use. In some embodiments, the pharmaceutical product is for medical use in a human subject.

As used herein "biological therapy" and variations of this root term has its ordinary and customary meaning as would be understood by one of ordinary skill in the art in view of this disclosure. It refers to a therapeutic composition comprising a biological macromolecule, for example a gene therapy, a therapeutic protein, a nucleic acid, a virus, or a cell or a portion thereof.

In methods described herein, the biological therapy may be selected from the group consisting of: an antibody, an antigen-binding antibody fragment, an antibody protein product, a Bi-specific T cell engager (BiTE®) molecule, a bispecific antibody, a trispecific antibody, an Fc fusion protein, a recombinant protein, a functional protein fragment, a recombinant virus, a recombinant T cell, a synthetic peptide, deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and an active fragment of a recombinant protein.

An "antibody" has its customary and ordinary meaning as understood by one of ordinary skill in the art in view of this disclosure. It refers to an immunoglobulin of any isotype with specific binding to the target antigen, and includes, for instance, chimeric, humanized, and fully human antibodies. By way of example, the antibody may be a monoclonal antibody. By way of example, human antibodies can be of any isotype, including IgG (including IgGl, lgG2, lgG3 and lgG4 subtypes). A human IgG antibody generally will comprise two full-length heavy chains and two full-length light chains. Antibodies may be derived solely from a single source, or may be "chimeric," that is, different portions of the antibody may be derived from two or more different antibodies from the same or different species. It will be understood that once an antibody is obtained from a source, it may undergo further engineering, for example to enhance stability and folding. Accordingly, it will be understood that a "human" antibody may be obtained from a source, and may undergo further engineering, for example in the Fc region. The engineered antibody may still be referred to as a type of human antibody. Similarly, variants of a human antibody, for example those that have undergone affinity maturation, will also be understood to be "human antibodies" unless stated otherwise. In some embodiments, an antibody comprises, consists essentially of, or consists of a human, humanized, or chimeric monoclonal antibody.

In various aspects, the biological therapy is an antibody protein product. As used herein, the term "antibody protein product" refers to any one of several antibody alternatives which in various instances is based on the architecture of an antibody but is not found in nature. In some aspects, the antibody protein product has a molecular-weight within the range of at least about 12-150 kDa. In certain aspects, the antibody protein product has a valency (n) range from monomeric (n = 1), to dimeric (n = 2), to trimeric (n = 3), to tetrameric (n = 4), if not higher order valency. Antibody protein products in some aspects are those based on the full antibody structure and/or those that mimic antibody fragments which retain full antigen-binding capacity, e.g., scFvs, Fabs and VHH/VH (discussed below). A small antigen binding antibody fragment that retains a complete antigen binding site is the Fv fragment, which consists entirely of variable (V) regions. A soluble, flexible amino acid peptide linker is used to connect the V regions to a scFv (single chain fragment variable) fragment for stabilization of the molecule, or the constant (C) domains are added to the V regions to generate a Fab fragment [fragment, antigen-binding]. Both scFv and Fab fragments can be easily produced in host cells, e.g., prokaryotic host cells. Other antibody protein products include disulfide-bond stabilized scFv (ds-scFv), single chain Fab (scFab), as well as di- and multimeric antibody formats like dia-, tria- and tetra-bodies, or minibodies (miniAbs) that comprise different formats consisting of scFvs linked to oligomerization domains. The smallest fragments are VHH/VH of camelid heavy chain Abs as well as single domain Abs (sdAb). Building blocks that are frequently used to create antibody formats include VH domains (e.g., for fragments comprising or based upon heavy chain only molecules), or the single-chain variable (V)-domain antibody fragment (scFv), which comprises V domains from the heavy and light chain (VH and VL domain) linked by a peptide linker of ~15 amino acid residues. A peptibody or peptide-Fc fusion is yet another antibody protein product. The structure of a peptibody consists of a biologically active peptide grafted onto an Fc domain. Peptibodies are well-described in the art. See, e.g., Shimamoto et al.., mAbs 4(5): 586-591 (2012).

Biological therapies suitable for the methods described herein can include polypeptides, including those that bind to one or more of the following: These include CD proteins, including CD3, CD4, CD8, CD19, CD20, CD22, CD30, and CD34; including those that interfere with receptor binding. HER receptor family proteins, including HER2, HER3, HER4, and the EGF receptor. Cell adhesion molecules, for example, LFA-I, Mol, pl50, 95, VLA-4, ICAM-I, VCAM, and alpha v/beta 3 integrin. Growth factors, such as vascular endothelial growth factor ("VEGF"), growth hormone, thyroid stimulating hormone, follicle stimulating hormone, luteinizing hormone, growth hormone releasing factor, parathyroid hormone, Mullerian-inhibiting substance, human macrophage inflammatory protein (MIP-lalpha), erythropoietin (EPO), nerve growth factor, such as NGF-beta, platelet-derived growth factor (PDGF), fibroblast growth factors, including, for instance, aFGF and bFGF, epidermal growth factor (EGF), transforming growth factors (TGF), including, among others, TGF- a and TGF- , including TGF-pi, TGF-P2, TGF-P3, TGF- P4, or TGF- P5, insulin-like growth factors-l and -II (IGF-I and IGF-II), des(l-3)-IGF-l (brain IGF-I), and osteoinductive factors. Insulins and insulin-related proteins, including insulin, insulin A-chain, insulin B-chain, proinsulin, and insulin-like growth factor binding proteins. Coagulation and coagulation-related proteins, such as, among others, factor VIII, tissue factor, von Willebrands factor, protein C, alpha-l-antitrypsin, plasminogen activators, such as urokinase and tissue plasminogen activator ("t-PA"), bombazine, thrombin, and thrombopoietin; (vii) other blood and serum proteins, including but not limited to albumin, IgE, and blood group antigens. Colony stimulating factors and receptors thereof, including the following, among others, M-CSF, GM- CSF, and G-CSF, and receptors thereof, such as CSF-1 receptor (c-fms). Receptors and receptor- associated proteins, including, for example, flk2/flt3 receptor, obesity (OB) receptor, LDL receptor, growth hormone receptors, thrombopoietin receptors ("TPO-R," "c-mpl"), glucagon receptors, interleukin receptors, interferon receptors, T-cell receptors, stem cell factor receptors, such as c-Kit, and other receptors. Receptor ligands, including, for example, OX40L, the ligand for the 0X40 receptor. Neurotrophic factors, including bone-derived neurotrophic factor (BDNF) and neurotrophin- 3, -4, -5, or -6 (NT-3, NT-4, NT-5, or NT-6). Relaxin A-chain, relaxin B-chain, and prorelaxin; interferons and interferon receptors, including for example, interferon-a, - , and -y, and their receptors. Interleukins and interleukin receptors, including IL-I to IL-33 and IL-I to IL-33 receptors, such as the IL- 8 receptor, among others. Viral antigens, including an AIDS envelope viral antigen. Lipoproteins, calcitonin, glucagon, atrial natriuretic factor, lung surfactant, tumor necrosis factor-alpha and -beta, enkephalinase, RANTES (regulated on activation normally T-cell expressed and secreted), mouse gonadotropin-associated peptide, DNAse, inhibin, and activin. Integrin, protein A or D, rheumatoid factors, immunotoxins, bone morphogenetic protein (BMP), superoxide dismutase, surface membrane proteins, decay accelerating factor (DAF), HIV envelope, transport proteins, homing receptors, addressins, regulatory proteins, immunoadhesins, antibodies. Myostatins, TALL proteins, including TALL-I, amyloid proteins, including but not limited to amyloid-beta proteins, thymic stromal lymphopoietins ("TSLP"), RANK ligand ("RANKL" or "OPGL"), c-kit, TNF receptors, including TNF Receptor Type 1, TRAIL-R2, angiopoietins, and biologically active fragments or analogs or variants of any of the foregoing.

Examples of biological therapies suitable for the methods described herein include antibodies such as infliximab, bevacizumab, cetuximab, ranibizumab, palivizumab, abagovomab, abciximab, actoxumab, adalimumab, afelimomab, afutuzumab, alacizumab, alacizumab pegol, ald518, alemtuzumab, alirocumab, altumomab, amatuximab, anatumomab mafenatox, anrukinzumab, apolizumab, arcitumomab, aselizumab, altinumab, atlizumab, atorolimiumab, tocilizumab, bapineuzumab, basiliximab, bavituximab, bectumomab, belimumab, bemarituzumab, benralizumab, bertilimumab, besilesomab, bevacizumab, bezlotoxumab, biciromab, bivatuzumab, bivatuzumab mertansine, blinatumomab, blosozumab, brentuximab vedotin, briakinumab, brodalumab, canakinumab, cantuzumab mertansine, cantuzumab mertansine, caplacizumab, capromab pendetide, carlumab, catumaxomab, cc49, cedelizumab, certolizumab pegol, cetuximab, citatuzumab bogatox, cixutumumab, clazakizumab, clenoliximab, clivatuzumab tetraxetan, conatumumab, crenezumab, cr6261, dacetuzumab, daclizumab, dalotuzumab, daratumumab, demcizumab, denosumab, detumomab, dorlimomab aritox, drozitumab, duligotumab, dupilumab, ecromeximab, eculizumab, edobacomab, edrecolomab, efalizumab, efungumab, elotuzumab, elsilimomab, enavatuzumab, enlimomab pegol, enokizumab, enoticumab, ensituximab, epitumomab cituxetan, epratuzumab, erenumab, erlizumab, ertumaxomab, etaracizumab, etrolizumab, evolocumab, exbivirumab, fanolesomab, faralimomab, farletuzumab, fasinumab, fbta05, felvizumab, fezakinumab, ficlatuzumab, figitumumab, flanvotumab, fontolizumab, foralumab, foravirumab, fresolimumab, fulranumab, futuximab, galiximab, ganitumab, gantenerumab, gavilimomab, gemtuzumab ozogamicin, gevokizumab, girentuximab, glembatumumab vedotin, golimumab, gomiliximab, gs6624, ibalizumab, ibritumomab tiuxetan, icrucumab, igovomab, imciromab, imgatuzumab, inclacumab, indatuximab ravtansine, infliximab, intetumumab, inolimomab, inotuzumab ozogamicin, ipilimumab, iratumumab, itolizumab, ixekizumab, keliximab, labetuzumab, lebrikizumab, lemalesomab, lerdelimumab, lexatumumab, libivirumab, ligelizumab, lintuzumab, lirilumab, lorvotuzumab mertansine, lucatumumab, lumiliximab, mapatumumab, maslimomab, mavrilimumab, matuzumab, mepolizumab, metelimumab, milatuzumab, minretumomab, mitumomab, mogamulizumab, morolimumab, motavizumab, moxetumomab pasudotox, muromonab-cd3, nacolomab tafenatox, namilumab, naptumomab estafenatox, narnatumab, natalizumab, nebacumab, necitumumab, nerelimomab, nesvacumab, nimotuzumab, nivolumab, nofetumomab merpentan, ocaratuzumab, ocrelizumab, odulimomab, ofatumumab, olaratumab, olokizumab, omalizumab, onartuzumab, oportuzumab monatox, oregovomab, orticumab, otelixizumab, oxelumab, ozanezumab, ozoralizumab, pagibaximab, palivizumab, panitumumab, panobacumab, parsatuzumab, pascolizumab, pateclizumab, patritumab, pemtumomab, perakizumab, pertuzumab, pexelizumab, pidilizumab, pintumomab, placulumab, ponezumab, priliximab, pritumumab, PRO 140, quilizumab, racotumomab, radretumab, rafivirumab, ramucirumab, ranibizumab, raxibacumab, regavirumab, reslizumab, rilotumumab, rituximab, robatumumab, roledumab, romosozumab, rontalizumab, rovelizumab, ruplizumab, samalizumab, sarilumab, satumomab pendetide, secukinumab, sevirumab, sibrotuzumab, sifalimumab, siltuximab, simtuzumab, siplizumab, sirukumab, solanezumab, solitomab, sonepcizumab, sontuzumab, stamulumab, sulesomab, suvizumab, tabalumab, tacatuzumab tetraxetan, tadocizumab, talizumab, tanezumab, taplitumomab paptox, tarlatamab, tefibazumab, telimomab aritox, tenatumomab, tefibazumab, teneliximab, teplizumab, teprotumumab, tezepelumab, TGN1412, tremelimumab, ticilimumab, tildrakizumab, tigatuzumab, TNX-650, tocilizumab, toralizumab, tositumomab, tralokinumab, trastuzumab, TRBS07, tregalizumab, tucotuzumab celmoleukin, tuvirumab, ublituximab, urelumab, urtoxazumab, ustekinumab, vapaliximab, vatelizumab, vedolizumab, veltuzumab, vepalimomab, vesencumab, visilizumab, volociximab, vorsetuzumab mafodotin, votumumab, zalutumumab, zanolimumab, zatuximab, ziralimumab, or zolimomab aritox.

In some embodiments, the biological therapy is a BiTE® molecule. BiTE® molecules are engineered bispecific antigen binding constructs which direct the cytotoxic activity of T cells against cancer cells. They are the fusion of two single-chain variable fragments (scFvs) of different antibodies, or amino acid sequences from four different genes, on a single peptide chain of about 55 kilodaltons. One of the scFvs binds to T cells via the CD3 receptor, and the other to a tumor cell via a tumor specific molecule. Blinatumomab (BLINCYTO®) is an example of a BiTE® molecule, specific for CD19. BiTE® molecules that are modified, such as those modified to extend their half-lives, can also be used in the disclosed methods. In various aspects, the polypeptide is an antigen binding protein, e.g., a BiTE® molecule. In some embodiments, an antibody protein product comprises a BiTE® molecule.

In some embodiments, the biological therapy is in a formulation. The formulation may be a pharmaceutically acceptable formulation. The formulation may comprise the biological therapy together with a pharmaceutically acceptable diluent, carrier, solubilizer, emulsifier, preservative, and/or adjuvant. Acceptable formulation materials for biological therapies as described herein preferably are nontoxic to recipients at the dosages and concentrations employed. In certain embodiments, the pharmaceutical composition may contain formulation materials for modifying, maintaining or preserving, for example, the pH, osmolality, viscosity, clarity, color, isotonicity, odor, sterility, stability, rate of dissolution or release, adsorption or penetration of the composition. In such embodiments, suitable formulation materials include, but are not limited to, amino acids (such as glycine, glutamine, asparagine, arginine or lysine); antimicrobials; antioxidants (such as ascorbic acid, sodium sulfite or sodium hydrogen-sulfite); buffers (such as borate, bicarbonate, Tris-HCI, citrates, phosphates or other organic acids); bulking agents (such as mannitol or glycine); chelating agents (such as ethylenediamine tetraacetic acid (EDTA)); complexing agents (such as caffeine, polyvinylpyrrolidone, beta-cyclodextrin or hydroxypropyl-beta-cyclodextrin); fillers; monosaccharides; disaccharides; and other carbohydrates (such as glucose, sucrose, mannose or dextrins); proteins (such as serum albumin, gelatin or immunoglobulins); coloring, flavoring and diluting agents; emulsifying agents; hydrophilic polymers (such as polyvinylpyrrolidone); low molecular weight polypeptides; salt-forming counterions (such as sodium); preservatives (such as benzalkonium chloride, benzoic acid, salicylic acid, thimerosal, phenethyl alcohol, methylparaben, propylparaben, chlorhexidine, sorbic acid or hydrogen peroxide); solvents (such as glycerin, propylene glycol or polyethylene glycol); sugar alcohols (such as mannitol or sorbitol); suspending agents; surfactants or wetting agents (such as pluronics, PEG, sorbitan esters, polysorbates such as polysorbate 20, polysorbate, triton, tromethamine, lecithin, cholesterol, tyloxapal); stability enhancing agents (such as sucrose or sorbitol); tonicity enhancing agents (such as alkali metal halides, preferably sodium or potassium chloride, mannitol sorbitol); delivery vehicles; diluents; excipients and/or pharmaceutical adjuvants. See, e.g., REMINGTON'S PHARMACEUTICAL SCIENCES, 18" Edition, (A. R. Genrmo, ed.), 1990, Mack Publishing Company.

A suitable vehicle or carrier for the formulation may be water for injection, physiological saline solution or artificial cerebrospinal fluid, possibly supplemented with other materials common in compositions for parenteral administration. Neutral buffered saline or saline mixed with serum albumin are further exemplary vehicles. In specific embodiments, pharmaceutical compositions comprise Tris buffer of about pH 7.0-8.5, or acetate buffer of about pH 4.0-5.5, and may further include sorbitol or a suitable substitute therefor.

Product quality attribute exposure

It will be appreciated that product quality attribute levels in a pharmaceutical product may change over time. For example, some product quality attribute levels may increase as a pharmaceutical product is stored over a period of time. It is contemplated herein that for methods herein, it is advantageous to ascertain product quality attribute levels at the time of pharmaceutical product administration to a subject, and this will enhance the accuracy of the determination of attribute impact.

By way of example, product quality attribute exposures may be calculated using gravimetric drug product exposure, and maximum specification doses of attributes (MSD) may be calculated from the maximum allowed dose. MSD is an example of a limit of a candidate product quality attribute target range. It will be understood that some product quality attributes, such as subvisible particles, are not typically measured in mass units, and thus attribute exposure may be calculated proportional to dose, but not necessarily gravimetrically. The difference in product quality attribute exposure and MSD may be calculated.

In some embodiments, the level of exposure of the product quality attribute comprises the calculation:

[Equation 1] in which A_t is the estimated level of product quality attribute exposure at the time of administration, summed from the contributions of each lot used at the time of administration, n is total number manufactured lots used at the time of administration, and for each manufactured lot /: %Ao,i is the percentage of the product quality attribute at time of lot release or analytical testing, %A& is the percent rate of change of the product quality attribute level over time at a given storage condition, t, is the time of storage at the given condition, and D, is a dose strength of the administration. In some embodiments, Di refers to the dose when n = i = 1, or a constituent dose component when n > 1.

In some embodiments, determining the level of exposure of the product quality attribute comprises the calculation:

% _rel = X 100% [Equation 2]

^Dref in which %A_rei is percentage of relative level of the product quality attribute exposure with respect to dosage, wherein A_t is level of the product quality attribute exposure computed using the equation 1 or 2, and wherein D_ref is dose strength in the dimension of mass of active pharmaceutical ingredient of the pharmaceutical product associated with each administration. It is contemplated that a lot (such as drug product) may be DP stored at different conditions, such as different temperatures. For such circumstances, the level of exposure of the product quality attribute may comprise the calculation: [Equation 3] in which the variables are defined as according to Equation, and in which h j is the condition (e.g. temperature), for up to k conditions.

In some embodiments, the level of exposure of the product quality attribute is calculated based on the release specification of a clinical lot, a stability specification, and the time until release of the clinical lot. Based on the dose of the pharmaceutical product, gravimetric calculation may be used to convert relative attribute levels to attribute exposure levels (and vice versa) for the purposes of using the same units for candidate target ranges and product quality attribute exposure in clinical data sets.

Optionally, clinical outcomes data can undergo pre-processing in conjunction with the calculation of attribute exposure. Exposures and specified clinical outcomes (such as adverse events) may be flagged according to logical rules. For example, exposures in excess of the MSD, and/or only clinical outcomes that occurred after an exposure may be flagged. For example, clinical outcomes may be clustered by subject and event date. This analysis may identify clinical outcomes that are associated with the administration of the pharmaceutical product, as opposed to artifactual or coincidental events.

Methods of selecting or rejecting a specification target range for a product quality attribute

Described herein are methods for selecting or rejecting a specification target range for a product quality attribute. Advantageously, the methods described herein can select specification target ranges for product quality attributes based on the highest magnitudes of attribute exposure in clinical study, decreasing the possibility of dilution of the impact of high-magnitude attribute exposure (which might occur if the subjects with high-magnitude attribute exposure are averaged across a large population). Thus, the methods herein have enhanced sensitivity to potential attribute impacts, and can be performed nimbly with fewer computational resources. The method can include a) providing a candidate target range for the product quality attribute of the pharmaceutical product. The method can further include b) obtaining clinical outcomes data for subjects that have received an administration of the pharmaceutical product. The method can further include c) determining a level of exposure of the product quality attribute upon said administration. The method can further include d) partitioning the clinical outcomes data according to the level of exposure of the product quality attribute, in which clinical outcomes data for exposure of the product quality attribute outside the candidate target range are apportioned to a first partition, and clinical outcomes data for exposure of the product quality attribute within the candidate target range are apportioned to a second partition. The method can further include e) determining an incidence rate of a specified clinical outcome in the first partition and an incidence rate of the specified clinical outcome in the second partition. The method can further include f) either: (i) selecting the specification target range for the product quality attribute based on the candidate target range if there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions; or (ii) rejecting basing the specification target range upon the candidate target range if there is a clinical difference between the incidence rate of the specified clinical outcome in said partitions. By way of example, the specified clinical outcome may be adverse events incidence. By way of example, the candidate target range may refer to a threshold value, and exposure of the product quality attribute within the candidate target range may refer to exposure levels below the threshold value, and exposure of the product quality attribute outside the candidate target range may refer to exposure levels above the threshold value.

As used herein, a "clinical difference" has its customary and ordinary meaning as would be understood by the skilled person in view of this disclosure. It refers to a qualitatively meaningful difference in the context of the applicable clinical outcome (or outcomes). For example, the clinical difference may refer to a qualitative difference in clinical outcome, a statistically significant difference in clinical outcome, or a quantifiable trend. It will be appreciated that not just any numerical difference is a clinical difference. For example, some parameters for clinical outcomes may numerically differ at some level of granularity, but will not represent a qualitatively meaningful difference (for example, two numerical parameters that are slightly different, but within the detection error of the relevant measuring assay). However, a clinical difference refers to a difference, that in the context of the clinical situation and the particular clinical outcomes, represents a qualitatively different clinical situation, for example a different adverse event incidence rate that would be recognized as a different clinical situation. In the method of some embodiments, the clinical difference comprises at least one of a qualitative difference, a statistically significant difference, or a quantifiable trend.

A "candidate target range" for the product quality attribute has its ordinary and customary meaning as will be understood by a person of ordinary skill in the art in view of this disclosure. It refers to a range of the product quality attribute levels of a pharmaceutical product that may be evaluated for possible impact of the product quality attribute within or outside that range when administered at clinically relevant doses of the pharmaceutical product. A candidate target range may be based, for example, upon prior knowledge, in vitro or animal model data, or pharmacokinetic modeling. A "specification target range" for the product quality attribute has its ordinary and customary meaning as will be understood by a person of ordinary skill in the art in view of this disclosure. It refers to a specified target range of product quality attribute levels of a pharmaceutical product for which there has been a determination that levels of the product quality attribute are not associated with undesired clinical outcomes when the pharmaceutical product is administered in a clinically relevant dose. The specification target range may be comprised by a quality target product profile, which may be used to develop manufacturing methods for the pharmaceutical product. The specification target range may be comprised by a release specification for manufacturing lots of the pharmaceutical product, including lots of drug substance or drug product, as applicable. For any of the methods described herein, the specification target range may comprise an action or rejection limit, an acceptance criteria, or a quality target.

Optionally, the method may be repeated for at least one additional candidate target range. It is contemplated that two or more different candidate target ranges may be selected based on prior knowledge, or for the purpose of hypothesis testing. The greatest magnitude target range for which there is no impact of the product quality attribute (no association between the clinical outcome and attribute exposures outside of the target range) may then be selected, for example, to improve manufacturing efficiency by avoiding unnecessary rejection of clinically acceptable lots of pharmaceutical product. Example 2 illustrates methods as described herein in which the method is performed for multiple candidate target ranges.

For some methods for selecting or rejecting the specification target range as described herein, the method further comprises repeating a) - e) for at least one additional candidate target range of the product quality attribute, and either: (iii) selecting the specification target range based upon the greatest magnitude candidate target range value for which there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions; or (iv) rejecting basing the specification target range upon any of the candidate target ranges if there is a clinical difference between the incidence rate of the specified clinical outcome in the partitions for each of the candidate target ranges. Optionally, if the method comprises (iv), the method may further comprise repeating the method with an additional candidate target range that is of lower magnitude than the other candidate target ranges (e.g., the candidate target range and the at least one additional candidate target range).

Some of the methods may be used to inform the selection or rejection of manufactured lots of the pharmaceutical product. For example, some methods for selecting or rejecting the specification target range as described herein may further comprise g) manufacturing a lot of the pharmaceutical product; and h) determining a level of the product quality attribute in the lot of the pharmaceutical product. The method may further comprise i) either: (i) rejecting the lot if the determined level of the product quality attribute is outside the specification target range for the product quality attribute; or (ii) applying an acceptance justification to the lot if the determined level of the product quality attribute is within the specification target range for the product quality attribute. Optionally, if the lot is rejected, the method further may comprise marking the lot for investigation. The investigation may assess, for example, lot history, raw materials, and/or the suitability of the specification target range for further manufacturing.

Some of the methods may be used to develop a manufacturing process for the pharmaceutical product based upon a quality target product profile comprising the specification target range. For example, the specification target range may be used to define a quality target product profile, and manufacturing parameters, equipment choices, raw material choices, and the like may be selected to develop a manufacturing process that yields pharmaceutical product that is justifiably acceptable according to the quality target product profile.

Methods of

Described herein are methods for manufacturing a pharmaceutical product. The methods may ensure that product quality attributes of the pharmaceutical product are within ranges associated with an acceptable clinical impact. For example, the methods may select a specification target range for the product quality attribute based upon a candidate target range that is not associated with an undesired clinical outcome (such as adverse events or loss of potency). Manufactured lots of the pharmaceutical product may be accepted or rejected based on whether the product quality attribute levels are within the specification target range.

The method of manufacturing a pharmaceutical product may comprise a) providing a candidate target range for a product quality attribute of the pharmaceutical product. The method may further comprise b) obtaining clinical outcomes data for subjects that have received an administration of the pharmaceutical product. The method may further comprise c) determining a level of exposure of the product quality attribute upon said administration. The method may further comprise d) partitioning the clinical outcomes data according to the level of exposure of the product quality attribute, wherein clinical outcomes data for exposure of the product quality attribute outside the candidate target range are apportioned to a first partition, and clinical outcomes data for exposure of the product quality attribute within the candidate target range are apportioned to a second partition. The method may further comprise e) determining that there is no clinical difference 1 between the incidence rate of the specified clinical outcome among the partitions. The method may further comprise f) selecting the specification target range for the product quality attribute based upon the candidate target range for the pharmaceutical product. The method may further comprise g) either: i) applying an acceptance justification to a lot of the pharmaceutical product comprising a level of the product quality attribute within the specification target range; or ii) rejecting a lot of the pharmaceutical product comprising a level of the product quality attribute outside of the specification target range.

Optionally, the method may be repeated for two or more different candidate target ranges. For example, the method of manufacturing a pharmaceutical product may comprise h) repeating a) - e) for at least one additional candidate target range of the product quality attribute. In such methods, f) selecting the specification target range for the product quality attribute, may be based upon the greatest magnitude candidate target range value for which there is no clinical difference between the incidence rate of the specified clinical outcome in the different partitions.

Methods of assessing an impact of product quality attributes of a pharmaceutical product

Methods of assessing an impact of a product quality attribute of a pharmaceutical product are described herein. The methods may assess the impact of one or more product quality attribute on one or more specified clinical outcomes, for example adverse events. The methods may be used, for example, to inform the design of a manufacturing process, to assess the impact of clinical attributes in real-world data sets, or to assess the clinical acceptability of a manufacturing process or manufacturing lot having certain product quality attribute ranges.

The method of assessing an impact of a product quality attribute of a pharmaceutical product may comprise a) providing a candidate target range for the product quality attribute of the pharmaceutical product. The method may further comprise b) obtaining clinical outcomes data for subjects that have received an administration of the pharmaceutical product. The method may further comprise c) determining a level of exposure of the product quality attribute upon said administration. The method may further comprise d) partitioning the clinical outcomes data according to the level of exposure of the product quality attribute. Clinical outcomes data for exposure of the product quality attribute outside the candidate target range may be apportioned to a first partition, and clinical outcomes data for exposure of the product quality attribute within the candidate target range may be apportioned to a second partition. Optionally, the clinical outcomes data within the candidate target range may be apportioned to two or more different subpartitions, for example based upon clinical characteristics of the subjects, or sub-ranges of the product quality attribute. The method may further comprise e) determining an incidence rate of a specified clinical outcome in the first partition and an incidence rate of the specified clinical outcome in the second partition. The method may further comprise f) either: (i) if there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions, determining that the product quality attribute does not have an impact on the specified clinical outcome within the candidate target range; or (ii) if there is a clinical difference between the incidence rate of the specified clinical outcome in said partitions, determining that the product quality attribute has a possible impact on the specified clinical outcome within the target range. Optionally, the method may be repeated for two or more different candidate target ranges.

It will be further appreciated that methods described herein may ascertain effects of modalities and strategies of drug delivery, such as drug product injection site, on a specified clinical outcome such as adverse events. Accordingly, for any of the methods described herein, "modality of deliver" or "injection site" may be substituted for "product quality attribute." As such, the method may include partitioning the clinical outcomes data according to modality of deliver (e.g., injection site), in which clinical outcomes data for a first modality of delivery (e.g., injection site) are apportioned to a first partition, and clinical outcomes data for a second modality of delivery (e.g., injection site) are apportioned to a second partition. Such a method can further include determining an incidence rate of a specified clinical outcome in the first partition and an incidence rate of the specified clinical outcome in the second partition. The specified clinical outcome may include injection site reactions. For such a method, if (i) there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions, it may be determined determining that the modality of delivery (e.g., injection site) does not have an impact on the specified clinical outcome within the candidate target range. Or, (ii) if there is a clinical difference between the incidence rate of the specified clinical outcome in said partitions, it can be determined that the modaility of delivery (e.g., injection site) has a possible impact on the specified clinical outcome. Optionally, the method may be repeated for two or more different injection sites.

Additional options

For any of the methods described herein, including any of the methods for selecting or rejecting a specification target range for a product quality attribute, methods for manufacturing a pharmaceutical product, or methods of assessing an impact of a product quality attribute of a pharmaceutical product, the following additional options apply in accordance with some embodiments:

For any of the methods described herein, the clinical outcomes data may comprise at least one of efficacy data or adverse events incidence data. In the method of some embodiments, the clinical outcomes data comprises or consists of adverse events incidence data.

For any of the methods described herein, the specified clinical outcome may comprise at least one of efficacy outcome or adverse events incidence. In the method of some embodiments, the specified clinical outcome comprises adverse events incidence. In the method of some embodiments, the specified clinical outcome consists of adverse events incidence. Adverse events incidence may be expressed as a rate, or an absolute value, as applicable for the particular method. Additionally, adverse events may comprise a categorization of severity or grade, in addition to occurrence.

For any of the methods described herein, the method may further comprise prior to the partitioning, filtering from the clinical outcomes data any clinical outcome that are scientifically inapplicable, for example adverse events clearly related to an irrelevant disease state (e.g., if the subject acquires an irrelevant viral infection), or that reflect a symptom of the disease state being treated, or that happened before the administration of the pharmaceutical product. For any of the methods described herein, in which the specified clinical outcome comprises or consists of adverse events incidence, the method may further comprise prior to the partitioning, filtering from the adverse event incidence data any adverse event that are scientifically inapplicable, for example, that happened before the administration of the pharmaceutical product. It is contemplated that removal of such "orphan adverse events" that occur before administration enhances the accuracy of determining an association (or lack of association) between the product quality attributes and adverse events, as such orphan adverse events may lead to false positives or false negatives (depending on the patient bin in which they occur) for determining associations or lack of associations between product quality attributes and adverse events. For any of the methods describe herein, the method may further comprise identifying the first clinical outcome event of its kind per subject (e.g., if the clinical outcome is an adverse event, the method may comprise identifying the first headache an individual experienced, even though these individuals may have endured many headaches during the clinical study). For some methods, repeat adverse events of the same kind in the same individual may be counted as a single instance for the purpose of determining associations or lack of associations.

For any of the methods describe herein, multiple partitions may be used, for example at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 partitions. For any of the methods described herein, apportioning the clinical outcomes data for exposure of the product quality attribute outside the candidate target range to the second partition comprises apportioning the clinical outcomes data into two or more different partitions. For example, the apportioning the clinical outcomes data for exposure of the product quality attribute outside the candidate target range to the second partition may comprise apportioning said clinical outcomes data into two or more sub-partitions. By way of example, the subpartitions may be based upon clinical characteristics of the patient group, type of clinical outcome (e.g., type of adverse event), or levels of product quality attribute exposure.

For any of the methods described herein, the specified clinical outcomes data may comprise two or more different categories or types of clinical outcomes, such as two or more different categories or types of adverse events. For any of the methods described herein, categories or types of clinical outcome to be used as specified clinical outcomes may be based upon incidence of these clinical outcomes. For example, the most frequent types or categories of clinical outcomes may be the specified clinical outcomes. By way of example, a histogram may be prepared listing the incidence of each type of adverse event (See FIG. 2). The top most frequent adverse events (e.g., the top 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 most frequent adverse events) may be selected as the specified clinical outcomes.

For any of the methods described herein, the method may further comprise determining an association or absence of an association between the product quality attribute exposure level and two or more different categories or types of clinical outcomes data. For any of the methods described herein, the method may further comprise determining an association or absence of an association between exposure levels of two or more different product quality attributes and at least one category or type of clinical outcomes data. Optionally, for any of the methods described herein, the association or absence of an association between exposure levels of two or more different product quality attributions and at least one category or type of clinical outcomes data may be determined automatically. Optionally, for any of the methods described herein the association or absence of an association between the product quality attribute exposure level and only some of the two or more different categories of clinical outcomes may be determined automatically.

For any of the methods described herein, the method may further comprise determining an association or lack of association between at least one clinical characteristic of the subjects of the first partition and the specified clinical outcome. Examples of a "clinical characteristic" include a preexisting condition, biomarker, laboratory result, or demographic information, or a combination of two or more of the listed items.

For any of the methods described herein, the clinical outcomes data for the pharmaceutical product comprise data for two or more different manufacturing lots of the pharmaceutical product. For example, the two lots may be manufactured using different processes, different raw materials, in different locations, and/or by different manufacturers, for example two different manufacturers of a product with the same nonproprietary name. It is contemplated that combining the data for two or more different lots may increase the diversity of product quality ranges, and thus enhance the power of the analysis for identifying associations (or lack of association) between product quality attributes and clinical outcomes. In some embodiments, the two or more different manufacturing lots of the pharmaceutical product comprise manufacturing lots from different manufacturers.

It is further contemplated that results of two or more clinical studies may be combined for some of the methods described herein. For any of the methods described herein, the method may be performed for two or more different sets of clinical outcomes data, in which each of the two or more different sets comprises clinical outcomes data for a different manufacturing lot of the pharmaceutical product. Optionally, the method may further comprise comparing the specification target range for each of the manufacturing lots of the two or more different sets of clinical outcomes data.

As used herein, the "greatest magnitude candidate target range value" refers to target range value that has greatest absolute difference from the base or null case for the particular product quality attribute (that is, the core API without any variation or modification). For most product quality attributes, the greatest magnitude candidate target range value will be the greatest numerical candidate target range value. However, for some product quality attributes, such as MMW species, in which a lower incidence of the product quality attributes represents a more substantial deviation from the base case, the greatest magnitude candidate target range value will be the lowest numerical candidate target range value. For any of the methods described herein, the greatest magnitude candidate target range value comprises a highest and/or lowest value of the product quality attribute.

It will be appreciated that a product quality attribute level outside the candidate or specification target range will often be a level that either exceeds the maximum value of the specification target, but may also be a value that is below the minimum of the specification target range (e.g, in the case of MMW species). It will be appreciated that for product quality attributes in which the specification target range is defined as a threshold, a product quality attribute level beyond that threshold will be outside the range (e.g., if the target range is for product quality attribute levels < X, a product quality attribute level > X is outside the target range). For any of the methods described herein, a product quality attribute level outside the candidate or specification target range may be a level (i) that exceeds the maximum value of the candidate or specification target range, (ii) that is below the minimum of the candidate or specification target range, or (iii) is either (i) or (ii). EXAMPLES

EXAMPLE 1: Establishing a product quality attribute specification target range for mAb 1

Methods as described herein were used to demonstrate safe clinical exposure for seven product quality attributes (CEX acidic peaks, CEX main peak, CEX basic peaks, CEX basic peak 3, SE- UHPLC HMW, SE-UHPLC main peak, rCE-SDS non HC+LC) for a provided candidate product quality attribute target range for mAb 1. mAb 1 is a therapeutic humanized lgG2 monoclonal antibody. As mAb 1 was contemplated to be administered in a flat dose for clinical purposes (mg), the percent value of each product quality attribute was converted into mg for the flat dose, using gravimetric calculation.

Product quality attribute exposures were determined from a completed clinical study of mAb 1, for which clinical outcomes data were available, including adverse event incidence data. The dosing in this study was weight based (mg/kg dosing), and attribute exposure was calculated using gravimetric drug product exposure. Drug product lots had been recorded in the clinical data, and lot genealogy was performed to trace individual drug product lots. For each drug product lot, drug product lot data in a stability database was obtained to find the associated product quality attribute levels with each lot. Product quality attribute levels at the time of administration to subjects were estimated using the lot release attribute data extrapolated against gravimetric DP weight-based exposure during the clinical trial. Accordingly, product quality attribute exposure at the time of administration was determined for each subject in the clinical study. For each product quality attribute, the clinical outcomes data included product quality attribute exposure levels that were in excess of the provided candidate target range (a candidate stability specification). The provided candidate product quality attribute target range, and the calculated clinical study attribute exposures are shown in Table 1.1.

Table 1.1 ^a the release specification and stability specification for rCE-SDS HC+LC are > 97.5% and > 96.4%, respectively. Therefore, the associated rCE-SDS non-HC+LC species are < 2.5% and < 3.6%, respectively.

Next, product quality attribute exposures exceeding the provided candidate target range were associated against incidence of adverse events from the clinical study data set. Only adverse events following exposure to mAb 1 and the product quality attributes were considered. That is, "orphan adverse events" that happened before administration of mAb 1 were filtered out, as it was contemplated that these events would not be informative of the impact of product quality attributes of mAb 1. Additionally, the first adverse event of its kind per subject was noted (e.g., the first headache an individual experienced, even though he or she may have experience multiple headaches during the clinical study).

Data on injection site reactions (ISRs) were also obtained, but ISRs could not be decoupled from injection rate in the course of the clinical study, and were not included in the analysis. In particular, ISRs were present in 12 subjects, 9 subjects with > 3 injections. The adverse event data are shown in Table 1.2. No hypersensitivity or autoimmune disorders were found in any subjects in the FIH study for any level of DP exposure. ADAs were found in up to 6 subjects (22% at N = 27) vs reported rate of clinical immunogenicity (18.1%).

Table 1.2

In this analysis, there were no incidences of hypersensitivity and autoimmune disorders. Moreover, incidence rates for ADAs were comparable to the known clinical immunogenicity rate. Accordingly, it was determined that there was no clinical difference between the adverse event rates for attribute exposures within and outside of the target specification. Moreover, since several maximum product quality attributes exposures were well in excess of the proposed target level (based on the clinical flat does and the provided stability specification), strong justifications for safety could be made.

Accordingly, the provided candidate product quality attribute target range was determined to be an acceptable basis for the product quality attribute specification target range. Based on the known flat dose of mAb 1, attribute exposure (in mass units) could be converted to percent attribute content by gravimetric calculation. Accordingly, the product quality attribute specification target range could be expressed as acceptable % product quality attribute target range for each lot of mAb 1. This product quality attribute specification may be used as a release specification for mAb 1.

EXAMPLE 2: Establishing a product quality attribute specification target range for Protein 1

Methods as described herein were used to demonstrate safe clinical exposure for a product quality attribute (host cell protein - HCP) for a provided product quality attribute stability specification of Protein 1.

Lots of Protein 1 manufactured with a method that yielded relatively high HCP were measured for HCP levels using a SF-ELISA HCP assay. Protein 1 clinical studies containing lots manufactured with this method (and thus expected to have high HCP) were identified, Study A and Study B. Clinical outcomes data, including adverse events incidence data, were obtained from these clinical studies (Study A and Study B). Associated HCP levels for each study as measured by SF-ELISA HCP assay were obtained, and are shown in Table 2.1. It was determined that the clinical studies included data for relatively high exposure to HCP. Table 2.1

Three candidate target ranges for HCP levels for Protein 1 product were provided (candidate target ranges 1-3), including two proposed release specifications, and one proposed rejection specification. Clinically relevant numerical values (in pg) for the candidate target range were calculated by extrapolating candidate target range HCP levels (ng/mg) against gravimetric DP weightbased exposures recorded in the clinical trial. Maximum clinical exposure for HCP levels in excess of the calculated maximum spec level based on maximum candidate target range (MSD) calculated as (gravimetric drug exposure * HCP spec) vs. (MSD * HCP spec). The highest level of HCP in the two clinical studies was 397% of the lowest HCP candidate target range maximum. These data are shown in Table 2.2

Table 2.2

Product quality attribute (HCP) levels outside of the candidate target range were identified, and associated against adverse events incidence data from the clinical study. Evidence for safety was evaluated for each of candidate target range 1 (up to 632 ng/mg HCP), candidate target range 2 (up to 924 ng/mg HCP), and candidate target range 3 (up to 1765 ng/mg HCP).

Four major adverse event classes were analyzed from the clinical outcomes data sets: AntiDrug Antibodies (ADA), Pure Red Cell Aplasia (PRCA), Hypertension, Allergic Reaction. No reported cases of ADA or PRCA regardless of Protein 1 exposure level. These data are shown in Table 2.3:

Table 2.3

All incidence levels of adverse events in above MSD population at each candidate target range were comparable to or below that of the below MSD population. ADA and PRCA was not reported in any subject regardless of attribute exposure level. Hypertension and Allergic Reaction levels in above MSD population were comparable or below that of below MSD population at all candidate target range levels (candidate target ranges 1-3). Accordingly, it was concluded that a target specification range for HCP based on any of candidate target ranges 1-3 would be acceptable. Candidate Target Range 3, which included the greatest magnitude of HCP level (< 1765 ng/ml) was selected as an acceptable specification target range.

General

All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

The use of the terms "a" and "an" and "the" and similar referents in the context of describing the disclosure (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms "comprising," "having," "including," and "containing" are to be construed as open-ended terms (i.e., meaning "including, but not limited to,") unless otherwise noted.

The terms "patient" and "subject" are used interchangeably herein. Generally, these terms will be understood to refer to humans. In the methods of some embodiments, the patient or subject is a human.

Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range and each endpoint, unless otherwise indicated herein, and each separate value and endpoint is incorporated into the specification as if it were individually recited herein.

All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate the disclosure and does not pose a limitation on the scope of the disclosure unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the disclosure.

Preferred embodiments of this disclosure are described herein, including the best mode known to the inventors for carrying out the disclosure. Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the disclosure to be practiced otherwise than as specifically described herein. Accordingly, this disclosure includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the elements described herein, in all possible variations thereof, is encompassed by the disclosure unless otherwise indicated herein or otherwise clearly contradicted by context.

Claims

What is claimed:

1. A method of selecting or rejecting a specification target range for a product quality attribute of a pharmaceutical product, the method comprising: a) providing a candidate target range for the product quality attribute of the pharmaceutical product; b) obtaining clinical outcomes data for subjects that have received an administration of the pharmaceutical product; c) determining a level of exposure of the product quality attribute upon said administration; d) partitioning the clinical outcomes data according to the level of exposure of the product quality attribute, wherein clinical outcomes data for exposure of the product quality attribute outside the candidate target range are apportioned to a first partition, and clinical outcomes data for exposure of the product quality attribute within the candidate target range are apportioned to a second partition; and e) determining an incidence rate of a specified clinical outcome in the first partition and an incidence rate of the specified clinical outcome in the second partition; and f) either:

2. The method of claim 1, further comprising: repeating a) - e) for at least one additional candidate target range of the product quality attribute, and either:

(iii) selecting the specification target range based upon the greatest magnitude candidate target range value for which there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions; or

(iv) rejecting basing the specification target range upon any of the candidate target ranges if there is a clinical difference between the incidence rate of the specified clinical outcome in said partitions for each of the candidate target ranges.

3. The method of claim 2, wherein if the method comprises (iv), the method further comprises repeating the method with an additional candidate target range that is of lower magnitude than the candidate target range and the at least one additional candidate target range.

4. The method of any one of claims 1-3, further comprising: g) manufacturing a lot of the pharmaceutical product; h) determining a level of the product quality attribute in the lot of the pharmaceutical product; and i) either:

5. The method of claim 4, wherein if the lot is rejected, the method further comprises marking the lot for investigation.

6. The method of any one of claims 1-3, further comprising developing a manufacturing process for the pharmaceutical product based upon a quality target product profile comprising the specification target range.

7. A method of manufacturing a pharmaceutical product comprising: a) providing a candidate target range for a product quality attribute of the pharmaceutical product; b) obtaining clinical outcomes data for subjects that have received an administration of the pharmaceutical product; c) determining a level of exposure of the product quality attribute upon said administration; d) partitioning the clinical outcomes data according to the level of exposure of the product quality attribute, wherein clinical outcomes data for exposure of the product quality attribute outside the candidate target range are apportioned to a first partition, and clinical outcomes data for exposure of the product quality attribute within the candidate target range are apportioned to a second partition; e) determining that there is no clinical difference between the incidence rate of the specified clinical outcome among the partitions; f) selecting the specification target range for the product quality attribute based upon the candidate target range as for the pharmaceutical product; and g) either: i) applying an acceptance justification to a lot of the pharmaceutical product comprising a level of the product quality attribute within the specification target range; or ii) rejecting a lot of the pharmaceutical product comprising a level of the product quality attribute outside of the specification target range.

8. The method of claim 7 , further comprising: h) repeating a) - e) for at least one additional candidate target range of the product quality attribute, wherein f) selecting the specification target range for the product quality attribute, is based upon the greatest magnitude candidate target range value for which there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions.

9. A method of assessing an impact of a product quality attribute of a pharmaceutical product, comprising: a) providing a candidate target range for the product quality attribute of the pharmaceutical product; b) obtaining clinical outcomes data for subjects that have received an administration of the pharmaceutical product; c) determining a level of exposure of the product quality attribute upon said administration; d) partitioning the clinical outcomes data according to the level of exposure of the product quality attribute, wherein clinical outcomes date for exposure of the product quality attribute outside the candidate target range are apportioned to a first partition, and clinical outcomes data for exposure of the product quality attribute within the candidate target range are apportioned to a second partition; e) determining an incidence rate of a specified clinical outcome in the first partition and an incidence rate of the specified clinical outcome in the second partition; and f) either: i) if there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions, determining that the product quality attribute does not have an impact on the specified clinical outcome within the candidate target range; or ii) if there is a clinical difference between the incidence rate of the specified clinical outcome in said partitions, determining that the product quality attribute has a possible impact on the specified clinical outcome within the target range.

10. The method of claim 9, wherein the method is repeated for two or more different candidate target ranges.

11. A method of assessing an impact of an injection site for a pharmaceutical product, the method comprising: a) providing two or more candidate injection sites for the pharmaceutical product; b) obtaining clinical outcomes data for subjects that have received an administration of the pharmaceutical product in at least one of the two or more candidate injection sites; c) partitioning the clinical outcomes data according to the injection site, wherein clinical outcomes data for a first injection site are apportioned to a first partition, and clinical outcomes data for exposure for a second injection site to a second partition; e) determining an incidence rate of a specified clinical outcome (such as adverse event incidence) in the first partition and an incidence rate of the specified clinical outcome in the second partition; and f) either: i) if there is no clinical difference between the incidence rate of the specified clinical outcome in said partitions, determining that the injection site does not have an impact on the specified clinical outcome within the candidate target range; or ii) if there is a clinical difference between the incidence rate of the specified clinical outcome in said partitions, determining that the injection site has a possible impact on the specified clinical outcome within the target range.

12. The method of any one of the preceding claims, wherein the clinical outcomes data comprise at least one of efficacy data, or adverse events incidence data.

13. The method of any one of the preceding claims, wherein the specified clinical outcome comprises at least one of efficacy outcome, or adverse events incidence.

14. The method of any one of the preceding claims, wherein the specified clinical outcome comprises adverse events incidence.

15. The method of claim 14, further comprising, prior to said partitioning, filtering from the adverse event incidence data any adverse event that happened before the administration.

16. The method of any one of the preceding claims, wherein the clinical difference comprises at least one of a qualitative difference, a statistically significant difference, or a quantifiable trend.

17. The method of any one of the preceding claims, wherein apportioning the clinical outcomes data for exposure of the product quality attribute outside the candidate target range to the second partition comprises apportioning said clinical outcomes data into two or more different partitions.

18. The method of any one of the preceding claims, wherein the specified clinical outcomes data comprise two or more different categories or types of clinical outcomes, such as two or more different categories or types of adverse events.

19. The method of any one of claims 9-18, further comprising determining an association or absence of an association between the product quality attribute exposure level and two or more different categories or types of clinical outcomes data.

20. The method of any one of claims 9-18, further comprising determining an association or absence of an association between exposure levels of two or more different product quality attributes and at least one category or type of clinical outcomes data.

21. The method of claim 19 or 20, wherein the association or absence of an association between the product quality attribute exposure level and only some of the two or more different categories of clinical outcomes is determined automatically, and/or wherein the association or absence of an association between exposure levels of two or more different product quality attributions and at least one category or type of clinical outcomes data is determined automatically.

22. The method of any one of the preceding claims, further comprising determining an association or lack of association between at least one clinical characteristic of the subjects of the first partition and the specified clinical outcome.

23. The method of claim 22, wherein the at least one clinical characteristic comprises one or more of: preexisting condition, biomarker, laboratory result, or demographic information.

24. The method of any one of the preceding claims, wherein the clinical outcomes data for the pharmaceutical product comprise data for two or more different manufacturing lots of the pharmaceutical product.

25. The method of claim 24, wherein the two or more different manufacturing lots of the pharmaceutical product comprise manufacturing lots from different manufacturers.

26. The method of any one of claims 1-23, wherein the method is performed for two or more different sets of clinical outcomes data, wherein each of the two or more different sets comprises clinical outcomes data for a different manufacturing lot of the pharmaceutical product.

27. The method of claim 26, further comprising comparing the specification target range for each of the manufacturing lots of the two or more different sets.

28. The method of any one of claims 2-8, 8, or 11-27, wherein the greatest magnitude candidate target range value comprises a highest and/or lowest value of the product quality attribute.

29. The method of any one of the preceding claims, wherein determining the level of exposure of the product quality attribute comprises the calculation: wherein A_t is the estimated level of product quality attribute exposure at the time of administration, summed from the contributions of each lot used at the time of administration, n is total number manufactured lots used at the time of administration, and for each manufactured lot /, %Ao,i is the percentage of the product quality attribute at time of lot release or analytical testing, %A& is the percent rate of change of the product quality attribute level over time at a given storage condition, t, is the time of storage at the given condition, and Di is a dose strength of the administration.

30. The method of any one of the preceding claims, wherein determining the level of exposure of the product quality attribute comprises the calculation: wherein % ei is percentage of relative level of the product quality attribute exposure with respect to dosage, wherein is level of the product quality attribute exposure computed using the equation 1 or 2, and wherein D_re/ is dose strength in the dimension of mass of active pharmaceutical ingredient of the pharmaceutical product associated with each administration.

31. The method of any one of the preceding claims wherein the product quality attribute comprises a molecular attribute, , endotoxin, color, clarity, polysorbate, nitrosamines, process related impurities, a process related impurity, or a drug product characteristic, or two or more of the listed items.

32. The method of claim 31, wherein the molecular attribute comprises at least one of: acidic species, basic species, high molecular weight species, subvisible particle number, visible particles, aggregation, low molecular weight, middle molecular weight, glycosylation (such as nonglycosylated heavy chain or high mannose), glycation, sialylation, non-heavy chain and light chain, deamidation, deamination, cyclization, oxidation, sulfation, hydroxylysine, isomerization, fragmentation/clipping, N-terminal and C-terminal variants, signal peptide, reduced and partial species, misfolding, disulfide scrambling, domain swapping, folded structure, surface hydrophobicity, chemical modification, covalent bond, mutations/misincorporations, a C-terminal amino acid motif PARG, a C-terminal amino acid motif PAR-Amide, drug antibody ratio (DAR), or peptide antibody ratio (PAR).

33. The method of claim 31 or 32, wherein the process related impurities comprise at least one of CHOP, HCP, residual host cell DNA, residual ProA, or a process reagent.

34. The method of any one of claims 31-34, wherein the drug product characteristic comprises an impurity, a particle, an excipient, as non-drug product feature, a process reagent, an extractable, a leachable, and/or a component feature.

35. The method of any one of the preceding claims, wherein a level of the product quality attribute is determined by one or more of mass spectrometry, chromatography, electrophoresis, spectroscopy, light obscuration, a particle method (such as nanoparticle/visible/micron-sized resonant mass or Brownian motion), analytical centrifugation, imaging or imaging characterization, or immunoassay.

36. The method of any one of the preceding claims, wherein the specification target range comprises an action limit, acceptance criteria, or quality target.

37. The method of any one of the preceding claims, wherein a product quality attribute level outside the candidate or specification target range is a level (i) that exceeds the maximum value of the candidate or specification target range, (ii) that is below the minimum of the candidate or specification target range, or (iii) is either (i) or (ii).

38. The method of any one of the preceding claims, wherein the pharmaceutical product comprises a biological therapy, a synthetic molecule, a small molecule, or a nucleic acid.

39. The method of claim 38, wherein the biological therapy is selected from the group consisting of: an antibody, an antigen-binding antibody fragment, an antibody protein product, a Bispecific T cell engager (BiTE®) molecule, a bispecific antibody, a trispecific antibody, an Fc fusion protein, a recombinant protein, a recombinant virus, a recombinant T cell, a synthetic peptide, and an active fragment of a recombinant protein.

40. The method of claim 38, wherein the pharmaceutical product comprises a synthetic small molecule.

41. The method of claim 38, wherein the nucleic acid comprises an siRNA, an mRNA or a DNA.

42. The method of any one of claims 4-8 or 11-39, wherein the pharmaceutical product comprises a biological therapy, and wherein manufacturing the pharmaceutical product comprises culturing a genetically engineered mammalian host cell comprising one or more nucleic acids encoding the biological therapy.