Methods for manufacturing biological drugs

Abstract

A method for manufacturing a biological therapeutic is described. The method may include detecting the level of a molecular attribute of the biological therapeutic in a formulation, determining a rate of change of the molecular attribute under storage conditions, and estimating the level of molecular attribute exposure experienced by a subject upon said administration. A manufacturing lot of the biological therapeutic containing the molecular attribute may be manufactured, the manufacturing lot containing the molecular attribute at or below a specified specification for an acceptable level of the molecular attribute based on the estimated level of molecular attribute exposure. A method for developing a manufacturing process for a biological therapeutic is described. A method for evaluating the clinical impact of a molecular attribute of a biological therapeutic is described.

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims the benefits of U.S. Provisional Patent Application No. 63 / 126,274, filed on 16 December 2020, entitled "Methods of Manufacturing Biological Therapies," and U.S. Provisional Patent Application No. 63 / 242,395, filed on 9 September 2021, entitled "Methods of Manufacturing Biological Therapies," each of which is incorporated herein by reference in whole.

[0002] Embodiments of this specification relate to methods for producing biological therapeutics and to levels of molecular attributes of biological therapeutics. [Background technology]

[0003] The natural structure or chemical properties of a biological molecule (e.g., a therapeutic protein) adapt or change in response to changes in the environment of this molecule. Other biological therapeutics (e.g., nucleic acid therapeutics and cell-based therapeutics) may also be subject to changes in their environment. This flexibility in structure or chemical properties is required for most, if not all, biological functions of biological molecules and cells, but this flexibility also presents many challenges during the development and manufacture of biological therapeutics for pharmaceutical use. For example, a therapeutic protein endures various conditions during many process steps before being administered to a patient. Many process steps include, for example, one or more of protein production (e.g., recombinant production), recovery, purification, formulation, filling, packaging, storage, distribution, and final preparation immediately before administration to the patient. During each of these steps, the therapeutic protein is placed in one or more environments where its structure or chemical properties may or may not change. This change in structure or chemical properties can result in the formation of various species of biological therapeutics that produce heterogeneous products. Some species retain their ability to bind to these targets and thus maintain therapeutic efficacy, while others lose their target-binding ability and thus become functionally inactive. To maximize and maintain the quality control of these biological therapeutics, the biopharmaceutical industry has made many efforts to understand why some species lose activity while others retain activity.

[0004] Molecular attributes define the physicochemical characteristics of a therapeutic biological molecule and can thus affect the safety and efficacy of a drug. The levels of attributes important for drug quality (i.e., critical quality attributes (CQAs)) are clearly defined by product purity specifications that require approval by a wide range of regulatory authorities. SUMMARY OF THE INVENTION MEANS FOR SOLVING THE PROBLEM

[0005] According to several embodiments, a method for manufacturing a biotherapy drug is described. This method may include detecting the level of molecular attributes of the biotherapy drug in the formulation at one or more time points under storage conditions. This method may include determining the rate of change of the molecular attributes under storage conditions. This method may include obtaining in vivo safety and / or efficacy data of the biotherapy drug with respect to a subject to which the biotherapy drug is administered. This method may include estimating the level of molecular attribute exposure that the subject will receive at the time of administration of the biotherapy drug, based on (i) the rate of change of the molecular attributes of the biotherapy drug in the formulation during storage, and (ii) the period during which the biotherapy drug in the formulation was under storage conditions prior to administration of the biotherapy drug. This method may include determining whether there is a correlation between the estimated level of molecular attribute exposure and safety and / or efficacy data regarding the biotherapy drug. If no such correlation exists, this method may include manufacturing a production lot of the biotherapy drug containing the molecular attributes at a level below the acceptable level of the designated molecular attributes, based on the estimated level of molecular attribute exposure. Where this correlation exists, this method may further include setting the specification level of the molecular attribute at the time of manufacture so as not to exceed the maximum permissible level of the molecular attribute of the biotherapy drug. This maximum permissible level of the molecular attribute may be based on the highest estimated level of exposure to the molecular attribute that is not associated with adverse events and / or inhibition of efficacy of the biotherapy drug. Manufacturing may further include rejecting manufacturing lots of the biotherapy drug that contain levels of the molecular attribute that exceed the maximum permissible level. In some cases, the maximum permissible level of the specified molecular attribute is calculated to result in an end-of-shelf attribute exposure level that is 90-100% or less of the highest estimated level of exposure to the molecular attribute that is not associated with adverse events and / or inhibition of efficacy in the subject.

[0006] According to some embodiments, a method of developing a manufacturing process for a biological therapeutic. The method can include detecting the level of a molecular attribute of the biological therapeutic in a formulation at one or more time points under storage conditions. The method can include determining the rate of change of the molecular attribute under storage conditions. The method can include obtaining data regarding the safety and / or efficacy of the biological therapeutic in a subject to whom the biological therapeutic is administered. The method can include estimating the level of molecular attribute exposure that a subject will receive upon administration of the biological therapeutic based on (i) the rate of change of the molecular attribute during storage of the biological therapeutic in the formulation and (ii) the period during which the biological therapeutic in the formulation was under storage conditions prior to administration of the biological therapeutic. The method can include determining the presence or absence of a correlation between the estimated level of molecular attribute exposure and data on the safety and / or efficacy of the biological therapeutic. (a) If no such correlation exists, the method can include establishing a manufacturing process to result in a level of the molecular attribute below a specified acceptable level based on the estimated level of molecular attribute exposure. Or, (b) if such correlation exists, the method can include establishing a manufacturing process to result in a level of the molecular attribute below a specified maximum acceptable level of the molecular attribute based on the highest level of the molecular attribute not associated with an adverse event and / or inhibition of efficacy of the biological therapeutic.

[0007] According to several embodiments, a method for evaluating the clinical impact of molecular attributes of a biotherapy drug is described. This method may include detecting the level of molecular attributes of the biotherapy drug in the formulation at one or more time points under storage conditions. This method may include determining the rate of change of the molecular attributes under storage conditions. This method may include obtaining data on the safety and / or efficacy of the biotherapy drug in subjects to whom it is administered. This method may include estimating the level of molecular attribute exposure received by subjects at the time of administration of the biotherapy drug, based on (i) the rate of change of molecular attributes of the biotherapy drug in the formulation during storage, and (ii) the period during which the biotherapy drug in the formulation was under storage conditions prior to such administration. This method may include determining whether there is a correlation between the estimated molecular attribute exposure and the safety and / or efficacy of the biotherapy drug. (a) If no correlation exists, this method may include determining that the molecular attributes do not affect the clinical safety or efficacy of the biotherapy drug. Or, (b) if a correlation exists, this method may include determining that the molecular attributes do affect the safety and / or efficacy of the biotherapy drug. In some cases, (a) if no correlation exists, the method may further include setting a standard for an acceptable level of the molecular attribute of the biotherapy drug, which is based on the highest estimated level of exposure to the molecular attribute received by the subject; or (b) if a correlation exists, the method may further include setting a standard for a maximum acceptable level of the molecular attribute of the biotherapy drug, which is based on the level of the molecular attribute associated with adverse events and / or inhibition of the efficacy of the biotherapy drug.

[0008] With respect to any of the methods described herein, estimations may be based on (iii) the dose of the biologic agent in the administration, and (iv) the amount of molecular attributes measured at the time of manufacture and / or lot delivery.

[0009] With respect to any of the methods described herein, the levels of molecular attributes of a biotherapeutic substance in a formulation can be detected at two or more time points under storage conditions.

[0010] With respect to any of the methods described herein, one or more time points, or two or more time points, may include the time of manufacture and at least two time points thereafter.

[0011] To estimate the level of molecular attribute exposure with respect to any of the methods described herein, the calculation is:

number

[0012] To estimate the level of molecular attribute exposure with respect to any of the methods described herein, the calculation is:

number

[0013] With respect to any of the methods described herein, the correlation may include a weighted correlation between attribute exposure and the occurrence of an adverse event.

[0014] Determining whether there is a correlation between estimated levels of molecular attribute exposure and safety and / or efficacy data of a biotherapeutic drug, with respect to any of the methods described herein, may include Bayesian estimation.

[0015] With respect to any of the methods described herein, the biological therapeutic agents in the formulations may be stored under various conditions for various periods of time in administration to various subjects.

[0016] In any of the methods described herein, the administration of a biologic drug may include two or more administration events. In some cases, the estimated level of molecular attribute exposure received by the subject at the time of administration of the biologic drug may be the maximum or average value of the two or more administration events.

[0017] With respect to any of the methods described herein, the administration (of a biopharmaceutical) may include a series of infusions, and estimation may include calculating estimated levels of molecular attribute exposure during two or more intervals of this series of infusions, such as 24-hour intervals.

[0018] With respect to any of the methods described herein, safety data may include data on adverse events. In some cases, safety data may include changes in adverse events over time.

[0019] With respect to any of the methods described herein, efficacy data may include data on clinical endpoints.

[0020] With respect to any of the methods described herein, molecular attributes may include at least one of the following: acidic species, basic species, high molecular weight species, invisible particle number, low molecular weight, medium molecular weight, glycosylation (e.g., non-glycosylated heavy chain or high mannose), non-heavy chain and light chain, deamidation, deamination, cyclization, oxidation, isomerization, fragmentation / clipping, N-terminal variant and C-terminal variant, reduced species and subspecies, folded structure, surface hydrophobicity, chemical modification, covalent bond, C-terminal amino acid motif PARG, or C-terminal amino acid motif PAR-amide. With respect to any of the methods described herein, molecular attributes may include at least one of the following: acidic species, basic species, high molecular weight species, amino acid isomer, or invisible particle number.

[0021] With respect to any of the methods described herein, the biotherapeutic agent may be selected from the group consisting of antibodies, antigen-binding antibody fragments, antibody protein products, bispecific T cell engager (BiTE®) molecules, bispecific antibodies, tripspecific antibodies, Fc fusion proteins, recombinant proteins, recombinant viruses, recombinant T cells, synthetic peptides, and active fragments of recombinant proteins.

[0022] With respect to any of the methods described herein, the formulation may be a pharmaceutically acceptable formulation. With respect to any of the methods described herein, the subject (or patient) may be a human subject (or human patient).

[0023] With respect to any of the methods described herein, detecting levels of molecular attributes of a biopharmaceutical agent may include mass spectrometry, chromatography, electrophoresis, spectroscopy, light shielding, particle methods (e.g., nanoparticle / visible / micron-sized resonance mass or Brownian motion), analytical centrifugation, imaging or image characterization, or immunoassays. [Brief explanation of the drawing]

[0024] [Figure 1A]This is a series of graphs illustrating the components of a calculation and statistical approach for determining the clinical impact (CIA) of an attribute in accordance with the embodiments of this specification. [Figure 1B] This is a series of graphs illustrating the components of a calculation and statistical approach for determining the clinical impact (CIA) of an attribute in accordance with the embodiments of this specification. [Figure 1C] This is a series of graphs illustrating the components of a calculation and statistical approach for determining the clinical impact (CIA) of an attribute in accordance with the embodiments of this specification. [Figure 2A] Figure 2B is a series of graphs showing the effects of mAb A (Figure 1A) and mAb B (Figure 1B) on ADA according to embodiments of this specification. In each graph in Figure 2B, the results for ADA-negative patients are shown on the left, and the results for ADA-positive patients are shown on the right. [Figure 2B] Figure 2B is a series of graphs showing the effects of mAb A (Figure 1A) and mAb B (Figure 1B) on ADA according to embodiments of this specification. In each graph in Figure 2B, the results for ADA-negative patients are shown on the left, and the results for ADA-positive patients are shown on the right. [Figure 3A] This is a series of graphs showing a CIA analysis of ADA incidence rates for mAb A (Figure 3A) and mAb B (Figure 3B) according to embodiments of this specification (subjects grouped according to representative At). [Figure 3B] This is a series of graphs showing a CIA analysis of ADA incidence rates for mAb A (Figure 3A) and mAb B (Figure 3B) according to embodiments of this specification (subjects grouped according to representative At). [Figure 4A] This is a series of graphs showing a CIA analysis of the ADA changes over time for mAb A (Figure 4A) and mAb B (Figure 4B) according to embodiments of this specification (subjects grouped according to a representative At). [Figure 4B] This is a series of graphs showing a CIA analysis of the ADA changes over time for mAb A (Figure 4A) and mAb B (Figure 4B) according to embodiments of this specification (subjects grouped according to a representative At). [Figure 5A] This is a series of graphs showing the relationship between the ADA response to mAb B and representative At (amount of attribute exposure at the time of treatment). In each graph in Figure 5A, the results of all treatments for ADA-positive patients are shown on the left, the results of all treatments for ADA-negative patients are shown in the center, and the results of all treatments for ADA-related treatments for ADA-positive patients are shown on the right. [Figure 5B] This is a series of graphs showing the relationship between the ADA response to mAb B and representative At (amount of attribute exposure at the time of treatment). In each graph in Figure 5A, the results of all treatments for ADA-positive patients are shown on the left, the results of all treatments for ADA-negative patients are shown in the center, and the results of all treatments for ADA-related treatments for ADA-positive patients are shown on the right. [Figure 5C] This is a series of graphs showing the relationship between the ADA response to mAb B and representative At (amount of attribute exposure at the time of treatment). In each graph in Figure 5A, the results of all treatments for ADA-positive patients are shown on the left, the results of all treatments for ADA-negative patients are shown in the center, and the results of all treatments for ADA-related treatments for ADA-positive patients are shown on the right. [Figure 6] This is a series of graphs showing the influence of treatment factors on ADA. In each graph in Figure 6, the results for ADA-negative patients are shown on the left, and the results for ADA-positive patients are shown on the right. [Figure 7A] Figure 7A shows a series of graphs illustrating the incidence of the impact on ADA based on mAb A attributes for subjects retrospectively grouped according to known clinical outcomes, and for subjects grouped according to representative At (At) characteristics (Figure 7B). In each graph in Figure 7A, the results for ADA-negative patients are shown on the left, and the results for ADA-positive patients are shown on the right. [Figure 7B]Figure 7A shows a series of graphs illustrating the incidence of the impact on ADA based on mAb A attributes for subjects retrospectively grouped according to known clinical outcomes, and for subjects grouped according to representative At (At) characteristics (Figure 7B). In each graph in Figure 7A, the results for ADA-negative patients are shown on the left, and the results for ADA-positive patients are shown on the right. [Figure 8] This graph shows a CIA analysis of subjects grouped by known clinical outcomes in a retrospective on the impact of mAb A attributes on ADA using advanced-stage clinical studies. [Figure 9] This graph shows the average or maximum At used as the representative At for subjects analyzed, grouped according to a representative At, regarding the impact of mAb B attributes on ADA. [Figure 10A] These graphs show examples of evaluating the effects of the molecular attributes and efficacy of product C, a standard BiTE® molecule, on six types of adverse events. In each graph in Figure 10A, for each X-axis value category (0, >0~15, or >15), patients without adverse events are shown on the left, and patients with adverse events are shown on the right. In each graph in Figure 10B, for each X-axis value category (0, >0~15, or >15), patients with no adverse events or G≦2 are shown on the left, and patients with adverse events G≧3 are shown on the right. [Figure 10B] These graphs show examples of evaluating the effects of the molecular attributes and efficacy of product C, a standard BiTE® molecule, on six types of adverse events. In each graph in Figure 10A, for each X-axis value category (0, >0~15, or >15), patients without adverse events are shown on the left, and patients with adverse events are shown on the right. In each graph in Figure 10B, for each X-axis value category (0, >0~15, or >15), patients with no adverse events or G≦2 are shown on the left, and patients with adverse events G≧3 are shown on the right. [Figure 11A]These are a series of graphs showing the analysis of the effect of high molecular weight (HMW) species on exothermic reactions using the method described herein without Bayesian estimation (Figures 11A-B) and the method described herein with Bayesian estimation (Figures 11C-D). [Figure 11B] These are a series of graphs showing the analysis of the effect of high molecular weight (HMW) species on exothermic reactions using the method described herein without Bayesian estimation (Figures 11A-B) and the method described herein with Bayesian estimation (Figures 11C-D). [Figure 11C] These are a series of graphs showing the analysis of the effect of high molecular weight (HMW) species on exothermic reactions using the method described herein without Bayesian estimation (Figures 11A-B) and the method described herein with Bayesian estimation (Figures 11C-D). [Figure 11D] These are a series of graphs showing the analysis of the effect of high molecular weight (HMW) species on exothermic reactions using the method described herein without Bayesian estimation (Figures 11A-B) and the method described herein with Bayesian estimation (Figures 11C-D). [Modes for carrying out the invention]

[0025] This specification describes methods for manufacturing biotherapeutic drugs (e.g., therapeutic drugs containing therapeutic proteins, nucleic acids, or cells), the safety and efficacy of which are controlled by limiting the level of attribute exposure to the subject. After a manufacturing lot of a biotherapeutic drug is produced, it is stored in the formulation for a certain period before being administered to the subject. During this storage period, the levels of molecular attributes of the biotherapeutic drug may change. In addition, the levels of molecular attributes may differ between different manufacturing lots, for example, reflecting differences in molecular attribute levels at different manufacturing stages between the initial cell culture and the final pharmaceutical product of the biotherapeutic drug. For example, the levels of attributes such as acidic species, basic species, high molecular weight species, amino acid isomers, or invisible particles may increase. Such attributes may cause a decrease in the efficacy of the biotherapeutic drug and / or cause adverse events in the subject to which the biotherapeutic drug is administered. Changes in molecular attribute levels during storage can be modeled as described herein. The level of molecular attributes at the time of administration to a subject can be calculated based on the level of molecular attributes at the time of manufacture of the manufacturing lot of the biotherapy drug, the period during which the biotherapy drug in the formulation is stored before administration to the subject, the rate of change in molecular attributes during storage, and the dose of the biotherapy drug administered to the subject. Furthermore, if the actual level of molecular attributes at the time of administration is not associated with adverse events or loss of efficacy, this level of molecular attributes can be determined to be safe and effective. Accordingly, using the methods described herein, manufacturing lots of biotherapy drugs can be manufactured at or below a specified level based on a level considered safe and effective at the time of administration.

[0026] Criticality and manufacturing specifications for molecular attributes are determined using conventional methods, but these conventionally determined criticality and specifications are expected to show little clinical relevance. These criticality levels are generally investigated using non-human model systems, and the specification limits reflect low levels of the attribute that are reasonably achievable during manufacturing and storage. Alternatively, the prior knowledge or clinical experience approach suggests that an attribute is unimportant if little clinical consequence is observed with respect to other pharmaceuticals containing that attribute. However, this approach ignores the possibility of product-specific variability in the effect of the attribute. Furthermore, adverse events can be caused by an attribute, and they may still appear rare if lots containing sufficiently high levels of the attribute to cause such events are not widely circulated in the clinical setting due to lot-to-lot variability.

[0027] The methods described herein may utilize a data analysis approach that tests whether a correlation exists between the estimated actual level of patient exposure to a given attribute and the degree of occurrence of clinical outcomes by analyzing data from clinical trials and product quality analysis studies. This approach may be referred to as the Clinical Impact of an Attribute (CIA). The methods described herein utilize actual clinical data and assessments of the level of attribute exposure at the time the biologic drug is administered. This method provides a realistic assessment of the impact of molecular attributes and overcomes the shortcomings of conventional approaches that use non-human model systems to determine attribute levels and do not account for attribute exposure at the time of administration.

[0028] molecule attributes The terms “molecular attribute” and variations thereof have the common, customary meaning that will be understood by those skilled in the art in light of this disclosure. This refers to a chemically or physically altered structure on a macromolecule such as a protein or nucleic acid, which may be characterized in terms of its physicochemical identity or attribute type and its position within the macromolecule's sequence (e.g., the position of the amino acid in which the attribute resides). For example, asparagine and glutamine residues exhibit susceptibility to deamidation. 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 brevity, molecular attributes may be simply referred to herein as “attributes.” The level of an attribute critical to the quality of a drug (i.e., a critical quality attribute (CQA)) may be clearly defined by a product purity standard. This standard typically requires approval through review by a broad range of regulatory authorities. In some embodiments, the standard may set acceptable levels for one or more molecular attributes in the manufacture of a biotherapeutic drug.

[0029] In some embodiments, molecular attributes include or consist of one or more of the following: acidic species, basic species, high molecular weight species, invisible particle number, low molecular weight, medium molecular weight, glycosylation (e.g., non-glycosylated heavy chain or high mannose), non-heavy chain and light chain, deamidation, deamination, cyclization, oxidation, isomerization, fragmentation / clipping, N-terminal variant and C-terminal variant, reduced species and subspecies, folded structure, surface hydrophobicity, chemical modification, covalent bond, C-terminal amino acid motif PARG, or C-terminal amino acid motif PAR-amide.

[0030] PARG is an alternative C-terminal variant of an antibody that can result from alternative splicing. It represents four amino acids (proline, alanine, arginine, and glycine), where "AR" was genetically inserted into the reference IgG2 C-terminal sequence. PAR-amide is another C-terminal variant that arises from further processing of PARG. This refers to the cleaved C-terminal glycine of the antibody ending in PARG, with an amide group remaining on the C-terminal arginine.

[0031] In some embodiments, the molecular attributes include or consist of at least one of the following: acidic species, basic species, high molecular weight species, amino acid isomers, or invisible particle count.

[0032] Techniques for detecting molecular attribute levels Any suitable analytical technique for detecting molecular attributes may be used in conjunction with the methods described herein. Techniques for detecting molecular attributes include, but are not limited to, mass spectrometry, chromatography, electrophoresis, spectroscopy, light shielding, particle methods (resonance mass or Brownian motion of nanoparticles / visible / micron-sized particles), analytical centrifugation, imaging and image characterization, and immunoassays.

[0033] Exemplary techniques for detecting molecular attributes include: reducing and non-reducing peptide mapping (which can detect chemical modifications), chromatography (e.g., size exclusion chromatography (SEC), ion exchange chromatography (IEX), e.g., cation exchange chromatography (CEX), hydrophobic interaction chromatography (HIC), affinity chromatography, e.g., protein A-column chromatography, or reversed-phase (RP) chromatography), capillary isoelectric focusing (cIEF), capillary zone electrophoresis (CZE), free-flow fractionation (FFF), or ultracentrifugation (UC), HIAC (e.g., for detecting the number of invisible particles), MFI (e.g., for detecting the number and morphology of invisible particles), visibility testing (for visible particles), SDS-PAGE (e.g., for detecting fragments and covalent aggregates), and color analysis (Trp Ox), rCE-SDS and nrCE-SDS (e.g., for the detection of partial molecular fragments), nanoparticle sizing, spectroscopy (e.g., FTIR, CD, autofluorescence, or ANS dye binding), Elman assay (free sulfhydryl), SEC-MALS, HILIC (glycan mapping), and ELISA (e.g., for the detection of HCP).

[0034] Biological therapeutic drugs As used herein, “biological therapeutic agent” and variations thereof have the common and customary meanings that will be understood by those skilled in the art in light of this disclosure. This refers to therapeutic compositions comprising biological polymers (e.g., gene therapies, therapeutic proteins, nucleic acids, viruses, or cells, or parts thereof).

[0035] In the methods described herein, the biopharmaceutical agents may be selected from the group consisting of antibodies, antigen-binding antibody fragments, antibody protein products, bispecific T cell engager (BiTE®) molecules, bispecific antibodies, tripspecific antibodies, Fc fusion proteins, recombinant proteins, recombinant viruses, recombinant T cells, synthetic peptides, deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and active fragments of recombinant proteins.

[0036] "Antibody" has the conventional and ordinary meaning as understood by those skilled in the art in light of this disclosure. It refers to any isotype of immunoglobulin that specifically binds to a target antigen, such as chimeric antibodies, humanized antibodies, and fully human antibodies. For example, an antibody may be a monoclonal antibody. For example, a human antibody may be of any isotype including IgG (including IgG1, IgG2, IgG3, and IgG4 subtypes), IgA (including IgA1 and IgA2 subtypes), IgM, and IgE. A human IgG antibody will generally consist of two full-length heavy chains and two full-length light chains. An antibody may originate from only a single source or it may be a "chimera," that is, different parts of the antibody may originate from two or more different antibodies from the same or different species. Once an antibody is obtained from a source, it will be understood that it may undergo further manipulation, for example, to enhance stability and folding. Therefore, it will be understood that a “human” antibody may be obtained from a certain source and then undergo further manipulation, for example, in the Fc region. The manipulated antibody may still be referred to as a type of human antibody. Similarly, a variant of a human antibody (e.g., one with high affinity maturation) will also be understood to be a “human antibody” unless otherwise specified. In some embodiments, the antibody includes, essentially consists of, or comprises a human antibody, a humanized antibody, or a chimeric monoclonal antibody.

[0037] The "heavy chain" of an antigen-binding protein (e.g., an antibody) includes a variable region ("VH") and three constant regions: CH1, CH2, and CH3. Exemplary heavy chain constant regions suitable for the antigen-binding proteins described herein (e.g., human IgG1, IgG2, IgG3, and IgG4 constant regions) are shown in Figures 9A-B. The "light chain" of an antigen-binding protein (e.g., an antibody) includes a variable region ("VL") and a constant region ("CL"). The human light chain includes a kappa chain and a lambda chain. Exemplary light chain constant regions suitable for the antigen-binding proteins described herein (e.g., human lambda constant region and human kappa constant region) are shown in Figure 9C.

[0038] In various embodiments, biological therapeutics are antibody protein products. As used herein, the term “antibody protein product” refers in various examples to one of several antibody substitutes that are based on the structure of an antibody but are not found in nature. In some embodiments, antibody protein products have a molecular weight in the range of at least about 12 to 150 kDa. In certain embodiments, antibody protein products have a valence (n) range from monomer (n=1) to dimer (n=2), trimer (n=3), and tetramer (n=4), if not higher valences. In some embodiments, antibody protein products are based on a complete antibody structure and / or mimic antibody fragments that retain complete antigen-binding ability, such as scFv, Fab, and VHH / VH (described later). The smallest antigen-binding antibody fragment that retains a complete antigen-binding site is the Fv fragment, which consists exclusively of a variable (V) region. A soluble and flexible amino acid peptide linker is used to stabilize the molecule by linking the V region to an scFv (single-stranded fragment variable) fragment, or to generate a Fab fragment [fragment, antigen-binding] by adding a constant (C) domain to the V region. Both scFv and Fab fragments can be readily produced in host cells (e.g., prokaryotic host cells). Other antibody protein products include: dimeric and multimeric antibody types such as diabodies, triabodies, and tetrabodies, or minibodies (mini-Ab), including various types consisting of disulfide-bonded scFv (ds-scFv), single-stranded Fab (scFab), and scFv linked to an oligomeric domain. The smallest fragment is VHH / VH of camelid heavy chain Ab and single-domain Ab (sdAb). The most frequently used building block for creating novel antibody types is a single-stranded variable (V)-domain antibody fragment (scFv) containing V domains (VH domain and VL domain) derived from the heavy and light chains, linked by a peptide linker of approximately 15 amino acid residues. Peptibodies, or peptide-Fc fusions, are yet another antibody protein product. The structure of a peptide body consists of a biologically active peptide grafted onto an Fc domain. Peptibodies have been well described in the art.For example, see Shimamoto et al., mAbs 4(5):586-591 (2012).

[0039] Suitable biological therapeutic agents for the methods described herein may include polypeptides that bind to one or more of the following: CD proteins, e.g., CD3, CD4, CD8, CD19, CD20, CD22, CD30, and CD34; e.g., those that interfere with receptor binding; HER receptor family proteins, e.g., HER2, HER3, HER4, and EGF receptors; cell adhesion molecules, e.g., LFA-I, MoI, pl50, 95, VLA-4, ICAM-I, VCAM, and αv / β3 integrins. 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, Müllerian duct inhibitors, human macrophage inflammatory protein (MIP-Iα), erythropoietin (EPO), nerve growth factors, such as NGF-β, platelet-derived growth factor (PDGF), fibroblast growth factors, such as aFGF and bFGF, epidermal growth factor (EGF), transforming growth factor (TGF), such as TGF-α and TGF-β, such as TGF-β1, TGF-β2, TGF-β3, TGF-β4, or TGF-β5, insulin-like growth factor-I and II (IGF-I and IGF-II), des(l-3)-IGF-I (brain IGF-I), and bone induction factors. Insulin and insulin-related proteins, e.g., insulin, insulin A chain, insulin B chain, proinsulin, and insulin-like growth factor-binding proteins. Coagulation and coagulation-related proteins, e.g., in particular factor VIII, tissue factor, von Willebrand factor, protein C, α-1-antitrypsin, plasminogen activators, e.g., urokinase and tissue plasminogen activator ("t-PA"), bombazine, thrombin, and thrombopoietin; (vii) albumin, IgE, and other blood and serum proteins, including but not limited to these. Colony-stimulating factors and their receptors, in particular M-CSF, GM-CSF, and G-CSF, and their receptors, e.g., CSF-1 receptor (c-fms).Receptors and receptor-related proteins, e.g., flk2 / flt3 receptor, obesity (OB) receptor, LDL receptor, growth hormone receptor, thrombopoietin receptor ("TPO-R", "c-mpl"), glucagon receptor, interleukin receptor, interferon receptor, T cell receptor, stem cell factor receptor, e.g., c-Kit, and other receptors. Receptor ligands, e.g., OX40L, the ligand for the OX40 receptor. Neurotrophic factors, e.g., bone-derived neurotrophic factor (BDNF), and neurotrophins-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, e.g., interferon-α, -β, and -γ, and their receptors. Interleukins and interleukin receptors, in particular IL-1~IL-33 and IL-1~IL-33 receptors (e.g., IL-8 receptor). Viral antigens, e.g., AIDS enveloped virus antigen. Lipoproteins, calcitonin, glucagon, atrial natriuretic factor, pulmonary surfactant, tumor necrosis factor alpha and beta, enkephalinase, RANTES (regulated on activation normally T-cell expressed and secreted), mouse gonadotropin-related peptides, DNAse, inhibin, and activin. Integrins, protein A or D, rheumatoid factor, immunotoxins, bone morphogenetic proteins (BMPs), superoxide dismutase, surface membrane proteins, degeneration factor (DAF), HIV envelope, transport proteins, homing receptors, adresin, regulatory proteins, immunoadhesins, antibodies. Myostatin, TALL protein, e.g., TALL-I, amyloid protein, e.g., amyloid-beta protein, but not limited to, thymic interstitial lymphocyte generating factor ("TSLP"), RANK ligand ("RANKL" or "OPGL"), c-kit, TNF receptor, e.g., TNF receptor type 1, TRAIL-R2, angiopoietin, and any biologically active fragment, analog, or variant of any of the foregoing.

[0040] Examples of biological therapeutic agents suitable for the methods described herein include: antibodies, e.g., infliximab, bevacizumab, cetuximab, ranivizumab, palivizumab, avagovomab, absiximab, actoxumab, adalimumab, aferimomab, aftuzumab, aracizumab, aracizumab pegol, ald518, alemtuzumab, alirocumab, artumomab, amatsuximab, anatumomab mafenatox, anlukinzumab, apolizumab, artumomab, aselizumab, altinumab, atliz Mab, atrolimumab, tocilizumab, bapinuzumab, basiliximab, bavituximab, vectumomab, belimumab, bemarituzumab, benralizumab, vertilimumab, besilesomab, bevacizumab, bezlotoxumab, bisilomab, vibatuzumab, vibatuzumab meltansine, blinatumomab, brosozumab, brentuximab vedotin, briakinumab, brodalumab, canakinumab, cantuzumab meltansine, cantuzumab meltansine, caplacizumab, capromab pendetide, carlumab, catsumakisomab, CC49, sedelizumab, Cel Tolizumab pegol, cetuximab, sitatuzumab bogatox, sixtumumab, crazakizumab, clenoliximab, cribatuzumab tetraxetan, conatumumab, crenezumab, cr6261, dasetuzumab, dacrizumab, darotuzumab, daratumumab, demicizumab, denosumab, detumomab, dorurimomab aritox, dorozizumab, duligotuzumab, dupilumab, eclomeximab, eculizumab, edovacomab, edrecolomab, efalizumab, efungumab, elotuzumab, elsilimomab, enabatuzumab, enlimomab pegol, e Nokizumab, Enoticumab, Encituximab, Epitumomab Citucetan, Epiratuzumab, Erenumab, Erlizumab, Erzmakisomab, Etalacizumab, Etrolizumab, Evolocumab, Exhibivirumab, Fanoresomab, Faralimomab, Farletuzumab, Facinumab, FBTA05, Felbizumab, Fezakinumab, Ficlatuzumab, Figitumumab, Flambotumab, Fontrizumab, Foralumab, Folavirumab, Fresolimmab, Fluranumab, Futuximab, Galiximab, Ganitumab, Gantenerumab, Gabirimomab,Gemtuzumab ozogamicin, gevokizumab, gylenetuximab, glembatumumab vedotin, golimumab, gomiliximab, gs6624, ibalizumab, ibritumomab tiuxetan, iclucumab, igovomab, imusilomab, imugatuzumab, incrakumab, indatuximab tansine, infliximab, intetumumab, inorimomab, inotuzumab ozogamicin, ipilimumab, iratumumab, itoliz Mab, ixekizumab, keriximab, rabetuzumab, lebrikizumab, remalesomab, reldelimumab, lexatumumab, rivivirumab, rigerizumab, lintuzumab, lirirumab, rorbotuzumab meltansine, lucatumumab, lumiliximab, mapatuzumab, masurimomab, mabrilimumab, matsuzumab, mepolizumab, meterimumab, milatuzumab, minretumomab, mitsumomab, mogamulizumab, morolimmab, mota Vizumab, Moxetumomab Pasdotox, Muromonab-cd3, Nacolomabutafenatox, Namilumab, Naptumomab Estafenatox, Narunatumab, Natalizumab, Nevacumab, Necitumumab, Nererimomab, Nesbacumab, Nimotuzumab, Nivolumab, Nofetumomab Merpentan, Okalatuzumab, Ocrelizumab, Odulimomab, Ofatumumab, Oraratuzumab, Orokizumab, Omalizumab, Onartuzumab Oportuzumab monatox, olegobomab, ortikumab, otelixizumab, oxerumab, ozanezumab, ozoralizumab, padibaximab, palivizumab, panitumumab, panobacumab, pulsatuzumab, pascorizumab, patechrizumab, patrizumab, pemtumomab, perakizumab, pertuzumab, paxerizumab, pizilizumab, pintumomab, prakmab, ponezumab, priliximab, pritumumab, PRO 140, quilizumab, lacosumomab, radrezumab, rafivirumab, ramucirumab, ranibizumab, laxibakumab, regavirumab, reslizumab, rilotumumab, rituximab, lobatumumab, loredumab, romosozumab, lontalizumab, loberizumab, luprizumab, samarizumab, sarilumab, satumomab pendecide, secukinumab, sevilumab, cibrotuzumab, cifalimumab, siltuximab, simtuzumab, ciprizumab, silucumab, solanezumab, soritomab, sonepcizumab,Sontuzumab, stamlumab, thresomab, subizumab, tavarumab, takatuzumab tetraxetan, tadocizumab, talizumab, tanezumab, tapritumomab paptox, tefibazumab, terimomab aritox, tenatumomab, tefibazumab, teneriximab, teprizumab, teprotumumab, tezeperumab, TGN1412, tremelimumab, tisilimucosa, tildrakizumab, tigatuzumab, TNX-650, tocilizumab, tralizumab, tocitsu Momab, tralokinumab, trastuzumab, TRBS07, tregalizumab, tucothuzumab cermoloykin, tuvirumab, ubrituximab, urerumab, urtoxazumab, ustekinumab, bapariximab, baterizumab, vedolizumab, bertuzumab, bepalimomab, besenkumab, vizilizumab, borosiximab, borsetuzumab mafodotin, botumumab, zaltumumab, zanorimumab, zatuximab, diralimumab, or zolimomab aritox.

[0041] In some embodiments, the biopharmaceutical agent is a BiTE® molecule. A BiTE® molecule is an engineered bispecific antigen-binding construct that directs the cytotoxic activity of T cells against cancer cells. It is a fusion of two single-chain variable fragments (scFv) of various antibodies or amino acid sequences from four different genes on a single peptide chain of approximately 55 kilodaltons. One scFv binds to T cells via the CD3 receptor, and the other binds to tumor cells via a tumor-specific molecule. Blinatumomab (BLINCYTO®) is an example of a BiTE® molecule that is specific to CD19. Modified BiTE® molecules (e.g., modified to extend half-life) may also be used in the manner of this disclosure. In various embodiments, the polypeptide is an antigen-binding protein, such as a BiTE® molecule. In some embodiments, the antibody protein product comprises a BiTE® molecule.

[0042] In some embodiments, the biological therapeutic agent is present in the formulation. This formulation may be a pharmaceutically acceptable formulation. This formulation may contain the biological therapeutic agent together with a pharmaceutically acceptable diluent, carrier, solubilizer, emulsifier, preservative, and / or adjuvant.

[0043] The acceptable formulation materials for the biological therapeutic agents described herein are preferably non-toxic to the recipient at the dosage and concentration used. In certain embodiments, the pharmaceutical composition may include, for example, formulation materials for modifying, maintaining, or preserving the pH, molar osmotic pressure, viscosity, clarity, color, isotonicity, odor, sterility, stability, rate of dissolution or release, adsorption or permeability of the composition. In such embodiments, suitable formulation materials include, but are not limited to, the following: amino acids (e.g., glycine, glutamine, asparagine, arginine, or lysine); antimicrobial agents; antioxidants (e.g., ascorbic acid, sodium sulfite, or sodium bisulfite); buffers (e.g., borates, bicarbonates, tris-HCl, citrates, phosphates, or other organic acids); fillers (e.g., mannitol or glycine); chelating agents (e.g., ethylenediaminetetraacetic acid (EDTA)); complexing agents (e.g., caffeine, polyvinylpyrrolidone, beta-cyclodextrin, or hydroxypropyl-beta-cyclodextrin); fillers; monosaccharides; disaccharides; and other carbohydrates (e.g., glucose, sucrose, mannose, or dextrin); proteins (e.g., serum albumin, gelatin, or immunoglobulins); colorants, flavorings, and diluents; emulsifiers; hydrophilic polymers (e.g., polyvinyl Pyrrolidone; low molecular weight polypeptides; salt-forming counterions (e.g., sodium); preservatives (e.g., benzalkonium chloride, benzoic acid, salicylic acid, thimerosal, phenethyl alcohol, methylparaben, propylparaben, chlorhexidine, sorbic acid, or hydrogen peroxide); solvents (e.g., glycerin, propylene glycol, or polyethylene glycol); sugar alcohols (e.g., mannitol or sorbitol); suspending agents; surfactants or wetting agents (e.g., Pluronic acid, PEG, sorbitan esters, polysorbates, e.g., polysorbate 20, polysorbate, Triton, tromethamine, lecithin, cholesterol, tyloxapal); stability enhancers (e.g., sucrose or sorbitol); isotonic enhancers (e.g., alkali metal halides, preferably sodium chloride or potassium chloride, mannitol, sorbitol); delivery vehicles; diluents; excipients, and / or pharmaceutical adjuvants.For example, see REMINGTON'S PHARMACEUTICAL SCIENCES, 18th Edition, (ARGenrmo, ed.), 1990, Mack Publishing Company.

[0044] Suitable vehicles or carriers for this formulation may be water for injection, saline solution, or artificial cerebrospinal fluid, and may be supplemented with other materials common in parenteral administration compositions. Neutral buffered saline, or saline mixed with serum albumin, are further exemplary vehicles. In certain embodiments, the pharmaceutical composition comprises Tris buffer at approximately pH 7.0–8.5, or acetate buffer at approximately pH 4.0–5.5, and may further comprise sorbitol or a suitable substitute thereof.

[0045] The formulation components are preferably present at the administration site in an acceptable concentration. In certain embodiments, a buffer is used to maintain the composition at a physiological pH or slightly lower (typically within a pH range of about 5 to about 8). This includes about 5.1, about 5.2, about 5.3, about 5.4, about 5.5, about 5.6, about 5.7, about 5.8, about 5.9, about 6.0, about 6.1, about 6.2, about 6.3, about 6.4, about 6.5, about 6.6, about 6.7, about 6.8, about 6.9, about 7.0, about 7.1, about 7.2, about 7.3, about 7.4, about 7.5, about 7.6, about 7.7, about 7.8, about 7.9, and about 8.0.

[0046] It should be noted that some biological therapies may be self-administered by the subject directly or via an auto-injector, and some may be administered to the subject by another individual, such as a healthcare provider. Accordingly, when used herein, the subject "receiving" or "being administered" a biological therapy, and variations of these terms, may refer to a biological therapy that is self-administered by the subject (directly or via a device such as an auto-injector) and / or administered to the subject by another individual, such as a healthcare provider.

[0047] Methods for manufacturing biological drugs It is intended that the methods described herein may be used to assess whether molecular attributes affect the efficacy of a biotherapy drug and / or its safety profile. This method may include determining the estimated actual level of exposure to the molecular attribute at the time the biotherapy drug is administered to the subject. Such a method may identify a suitable level or range of molecular attributes at the time of manufacture, lot delivery, and / or administration.

[0048] In some embodiments, methods for manufacturing biopharmaceuticals are described. These methods may include detecting the levels of molecular attributes of the biopharmaceutical in the formulation at one or more time points under storage conditions (optionally, at two or more time points under storage conditions). Optionally, for example, if the molecular attributes do not change during storage (e.g., a drug stored under cryogenic conditions), detecting the levels of molecular attributes at one time point may suffice. With respect to levels of molecular attributes that may change during storage, it is further intended that the rate of change of the molecular attributes described herein can be calculated by using the detection of levels of molecular attributes at two or more time points under storage conditions. For example, this time point may include the time of manufacture. For example, this time point may include the time of manufacture and at least one other time point. These methods may include determining the rate of change of molecular attributes under storage conditions (for example, with respect to certain molecular attributes of a biopharmaceutical under certain storage conditions (e.g., a biopharmaceutical stored under cryogenic conditions), the rate of change may be calculated as zero). These methods may include obtaining in vivo safety and / or efficacy data of the biopharmaceutical with respect to subjects to whom the biopharmaceutical is administered. This method may include estimating the level of molecular attribute exposure a subject will receive at the time of administration, based on (i) the rate of change of molecular attributes during storage, and (ii) the period during which the biopharmaceutical formulation was under storage conditions prior to such administration. This estimation may also take into account (iii) the level of molecular attributes at the time of manufacture and / or lot delivery, and / or (iv) the dose of the biopharmaceutical to be administered to the subject. For example, this estimation may use equation 1, equation 2, or equation 3. This method may include determining whether there is a correlation between the estimated level of molecular attribute exposure a subject will receive and safety and / or efficacy data for the biopharmaceutical. If no correlation exists, this method may include manufacturing a production lot of the biopharmaceutical containing the molecular attribute at a level below the acceptable level for the designation of the molecular attribute, based on the estimated level of molecular attribute exposure.For example, the acceptable level of this designation may be a level of molecular attribute calculated to result in a level of attribute exposure at the end of storage that is below the highest estimated level of molecular attribute exposure in the subject. For example, the acceptable level of this designation may be a level of molecular attribute calculated to result in a level of attribute exposure at the end of storage that is 90-100% or less of the highest estimated level of molecular attribute exposure in the subject. Therefore, this acceptable level is expected to result in a level of attribute exposure that has been shown to be safe and effective in the subject throughout the entire storage period of the biotherapy drug. Manufacturing lots in which the level of molecular attribute exceeds the acceptable level of this designation may be rejected.

[0049] Where a correlation exists, this method may further include setting the specification level of the molecular attribute at manufacturing so as not to exceed the maximum acceptable level of the molecular attribute of the biotherapy drug. This maximum acceptable level of the molecular attribute may be based on the highest estimated level of exposure to the molecular attribute in the subject that is not associated with adverse events and / or inhibition of efficacy of the biotherapy drug. This manufacturing method may further include rejecting manufacturing lots of the biotherapy drug that contain levels of the molecular attribute that exceed the maximum acceptable level (and are therefore out of specification). Manufacturing lots in which the level of the molecular attribute does not exceed the specified maximum acceptable level may be accepted. For example, the specified maximum acceptable level may be a level of the molecular attribute calculated to produce a level of attribute exposure at the end of storage that is less than or equal to the highest estimated level of exposure to the molecular attribute that is not associated with adverse events and / or inhibition of efficacy in the subject. This calculation may use equation 1, or equation 2, or equation 3, and may take into account the rate of change of the molecular attribute in the formulation during storage and the dose of the molecular attribute administered to the subject. For example, the specified maximum permissible level may be a molecular attribute level calculated to result in an attribute exposure level at the end of storage that is 90-100% or less of the highest estimated level of molecular attribute exposure not associated with adverse events and / or inhibition of efficacy in the subject.

[0050] As used herein, “acceptable level” of a molecular attribute refers to the level of the molecular attribute of a biotherapeutic drug in a formulation that is within the specified limits at the time of manufacture. As described herein, this acceptable level may be calculated such that the designated level of molecular attribute exposure (to the subject to which the biotherapeutic drug in the formulation will be administered) at end of storage or expiration is below the estimated level of molecular attribute exposure that was not associated with adverse events and / or loss of efficacy (which may be referred to as the “acceptable level” based on the estimated level of attribute exposure). Thus, the acceptable level of a molecular attribute can provide confidence that the level of molecular attribute exposure from the biotherapeutic drug in the formulation is safe and effective when administered to a subject, even if administered very close to the end of storage or expiration. “Maximum acceptable level” refers to a scenario in which a correlation exists between the estimated level of molecular attribute exposure and safety and / or efficacy data. “Maximum acceptable level” refers to the highest level of molecular attribute in a formulation that results in a level of molecular attribute exposure (to the subject to which the biotherapeutic drug in the formulation will be administered) at end of storage or expiration that is below the highest level of molecular attribute exposure that was not associated with adverse events. The tolerable level or maximum tolerable level can be calculated using Equation 1, Equation 2, or Equation 3, based on the estimated level of attribute exposure not associated with adverse events, the time remaining until the end of storage or expiration, the rate of change in the molecular attribute level, and the dose of the biotherapy drug. That is, the tolerable level (and, where applicable, the maximum tolerable level) can be determined using the level of molecular attribute exposure not associated with adverse events and / or inhibition of efficacy, the rate of change in the molecular attribute in the formulation, and this time. Therefore, if a biotherapy drug is manufactured with molecular attributes below the tolerable level (and, where applicable, the maximum tolerable level), it can be expected that the estimated level of molecular attribute exposure to the subject at the time of administration will be below the level not associated with adverse events and / or loss of efficacy.

[0051] As used herein, an acceptable level or maximum acceptable level "based on" an estimated level of molecular attribute exposure refers to an acceptable level (or maximum acceptable level) calculated so as not to exceed the estimated level of molecular attribute exposure at the end of storage or expiration. If multiple doses are suitable for the biotherapeutic drug in the formulation, the best preferred dose may be used to calculate the acceptable level or maximum acceptable level "based on" the estimated level of molecular attribute exposure (for lower doses result in even lower levels of molecular attribute exposure). As an example, an estimated level of attribute exposure may be selected to fall within the confidence interval of the estimated distribution or spread of molecular attribute exposure levels determined for a group of subjects. It is intended that the estimated level of attribute exposure received by subjects does not necessarily imply that all subjects received the same numerical level of attribute exposure. Rather, the estimated level of attribute exposure received by these subjects may include the distribution of estimated attribute levels received by individual subjects. Therefore, a maximum permissible level can be selected using a confidence interval such that, with at least 85%, 90%, 95%, 97%, or 99% probability, the level of attribute exposure at the end of storage is less than or equal to the highest level of attribute exposure not associated with loss of safety or efficacy. It is further intended that the actual values ​​of the permissible level or maximum permissible level "based" on the level of molecular attribute exposure may be rounded. Rounding, such as rounding to one, two, or three significant figures in a unit suitable for measuring molecular attributes, may provide administrative or mathematical convenience. As further caution, this rounding may be truncation. For example, calculating the permissible level or maximum permissible level of an attribute "based" on the estimated level of attribute exposure yields a level of attribute exposure at the end of storage that is less than or equal to 99%, 97%, 95%, 90%, 85%, or 80% of the reference level of the molecular attribute (not associated with loss of safety and / or efficacy) calculated using equation 1 or 2 described herein. This truncation may further ensure that the level of attribute exposure from the biological therapeutic agent at the end of storage remains safe and / or effective.

[0052] In some embodiments, a production lot is manufactured at the level of multiple molecular attributes, each below its permissible (or maximum permissible) level. For example, a production lot may be manufactured with at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 molecular attributes (e.g., a range between any two of the enumerated values, e.g., 1-10, 1-5, 2-10, 2-5, 3-10, 3-5, or 5-10 molecular attributes), each below a specified permissible (or maximum permissible) level.

[0053] manufacturing technology The methods for producing biological therapeutics (e.g., therapeutic proteins) described herein may utilize recombinant DNA technology. Recombinant DNA methods for producing therapeutic proteins such as antibodies or antibody protein products are well known. DNA can encode these therapeutic proteins. For example, DNA can encode antibodies, and DNA encoding, for example, a VH domain, a VL domain, a single-stranded variable fragment (scFv), or a combination of these fragments (target polynucleotide) can be inserted into a suitable expression vector, and this vector can then be transfected into suitable host cells that do not otherwise produce antibodies (e.g., Escherichia coli cells, COS cells, Chinese hamster ovary (CHO) cells, or myeloma cells) to obtain the desired antibody.

[0054] For example, suitable expression vectors containing polynucleotides encoding a target polypeptide linked to a promoter are known in the art. Such vectors may contain nucleotide sequences encoding the constant region of an antibody molecule, and variable domains of the antibody may be cloned into such vectors to express the heavy chain, the entire light chain, or both the entire heavy chain and the light chain (or fragments thereof). This expression vector can be transferred to host cells by conventional techniques, and the transfected cells can be cultured to produce antibodies.

[0055] Any cell line capable of expressing or engineered to express functional antibodies or antibody fragments or other proteins may be used. For example, suitable mammalian cell lines include immortalized cell lines available from the American Type Culture Collection (Manassas, VA), such as Chinese hamster ovary (CH) cells, HeLa cells, baby hamster kidney (BHK) cells, monkey kidney cells (COS), human hepatocellular carcinoma cells (e.g., Hep G2), and human epithelial kidney 293 cells. Furthermore, cell lines or host systems may be selected to ensure accurate modification and processing of antibodies. Eukaryotic host cells with cellular mechanisms for proper processing of primary transcripts, glycosylation, and phosphorylation of gene products may be used. Examples of eukaryotic host cells include CHO, VERY, BHK, Hela, COS, MDCK, 293, 3T3, W138, BT483, Hs578T, HTB2, BT20, and T47D, NS0 (a mouse myeloma cell line that does not endogenously produce any functional immunoglobulin chains), SP20, CRL7030, and HsS78Bst cells. Human cell lines developed by immortalizing human lymphocytes may also be used. Monoclonal antibodies can be recombinantly produced using the human cell line PER.C6® (Janssen;Titusville, NJ). Examples of non-mammalian cells that can be used similarly include insect cells (e.g., Sf21 / Sf9, Trichoplusia ni Bti-Tn5bl-4), yeast cells (e.g., Saccharomyces (e.g., S. cerevisiae, Pichia genus, etc.)), plant cells, or chicken cells.

[0056] Antibodies and other proteins can be stably expressed in cell lines using conventional methods. Stable expression can be used for long-term, high-yield production of recombinant proteins. For stable expression, host cells can be transformed with appropriate manipulation vectors containing expression regulatory elements (e.g., promoters, enhancers, transcriptional terminators, polyadenylation sites, etc.) and selective marker genes. Methods for producing stable cell lines in high yield are known in the art, and reagents are commercially available. Transient expression can also be performed using conventional methods.

[0057] Cell lines expressing proteins such as antibodies can be maintained in cell culture media and under culture conditions that induce antibody expression and production. The cell culture media may be based on commercially available culture medium preparations, such as DMEM or Ham's F12. Furthermore, the cell culture media can be modified to support increased cell proliferation and biological protein expression. Naturally, cell culture media can be optimized for specific cell cultures, including cell growth media formulated to promote cell proliferation, or cell production media formulated to promote recombinant protein production.

[0058] Numerous cell culture media, as well as cell culture nutrients and supplements, are known. For example, suitable basic media include Dulbecco's modified Eagle medium (DMEM), DME / F12, Minimum Essential Medium (MEM), Eagle basal medium (BME), RPMI 1640, F-10, F-12, α-Minimum Essential Medium (α-MEM), Glasgow's Minimum Essential Medium (G-MEM), PF CHO, and Iskov's modified Dulbecco medium. Other examples of basal media that can be used include BME basal medium and Dulbecco's modified Eagle medium.

[0059] A basal medium may be serum-free, meaning that the medium does not contain serum (e.g., fetal bovine serum (FBS)), or is an animal protein-free medium, or a chemically defined medium. A basal medium may be modified to remove certain non-nutrient components found in the basal medium, such as various inorganic and organic buffers, surfactants, and sodium chloride. A cell culture medium may contain (modified or unmodified) basal cell medium and at least one of the following: iron sources, recombinant growth factors, buffers; surfactants; osmolality regulators; energy sources; and non-animal hydrolysis products. Furthermore, a modified basal cell medium may optionally contain amino acids, vitamins, or a combination of both amino acids and vitamins. A modified basal medium may further contain glutamine (e.g., L-glutamine) and / or methotrexate.

[0060] Once a therapeutic protein (e.g., an antibody or antibody protein product) is produced, this therapeutic protein can be purified by conventional methods, such as chromatography (e.g., ion exchange, affinity, particularly affinity for specific antigens, protein A, protein G, or sizing column chromatography), centrifugation, differential solubility, or any other standard technique for protein purification. Furthermore, the purification of this protein can be facilitated by fusing it to a heterologous polypeptide sequence ("tag").

[0061] Purified proteins are typically formulated with excipients to produce sterile solutions that can be injected or infused. For example, purified proteins can be formulated with the formulations described herein. Following formulation, filling, packaging, storage, transport, and final preparation immediately before administration to the subject may be performed.

[0062] How to develop manufacturing processes for biological therapeutics It is intended that methods for manufacturing biotherapeutic drugs may also be developed using the methods described herein. In some embodiments, methods for developing a manufacturing process for biotherapeutic drugs are described. This method may include detecting the levels of molecular attributes of the biotherapeutic drug in the formulation at one or more time points (optionally, two or more time points under storage conditions) under storage conditions. For example, this time point may include the time of manufacture. For example, this time point may include the time of manufacture and at least one other time point. This method may include determining the rate of change of molecular attributes under storage conditions. This method may include obtaining data on the safety and / or efficacy of the biotherapeutic drug in subjects to whom the biotherapeutic drug is administered. This method may include estimating the level of molecular attribute exposure the subject receives at the time of administration, based on (i) the rate of change of molecular attributes of the biotherapeutic drug in the formulation during storage, and (ii) the period during which the biotherapeutic drug in the formulation was under storage conditions prior to such administration. This estimation may also take into account (iii) the levels of molecular attributes at the time of manufacture and / or at the time of lot delivery, and / or (iv) the dose of the biotherapeutic drug administered to the subject. For example, this estimation may use Equation 1, Equation 2, or Equation 3. This method may involve determining whether there is a correlation between the estimated level of molecular attribute exposure and data on the safety and / or efficacy of the biotherapy drug.

[0063] (a) If no correlation exists, this method may include establishing a manufacturing process that produces a level of molecular attribute below a specified tolerance level, based on the estimated level of molecular attribute exposure. For example, the specified tolerance level may be a molecular attribute level calculated to produce a level of attribute exposure at the end of storage that is below the highest estimated level of molecular attribute exposure in the subject. This calculation may use equation 1, equation 2, or equation 3. For example, the specified tolerance level may be a molecular attribute level calculated to produce a level of attribute exposure at the end of storage that is 90-100% or less of the highest estimated level of molecular attribute exposure in the subject. Manufacturing lots in which the level of molecular attribute exceeds this specified tolerance level may be rejected.

[0064] (b) Where a correlation exists, this method may include establishing a manufacturing process to produce a level of molecular attribute below the maximum permissible level of the molecular attribute designation, based on the highest level of molecular attribute not associated with adverse events and / or inhibition of efficacy of the biotherapy drug. For example, the maximum permissible level of the designation may be a level of molecular attribute calculated to produce a level of attribute exposure at the end of storage that is below the highest estimated level of molecular attribute exposure not associated with adverse events and / or inhibition of efficacy in the subject. This calculation may use equation 1, or equation 2, or equation 3. For example, this maximum permissible level of the designation may be a level of molecular attribute calculated to produce a level of attribute exposure at the end of storage that is 90-100% or less of the highest estimated level of molecular attribute exposure not associated with adverse events and / or inhibition of efficacy in the subject. The permissible level or maximum permissible level may be part of the specification at the time of manufacture or at the time of lot delivery.

[0065] Methods for evaluating the clinical impact of molecular attributes of biological therapeutics In some embodiments, methods for evaluating the clinical effects of molecular attributes are described. Such methods may be used further in methods for manufacturing biotherapeutic drugs and in methods for developing manufacturing processes for biotherapeutic drugs as described herein. This method may include detecting the level of molecular attributes of the biotherapeutic drug in the formulation at one or more time points under storage conditions. The level of attribute exposure may be detected at two or more time points under storage conditions. For example, the time points at which the level of molecular attributes is detected may include the time of manufacture or at least one other time point. For example, the two or more time points at which the level of molecular attributes is detected may include the time of manufacture and at least one other time point. This method may include determining the rate of change of molecular attributes under storage conditions. This method may include obtaining data on the safety and / or efficacy of the biotherapeutic drug in subjects to whom the biotherapeutic drug is administered. This method may include estimating the level of molecular attribute exposure received by the subject at the time of administration, based on (i) the rate of change of molecular attributes of the biotherapeutic drug in the formulation during storage, and (ii) the period during which the biotherapeutic drug in the formulation was under the storage conditions prior to the administration. This estimation may also take into account (iii) the level of molecular attributes at the time of manufacture and / or lot delivery, and / or (iv) the dose of the biological product administered to the subject. For example, this estimation may use equation 1, or equation 2, or equation 3 as described herein. This method may include determining whether there is a correlation between the estimated molecular attribute exposure and the safety and / or efficacy of the biological therapeutic agent. (a) If no correlation exists, it can be determined that the molecular attribute does not affect the clinical safety or efficacy of the biological therapeutic agent. (b) If a correlation exists, it can be determined that the molecular attribute does affect the clinical safety and / or efficacy of the biological therapeutic agent.

[0066] (a) Where no correlation exists, this method may further include setting standards for acceptable levels of molecular attributes of the biotherapy drug, the acceptable levels of molecular attributes being based on the highest estimated level of molecular attribute exposure to the subject. For example, the standards for acceptable levels may be levels of molecular attributes at manufacturing calculated to produce levels of attribute exposure at end of storage that are below the highest estimated level of molecular attribute exposure in the subject. For example, the standards for acceptable levels may be levels of molecular attributes calculated to produce levels of attribute exposure at end of storage that are 90-100% or less of the highest estimated level of molecular attribute exposure in the subject. This standard may be used in manufacturing and / or delivery testing of the biotherapy drug. If a lot or product of the biotherapy drug contains molecular attributes at levels exceeding the acceptable level, the lot or product may be deemed substandard and rejected. The biotherapy drug may be manufactured in accordance with this standard.

[0067] (b) Where a correlation exists, this method may further include setting a maximum permissible level specification for the molecular attribute of the biotherapy drug, the maximum permissible level of the molecular attribute being based on the highest estimated molecular attribute exposure level that was not associated with adverse events and / or inhibition of efficacy of the biotherapy drug. This specification may be used in manufacturing and / or delivery testing of the biotherapy drug. For example, the maximum permissible level specification may be a molecular attribute level calculated to produce an attribute exposure level at the end of storage that is less than or equal to the highest estimated molecular attribute exposure that was not associated with adverse events and / or inhibition of efficacy in the subject. This calculation may be performed using equation 1, equation 2, or equation 3. For example, the specified maximum permissible level may be a molecular attribute level calculated to produce an attribute exposure level at the end of storage that is 90-100% or less of the highest estimated molecular attribute exposure that was not associated with adverse events and / or inhibition of efficacy in the subject. If a manufacturing lot or product of a biotherapy drug contains a molecular attribute at a level exceeding the maximum permissible level, the lot or product may be deemed substandard and rejected. The biotherapy drug may be manufactured in accordance with this specification.

[0068] Further aspects of the method Any of the methods described herein may include one or more further embodiments.

[0069] In some embodiments, with respect to any method described herein, estimation is further based on (iii) the dose of the biotherapy drug in the administration, and (iv) the amount of molecular attribute measured at the time of manufacture and / or lot delivery. With respect to (iii), it should be noted that a higher dose of the biotherapy drug will administer a greater amount of molecular attribute compared to a lower dose with the same molecular attribute content. Also, in some cases, the level of molecular attribute at the time of manufacture and / or administration may not change significantly, and therefore estimation may be carried out from (i) and (ii) alone. For example, the manufacturing process may be well-established, strictly controlled, and shown to result in a consistent level of molecular attribute at the time of manufacture. For example, the biotherapy drug may be administered only in single doses, and therefore, dose-based calculations do not need to be considered.

[0070] In some embodiments, with respect to any method described herein, the levels of molecular attributes of the biotherapy drug in the formulation are measured at time of manufacture or at one or more points in time thereafter (e.g., at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 points in time, e.g., a range between any two of the enumerated values, e.g., 1 to 2 points in time, 1 to 5 points in time, or 1 to 10 points in time). Not limited to theory, for some biotherapy drugs in a formulation whose levels of molecular attributes do not change during storage, it may be sufficient to measure the levels of molecular attributes at a single point in time, as in the case of some biotherapy drugs in a formulation that is cryopreserved. In some embodiments, with respect to any method described herein, the levels of molecular attributes of the biotherapy drug in the formulation are measured at two or more points in time (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 points in time, e.g., a range between any two of the enumerated values, e.g., 2 to 5 points in time, 2 to 10 points in time, 3 to 5 points in time, 3 to 10 points in time, or 5 to 10 points in time). The rate of change in the level of molecular attributes under storage conditions can be calculated using the levels of molecular attributes at two or more time points. These time points may be at the time of manufacture or thereafter. In some embodiments, with respect to any method described herein, the two or more time points include the time of manufacture. In some embodiments, with respect to any method described herein, the two or more time points include the time of manufacture and at least one subsequent time point. In some embodiments, with respect to any method described herein, the two or more time points include the time of manufacture and at least two subsequent time points.

[0071] In some embodiments, estimating the level of molecular attribute exposure with respect to any method described herein involves using the equations described herein. In some embodiments, estimating the level of molecular attribute exposure involves equation 1: A t =(%A0+%A Δ ×t)D (1) (In the formula, A t %A0 is the estimated level of molecular attribute exposure, %A0 is the percentage of the attribute at the time of lot delivery, and %AΔ is the rate of change of the ratio of the attribute level, t is the storage time between the lot delivery and the treatment implementation, and D is the dosage strength in terms of the weight dimension of the active pharmaceutical ingredient associated with each treatment) includes using

[0072] In some embodiments, estimating the level of molecular attribute exposure involves Equation 2: [Number] (where A t is the estimated level of molecular attribute exposure, %A0 is the ratio of the molecular attribute at the time of lot delivery, and %A Δi is the rate of change of the ratio of the molecular attribute level over time under given storage conditions, t i is the storage time under given conditions, and D is the dosage strength of administration) includes using

[0073] In some embodiments, estimating the level of molecular attribute exposure involves Equation 3: [Number] (where %A rel. is the ratio of the relative level of molecular attribute exposure to the dosage, A t is the level of molecular attribute exposure calculated using Equation 1 or 2, and D is the dosage strength in terms of the weight dimension of the active pharmaceutical ingredient associated with each treatment). It is noted that Equation 3 can consider the relative level of molecular attribute exposure to the dosage strength. Without being limited to theory, when the molecular attribute affects efficacy, it can be speculated that the non-attribute may also affect efficacy. For example, the relative concentration of the active pharmaceutical ingredient (API) to the dosage strength may affect efficacy. Therefore, the % of the molecular attribute to the dosage strength may be useful for measuring the influence of the molecular attribute on efficacy. Thus, in the methods of some embodiments, Equation 3 can be used when determining the presence or absence of a correlation between the estimated level of molecular attribute exposure and the efficacy data.

[0074] In some embodiments, estimating the level of molecular attribute exposure a subject receives with respect to the methods described herein includes assigning the estimated attribute exposure levels as follows: (A) For all related procedures described below, use equation 1, equation 2, or equation 3, A t First, decide on that. (1) In the case of a patient who does not demonstrate the desired clinical outcome throughout the course of the clinical trial, related treatment refers to all treatments administered to the patient throughout the trial. (2) In the case of a patient exhibiting this clinical outcome, the relevant treatment refers to all treatments performed on the patient prior to the occurrence of this clinical outcome. (3) In patients who develop this clinical outcome before receiving any treatment, no relevant treatment exists. In this patient, A t It is 0. (4) In the case of a biological drug administered by continuous infusion, A occurs in the relevant portion of the infusion. t This can be determined as follows (item (4) may be omitted in the case of biological therapeutics that are not administered by continuous infusion): a. In the case of infusions administered continuously for more than 12 or 24 hours, A per 12 or 24-hour segment of the relevant infusion prior to the occurrence of the clinical outcome. t To decide. b. For infusions shorter than 12 hours (or 24 hours, if applicable), A is associated with each of the relevant infusions prior to the occurrence of the clinical outcome. t To decide. c. In either of the above two cases (a and b), if the clinical outcome occurs during the infusion, the portion of the infusion prior to the occurrence of the clinical outcome is associated with A t To decide. (B) A of all related measures t When this is determined for each patient, regardless of the presence or absence of clinical outcomes, all A related to the relevant part of the relevant procedure or infusion is determined. t The average or maximum value is calculated as defined above.

[0075] It should be noted that further mathematical operations may be applied to further refine the determination of the correlation between the level of molecular attribute exposure and data on safety and efficacy. In some embodiments, with respect to any method described herein, this correlation includes a weighted correlation between attribute exposure and the occurrence of adverse events. This weighting may use the size of each group of subjects. Not limited to theory, it is intended that if a subject experiences an adverse event in a multi-dose regimen of a biotherapy drug, that subject may discontinue the use of the biotherapy drug. Overall, the data for subjects experiencing adverse events may be smaller than that for subjects who did not experience adverse events, which may affect the determination of the correlation (or lack thereof). Therefore, the data may be selectively weighted to account for subjects who discontinued the multi-dose regimen of a biotherapy drug before completion (e.g., subjects who experienced adverse events).

[0076] For various targets, it is further intended that the biotherapeutic agents in the formulation may be stored under various conditions for various periods of time for various targets. Therefore, the period during which the biotherapeutic agents in the formulation were stored may not be a single instance. Rather, various periods may be applied to various administrations of the biotherapeutic agents as needed. Estimating the level of molecular attribute exposure requires determining the appropriate storage time (t) in each case. i These different periods can be taken into account by selecting ). For this reason, in some embodiments of the method, the biological therapeutic agent was stored under various conditions over different periods of administration to different subjects.

[0077] With respect to the methods described herein, the administration of a biological therapeutic agent may include two or more administration events, and is further intended to include, for example, at least two, three, four, five, or ten administration events (including a range between any two of the enumerated values, e.g., 2-3, 2-5, 2-10, 3-5, 3-10, or 5-10 administration events). Accordingly, in some embodiments, the estimated level of attribute exposure received by the subject at the time of administration is the maximum or average value of the two or more administration events.

[0078] With regard to the methods described herein, it is further intended that the administration of a biopharmaceutical agent may include a series of infusions. This series of infusions may, for example, span a period of several hours or several days. In some embodiments, the administration includes a series of infusions, and estimating the level of attribute exposure includes calculating the estimated level of molecular attribute exposure during two or more intervals of the series of infusions, such as 12-hour or 24-hour intervals.

[0079] The methods described herein may utilize clinical trial data, including, but not limited to, the number of subjects, clinical outcome information (date, time, and extent (severity or grade)), treatment lot number, and treatment (date, time, and duration). Data may be grouped according to clinical outcomes (e.g., clinical endpoints and / or adverse events). Adverse events may represent safety data, and clinical endpoints may represent efficacy data. In clinical outcome-based grouping, subjects are grouped according to the occurrence of a clinical outcome with the desired severity or extent. For example, to analyze the impact of molecular attributes on the severity of safety adverse events, subjects are grouped according to the occurrence of AEs with the desired severity grade (e.g., the severe fever positive group would include patients with fever of grade 3 or higher, and the severe fever negative group would include subjects without fever of grade 3 or higher). The estimated level of the molecular attribute for each subject is then determined as described.

[0080] For example, safety data for the methods described herein may include adverse event data. These adverse events may include adverse events related to the treatment. For example, adverse events related to treatment with a biological agent may include immunoadverse events such as an immune response to the biological agent (e.g., anti-drug antibodies (ADAs)). Further examples of adverse events include anemia, cytokine release syndrome (CRS), fever, infusion-related reactions (IRRs), lymphopenia, or neurological events. In some embodiments, with respect to any method described herein, safety data may include adverse event data. For example, safety data may include changes in adverse events over time.

[0081] For example, efficacy data of the methods described herein may include clinical evidence of efficacy (e.g., treatment, prevention, remission, or delay of the onset of a disease or disorder). Clinical endpoint data may demonstrate efficacy. Therefore, in some embodiments, efficacy data includes clinical endpoint data.

[0082] With respect to some of the methods described herein, it is intended that statistical methods may be used to calculate whether or not there is a correlation between the estimated level of molecular attribute exposure and the safety and / or efficacy data of the biotherapeutic drug. For example, a linear regression correlation between the clinical event rate from safety and / or efficacy data and the attribute exposure level may be calculated, and a p-value may be determined (Figures 3A, 3B, 7B, and 9). For example, the p-value may be calculated from a t-test or an F-test (Figures 2A, 2B, 5A, 5B, 6, 7A, 8, 10A, and 10B). For example, a log-rank test or a Mantelcox test may be performed to calculate a p-value for verifying the earlier onset of clinical safety events in clinical control groups with higher levels of attribute exposure (Figures 4A and 4B).

[0083] Some implementations of the methods described herein utilize a Bayesian estimation approach. The Bayesian estimation approach can be used to determine whether there is a correlation between estimated levels of molecular attribute exposure and safety and / or efficacy data of biotherapeutic drugs. Due to the stepwise learning nature of the Bayesian approach, all evidence can be used completely and fairly beforehand. Specifically, when applying the Bayesian estimation approach to determining whether there is a correlation between estimated levels of molecular attribute exposure and safety and / or efficacy data of biotherapeutic drugs, the method can modify the probability density function of the clinical impact of each attribute to take a specific value each time new evidence becomes available, until all evidence is used. The probability that each attribute is associated with each adverse event is derived at the end of the analysis. To leverage the high computational power required for the Bayesian approach, a computer program has been developed (see Example 12). This program provides an interactive platform for visualizing the results of the Bayesian estimation approach using a spreadsheet provided by the user. The program also provides parameter tuning modules for defining initial priors, removing outliers, and selecting multiple attributes. A small set of previous CIA actual clinical data for mAb D, and a small amount of model data based on mAb D CIA data, were validated with this system. The Bayesian estimation method yielded the expected results, thus demonstrating the effectiveness of this system. [Examples]

[0084] Example 1: Method for molecular attribute analysis Here, the inventors use a data analysis approach called Clinical Impact Analysis (CIA) to investigate the clinically relevant immunological safety impact of 15 molecular attributes between two immunoglobulin G2 (IgG2) monoclonal antibody (mAb) drugs, mAb A and mAb B. CIA examines whether there is a correlation between the level of patient exposure to a given attribute and the incidence of clinical outcomes by analyzing data from clinical trials and product quality analysis studies. No association was found between the level of patient exposure to any of the attributes and the ADA response, suggesting that none of the attributes analyzed at the examined exposure levels elicited an ADA response. Furthermore, the upper limit of the examined exposure levels was higher than or close to the purity specification limits for the attributes of mAb A, demonstrating the feasibility of creating clinically relevant evidence to justify these attributes, which is possible with CIA. These results demonstrate that integrating clinical and analytical information can provide clinically relevant insights into the impact of attributes applicable to drug development and manufacturing.

[0085] The inventors evaluate the clinically relevant safety impact of attributes by integrating clinical data and attribute analysis data (Figure 1, Equation 1). Briefly, attribute levels measured at lot delivery are combined with changes in attribute levels between lot delivery and treatment time to approximate attribute exposure levels at treatment time (Figure 1A). Then, attribute exposure is determined for each treatment performed on the patient during the treatment regimen. If an attribute causes a clinical event, patients experiencing this event will, on average, be associated with higher levels of exposure to this attribute (Figure 1B), or a positive correlation will be found between attribute exposure levels and the incidence of the clinical event (Figure 1C, left) or its change over time (Figure 1C, right). Conversely, no association between attribute exposure and a clinical event will indicate that this attribute does not cause the event at the exposure level examined.

[0086] As shown in Figure 1A, the attribute exposure level at the time of treatment is A t This is approximated by considering attribute levels measured at the time of manufacture and changes in levels over time. In retrospective analysis, patients are grouped according to retrospectively known clinical outcomes such as ADA trial results (Figure 1B). Then, A represents each individual patient. t This will be compared among the indicated groups. In prospective analysis, patients will be treated as representative A t The groups are divided according to the following criteria (Figure 1C). The incidence (left) or change over time (right) of the clinical outcome for each group is shown as the average representative A of the group. t Let's compare them in this regard.

[0087] The inventors investigated the immune responses of patients exposed to 15 attributes between two separate pharmaceuticals, mAb A and mAb B, by examining attribute exposure and ADA test results for 1,398 patients from eight separate clinical trials for mAb A and 5,439 patients from two combined clinical trials for mAb B. The ADA included various effects on safety and efficacy (e.g., neutralization of drug target binding function and other more serious adverse events). 17,18 Significant regulatory concerns remain as a result.

[0088] The attributes analyzed for mAb A were: high molecular weight (HMW) species by size exclusion chromatography (SEC), acidic species by cation exchange (CEX), non-heavy chain and light chain (non-HC+LC) species by capillary electrophoresis under reducing / denaturing conditions (rCE-SDS), and hydrophilic species in the pre-peak eluting before the major species by hydrophobic interaction chromatography (HIC). The attributes analyzed for mAb B were: SEC HMW; rCE-SDS separable low molecular weight (LMW), medium molecular weight (MMW), non-glycosylated heavy chain (NGHC), LMW MMW and NGHC sum (L+M+NG), and non-HC+LC; high mannose (HM) species analyzed by hydrophilic interaction chromatography, basic species 3 and all basic species separable by CEX; C-terminal sequence variant 1 (CSV1) and C-terminal sequence variant 2 (CSV2) analyzed by mass spectrometry. With regard to the methods described herein, it is intended that any attribute or combination of attributes can be analyzed.

[0089] Based on the attribute exposure levels investigated, the inventors found that none of the four attributes of mAb A were determinants of ADA. Similarly, they found that none of the eleven attributes of mAb B were determinants of ADA. Furthermore, the upper limits of the attribute exposure levels investigated were higher than or close to the proposed purity standards for the corresponding attributes of mAb A, demonstrating that CIA can be used to create clinically relevant evidence justifying the proposed standards.

[0090] A Clinical Investigative Analysis (CIA) involves analyzing the prevalence or degree of change over time of clinical outcomes for each given variable, such as various levels of the attributes to which patients are exposed. In conducting the CIA, the following approach was used to integrate the ADA trial results and attribute exposure levels for individual patients.

[0091] Clinical trial selection The selection criteria for clinical trials concerning CIA were (1) the presence of significant variability in attribute exposure levels, and (2) the enrollment of a large number of patients. In this way, statistically significant results can be obtained from the analysis of clinical effects under a wide range of attribute exposure levels. Therefore, dose-escalation studies or studies using multiple treatment lots were selected, and if a larger number of patients were needed, patients from equivalent studies were combined for analysis.

[0092] ADA trial results from clinical trials ADA test results from electron chemiluminescence (ECL)-based bridging immunoassays, developed to specifically detect mAb A-bound or mAb B-bound ADA in patients' serum, were analyzed for analysis. It should be noted that bound ADA encompasses both neutralized and unneutralized ADA. However, neutralized ADA is far less prevalent than bound ADA for both mAb A and mAb B, and a much larger number of patients are required for statistically significant analysis; therefore, analysis of bound ADA is suitable for this embodiment to test whether any clinical outcome is associated with the treatment variable.

[0093] The clinical trials selected for mAb A included four initial dose-escalation studies with a maximum total of 218 patients, two phase 2 studies with a maximum of 627 patients, and two phase 3 studies with a maximum of 542 patients. These three sets of trials were individually analyzed using retrospective CIA or prospective CIA, as described below. The clinical trials selected for mAb B included two phase 2 studies with a maximum of 5,440 patients. These sets of trials were analyzed using both retrospective and prospective CIA.

[0094] Data from attribute analysis and stability testing By tracking drug batches administered in clinical trials selected for CIA using batch trace records, it was possible to identify the drug lots examined in product quality analysis testing, thereby obtaining attribute levels specific to each treatment lot, determined at the time of lot delivery.

[0095] The tests for lot delivery analysis were SEC (size exclusion chromatography) for HMW, rCE-SDS (capillary electrophoresis under reduction and SDS denaturation conditions) for HC, LC, LMW, MMW, and NGHC, and CEX (cation exchange chromatography) for acidic or basic species, each developed individually for mAb A or mAb B. Other analytical data from peptide mapping by HIC (hydrophobic interaction chromatography), hydrophilic chromatography (HILIC) for HM, and LCMSMS (liquid chromatography-tandem mass spectrometry) for C-terminal sequence variant species (CSV1 and CSV2) were available for limited batches of mAb A or mAb B, as they were not included in the panel of lot delivery assays.

[0096] Data from attribute stability tests were also used for each drug. The stability data represents the changes in the levels of individual attributes over time under storage conditions, which are important for determining the attribute exposure levels at the time of treatment, as shown below.

[0097] Decision on attribute disclosure Amount of attribute exposure at the time of each treatment and related treatments t Equation 1: A t =(%A0+%A Δ ×t)D (Equation 1) (In the formula, %A0 is the percentage of attributes at the time of lot delivery, and %A Δ (where is the rate of change in the percentage of attribute levels, t is the storage time between lot delivery and treatment implementation, and D is the dose intensity in the weight dimension of the active pharmaceutical ingredient associated with each treatment.) This was determined using the information above.

[0098] Next, for each patient, representative A t A was assigned. In the case of an initial trial in which each patient receives a single treatment, representative A t This is related to A t That was the case.

[0099] In studies involving multiple treatment regimens, treatments administered after a patient is first tested positive for ADA are unrelated to the investigation of the cause of ADA. Therefore, in the case of ADA-positive patients, representative A t This was the mean or maximum attribute exposure level associated with the treatment administered before the first ADA-positive test. In the case of ADA-negative patients, representative A t To obtain this result, all treatments in the entire regimen were included in the determination of mean or maximum exposure.

[0100] CIA patient group As explained above, only patients from selected trials that met the following criteria were included in the CIA: (1) Batch information was available for all procedures in the regimen; (2) Attribute exposure could be determined for all procedures in the regimen; (3) ADA trial results were available; and (4) No positive results were obtained in ADA trials performed before the first treatment. For example, one patient from the mAb B clinical trial was excluded from the CIA because the drug lot number was not registered for one of the procedures. Also, 29 patients from the mAb B trial were excluded because these patients tested positive for ADA before treatment.

[0101] Applying these criteria, the patient counts were as follows: For CIAs of mAb A HMW, non-HC+LC, or acidic species, 218 patients from four combined initial trials and 627 patients from two combined phase 2 trials were analyzed separately. For CIAs of mAb A HIC pre-peak, 90 patients from two combined initial trials and 24 patients from two combined phase 2 trials were analyzed separately. In addition, a separate CIA for mAb A HMW analyzed 553 patients from two combined phase 3 trials.

[0102] For mAb B HMW, LMW, MMW, NGHC, M+N+NG, non-HC+LC, basic species 3, and all basic species, all three CIA approaches were used to analyze 5,439 patients from two combined phase 2 trials (see Figures 1B and C below). CIA for mAb B HM was performed by analyzing 127 patients from the same set of clinical trials, and CIA for CSV1 and CSV2 was performed by analyzing 1,671 patients.

[0103] Example 2: Analysis results of the impact of individual patient attributes If an attribute triggers an ADA response, a positive correlation will exist, and therefore, ADA-positive patients will be found to be associated with higher attribute exposure levels compared to ADA-negative patients (Figures 1A-C). While the discovery of an association does not prove causality, such a finding may prompt further research to verify causality. However, the finding that no such association exists would rule out a causal relationship between this attribute and ADA at the exposure levels examined. Three separate analyses were conducted to examine the association between attribute-exposure levels and the occurrence of an ADA response.

[0104] (As described in Example 1) Representative attribute-exposure levels analyzed for individual patients were determined from the patient's single exposure in the Phase 1 trial for mAb A, the patient's maximum exposure in the Phase 2 and Phase 3 trials for mAb B, and the patient's maximum exposure for mAb B. Mean A t This is a statistical representation of multiple attribute exposures. Maximum A t This is immunologically related because patients can only develop ADA when exposed to immunogens exceeding a certain threshold.

[0105] Example 3: Retrospective CIA on the Attributes of mAb A and mAb B A retrospective analysis (grouping subjects retrospectively based on known clinical outcomes) was developed to investigate whether ADA-positive patients correlated with higher attribute exposure levels compared to ADA-negative patients. Patients were retrospectively grouped according to known ADA trial results (Figure 1B). Next, the distribution of representative attribute exposure levels for each attribute was compared between the ADA-positive and ADA-negative patient groups. If the representative attribute exposure levels in the ADA-positive patient group were higher on average than in the ADA-negative patient group, Student's t-test was performed to evaluate whether this difference was statistically significant.

[0106] No statistical association was found between ADA-positive patients and higher exposure to either mAb A or mAb B attributes (Figure 2), indicating that none of these attributes cause ADA at the exposure levels examined. This finding holds true regardless of whether single exposure (Figure 2A), mean exposure (Appendix Figure 1), or maximum exposure (Figure 2B) was used as the representative exposure level (see Methods section). Investigations of multiple sets of mAb A clinical trials in various phases synonymously demonstrate that none of the four attributes are determinants of ADA (Figures 2A, 7A, and 8).

[0107] Figures 2A and 2B show a comparison of attribute exposure levels between ADA-positive and ADA-negative patients, indicating that there is no statistically significant association between high exposure levels and ADA-positive patients for all attributes analyzed. This suggests that these attributes do not cause ADA under the clinical conditions examined. The numbers in parentheses are p-values ​​determined as needed. The straight lines represent the mean ± standard deviation of representative attribute exposures for individual patients in each group.

[0108] Example 4: Prospective CIA of ADA incidence rates related to the attributes of mAb A and mAb B Typical A t We developed an incidence analysis of subjects grouped according to the following criteria to verify whether a positive correlation exists between attribute exposure levels and the incidence of ADA response. Patients were grouped into quartiles according to attribute exposure levels, and the percentage of ADA-positive patients determined for each group was compared with respect to the group mean of representative attribute exposure levels (Figure 1C, left panel).

[0109] The range of exposure levels applied to create the quartile bins was optimized to achieve minimal variability in exposure levels within each patient group while simultaneously ensuring a sufficient number of patients in each group. Where a positive correlation was observed between ADA incidence and attribute exposure levels, a weighted correlation was calculated using the number of patients in each group as a weighting to verify whether this correlation was statistically significant.

[0110] Consistent with the results of the CIA analysis in Example 3 (in this analysis, subjects were retrospectively grouped according to known clinical outcomes), the correlation between the incidence of ADA and the exposure levels of all attributes analyzed for mAb A or mAb B is not statistically positive (Figures 3A-B, Figure 7B). In fact, lower exposure appears to be associated with ADA-positive patients with mAb B, and representative A tThis is reflected as a negative correlation in the incidence rates of subjects grouped according to [the specified criteria] (Figure 3B). This negative correlation is a statistically useful outcome that further supports the absence of a causal relationship (see below; Figures 5A-B, Figure 9).

[0111] Figures 3A and 3B show the proportion of ADA-positive patients in patient quartiles, grouped according to representative attribute exposure levels ranging from low to high, plotted as a function of the group mean of representative exposure levels. The numbers in parentheses are the weighted p-values ​​determined when a positive correlation is found. The number of patients in each quartile is indicated next to the plot. Error bars represent the standard deviation. The results for mAb A are shown in Figure 3A, and the results for mAb B are shown in Figure 3B.

[0112] Example 5: Prospective CIA analysis of ADA changes over time regarding the attributes of mAb A and mAb B Develop a different prospect analysis (representative A t Subjects were grouped according to the criteria (see Figure 1B, right panel), and the time course of ADA response was compared among patients grouped in the same way as in Example 4 above. If higher attribute exposure levels were associated with a wider incidence over time, the Mantelcox log-rank test23,24 was performed to verify whether the difference in time course was statistically significant.

[0113] Consistent with the above analysis, no association was found between the time course of ADA and exposure levels for any attribute of mAb A or mAb B (Figures 4A-B). Regarding the HIC pre-peak of mAb A (Figure 4A), the result indicating no association with ADA was not useful due to the limited number of patients analyzed. Other analyses performed using data from a larger number of patients in a different set of clinical trials (Figures 2A and 3A) indicate that the HIC pre-peak is not actually a determinant of ADA.

[0114] Example 6: ADA response and typical A tThe relationship between the decline and A t The association, or negative correlation, between the decrease in mAb A and the ADA response is found for both mAb A and mAb B, in both incidence and time course (Figures 2B, 3B, 4A, and 4B). This negative correlation is found in the maximum A in CIA for mAb B. t This becomes particularly evident when analyzing (Figures 2B, 3B, and 4B).

[0115] Using HMW exposure in patients treated with mAb B, the inventors show that a negative correlation supports the CIA's results indicating that attribute exposure does not evoke an ADA response. There is no bias between ADA-negative and ADA-positive patients regarding how maximum HMW exposure occurs: generally speaking, between ADA-negative and ADA-positive patients, maximum A t There is no difference in the levels (Figure 5A); the treatment time at which maximum HMW exposure occurs is similarly distributed between ADA-negative and ADA-positive patients (Figure 5B). However, most treatments are associated with lower HMW exposures, particularly those lower than the mean maximum HMW exposure of 2 mg, as shown in Figure 5A (Figure 5C). Without being limited by theory, it is intended that the next highest HMW exposure can be analyzed as representative exposure for ADA-positive patients by excluding some treatments at later administration times (due to their lack of association with causality in the occurrence of ADA). The use of this next highest HMW exposure may introduce a bias that leads the correlation in a negative direction. This potential bias should be considered when conducting the analysis.

[0116] Figure 5A shows the maximum A determined using all treatments or ADA-related treatments. t However, this is plotted for ADA-negative or ADA-positive patients. The maximum A value determined using all treatments for ADA-negative patients. tThe mean ± standard deviation is shown; for other groups, it is as analyzed for the same corresponding group in Figure 2B. The p-value for the t-test is greater than 0.05 for comparisons indicated as ns (not significant) unless otherwise indicated. Error bars are the standard deviation. In Figure 5B, the frequency of monthly procedures associated with maximum HMW exposure for individual patients is shown for the ADA-negative and ADA-positive groups. In Figure 5C, the frequency of procedures resulting in various levels of HMW exposure is plotted for individual mAb2 patients, along with the cumulative rate of procedures.

[0117] Only attribute exposure due to treatment performed prior to the ADA response is associated with the ADA response. Furthermore, ADA can only be induced by immunogens exceeding the threshold concentration. 20,21 The analysis of maximum attribute exposure takes immunological mechanisms into account. In summary, the negative correlation found here provides further evidence that attribute exposure does not cause ADA (see Example 8).

[0118] Example 7: Application of the CIA approach to the analysis of the clinical impact of treatment regimens In addition to analyzing the influence of attributes, the inventors investigated whether there was a correlation between the ADA response and various factors of the treatment regimen (e.g., the number of treatments performed, the dose intensity of each treatment, and the number of treatment lots associated with the treatment course for each patient).

[0119] None of these factors were found to correlate with the incidence of ADA. In fact, ADA-positive patients were associated with fewer treatments, shorter treatment durations, and fewer treatment batches, because this analysis only included treatments performed before the initial positive ADA test.

[0120] This demonstrates that the CIA's approach can be applied to other aspects of treatment in addition to attribute exposure. The inventors hypothesize that combining attribute exposure information with patient information (e.g., demographic and even genotype information) can enable powerful analyses that expand knowledge about drug safety and efficacy.

[0121] Example 8: Further analysis of prospective CIA incidence rates Typical A t For the CIA incidence rates of subjects grouped according to (Figure 9A-B), randomly selected points were used to determine whether the slope of the linear regression was significantly different from zero, based on the observed linear regression correlation coefficient R. 2 We verified this using an F-test to calculate the probability p that the result will be or will exceed the maximum A. t The p-values ​​from F-tests used to determine significant non-zero slopes in the regressions of rCE-SDS non-HC+LC, CEX basic species 3, CEX all basic species, and CSV2, analyzed using the following methods, were 0.017, 0.049, 0.031, and 0.045, respectively. Other slopes were not significantly different from zero.

[0122] Summarizing Figure 9B, the maximum A is as shown in Figure 9B. t Instead of average A t Compare with the same set of data reanalyzed using [method name]. The straight line is an unweighted linear regression, and the slope of all regressions is [value]. rCE-SDS non-HC+LC analyzed using maximal At, CEX basic species 3 analyzed using maximal At, maximal A t The total basic species of CEX, and the maximum A, were analyzed using [this method]. t Except for CSV2, which was analyzed using [the specified method], the results are statistically zero. *Unless otherwise specified, the number of patients divided into quartiles for reanalysis is the same as the corresponding quartiles in Figure 9B. The correlations of the reanalyzed data are generally low negative, except for HM and CSV1, and the slope of all these goodness-of-fit data is statistically zero.

[0123] Example 9: CIA using further clinical trials A CIA analysis of the influence of mAb A attributes on ADA, conducted using data from the Phase 2 clinical trial, showed that CQA was not a determinant of ADA response at any of the exposure levels examined (Figure 7A). Attribute exposure levels in the Phase 3 clinical trial of mAb A were lower than those in previous phases, and representative A t As shown by the CIA analysis of subjects grouped according to the criteria, they do not cause ADA (Figure 8).

[0124] As shown in Figure 7B, patients with different clinical trial regimens than those analyzed in Figure 2A were analyzed. Average A per treatment t However, the representative A of each procedure t It is used as such. Error bars represent the individual average A t This is the uncertainty propagated from the associated standard deviation. The quartiles analyzed in (Figure 7B), and the attribute exposure ranges used to group these quartiles, are the same as those in Figure 4.

[0125] Low A t and correlation with ADA As demonstrated herein, if attributes do not cause ADA and only ADA-related treatments are analyzed in the CIA of ADA-positive patients, a bias in maximum attribute exposure will be found between ADA-negative and ADA-positive patients. Such a bias will be reduced if the mean exposure per treatment is analyzed as the representative exposure for each patient. For example, perhaps the bias of reduced maximum HMW exposure when ADA-unrelated treatments are excluded from the CIA of ADA-positive patients may be offset by a similar bias of increased minimum HMW exposure. Consistent with this explanation, using mean exposure as the representative exposure for the CIA generally reduces or eliminates these negative correlations (Figure 9). t Since this is related to ADA and analyzing maximum exposure is immunologically important,25,26 the negative correlation described in the text provides further evidence that the attribute does not cause ADA under the clinical conditions examined.

[0126] Example 10: Analysis and conclusions of Examples 1-9 The inventors demonstrate that by integrating information on treatment lots, clinical outcomes, and attribute analysis data, it is possible to approximate the attribute exposure levels associated with individual treatments administered to patients. Subsequently, it is possible to determine whether a certain attribute causes ADA at a higher A level. t This can be verified by statistical tests that examine the existence of a correlation between the occurrence of an ADA response and the symptoms. If an association exists, further focused investigation is needed to prove causality; however, if no association exists, causality is ruled out.

[0127] The research described herein describes an effort to integrate treatment information from individual patients and evaluate the clinically relevant impact of molecular attributes of therapeutic agents. Here, no significant positive correlation was found between ADA response and exposure levels for any of the 15 attributes examined between the two therapeutic agents, mAb A and mAb B. This indicates that, at the levels examined for the corresponding pharmaceutical agents, none of these attributes are determinants of ADA (Table 1). This study does not establish that these attributes do not increase safety risks at various exposure levels or with respect to other therapeutic mAb analyses.

[0128] This approach analyzes passive data taken from clinical trials originally intended to demonstrate the safety or efficacy of pharmaceuticals. Ethical concerns regarding patient exposure to any level of attribute make it impossible to generate active data by designing studies specifically to investigate the effects of attributes in patients. Nevertheless, CIA of passive data offers rich insights and suggestions for both applied and fundamental scientific exploration in biotechnology.

[0129] For example, CIA can be useful in determining patient-centered limits for product purity specifications. As demonstrated above, the lack of a causal link between attribute exposure and ADA indicates that, at the exposure levels examined, the attribute does not increase the risk of immunological safety. In particular, the upper limits of these attribute exposure levels analyzed for mAb A (Table 1) are near or higher than the limits of the corresponding attribute imposed by the specification of this drug, demonstrating that patient-focused evidence generated by CIA can be used to determine specification limits for therapeutic drugs.

[0130] Figure 6 shows the influence of treatment factors on ADA. Various factors, including mAb B treatment regimen, mean dose intensity per treatment, treatment duration, number of treatments, and treatment lot number, are compared between ADA-negative and ADA-positive patients. These factors are either for all treatments in ADA-negative patients or for treatments prior to the first ADA-positive test result in ADA-positive patients. Error is expressed as standard deviation.

[0131] [Table 1]

[0132] CIA can also deepen foundational knowledge in clinical immunology. Any correlation found between ADA response and attribute exposure levels may facilitate intensive in vitro or in vivo studies to investigate the causal relationship or mechanism of ADA generation. t Depending on which of the two better represents any attribute exposure found to correlate with ADA, hypotheses can be refined to also examine whether repeated high-attribute exposure or single high-attribute exposure is important in eliciting an ADA response.

[0133] The inventors further demonstrate that CIA, when combined with other analyses, can provide powerful insights into therapeutic drug development and address critical, unknown problems. Can certain attributes influence the clinical outcomes of a subset of a population defined by demographic or genomic information? Can knowledge gained from demographics and genomics advance clinical trial design? Do attributes alter the biophysical behavior, such as the structure and conformational dynamics, of various therapeutic protein molecules, and consequently affect clinical outcomes? This study reports data and analyses that pave the way for these and further discussions.

[0134] In summary, understanding the impact of molecular attributes on drug safety and efficacy is crucial for therapeutic drug development. For example, some attributes can trigger anti-drug antibody (ADA) responses in patients, which remains a significant regulatory concern due to potential clinical effects, including drug neutralization. Non-human model systems (e.g., cells, tissues, and animals) are viable experimental systems for examining the safety impact of attributes, but insights into the clinical outcomes of patient exposure to attributes are limited. Here, we use a data analysis approach called Clinical Impact Analysis (CIA) to investigate the clinically relevant immunological safety impact of 15 attributes between two immunoglobulin G2 (IgG2) monoclonal antibody (mAb) drugs, mAb A and mAb B. CIA examines whether there is a correlation between the level of patient exposure to a given attribute and the incidence of clinical outcomes by analyzing data from clinical trials and product quality analysis studies. No association was found between the level of patient exposure to any of the attributes and ADA responses, suggesting that none of the attributes analyzed at the exposure levels examined trigger an ADA response. Furthermore, the upper limits of the exposure levels examined were higher than or close to the purity specification limits for the attributes of mAb A, demonstrating the feasibility of generating clinically relevant evidence to justify these attributes, which is possible through CIA. These results indicate that integrating clinical and analytical information can provide clinically relevant insights into the impact of attributes applicable to drug development.

[0135] Example 11: Analysis of Product C, a canonical BiTE® molecule Product C, a canonical BiTE® molecule, was further analyzed. The measured molecular attributes were Tyr sulfated, Asp isomerized, Lys hydroxylysine, Lys glucosyl-galactosyl hydroxylysine, Met oxidized, and SE-HPLC aggregates. Efficacy was also evaluated. Safety data included the identification of the following adverse events: anemia, cytokine release syndrome (CRS), fever, infusion-related reactions (IRR), lymphopenia, and neurological events.

[0136] Attribute exposure was estimated based on 1) the percentage of attribute levels for various drug lots at the time of manufacture, 2) the stability of the attribute, i.e., the rate of change in attribute levels during DP storage, and 3) the date and time each individual treatment was administered to each patient, along with a record of which drug lot was administered for each treatment. Then, 4) the date and time of occurrence of a given clinical outcome and the grade (or severity) of the target outcome were incorporated into the next step in the CIA (Figure 1), 5) the CIA combined the information to determine attribute exposure, and 6) analyzed whether there was a correlation between the level of attribute exposure and the occurrence of the target clinical outcome. (A)A t This was initially determined using Equation 1 for all related measures defined below. (1) For patients who do not demonstrate the desired clinical outcome throughout the entire course of the clinical trial, the relevant treatment is all treatment administered to the patient throughout the entire trial. (2) For patients showing a clinical outcome, the relevant treatment is all treatment administered to the patient prior to the occurrence of that clinical outcome. (3) For patients who developed a clinical outcome before receiving treatment, there is no relevant treatment. For this patient, A t The value was 0. (4) With regard to administration by continuous infusion, A occurs in the relevant portion of this infusion. t The following decision was made: a. For infusions administered continuously for more than 24 hours, A per 24-hour interval of the relevant infusion prior to the occurrence of the clinical outcome t To decide. b. For infusions of less than 24 hours, A is associated with each of the relevant infusions prior to the occurrence of the clinical outcome. t To decide. c. In either of the above two cases, if the clinical outcome occurred during the infusion, the portion of the infusion prior to the occurrence of the clinical outcome is associated with A t To decide.

[0137] The analysis results for product C are shown in Figures 10A and 10B. In addition to the six CQAs listed for product CIA, the effect of potency on AEs was analyzed by examining whether there was a correlation between the potency-adjusted dose of product C and the occurrence of AEs in patients administered no-dose, medium-dose, or high-dose levels. As shown in Figure 10A, the relationship between potency and the occurrence of AEs of any grade was analyzed. As shown in Figure 10B, the relationship between potency and the occurrence of AEs of grade 3 or higher was analyzed.

[0138] None of the attributes were observed to correlate with any of the adverse events tested. Therefore, the estimated levels of attribute exposure were determined to be safe.

[0139] Example 12: Computer-assisted implementation of Bayesian estimation To leverage the high computational power required for the Bayesian estimation approach, a computer program was developed. This program provides an interactive platform for visualizing the results of the Bayesian estimation approach using spreadsheets provided by the user. The program also provides a parameter tuning module for defining initial prior probabilities, removing outliers, and selecting multiple attributes. A set of processed clinical data for evaluating the clinical impact of molecular attributes of mAb D, and a small amount of modeling data based on mAb D CIA data, were validated in this system. This method was used to verify whether a positive correlation exists between mAb D HMW exposure and the occurrence of fever adverse events by comparing the maximum daily HMW exposure in patients who were negative and positive for adverse events. This method failed to find a correlation using conventional methods (Figure 11A). The Bayesian method (Figures 11C-D) produced similar results to the conventional system (Figures 11A-B), thereby demonstrating the effectiveness of this system: (1) When analyzing clinical data from the mAb D clinical trial, Bayesian estimation showed that there was no probability that attributes correlated with clinical outcomes (Figure 11C). When the data was processed using conventional statistics, the same results were obtained (Figure 11A), (2) When the modeled dataset (based on the clinical data mentioned above) was analyzed (however, attribute exposure levels were arbitrarily increased for the clinical trial subjects), the Bayesian estimation method showed the probability that attributes correlated with clinical outcomes, similar to conventional statistics (Figure 11B) (Figure 11D). Therefore, both conventional statistics and the Bayesian estimation method showed a correlation between clinical outcomes and arbitrarily increased attribute exposure levels.

[0140] References The following documents are incorporated herein by reference in their entirety. 1 Rosenberg, AS, Verthelyi, D. & Cherney, BW Managing uncertainty: a perspective on risk pertaining to product quality attributes as they bear on immunogenicity of therapeutic proteins. J Pharm Sci 101, 3560-3567, doi:10.1002 / jps.23244 (2012). 2 Goetze, AM, Schenauer, MR & Flynn, GC Assessing monoclonal antibody product quality attribute criticality through clinical studies. MAbs 2, 500-507 (2010). 3 Kelley, B., Cromwell, M. & Jerkins, J. Integration of QbD risk assessment tools and overall risk management. Biologicals 44, 341-351, doi:10.1016 / j.biologicals.2016.06.001 (2016). 4 Bessa, J. et al. The immunogenicity of antibody aggregates in a novel transgenic mouse model. Pharm Res 32, 2344-2359, doi:10.1007 / s11095-015-1627-0 (2015). 5 Bi, V. et al. Development of a human antibody tolerant mouse model to assess the immunogenicity risk due to aggregated biotherapeutics. J Pharm Sci 102, 3545-3555, doi:10.1002 / jps.23663 (2013). 6 Bee, J. S., Goletz, T. J. & Ragheb, J. A. The future of protein particle characterization and understanding its potential to diminish the immunogenicity of biopharmaceuticals: a shared perspective. J Pharm Sci 101, 3580-3585, doi:10.1002 / jps.23247 (2012). 7 Goetze, A. M., Liu, Y. D., Arroll, T., Chu, L. & Flynn, G. C. Rates and impact of human antibody glycation in vivo. Glycobiology 22, 221-234, doi:10.1093 / glycob / cwr141 (2012). 8 Jawa, V. et al. Evaluating Immunogenicity Risk Due to Host Cell Protein Impurities in Antibody-Based Biotherapeutics. AAPS J 18, 1439-1452, doi:10.1208 / s12248-016-9948-4 (2016). 9 Joubert, M. K. et al. Use of In Vitro Assays to Assess Immunogenicity Risk of Antibody-Based Biotherapeutics. PLoS One 11, e0159328, doi:10.1371 / journal.pone.0159328 (2016). 10 Liu, Y. D. et al. Human IgG2 antibody disulfide rearrangement in vivo. J Biol Chem 283, 29266-29272, doi:10.1074 / jbc.M804787200 (2008). 11 Liu, Y. D., van Enk, J. Z. & Flynn, G. C. Human antibody Fc deamidation in vivo. Biologicals 37, 313-322, doi:10.1016 / j.biologicals.2009.06.001 (2009). 12 Yang, J., Goetze, A. M. & Flynn, G. C. Assessment of naturally occurring covalent and total dimer levels in human IgG1 and IgG2. Mol Immunol 58, 108-115, doi:10.1016 / j.molimm.2013.11.011 (2014). 13 Zhang, Q. et al. Characterization of the co-elution of host cell proteins with monoclonal antibodies during protein A purification. Biotechnol Prog 32, 708-717, doi:10.1002 / btpr.2272 (2016). 14 Seamon, K. B. Specifications for biotechnology-derived protein drugs. Curr Opin Biotechnol 9, 319-325 (1998). 15 Rathore, A. S. Setting Specifications for a Biotech Therapeutic Product in the Quality by Design Paradigm. BioPharm Internaltional 23 (2010). accessible on the world wide web at www dot biopharminternational dot com / setting-specifications-biotech-therapeutic-product-quality-design-paradigm?id=&pageID=1&sk=&date=. 16 Rathore, A. S. Roadmap for implementation of quality by design (QbD) for biotechnology products. Trends Biotechnol 27, 546-553, doi:10.1016 / j.tibtech.2009.06.006 (2009). 17 Casadevall, N. et al. Pure red-cell aplasia and antierythropoietin antibodies in patients treated with recombinant erythropoietin. N Engl J Med 346, 469-475, doi:10.1056 / NEJMoa011931 (2002). 18 Li, J. et al. Thrombocytopenia caused by the development of antibodies to thrombopoietin. Blood 98, 3241-3248 (2001). 19 Bautista, A. C., Salimi-Moosavi, H. & Jawa, V. Universal immunoassay applied during early development of large molecules to understand impact of immunogenicity on biotherapeutic exposure. AAPS J 14, 843-849, doi:10.1208 / s12248-012-9403-0 (2012). 20 Sauerborn, M., Brinks, V., Jiskoot, W. & Schellekens, H. Immunological mechanism underlying the immune response to recombinant human protein therapeutics. Trends Pharmacol Sci 31, 53-59, doi:10.1016 / j.tips.2009.11.001 (2010). 21 Baker, M. P., Reynolds, H. M., Lumicisi, B. & Bryson, C. J. Immunogenicity of protein therapeutics: The key causes, consequences and challenges. Self Nonself 1, 314-322, doi:10.4161 / self.1.4.13904 (2010). 22 Sauna, Z. E., Lagasse, D., Pedras-Vasconcelos, J., Golding, B. & Rosenberg, A. S. Evaluating and Mitigating the Immunogenicity of Therapeutic Proteins. Trends Biotechnol 36, 1068-1084, doi:10.1016 / j.tibtech.2018.05.008 (2018). 23 Mantel, N. Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother Rep 50, 163-170 (1966). 24 Peto, R. & Peto, J. Asymptotically Efficient Rank Invariant Test Procedures. Journal of the Royal Statistical Society. Series A (General) 135, 185-207, doi:10.2307 / 2344317 (1972). 25 Sauerborn, M., Brinks, V., Jiskoot, W. & Schellekens, H. Immunological mechanisms underlying the immune response to recombinant human protein therapeutics. Trends Pharmacol Sci 31, 53-59, doi:10.1016 / j.tips.2009.11.001 (2010). 26 Baker, MP, Reynolds, HM, Lumicisi, B. & Bryson, CJ Immunogenicity of protein therapeutics: The key causes, consequences and challenges. Self Nonself 1, 314-322, doi:10.4161 / self.1.4.13904 (2010).

[0141] All references cited herein, including publications, patent applications, and patents, are incorporated herein by reference to the same extent as each reference is incorporated individually and specifically by reference, and to the same extent as they are incorporated in whole herein.

[0142] In connection with the description of this disclosure (and in particular with the claims below), the terms “one (a),” “one (an),” and “it,” as well as similar reference subjects, should be construed to encompass both singular and plural unless otherwise indicated herein or unless clearly inconsistent with the context. The terms “include,” “have,” “include,” and “contain” should be construed as open-ended terms (i.e., “include, but not limited to”) unless otherwise noted.

[0143] The terms “patient” and “subject” are used interchangeably herein. Generally, these terms are understood to refer to a human being. In some embodiments, the patient or subject is a human being.

[0144] The descriptions of value ranges in this specification are intended solely as a simplified way of referring to each distinct value and each boundary value within that range, unless otherwise specified herein, and each distinct value and boundary value are incorporated herein as if they were individually described herein.

[0145] All methods described herein may be carried out in any suitable order, unless otherwise specified herein or unless it is clearly inconsistent with the context. The use of any examples or exemplary language provided herein (e.g., "etc.") is intended solely to clarify the disclosure and does not impose any limitation on the scope of the disclosure unless otherwise claimed. No language herein should be construed as indicating any unclaimed element as essential to carrying out the disclosure.

[0146] Preferred embodiments of the Disclosure, including the best modes known to the inventors for carrying out the Disclosure, are described herein. Variations of these preferred embodiments will be apparent to those skilled in the art by reading the above description. The inventors expect that those skilled in the art will use such variations as needed, and the inventors intend that the Disclosure will be carried out in ways other than those specifically described herein. Accordingly, the Disclosure encompasses all modifications and equivalents of the subject matter enumerated in the claims appended herein, as permitted by applicable law. Furthermore, any combination of the above elements is incorporated herein in all possible variations unless otherwise indicated herein or unless it is clearly inconsistent with the context.

Claims

1. A method for manufacturing a biological drug, To detect the level of the molecular attributes of the biopharmaceutical agent in the formulation at one or more time points under storage conditions; To determine the rate of change of the molecular attributes under the aforementioned storage conditions; With respect to subjects receiving the said biological drug, obtain data on the in vivo safety and / or efficacy of the said biological drug: (i) the rate of change of the molecular attributes of the biological therapeutic agent in the formulation during storage, and (ii) the period during which the biological therapeutic agent in the formulation was under the storage conditions prior to administration, to estimate the level of molecular attribute exposure the subject will receive at the time of administration; To determine whether there is a correlation between the estimated level of exposure to the molecular attribute and the safety and / or efficacy data relating to the biological therapeutic agent; and If the aforementioned correlation does not exist, the manufacturing lot of the biopharmaceutical product containing the molecular attribute shall be manufactured at a level below the acceptable level for designating the molecular attribute, based on the estimated level of exposure to the molecular attribute. A method that includes this.

2. If such correlation exists, the method further includes setting the standard level of the molecular attribute at the time of manufacture so as not to exceed the maximum permissible level of the molecular attribute of the biopharmaceutical, The maximum permissible level of the molecular attribute is based on the highest estimated level of exposure to the molecular attribute that is not associated with adverse events and / or inhibition of the efficacy of the biopharmaceutical, The manufacturing further includes rejecting manufacturing lots of the biopharmaceutical that contain levels of the molecular attributes exceeding the maximum permissible level. The method according to claim 1.

3. The method according to claim 2, wherein the maximum permissible level of the specified molecular attribute is calculated to produce a level of attribute exposure at the end of storage that is 90 to 100% or less of the highest estimated level of molecular attribute exposure that is not associated with adverse events and / or inhibition of efficacy in the subject.

4. A method for developing a manufacturing process for biological therapeutic drugs, To detect the level of the molecular attributes of the biopharmaceutical agent in the formulation at one or more time points under storage conditions; To determine the rate of change of the molecular attributes under the aforementioned storage conditions; To obtain data on the safety and / or efficacy of the said biological agent in subjects to whom the said biological agent is administered: (i) the rate of change of the molecular attributes of the biological therapeutic agent in the formulation during storage, and (ii) the period during which the biological therapeutic agent in the formulation was under the storage conditions prior to administration, to estimate the level of molecular attribute exposure the subject will receive at the time of administration; To determine whether there is a correlation between the estimated level of exposure to the molecular attribute and the safety and / or efficacy data of the biological therapeutic agent; and (a) If the correlation does not exist, establish the manufacturing process to produce a level of the molecular attribute below a specified tolerance level based on the estimated level of exposure to the molecular attribute; or (b) Where such correlation exists, establish the manufacturing process such that the level of the molecular attribute is below the maximum permissible level for which the molecular attribute is specified, based on the highest level of the molecular attribute that is not associated with adverse events and / or inhibition of the efficacy of the biological therapeutic agent. A method that includes this.

5. A method for evaluating the clinical impact of molecular properties of biological therapeutic agents, To detect the level of the molecular attributes of the biopharmaceutical agent in the formulation at one or more time points under storage conditions; To determine the rate of change of the molecular attributes under the aforementioned storage conditions; To obtain data on the safety and / or efficacy of the said biological agent in subjects to whom the said biological agent is administered: (i) the rate of change of the molecular attributes of the biological therapeutic agent in the formulation during storage, and (ii) the period during which the biological therapeutic agent in the formulation was under the storage conditions prior to administration, to estimate the level of molecular attribute exposure the subject will receive at the time of administration; To determine whether there is a correlation between the estimated molecular attribute exposure and the safety and / or efficacy of the biological therapeutic agent; (a) If the aforementioned correlation does not exist, it is determined that the molecular attribute does not affect the clinical safety or efficacy of the biological therapeutic agent. or (b) If such correlation exists, determine that the molecular attribute affects the safety and / or efficacy of the biological therapeutic agent. A method that includes this.

6. (a) If the correlation does not exist, the method further includes setting a standard for an acceptable level of the molecular attribute of the biopharmaceutical, the acceptable level of the molecular attribute being based on the highest estimated level of exposure to the molecular attribute received by the subject, or (b) Where the correlation exists, the method further includes setting a standard for the maximum permissible level of the molecular attribute of the biological therapeutic agent, the maximum permissible level of the molecular attribute being based on the level of the molecular attribute associated with the adverse events and / or inhibition of the efficacy of the biological therapeutic agent. The method according to claim 5.

7. The method according to any one of claims 1 to 6, wherein the estimation is further based on (iii) the dose of the biological therapeutic agent in the administration, and (iv) the amount of the molecular attribute measured at the time of manufacture and / or at the time of lot delivery.

8. The method according to any one of claims 1 to 7, comprising detecting the level of the molecular attribute of the biological therapeutic agent in the formulation at two or more time points under storage conditions.

9. The method according to any one of claims 1 to 8, wherein the one or more of the aforementioned time points include the time of manufacture and at least two subsequent time points.

10. To estimate the level of exposure to the aforementioned molecular attributes, the following calculation is performed: [Math 1] (In the formula, A ｔ This is the estimated level of exposure to the aforementioned molecular attributes, and %A ０ This is the percentage of the aforementioned molecular attribute at the time of lot delivery, and %A Δｉ t is the rate of change in the percentage of the molecular attribute level over time under given storage conditions, ｉ (where is the storage time under the given conditions, and D is the dose intensity of the administration.) The method according to any one of claims 1 to 9, including the method described in any one of claims 1 to 9.

11. To estimate the level of exposure to the aforementioned molecular attributes, the following calculation is performed: [Math 2] (In the formula, %A ｒｅｌ． This is the ratio of the relative level of exposure to the molecular attribute to the dose, and A ｔ (where D is the level of exposure to the molecular attribute calculated using equation 1 or 2, and D is the dose intensity in the weight dimension of the active pharmaceutical ingredient associated with each treatment.) The method according to any one of claims 1 to 10, including the method described in any one of claims 1 to 10.

12. The method according to any one of claims 1 to 11, wherein the correlation includes a weighted correlation between attribute exposure and the occurrence of an adverse event.

13. The method according to any one of claims 1 to 12, wherein the biological therapeutic agent in the formulation has been under the storage conditions for various periods of time in administration to various subjects.

14. The method according to any one of claims 1 to 13, wherein the administration includes two or more administration events.

15. The method according to claim 14, wherein the estimated level of exposure to the molecular attribute received by the subject at the time of administration is the maximum or average value of two or more administration events.

16. The method according to any one of claims 1 to 15, wherein the administration comprises continuous infusions, and the estimation comprises calculating estimated levels of molecular attribute exposure during two or more intervals of the continuous infusions, such as 24-hour intervals.

17. The method according to any one of claims 1 to 16, wherein the safety data includes data on adverse events.

18. The method according to claim 17, wherein the safety data includes changes in adverse events over time.

19. The method according to any one of claims 1 to 18, wherein the efficacy data includes data from clinical endpoints.

20. The method according to any one of claims 1 to 19, wherein the molecular attributes include at least one of acidic species, basic species, high molecular weight species, number of invisible particles, low molecular weight, medium molecular weight, glycosylation (e.g., non-glycosylated heavy chain or high mannose), non-heavy chain and light chain, deamidation, deamination, cyclization, oxidation, isomerization, fragmentation / clipping, N-terminal variant and C-terminal variant, reduced species and subspecies, folded structure, surface hydrophobicity, chemical modification, covalent bond, C-terminal amino acid motif PARG, or C-terminal amino acid motif PAR-amide.

21. The method according to any one of claims 1 to 20, wherein the molecular attribute includes at least one of acidic species, basic species, high molecular weight species, amino acid isomers, or the number of invisible particles.

22. The method according to any one of claims 1 to 21, wherein the biological therapeutic agent is selected from the group consisting of antibodies, antigen-binding antibody fragments, antibody protein products, bispecific T cell engager (BiTE®) molecules, bispecific antibodies, tripspecific antibodies, Fc fusion proteins, recombinant proteins, recombinant viruses, recombinant T cells, synthetic peptides, and active fragments of recombinant proteins.

23. The method according to any one of claims 1 to 22, wherein the preparation is a pharmaceutically acceptable preparation.

24. The method according to any one of claims 1 to 23, wherein detecting the level of molecular attributes of the said biological therapeutic agent includes mass spectrometry, chromatography, electrophoresis, spectroscopy, light shielding, particle methods (e.g., nanoparticle / visible / micron-sized resonance mass or Brownian motion), analytical centrifugation, imaging or image characterization, or immunoassay.

25. The method according to any one of claims 1 to 24, wherein determining whether or not there is a correlation between the estimated level of exposure to the molecular attribute and the safety and / or efficacy data of the biological therapeutic agent includes Bayesian estimation.

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