Quantitation and modeling of quality attributes of therapeutic monoclonal antibodies

Abstract

To provide methods of predicting an in vivo serum concentration of an antibody with a post-translational modification of interest after administration of the antibody, and methods for predicting a subject's exposure to post-translational variants of the antibody.SOLUTION: The methods include predicting a percentage of the antibody with the post-translational modification of interest by using an in vivo rate constant determined for the post-translational modification, and multiplying the predicted percentage of the antibody with the post-translational modification of interest by the in vivo concentration of the antibody to determine the concentration of the antibody with the post-translational modification of interest.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] Sequence Listing This application incorporates by reference the Sequence Listing submitted in computer readable format as 10421WO01-Sequence.txt, created on January 31, 2019, and containing 514 bytes.

[0002] FIELD OF THEINVENTION The present invention relates to biopharmaceuticals and to the prediction and modeling of in vivo post-translational modifications of therapeutic antibodies. [Background technology]

[0003] background Therapeutic monoclonal antibodies (mAbs) are heterogeneous molecules produced in mammalian cells that contain many product variants, including those resulting from post-translational modifications (PTMs). Variants produced through PTMs can arise during manufacturing, purification, storage, and throughout the lifespan of the mAb after administration. These variants or product-related modifications are also referred to as product quality attributes (PQAs). Controlling PQAs within predefined acceptance criteria is essential for the biopharmaceutical industry as it ensures consistent product quality and reduces potential impacts on drug safety and efficacy. Modifications that occur during drug manufacturing and storage can often be reliably monitored and controlled. However, after drug administration, further modifications can occur due to the significantly different environment of the bloodstream relative to the formulation buffer. In vivo modifications of mAbs are typically difficult to monitor and are less studied. Assessing and predicting in vivo changes in PTMs not only facilitates understanding of the importance of quality attributes in product risk assessment, but also helps product design and development teams design mAb drug candidates with improved in vivo stability.

[0004] The Food and Drug Administration's guidance for industry recommends that sponsors should assess the susceptibility of therapeutic proteins to modification in the in vivo environment (see Guidance for Industry, Immunogenicity Assessment for Therapeutic Protein Products. 2014).As a result, the proteins undergo a number of processes, including deamidation (see, e.g., Huang et al., Analytical chemistry 2005;77:1432-9; Ouellette et al., mAbs 2013;5:432-44; Yin et al., Pharmaceutical research 2013;30:167-78; Li et al., mAbs 2016:0), oxidation (see, e.g., Yin et al., Pharmaceutical research 2013;30:167-78; Li et al., mAbs 2016:0), glycation (see, e.g., Goetze et al., Glycobiology 2012;22:221-34), glycosylation (see, e.g., Li et al., mAbs 2016:0, Li et al., mAbs 2016:0), and cleavage (see, e.g., Li et al., mAbs 2016:1). al., mAbs 2016:0, Goetze et al., Glycobiology 2011;21:949-59, Alessandri et al., mAbs 2012;4:509-20.), disulfides (e.g., Li Yet al., mAbs 2016:0, Liu et al., The Journal of biological chemistry 2008;283:29266-72), N-terminal pyroglutamic acid (e.g. Yin et al., Pharmaceutical research 2013;30:167-78, Li et al., mAbs 2016:0, Li et al., mAbs 2016:0, Liu et al., The Journal of biological chemistry 2011;286:11211-7), and C-terminal lysine removal (e.g., Li et al., mAbs The in vivo behavior of many PQAs, including EDTA-dependent agonists, EDTA-dependent agonists, and EDTA-dependent agonists (see Cai et al., Biotechnology and bioengineering 2011;108:404-12), has been investigated in animal or human samples.Affinity purification is often used to extract therapeutic mAbs from serum samples and reduce the interference of endogenous protein background in the analysis (see, for example, Li et al., mAbs 2016:0, Li et al., mAbs 2016:0). For animal studies, anti-human Fc antibodies or antigens can be easily used as capture reagents. For human studies, antigens or antibodies that specifically recognize the unique complementarity determining regions (CDRs) of the therapeutic mAb can be used as capture reagents. After affinity purification, liquid chromatography-tandem mass spectrometry (LC-MS / MS) peptide mapping is often used to quantify the relative abundance of PTM product variants. Recently, in vivo PTM quantification has been combined with pharmacokinetic (PK) models to assess the formation and elimination of PTM product variants and quantitatively assess the subject's exposure to PQAs in single-dose regimens (see, e.g., Li et al., mAbs 2016:0; Li et al., mAbs 2016:0; Goetze et al., mAbs 2010;2:500-7) to help establish the importance of PQAs for product risk assessment. However, no modeling has been attempted to simulate the in vivo progression of PTMs and the subject's exposure to PQAs in multiple-dose regimens and quantitatively assess the importance of PQAs. Summary of the Invention

[0005] In one aspect, the invention provides a method of predicting the in vivo serum concentration of an antibody with a post-translational modification of interest following a single administration of the antibody, the method comprising: (a) predicting the percentage of the antibody with the post-translational modification of interest using an in vivo rate constant identified for the post-translational modification, and (b) multiplying the predicted percentage of the antibody with the post-translational modification of interest by the in vivo concentration of the antibody to determine the concentration of the antibody with the post-translational modification of interest. In some cases, the following equation is used for the calculation: C PTM (t)=C(t)·P(t), where C PTM (t) is the serum concentration of the antibody with the post-translational modification of interest, C(t) is the serum concentration of the antibody, and P(t) is the percentage of the post-translational modification of interest.

[0006] In some embodiments, the in vivo rate constant for a post-translational modification is determined by quantifying the percentage of a post-translational modification of interest as a function of time and calculating the quantified percentage of the post-translational modification of interest according to the equation: TIFF2025010564000002.tif5128. PTM (t) is the percentage of post-translational modification as a function of time, P0 is the initial percentage of post-translational modification, and k PTM is the rate constant for the post-translational modification.

[0007] In some embodiments, the method further includes determining the subject's exposure to an antibody with a post-translational modification of interest. Optionally, determining the subject's exposure to an antibody with a post-translational modification of interest includes determining the concentration of the antibody with the post-translational modification of interest (C PTM This involves determining the area under the curve (AUC) of (t).

[0008] In some embodiments, the serum concentration of the antibody is calculated according to the equation C(t)=Ae -αt +Be -βt where A and B are hybrid coefficients where α and β are hybrid linear constants, respectively.

[0009] In some embodiments, the method further comprises predicting the in vivo serum concentration of the antibody with the post-translational modification of interest after multiple administrations of the antibody. In some cases, predicting the in vivo serum concentration of the antibody with the post-translational modification of interest after multiple administrations of the antibody comprises overlaying multiple single dose models, where the serum concentration of the antibody at each administration is calculated according to the equation C(t)=Ae of a two-compartment pharmacokinetic model. -αt +Be -βt where A and B are hybrid coefficients where α and β are hybrid linear constants, respectively.

[0010] In various embodiments of the method, the antibody comprises a therapeutic recombinant monoclonal antibody. In some cases, the post-translational modification comprises a product quality attribute that can affect drug stability, safety, and / or efficacy.

[0011] In some embodiments, the methods prospectively monitor the post-translational modification profile of a monoclonal antibody.

[0012] In some embodiments, the method further comprises generating a predictive model for the in vivo progression of post-translational modifications and subject exposure to product quality attributes.

[0013] In some embodiments, the method further comprises modeling the correlation between PK concentrations and proportions of post-translationally modified variants.

[0014] In some embodiments, the methods further comprise quantitatively assessing and predicting the subject's exposure to representative post-translational modifications in both single and multiple dose regimens.

[0015] In some embodiments, the method further comprises predicting the maximum and minimum post-translational modification levels to be observed in a multiple dose regimen.

[0016] In another aspect, the invention provides a method for predicting a subject's exposure to a variant of a therapeutic recombinant monoclonal antibody following administration to said subject, the method comprising: (a) identifying a rate constant for a post-translational modification (PTM) process that leads to the formation of a variant; (b) calculating a predicted fraction of said variant as a function of time; (c) calculating a predicted serum concentration of said variant in said subject as a function of time based on said predicted fraction of said variant; and (d) predicting the subject's exposure to said variant following administration of the antibody based on the predicted serum concentration of said variant.

[0017] In some embodiments of the method, the calculation of the predicted proportion of the variant as a function of time is performed via the formula: TIFF2025010564000003.tif4128 formula, P PTM (t) is the proportion of the variant as a function of time, P0 is the initial level of the variant at the time of administration, and k PTM is the rate constant for the PTM process and t is time.

[0018] In some embodiments of the method, the calculation of the predicted serum concentration of the variant in the subject as a function of time is performed via the formula: PTM (t)=C(t) P PTM (t), where C PTM (t) is the serum concentration of the variant, C(t) is the serum concentration of the antibody, and P PTM (t) is the proportion of the variant at time t.

[0019] In some embodiments, the method further comprises administering the antibody to the subject.

[0020] In some embodiments of the method, the antibody is a mAb.

[0021] In some embodiments, the method further comprises determining the total in vivo concentration of the antibody in the subject.

[0022] In another aspect, the invention provides a method of predicting a subject's exposure to a variant of a therapeutic recombinant monoclonal antibody following multiple administrations of the antibody to the subject, the method comprising: (a) performing the method directly discussed above; (b) calculating a predicted proportion of the variant before and after each subsequent administration of the antibody based on administration intervals; (c) calculating a predicted serum concentration of the variant in the subject for each administration interval based on the predicted proportion of the variant; and (d) predicting the subject's exposure to the variant across two or more administrations of the antibody based on the predicted serum concentrations of the variant.

[0023] In various embodiments of the methods discussed above or herein, the post-translational modification comprises one or more of deamidation, oxidation, glycation, disulfide formation, N-terminal pyroglutamic acid formation, C-terminal lysine removal, and high mannose glycosylation.

[0024] In some embodiments, the method further comprises administering the antibody to the subject.

[0025] In some embodiments of the method, the antibody is a mAb.

[0026] In some embodiments, the method further comprises determining the total in vivo concentration of the antibody in the subject.

[0027] Any of the various aspects or embodiments described above or discussed herein may be combined with other embodiments, each of which is encompassed within the scope of the present disclosure. [The present invention 1001] 1. A method for predicting the in vivo serum concentration of an antibody with a post-translational modification of interest following a single administration of the antibody, comprising: predicting the percentage of said antibody with said post-translational modification of interest using an in vivo rate constant identified for said post-translational modification; and Multiplying the predicted percentage of the antibody with the post-translational modification of interest by the total in vivo concentration of the antibody to identify the concentration of the antibody with the post-translational modification of interest (C PTM (t)=C(t) P(t), where C PTM (t) is the serum concentration of the antibody with the post-translational modification of interest, C(t) is the serum concentration of the antibody, and P(t) is the percentage of the post-translational modification of interest. The method comprising: [The present invention 1002] The in vivo rate constant for the post-translational modification is Quantifying the percentage of said post-translational modification of interest as a function of time; and The quantified percentage of the post-translational modification of interest is calculated according to the equation Fit to TIFF2025010564000004.tif5128 Identified by In the formula, P PTM (t) is the proportion of said post-translational modification as a function of time, P0 is the initial percentage of the post-translational modification, and k PTM is the rate constant of the post-translational modification; The method of the present invention 1001. [The present invention 1003] The method of any one of claims 1001 to 1002, further comprising identifying exposure of said subject to said antibody with said post-translational modification of interest. [The present invention 1004] Identifying the subject's exposure to the antibody with the post-translational modification of interest includes determining the concentration of the antibody with the post-translational modification of interest (C PTM The method of the present invention 1003, comprising determining the area under the curve (AUC) of (t). [The present invention 1005] The serum concentration of the antibody is expressed by a two-compartment pharmacokinetic model of the equation C(t)=Ae -αt +Be -βt wherein A and B are hybrid coefficients where α and β are hybrid linear constants, respectively. [The present invention 1006] 6. The method of any of claims 1001 to 1005, further comprising predicting said in vivo serum concentration of said antibody with said post-translational modification of interest after multiple administrations of said antibody. [The present invention 1007] Predicting the in vivo serum concentration of the antibody with the post-translational modification of interest after multiple doses of the antibody includes overlaying multiple single dose models, where the serum concentration of the antibody at each dose is calculated according to a two-compartment pharmacokinetic model equation, C(t)=Ae -αt +Be -βtwhere A and B are hybrid coefficients where α and β are hybrid linear constants, respectively. [The present invention 1008] The method of any one of claims 1001 to 1007, wherein the antibody comprises a therapeutic recombinant monoclonal antibody. [The present invention 1009] The method of any of claims 1001 to 1008, wherein said post-translational modification comprises a product quality attribute that may affect drug stability, safety, and / or efficacy. [The present invention 1010] The method of any of claims 1001 to 1009, wherein the post-translational modification profile of a monoclonal antibody is prospectively monitored. [The present invention 1011] Any of the methods of 1001 to 1010, further comprising generating a predictive model for the in vivo progression of post-translational modifications and the subject's exposure to product quality attributes. [The present invention 1012] The method of any of claims 1001 to 1011, wherein the post-translational modifications include one or more of deamidation, oxidation, glycation, disulfide formation, N-terminal pyroglutamic acid formation, C-terminal lysine removal, and high mannose glycosylation. [The present invention 1013] The method of any of claims 1001 to 1012, further comprising modeling the correlation between PK concentration and proportion of post-translationally modified variants. [The present invention 1014] Any of the methods of claims 1001 to 1013, further comprising quantitatively assessing and predicting the subject's exposure to representative post-translational modifications in both single and multiple administration regimens. [The present invention 1015] The method of any of claims 1001 to 1013, further comprising predicting maximum and minimum post-translational modification levels observed in said multiple dose regimen. [The present invention 1016] Any of the methods of claims 1001 to 1015, further comprising administering said antibody to said subject. [The present invention 1017] The method of any one of claims 1001 to 1016, wherein the antibody is a mAb. [The present invention 1018] Any of the methods of claims 1001 to 1017, further comprising determining the total in vivo concentration of said antibody in said subject. [The present invention 1019] 1. A method for predicting exposure of a subject to a variant of a therapeutic recombinant monoclonal antibody following administration of said antibody, comprising: Identifying rate constants for post-translational modification (PTM) processes that lead to the formation of variants; calculating a predicted proportion of said variant as a function of time; calculating a predicted serum concentration of the variant in the subject as a function of time based on the predicted fraction of the variant; and predicting the subject's exposure to the variant following administration of the antibody based on the predicted serum concentration of the variant. The method comprising: [The present invention 1020] Calculating the predicted proportion of the variant as a function of time comprises the steps of: This is done by TIFF2025010564000005.tif4128, In the formula, P PTM (t) is the proportion of the variant as a function of time, P0 is the initial level of the variant at the time of administration, and k PTM is the rate constant for the PTM process and t is time. The method of the present invention 1019. [The present invention 1021] Calculating the predicted serum concentration of the variant in the subject as a function of time is performed by the formula: C PTM (t)=C(t) P PTM (t) It is carried out by In the formula, C PTM (t) is the serum concentration of the variant, C(t) is the serum concentration of the antibody, and P PTM(t) is the proportion of the variant at time t; The method of the present invention 1019 or 1020. [The present invention 1022] 1. A method for predicting exposure of a subject to a variant of a therapeutic recombinant monoclonal antibody following multiple administrations of said antibody, comprising: Carrying out any one of the methods of the present invention 1019 to 1021; calculating a predicted proportion of said variant before and after each subsequent administration of said antibody based on the administration interval; calculating a predicted serum concentration of the variant in the subject based on the predicted proportion of the variant at each dosing interval; and predicting the subject's exposure to the variant across two or more administrations of the antibody based on the predicted serum concentration of the variant. The method comprising: [The present invention 1023] The method of any of claims 1019 to 1022, wherein the post-translational modifications include one or more of deamidation, oxidation, glycation, disulfide formation, N-terminal pyroglutamic acid formation, C-terminal lysine removal, and high mannose glycosylation. [The present invention 1024] The method of any of claims 1019 to 1023, further comprising administering said antibody to said subject. [The present invention 1025] The method of any one of claims 1018 to 1024, wherein the antibody is a mAb. [The present invention 1026] Any of the methods of claims 1018 to 1025, further comprising determining the total in vivo concentration of said antibody in said subject. [Brief description of the drawings]

[0028] (FIG. 1) A series of traces showing recovery of MAB1 protein by affinity purification. Recovery of affinity purification of MAB1 was >99.5%, with <0.5% total MAB1 detected in the flow-through. Recovery calculations were as described in the Materials and Methods section of Example 1. A known amount of a high molecular weight standard (shown as "upper marker") was run alongside each sample as an internal normalization control. The ratio of the MAB1 peak area to the upper marker peak area for each sample was calculated to correct for run-to-run variability. (FIG. 2) FIG. 2A and FIG. 2B are examples of MS / MS spectra of peptide identification (FIG. 2A) and extracted ion chromatograms of peptide peak integration and PTM quantification (FIG. 2B). (FIG. 2A) MS / MS spectrum of an example of peptide DTLMISR (SEQ ID NO: 1) with oxidized Met (upper panel) and an example of wild-type peptide DTLMISR (SEQ ID NO: 1) (lower panel). (FIG. 2B) Extracted ion chromatograph of peptide DTLMISR (SEQ ID NO: 1) with oxidized Met (upper panel) and an example of wild-type peptide DTLMISR (SEQ ID NO: 1) (lower panel). (FIG. 3) FIGS. 3A and 3B are graphs showing the relative abundance of deamidation at each of the three Asn sites in the Fc region of MAB1 from a single-dose PK study (FIG. 3A) and a multiple-dose PK study (FIG. 3B). (FIG. 3A) In the single-dose study, the relative abundance of deamidation at each of the three Asn sites was determined by first-order kinetics: The relative abundance of deamidation at each of the three Asn sites increased over time at different rates according to TIFF2025010564000006.tif5128. Using nonlinear regression, the in vivo deamidation rate constants at Asn site 1, Asn site 2, and Asn site 3 were determined to be 0.003523% day-1, 0.5394% day-1, and 0.1546% day-1, respectively. (Figure B) In a multiple-dose study, the relative abundance of deamidation at each of the three Asn sites increased over time during each dosing interval according to the first-order kinetics, but rapidly decreased after each subsequent dose due to dilution by newly administered unmodified MAB1, showing an upward-trending sawtooth pattern. Each dosing time point is indicated by an arrow "↑". (FIG. 4) FIG. 4A and FIG. 4B are graphs showing the relative abundance of oxidation at each of the three Met sites in the Fc region of MAB1 from a single-dose PK study (FIG. 4A) and a multiple-dose PK study (FIG. 4B). (FIG. 4A) In the single-dose study, the relative abundance of Met oxidation fluctuated slightly but remained nearly unchanged at all three sites. (FIG. 4B) In the multiple-dose study, the relative abundance of oxidation at Met site 1 decreased slightly during each dosing interval and increased slightly after each dose. The relative abundance of oxidation at Met sites 2 and 3 remained stable. Each dosing time point is indicated by an arrow "↑". (Figure 5) Figures 5A and 5B are graphs showing the relative abundance of N-terminal pyroglutamic acid from a single-dose PK study (Figure 5A) and a multiple-dose PK study (Figure 5B). (Figure 5A) In the single-dose study, the relative abundance of N-terminal pyroglutamic acid increased over time. (Figure 5B) In the multiple-dose study, the relative abundance of N-terminal pyroglutamic acid increased during each dosing interval, but decreased rapidly after each subsequent MAB1 dose due to dilution by newly administered unmodified MAB1, showing an upward-trending sawtooth pattern. Each dosing time point is indicated by an arrow "↑". (FIG. 6) FIG. 6A and FIG. 6B are graphs showing the relative abundance of MAB1 with a heavy chain C-terminal lysine from a single-dose PK study (FIG. 6A) and a multiple-dose PK study (FIG. 6B). In both single-dose and multiple-dose studies, the C-terminal lysine was rapidly cleared within 1 day after each dose. Each dose time point is indicated by an arrow "↑". (FIG. 7) Figures 7A and 7B are graphs showing the relative abundance of mannose 5 glycoforms from a single-dose PK study (FIG. 7A) and a multiple-dose PK study (FIG. 7B). (FIG. 7A) In the single-dose study, the relative abundance of mannose 5 decreased from 0.5% to undetectable within 6 weeks. (FIG. 7B) In the multiple-dose study, the relative abundance of mannose 5 decreased during each dosing interval, but increased sharply at each subsequent new dose due to the newly administered MAB1 having high levels of mannose 5, showing a downward-trending sawtooth pattern. Each dosing time point is indicated by an arrow "↑". (FIG. 8) FIG. 8A is a graph showing subject exposure to total MAB1 from a single-dose PK study. The equation for the two-compartment pharmacokinetic model, C(t)=Ae -αt +Be -βt The serum concentration-time curve of MAB1, represented by the AUC of C(t), was fitted to the serum concentrations of MAB1 measured by ELISA (black dots). Subject exposure to total MAB1 over 56 days, represented by the AUC of C(t), was determined to be 4302.0 μg / mL·days. Figure 8B is a graph showing subject exposure to MAB1 at PQA from a single-dose study. The serum concentration-time curve of MAB1 with deamidation was fitted to the AUC of C(t), represented by the AUC of C(t). PQA (t)=C(t) P PQA (t) and C PQA The AUC of the (t) curve corresponds to the subject's exposure to MAB1 at PQA. The subject's exposure to MAB1 with deamidation at Asn site 2 over 56 days was determined to be 426.7 μg / mL·day (solid line). The subject's exposure to MAB1 with deamidation at Asn site 3 over 56 days was determined to be 152.5 μg / mL·day (dashed line). The subject's exposure to MAB1 with N-terminal pyroglutamic acid over 56 days was determined to be 191.5 μg / mL·day (dotted line). (FIG. 9) FIG. 9A is a graph showing model predictions and experimental measurements of serum concentrations of total MAB1 and MAB1 with deamidation at Asn site 2 or Asn site 3 from a multiple-dose PK study. The predicted pre- and post-dose serum concentrations of total MAB1 and MAB1 with deamidation at Asn site 2 or Asn site 3 are in good agreement with the experimental measurements. The pre- and post-dose concentrations of total MAB1 and MAB1 with deamidation at Asn site 2 or Asn site 3 approach steady-state levels after prolonged administration. FIG. 9B is a graph showing model predictions and experimental measurements of the relative abundance of deamidation at Asn site 2 or Asn site 3. The predicted levels are in good agreement with the experimental values. The pre- and post-dose relative abundance of deamidation at Asn site 2 or Asn site 3 approach steady-state levels after prolonged administration. Each administration time point is indicated by an arrow "↑". (FIG. 10) FIG. 10A is a graph showing model predictions and experimental measurements of serum concentrations of MAB1 with N-terminal pyroglutamic acid from a multiple-dose PK study. The predicted pre- and post-dose serum concentrations of MAB1 with N-terminal pyroglutamic acid are in good agreement with the experimental measurements. The pre- and post-dose concentrations of MAB1 with N-terminal pyroglutamic acid approach steady-state levels after prolonged administration. FIG. 10B is a graph showing model predictions and experimental measurements of the relative abundance of N-terminal pyroglutamic acid. The predicted levels are in good agreement with the experimental values. The pre- and post-dose relative abundance of N-terminal pyroglutamine approach steady-state levels after prolonged administration. Each administration time point is indicated by an arrow "↑". (Figure 11) Figures 11A and 11B show the daily doses in a single-dose study (Figure 11A) and a multiple-dose study (Figure 11B). -1 11A-11C are graphs showing modeled subject exposure to CDR deamidation based on a hypothetical in vivo deamidation rate of 2.5% per dose and initial deamidation levels of 0%, 10%, and 20% (FIG. 11A). In a single-dose study, subject exposure over 56 days to hypothetical CDR deamidation variants with 0%, 10%, and 20% initial deamidation was 1385 μg / mL·day, 1653 μg / mL·day, and 1921 μg / mL·day, respectively, accounting for 32.5%, 38.8%, and 45.1% of subject exposure to the total mAb, respectively. (FIG. 11B) In a multiple-dose study, subject exposure over five doses (56 days) to hypothetical CDR deamidation variants with 0%, 10%, and 20% initial deamidation was 1086 μg / mL·day, 1478 μg / mL·day, and 1871 μg / mL·day, respectively, accounting for 21.7%, 29.5%, and 37.3% of subject exposure to the total mAb, respectively. Each dose time point is indicated by an arrow "↑". FIG. 12 is a block diagram of a computer system that can be used to implement aspects of the present disclosure. FIG. 13 is a diagram of a distributed computing environment in which aspects of the present disclosure can be implemented. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] Detailed Description of the Invention Before describing the method, it is to be understood that the invention is not limited to the particular methods and experimental conditions described, as such methods and conditions may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the invention will be limited only by the appended claims.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are described herein. All patents, applications and non-patent publications cited herein are incorporated herein by reference in their entirety.

[0031] Abbreviations used herein Asn: Asparagine AUC: Area under the curve CDR: Complementarity determining region CV: Coefficient of variation EIC: Extracted ion chromatography ELISA: Enzyme-linked immunosorbent assay FDA: Food and Drug Administration HC: Heavy chain IgG: immunoglobulin G LC: light chain mAb: Monoclonal antibody MAB1: Therapeutic human monoclonal antibody used in Example 1 Met: methionine PK: Pharmacokinetics PQA: Product quality characteristics PTM: Post-translational modification RP-LC-MS / MS: Reversed-phase liquid chromatography-tandem mass spectrometry

[0032] definition As used herein, the term "antibody" is intended to refer to an immunoglobulin molecule (i.e., an "intact antibody molecule") consisting of four polypeptide chains, two heavy (H) chains and two light (L) chains, interconnected by disulfide bonds, as well as multimers thereof (e.g., IgM) or antigen-binding fragments thereof. Each heavy chain contains a heavy chain variable region ("HCVR" or "V H ") and the heavy chain constant region (C H 1. C H 2 and C H In various embodiments, the heavy chain may be of the IgG isotype. Optionally, the heavy chain is selected from IgG1, IgG2, IgG3 or IgG4. In some embodiments, the heavy chain is of isotype IgG1 or IgG4, and optionally includes a chimeric hinge region of isotype IgG1 / IgG2 or IgG4 / IgG2. Each light chain comprises a light chain variable region ("LCVR" or "VVR"). L ) and light chain constant region (C L ). H and V L The regions are further subdivided into regions of hypervariability, called complementarity determining regions (CDRs), interspersed with more conserved regions, called framework regions (FRs). H and V Lis composed of three CDRs and four FRs, arranged from amino-terminus to carboxy-terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. The term "antibody" includes reference to both glycosylated and non-glycosylated immunoglobulins of any isotype or subclass. The term "antibody" includes antibody molecules prepared, expressed, produced, or isolated by recombinant means, e.g., antibodies isolated from a host cell transfected to express the antibody. For a review of antibody structure, see Lefranc et al., IMGT unique numbering for immunoglobulin and T cell receptor variable domains and Ig superfamily V-like domains, 27(1) Dev. Comp. Immunol. 55-77 (2003), and M. Potter, Structural correlates of immunoglobulin diversity, 2(1) Surv. Immunol. Res. 27-42 (1983).

[0033] The term antibody also encompasses "bispecific antibodies," which include heterotetrameric immunoglobulins that can bind to multiple different epitopes. One half of a bispecific antibody, which contains one heavy chain and one light chain and six CDRs, binds to one antigen or epitope, and the other half of the antibody binds to a different antigen or epitope. In some cases, the bispecific antibody can bind to the same antigen, but at different or non-overlapping epitopes. In some cases, both halves of the bispecific antibody have the same light chain while retaining the bispecificity. Bispecific antibodies are generally described in U.S. Patent Application Publication No. 2010 / 0331527 (December 30, 2010).

[0034] The term "antigen-binding portion" (or "antibody fragment") of an antibody refers to one or more fragments of an antibody that retain the ability to specifically bind to an antigen. Examples of binding fragments encompassed by the term "antigen-binding portion" of an antibody include (i) a Fab fragment, i.e., a monovalent fragment consisting of the VL, VH, CL, and CH1 domains, (ii) a F(ab')2 fragment, i.e., a bivalent fragment comprising two Fab fragments linked by a disulfide bridge at the hinge region, (iii) a Fd fragment consisting of the VH and CH1 domains, (iv) a Fv fragment consisting of the VL and VH domains of a single arm of an antibody, (v) a dAb fragment consisting of the VH domain (Ward et al. (1989) Nature 241:544-546), (vi) an isolated CDR, and (vii) a scFv consisting of the two domains of the Fv fragment, VL and VH, that form a single protein chain linked by a synthetic linker, with the VL and VH domains combining to form a monovalent molecule. Other forms of single chain antibodies, such as diabodies, are also encompassed by the term "antibody" (see, e.g., Holliger et al. (1993) 90 PNAS USA 6444-6448, and Poljak et al. (1994) 2 Structure 1121-1123).

[0035] Furthermore, antibodies and antigen-binding fragments thereof can be obtained using standard recombinant DNA techniques generally known in the art (see Sambrook et al., 1989).

[0036] An "Fc fusion protein" comprises part or all of two or more proteins, one of which is the Fc portion of an immunoglobulin molecule that is not found together in nature. The preparation of fusion proteins comprising certain heterologous polypeptides fused to various portions of antibody-derived polypeptides (including Fc domains) is described, for example, by Ashkenazi et al., (1991) 88 Proc. Natl. Acad. Sci. USA 10535, Byrn et al., (1990) 344 Nature 677, and Hollenbaugh et al., (1992) "Construction of Immunoglobulin Fusion Proteins", in Current Protocols in Immunology, Suppl. 4, pages 10.19.1-10.19.11. A "receptor Fc fusion protein" comprises one or more extracellular domains of a receptor bound to an Fc portion, which in some embodiments includes the hinge region, followed by the CH2 and CH3 domains of an immunoglobulin. In some embodiments, the Fc fusion protein comprises two or more distinct receptor chains that bind to one or more ligands. For example, the Fc fusion protein is a trap, such as an IL-1 trap (e.g., rilonacept, which comprises the IL-1RAcP ligand binding domain fused to the extracellular domain of IL-1R1 fused to the Fc of hIgG1; see U.S. Patent No. 6,927,004), or a VEGF trap (e.g., aflibercept, which comprises the Ig domain 2 of the VEGF receptor Flt1 fused to the Ig domain 3 of the VEGF receptor Flk1 fused to the Fc of hIgG1; see U.S. Patent No. 7,087,411, issued August 8, 2006, and U.S. Patent No. 7,279,159, issued September 9, 2007).

[0037] The term "human antibody" is intended to include antibodies having variable and constant regions derived from human germline immunoglobulin sequences. Human mAbs of the invention may contain amino acid residues not encoded by human germline immunoglobulin sequences (e.g., mutations introduced by random or site-specific mutagenesis in vitro or by somatic mutation in vivo), for example in the CDRs, and particularly in CDR3. However, as used herein, the term "human antibody" is not intended to include mAbs in which CDR sequences derived from the germline of another mammalian species (e.g., mouse) are grafted onto human FR sequences. The term includes antibodies recombinantly produced in non-human mammals or in cells of non-human mammals. The term is not intended to include antibodies isolated from or generated in a human subject.

[0038] As used herein, the term "subject" refers to an animal, preferably a mammal, more preferably a human, in need of amelioration, prevention and / or treatment of, for example, a disease or disorder.

[0039] As used herein, the terms "treat", "treating" or "treatment" refer to the reduction or amelioration of the severity of at least one symptom or sign resulting from administering a therapeutic agent, such as an antibody of the present invention, to a subject in need thereof. The term includes the inhibition of progression of a disease. The therapeutic agent may be administered to the subject in a therapeutic dose.

[0040] "Post-translational modification" (PTM) refers to the covalent modification of a protein after protein biosynthesis. Post-translational modifications may occur at the side chain of an amino acid or at the C-terminus or N-terminus of a protein. Exemplary post-translational modifications of antibodies include deamidation, oxidation, glycation, disulfide formation, N-terminal pyroglutamic acid formation, C-terminal lysine removal, and glycosylation.

[0041] overview Therapeutic monoclonal antibodies (mAbs) produced in mammalian cells are heterogeneous as a result of post-translational modifications (PTMs). PTMs of therapeutic monoclonal antibodies (mAbs) are important product quality attributes (PQAs) that can affect drug stability, safety, and efficacy (Xu et al., Journal of Applied Bioanalysis 2017;3:21-5). PTMs can occur during mAb production, purification, storage, and post-administration (Kozlowski et al., Advanced drug delivery reviews 2006;58:707-22; Liu et al., Journal of pharmaceutical sciences 2008;97:2426-47; Goetze et al., mAbs 2010;2:500-7; Wang et al., Journal of pharmaceutical sciences 2007;96:1-26). Modifications occurring during drug production and storage can be reliably monitored and controlled. However, after administration of the drug, further modifications may occur due to the different environment that the mAb encounters in vivo versus in vitro. The PTMs of mAbs change significantly after administration into the bloodstream compared to in vitro conditions. Thus, monitoring the in vivo PTM changes of mAbs helps evaluate the criticality of PQAs during product risk assessment. Furthermore, quantitative evaluation of subject exposure to PTM variants helps evaluate the impact of PTMs on the safety and efficacy of therapeutic mAbs. Evaluation and prediction of possible PTMs in vivo not only facilitates understanding of the criticality of quality attributes in product risk assessment, but also helps product development teams design mAb drug candidates with improved stability in vivo.

[0042] As disclosed herein, the inventors undertook a detailed analysis and identification of product quality attributes (PQAs) present in therapeutic monoclonal antibodies. Using therapeutic monoclonal antibody MAB1 as a model, the inventors evaluated a number of post-translational modifications, including, for example, deamidation, oxidation, N-terminal pyroglutamic acid formation, and C-terminal lysine removal. As disclosed herein, the inventors identified PTMs (e.g., deamidation, oxidation, N-terminal pyroglutamic acid formation, C-terminal lysine removal, and high mannose glycosylation) of therapeutic mAb MAB1 in single and multiple dose monkey pharmacokinetic (PK) studies. Using the resulting data as an example, the inventors constructed modeling equations to calculate in vivo serum concentrations of PQAs, subject exposure to PQAs, and relative abundance of PQAs with three common PTMs in single and multiple dose regimens. The model predictions showed agreement with experimental results. The application of the model was elucidated using hypothetical PTMs. The model provides bioanalytical chemists with a simplified modeling tool to quantitatively evaluate the importance of PQAs when advanced PK models are not available. As detailed in the following examples, monkey serum samples from single and multiple dose PK studies were analyzed by affinity purification followed by LC-MS / MS to identify several post-translational properties of MAB1. Using the data obtained from this analysis, the inventors were able to build a mathematical model and / or simulation of the in vivo progression of antibody post-translational modifications to predict subject exposure to product quality attributes of therapeutic antibodies. The simulation provides a quantitative approach to evaluate the importance of PQAs.

[0043] Based on the single-dose PK study of MAB1 PQA, we were able to model the PK concentrations and proportions of PTM variants, as well as quantitatively assess and predict subject exposure to representative PTM features (e.g., asparagine deamidation) in both single and multiple dose regimens. The model also predicted the maximum and minimum PTM levels observed in multiple dose regimens. This modeling approach provides bioanalytical chemists with a new tool to quantitatively assess the importance of PQAs for therapeutic mAbs. The model could be used to assess the impact of PQAs caused by process changes and lot-to-lot variations by adjusting the initial PQA level (P0). If the modeling shows that the progression of a PQA or the subject's exposure to the PQA is insensitive to the initial level of the PQA, the process change can be adopted with a wider tolerance for the PQA.

[0044] The model can also be used to simulate the subject's exposure by extending the dosing period (t) or changing the dosing interval (τ), providing a quantitative assessment of the subject's exposure to PQA in preclinical and clinical trials. The quantitative assessment of the subject's exposure to PQA can help evaluate the risk associated with the PQA. PQA with known adverse immune reaction identified as having high exposure level during PK testing will have high potential risk. In such cases, certain strategies (e.g., protein redesign to increase in vivo stability) can be implemented to eliminate or reduce the level of the PQA.

[0045] Methods for predicting exposure to quality attributes of antibody products Disclosed herein is a method for predicting the in vivo serum concentration of an antibody with a post-translational modification of interest after administration of the antibody (e.g., a therapeutic monoclonal antibody) to a subject. The disclosed method includes predicting the percentage of the antibody with the post-translational modification of interest using an in vivo rate constant specified for the post-translational modification of interest. The percentage of the antibody with the post-translational modification can be multiplied by the total in vivo concentration of the antibody at any time to determine the concentration of the antibody with the post-translational modification of interest at that time. For example, Equation C PTM Using the formula (t) = C(t) P(t), where C(t) is the serum concentration of the antibody and P(t) is the percentage of the post-translational modification of interest. PTM In other words, the serum concentration of an antibody with a post-translational modification of interest is the percentage of the total antibody that has that post-translational modification. As disclosed herein, the serum concentration of the antibody can be determined by the two-compartment pharmacokinetic model equation C(t)=Ae -αt +Be -βt where A and B are hybrid coefficients where α and β are hybrid linear constants, respectively.

[0046] In embodiments, the in vivo rate constant for a post-translational modification of interest is determined by quantifying the percentage of the post-translational modification of interest as a function of time following a single dose, and calculating the quantified percentage of the post-translational modification of interest according to the equation: TIFF2025010564000007.tif5128. PTM (t) is the percentage of the post-translational modification of interest as a function of time, P0 is the initial percentage of the post-translational modification of interest, and k PTM is the rate constant of the post-translational modification of interest for that post-translational modification. By fitting a curve to the measured percentage of the post-translational modification of interest, the rate constant k PTM can be determined using, for example, non-linear regression or other mathematical fitting algorithms.

[0047] In embodiments, the method may also include determining the subject's exposure to an amount of an antibody with a post-translational modification of interest. For example, the concentration of an antibody with a post-translational modification of interest (C) over a period of interest, e.g., days, weeks, or months. PTM Using area under the curve (AUC) analysis of (t)), a value for the total exposure of the subject relative to the amount of antibody having the post-translational modification of interest is obtained.

[0048] It is also possible to look at multiple sites simultaneously. For example, the proportion of antibodies with post-translational modifications at a particular site can be determined using first-order kinetics. TIFF2025010564000008.tif5128, where P PTM_部位_i (t) is the rate of post-translational modification at site i as a function of time, P0 is the initial post-translational modification level at site i, and k PTM_部位_i is the rate constant for post-translational modification at site i. Thus, the PK concentration of an antibody with a particular site i in the post-translationally modified antibody is C PTM_部位_i (t)=C(t) P PTM_部位_i (t) can be shown as C PTM_部位_i The area under the curve (AUC) of the (t) plot indicates the subject's exposure to the quality attribute. Additionally, the approximate concentration of each species of antibody variant, e.g., having one, two, three, or more different post-translational modifications, can be calculated.

[0049] As discussed in the example below regarding deamidation, the methods disclosed herein may be used to predict the in vivo serum concentration of an antibody with a post-translational modification of interest after multiple doses of the antibody by extending the model to multiple doses. For example, predicting the in vivo serum concentration of an antibody with a post-translational modification of interest after multiple doses of the antibody may include overlaying multiple single dose models, where the serum concentration of the antibody at each dose is calculated according to the equation C(t)=Ae for a two-compartment pharmacokinetic model: -αt +Be -βt where A and B are hybrid coefficients where α and β are hybrid linear constants, respectively.

[0050] The serum concentration of an antibody with a desired post-translational modification immediately prior to the mth administration can be expressed as follows: C PTM,m,投与前 (m τ)=P PTM [(m-1) τ]C[(m-1) τ]+…+P PTM (1 τ)C(1 τ)

[0051] The serum concentration of an antibody with a desired post-translational modification immediately after the mth dose can be expressed as follows: C PTM,m,投与後 (m τ)=P PTM [(m-1) τ]C[(m-1) τ]+…+P PTM (1·τ)C(1·τ)+P PTM (0)C(0) Here, P PTM (t) is the first-order reaction equation describing a single dose. Thus, the percentage of antibody with the desired post-translational modification just prior to the mth dose can be expressed as: TIFF2025010564000009.tif10128

[0052] The percentage of antibodies with the desired post-translational modification immediately after the mth administration can be expressed as follows: TIFF2025010564000010.tif10128

[0053] As m approaches infinity, P 脱アミド化,m,投与前 and P PTM,m,投与後 reaches a plateau, which can be shown as follows: TIFF2025010564000011.tif15170 Therefore: TIFF2025010564000012.tif45128

[0054] The constants A, B, α, β, and k in the equation can be solved by fitting the equation to the experimental results obtained at the first dosing interval.

[0055] In various embodiments, the post-translational modifications include product quality attributes that can potentially affect the stability, safety, and / or efficacy of a drug. In certain embodiments, the method is used to monitor the in vivo post-translational modification profile of a monoclonal antibody, such as a therapeutic recombinant monoclonal antibody. In some cases, the method further comprises generating a predictive model for the in vivo progression of post-translational modifications and the subject's exposure to the product quality attributes. In some embodiments, the post-translational modifications include one or more of deamidation, oxidation, glycation, disulfide formation, N-terminal pyroglutamic acid formation, C-terminal lysine removal, and / or high mannose glycosylation. In some embodiments, the method further comprises modeling the correlation between PK concentration and the proportion of post-translational modification variants. In some embodiments, the method further comprises quantitatively assessing and predicting the subject's exposure to representative post-translational modifications in both single and multiple dose regimens. In some embodiments, the method further comprises predicting the maximum and minimum post-translational modification levels observed in multiple dose regimens.

[0056] Exemplary Computer System 12, an exemplary computer system for implementing the methods of the present disclosure includes a computer 120 (e.g., personal computers, laptops, palmtops, set-tops, servers, mainframes, handheld devices, and other types of computers) that includes a processing unit 121, a system memory 122, and a system bus 123 that couples various system components, including the system memory, to the processing unit 121. The processing unit may be any of a variety of commercially available processors, including INTEL® x86, PENTIUM®, and compatible microprocessors from INTEL® and other companies, including Cyrix, AMD, and Nexgen, Alpha, MIPS Technology, NEC, IDT®, Siemens, and other MIPS, and PowerPC from IBM® and Motorola. Dual microprocessors and other multiprocessor architectures may also be used as the processing unit 121.

[0057] The system bus may be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of conventional bus architectures, e.g., PCI, VESA, AGP, MicroChannel, ISA, and EISA, to name a few. A basic input / output system (BIOS), containing the basic routines that help to transfer information between elements within the computer 120, such as during start-up, is stored in ROM 124. The system memory includes read only memory (ROM) 124 and random access memory (RAM) 125. The computer 120 may further include a hard disk drive 127, a magnetic disk drive 128, e.g., for reading from or writing to a removable disk 129, and an optical disk drive 130, e.g., for reading from a CD-ROM disk 131 or other optical media. Hard disk drive 127, magnetic disk drive 128, and optical disk drive 130 are connected to system bus 123 by hard disk drive interface 132, magnetic disk drive interface 133, and optical drive interface 134, respectively. These drives and associated computer-readable media provide non-volatile storage of data, data structures (databases), computer-executable instructions, and the like for computer 120. Although the above description of computer-readable media refers to hard disks, removable magnetic disks, and CDs, those skilled in the art will appreciate that other types of computer-readable media, such as magnetic cassettes, flash memory cards, digital video disks, Bernoulli cartridges, and the like, may also be used in the exemplary operating environment.

[0058] An operating system 135, one or more application programs 136, other program modules 137, and data, such as program data 138, may be stored in these drives and in RAM 125. A user may enter commands and information into the computer 120 using a variety of input devices, such as a keyboard 140 and a pointing device, such as a mouse 142. Other input devices (not shown) may include a microphone, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 121 through a serial port interface 146 that is coupled to the system bus, but may also be connected by other interfaces, such as a parallel port, game port, or universal serial bus (USB). A monitor 147 or other type of display device is also connected to the system bus 123 via an interface, such as a video adapter 148. In addition to a monitor, computers typically include other peripheral output devices (not shown), such as printers. Alternatively, the display medium may be a printout or other tangible medium. The output information may also be stored on a computer-readable medium for storage and / or subsequent use or display.

[0059] Computer 120 may operate in a networked environment using logical connections to one or more other computer systems, such as computer 102. The other computer systems may be servers, routers, peer devices or other common network nodes, and typically include many or all of the elements shown relative to computer 120, although only storage device 149 is shown in Figure 12. The logical connections shown in Figure 12 include a local area network (LAN) 151 and a wide area network (WAN) 152. Such networking environments are common in offices, enterprise-wide computer networks, intranets, and the Internet.

[0060] When used in a LAN networking environment, the computer 120 is connected to the local network 151 via a network interface or adapter 153. When used in a WAN networking environment, the computer 120 typically includes a modem 154 or other means for establishing communications over a wide area network 152, such as the Internet (e.g., via the LAN 151 and a gateway or proxy server 155). The modem 154, which may be internal or external, is connected to the system bus 123 via a serial port interface 146. In a networked environment, program modules depicted relative to the computer 120, or portions thereof, may be stored in a remote storage device. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between computer systems may be used, including an Ethernet card, an ISDN terminal adapter, an ADSL modem, a 10BaseT adapter, a 100BaseT adapter, an ATM adapter, or the like.

[0061] The method, including the above-mentioned executions and operations that the method includes, may be performed by the computer 120 or by a specialized instrument or other device specifically programmed or specially designed to perform the method of the present disclosure. Thus, the method may be implemented on a specialized machine, e.g., an apparatus other than a general-purpose computer. Such executions and operations may be referred to as being computer-executed. It will be understood that the executions and symbolically represented operations include the manipulation by the processor 121 of electrical signals representing data bits, resulting in the change or reduction of the electrical signal representations and the maintenance of the data bits in memory locations of the storage devices (including the system memory 122, hard drive 127, floppy disk 129, and CD-ROM 131), thereby reconfiguring or altering the operation of the computer system and the processing of other signals. The memory locations where data bits are maintained are physical locations that have specific electrical, magnetic, or optical properties corresponding to the data bits.

[0062] Exemplary Distributed Computing Environment 13 illustrates a distributed computing environment in which software and / or database elements used to execute the methods of the present disclosure may reside. The distributed computing environment 200 includes two computer systems 202, 204 connected by a connection medium 206, although the methods of the present disclosure are equally applicable to any number of computer systems connected by a connection medium 206. The computer systems 202, 204 may be any of several types of computer system configurations, including personal computers, multiprocessor systems, handheld devices, and the like. In terms of logical relationships with other computer systems, a computer system may be a client, a server, a router, a peer device, or other common network node. The additional computer systems 202 or 204 may be connected by any number of connection media 206. The connection medium 206 may include any local area network (LAN), wide area network (WAN), or other computer network, including but not limited to Ethernet, enterprise-wide computer networks, intranets, and the Internet.

[0063] Portions of the software and databases for data storage may be incorporated in a single computer system 202 or 204, and the application is then distributed to other computer systems 202, 204 in the distributed computing environment 200. Portions of the software may be executed in the distributed computing environment 200, where tasks are performed by a single computer system 202 or 204 functioning as a remote processing device accessed over a communications network, and the distributed application is then distributed to other computer systems in the distributed computing environment 200. In a networked environment, program modules and databases for data storage may be located in multiple computer systems 202 or 204. Communications between computer systems in a distributed computing network may advantageously include encryption of the communicated data. EXAMPLES

[0064] The following examples are presented to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the methods and compositions of the present invention, and are not intended to limit the scope of what the inventors regard as their invention. Efforts have been made to ensure accuracy with respect to numbers used (e.g., amounts, temperature, etc.), but some experimental error and deviation should be accounted for. Unless otherwise specified, parts are parts by weight, molecular weight is average molecular weight, temperature is in degrees Celsius, room temperature is about 25° C., and pressure is at or near atmospheric pressure.

[0065] Example 1 Quantifying and modeling product quality attributes of a therapeutic monoclonal antibody in single- and multiple-dose monkey pharmacokinetic studies This example describes the investigation of a number of PQAs of therapeutic monoclonal antibody MAB1 using monkey serum samples from single and multiple dose PK studies. Several PTM features of MAB1 were assessed by affinity purification followed by LC-MS / MS, including deamidation, oxidation, N-terminal pyroglutamic acid, C-terminal lysine removal, and high mannose glycosylation. In addition, models were constructed to simulate the in vivo progression of PTMs and subject exposure to PQAs, providing a quantitative approach to assess the importance of PQAs. These models were evaluated using asparagine deamidation as a representative PTM feature. Subject exposure to deamidated MAB1 simulated by the disclosed models was in good agreement with the experimental results of both single and multiple dose studies.

[0066] Materials and Methods material The human IgG4 monoclonal antibody (MAB1) and anti-human antibody used in this study were manufactured by Regeneron Pharmaceuticals, Inc. (Tarrytown, NY). The average half-life of the MAB was approximately 11.5 days. Unless otherwise stated, all reagents were obtained from Sigma-Aldrich (St. Louis, MO) or Thermo Fisher Scientific (Waltham, MA).

[0067] Preclinical sample information Preclinical serum samples were obtained from either single-dose or multiple-dose cynomolgus monkey PK studies. Subjects were administered MAB1 intravenously (IV). In single-dose studies, animals were dosed with 10 mg / kg and serum samples were collected at designated time points (pre-dose, 5 min, 4 hr, 12 hr, 1 day, 3 days, 7 days, 14 days, 18 days, 30 days, 42 days, and 56 days) over a period of 8 weeks (56 days). In multiple-dose studies, animals were dosed with 3 mg / kg every 2 weeks (14 days) and serum samples were collected at designated time points (pre-dose, 1 hr, 4 hr, 1 day, 3 days, 7 days) within the first dosing interval and before and after each new dose (14 days, 28 days, 42 days, 56 days) over a period of 8 weeks (56 days). Serum samples were stored at -80°C until analysis. Serum concentrations of MAB1 at each collection time point were measured using an enzyme-linked immunosorbent assay (ELISA). Briefly, MAB1 was captured on a microtiter plate coated with the drug target. MAB1 captured on the plate was detected using a biotinylated mouse anti-human IgG4 monoclonal antibody followed by NeutrAvidin conjugated to horseradish peroxidase (NeutraAvidin-HRP). A luminol-based substrate specific for peroxidase was then added to obtain a signal intensity proportional to the concentration of MAB1.

[0068] Affinity purification of MAB1 from serum samples MAB1 was purified by affinity purification from collected monkey serum samples. Briefly, biotinylated anti-human antibodies were conjugated to Dynabeads MyOne Streptavidin T1 magnetic beads (Invitrogen, Carlsbad, CA) for 10 min at room temperature. The conjugated beads were incubated with serum samples for 30 min at room temperature. The beads were washed with HBS-EP buffer (GE Healthcare, Pittsburgh, PA) and then eluted with 0.1% formic acid and 50% acetonitrile. A bioanalyzer (Agilent Technologies, Santa Clara, CA) was used to assess the recovery of MAB1 from affinity purification. A known amount of an Agilent 230 kDa high molecular weight standard (shown as "upper marker") was run with each sample as an internal normalization control. The ratio of the peak area of ​​MAB1 to the peak area of ​​the upper marker in each sample was calculated to correct for run-to-run variability. The recovery of MAB1 from affinity purification was calculated using the following formula: TIFF2025010564000013.tif7128

[0069] Trypsin digestion Purified MAB1 samples were dried using a vacuum concentrator (LABCONCO, Kansas City, MO). Dried samples were resuspended in 8 M urea and 10 mM TCEP and incubated at 37°C for 30 min. Reduced cysteine ​​residues were alkylated with 10 mM iodoacetamide for 30 min at room temperature in the dark. After alkylation, the urea concentration was diluted to 1.25 M before digestion. Trypsin (Promega, Sunnyvale, CA) was added to the samples at an enzyme:substrate ratio of 1:10 and incubated at 37°C for 4 h. Digestion was stopped by the addition of 20% formic acid (FA, Thermo Scientific, San Jose, CA). Digested samples were stored at -80°C until analysis.

[0070] LC-MS / MS and data analysis Peptides generated by trypsin digestion were separated using an Acquity UPLC CSH C18 1.7 μm, 2.1 mm × 150 mm column (Waters, Milford, MA) on an Acquity I-Class UPLC system (Waters, Milford, MA) interfaced with a Q Exactive and mass spectrometer (Thermo Fisher Scientific, San Jose, CA). Mobile phase A was 0.1% FA in water, and mobile phase B was 0.1% FA in acetonitrile. A gradient from 2% mobile phase B to 30% mobile phase B in 56 min at a flow rate of 0.25 mL / min was used for peptide separation. MS acquisition consisted of a full mass scan followed by a tandem mass (MS / MS) scan of the top five most intense ions of each full scan. Peptide and PTM identifications were performed by Byonic (version 2.16.11, Protein Metrics Inc., San Carlos, CA) and manually verified. To quantify the relative abundance of PTMs, extracted ion chromatograms based on the m / z of the first isotopic peak of both modified and native peptides were generated and the extracted peak areas were integrated using Skyline-daily (version 4.1.1.18151) using a mass window of 5 ppm. The percentage of each PTM variant was calculated using the peak area of ​​the modified peptide relative to the sum of the peak areas of the modified and native peptides in this extracted ion chromatogram (EIC).

[0071] Calculating Subject Exposure to Quality Attributes in Single-Dose PK Studies Quantitative assessment of subject exposure to quality attributes in single-dose PK studies was previously described by Flynn et al. (Goetze et al., mAbs 2010;2:500-7). The serum concentration-time equation for MAB1 can be expressed as a two-compartment model (Rowland and Tozer Wolters Kluwer Health / Lippincott William & Wilkins, 2011): TIFF2025010564000014.tif5128 where C(t) is the serum concentration of MAB1, and C1, C2, λ1, and λ1 are hybrid constants.

[0072] For ease of reading, the following format has been adopted: TIFF2025010564000015.tif5128 where A and B are the zero time intercepts (hybrid coefficients) and α and β are the hybrid linear constants, respectively. This equation was fitted to the serum concentrations of MAB1 measured by ELISA at the time points of collection to find the best fit parameters. The fitting constraints are: C(0) must be higher than the serum concentration of MAB1 measured by ELISA after the first dose, the C(infinity) constant must be 0, and A and B must be greater than 0. R-squared was used to quantify the goodness of fit, and an R-squared greater than 0.95 was considered a good fit.

[0073] The relative abundance of MAB1 with site-specific Asn deamidation can be expressed by the equation for the first order rate constant: TIFF2025010564000016.tif5128 formula, P 脱アミド化 (t) is the percentage of deamidation, P0 is the initial deamidation level on day 0, and k 脱アミド化 is the deamidation rate constant.

[0074] Serum concentrations of MAB1 with deamidation at specific Asn sites can be shown as follows: TIFF2025010564000017.tif4128In formula, C 脱アミド化 (t) is the serum concentration of MAB1 with deamidation at a specific Asn site. 脱アミド化 The area under the curve (AUC) of C(t) represents the subject's exposure to this quality trait (i.e., MAB1 with deamidation at a specific Asn site). C(t) and P 脱アミド化 The equation for (t) was solved by nonlinear regression using JMP (version 13.2.1, SAS, Cary, NC).脱アミド化 (t) was calculated using Excel (Microsoft, Redmond, WA) and plotted using JMP. 脱アミド化 Subject exposure, expressed as the AUC of (t), was calculated using JMP.

[0075] Calculating subject exposure to quality attributes in multiple-dose PK studies Modeling of the subject's exposure to the quality attribute in the multiple-dose PK study was mathematically simplified using the principle of superposition as a linear accumulation of multiple single doses. The serum concentration of MAB1 after each dose was represented by the two-compartment model discussed in the single-dose PK study above. The serum concentration of MAB1 immediately prior to the mth dose can be represented as follows: TIFF2025010564000018.tif5128In the formula, m is the number of administrations and τ is the time interval between administrations.

[0076] The serum concentration of MAB1 immediately after the mth dose can be shown as follows: TIFF2025010564000019.tif5128

[0077] As m approaches infinity, C m,投与前 and C m,投与後 approaches a steady state concentration given by: TIFF2025010564000020.tif24128

[0078] The serum concentration of MAB1 with deamidation at a specific Asn site just prior to the mth dose can be shown as follows: TIFF2025010564000021.tif11147

[0079] The serum concentration of MAB1 with deamidation at the specific Asn site identified immediately after the mth dose can be shown as follows: TIFF2025010564000022.tif12146Here, P 脱アミド化(t) is the first-order reaction equation discussed in the single-dose PK study above.

[0080] Thus, the relative abundance of MAB1 with deamidation at a particular Asn site just prior to the mth dose can be expressed as follows: TIFF2025010564000023.tif8128

[0081] The relative abundance of MAB1 with deamidation at a particular Asn site immediately after the mth dose can be shown as follows: TIFF2025010564000024.tif8128

[0082] As m approaches infinity, P 脱アミド化,m,投与前 and P 脱アミド化,m,投与後 approaches a steady-state level given by: TIFF2025010564000025.tif33168

[0083] Because the calculation of the summation term converges, the pre-dose level of deamidation at steady state can be simplified to: TIFF2025010564000026.tif13128

[0084] Therefore, the level of deamidation after administration at steady state can be simplified as follows: TIFF2025010564000027.tif14143The constants A, B, α, β, and k in this equation were solved by fitting the equation to the experimental results within the first dosing interval.The simulated and predicted values ​​were calculated using Excel and plotted using JMP.The exposure of interest (i.e., AUC) was calculated using JMP.

[0085] result Quantification of post-translational modifications of MAB1 in vivo The in vivo kinetics of MAB1 variants with different PTMs were evaluated in both single- and multiple-dose monkey PK studies.

[0086] MAB1 was extracted from monkey serum samples using biotinylated anti-human Fc antibody conjugated to streptavidin magnetic beads. The recovery rate of MAB1 by affinity purification was >99.5% (Figure 1), indicating that the affinity purification approach could completely extract both native MAB1 and its PTM variants, ensuring accurate quantification of the relative PTM abundance of each PTM.

[0087] Figure 2A shows exemplary tandem MS spectra of the wild-type Fc tryptic peptide "DTLMISR" (SEQ ID NO: 1) (lower panel) and the corresponding methionine (Met) oxidized peptide (upper panel). Figure 2B shows the extracted ion chromatographic peak areas of the wild-type peptide (lower panel) and the corresponding Met oxidized peptide (upper panel).

[0088] Asparagine (Asn) deamidation is a common PTM in the highly conserved fragment crystallizable (Fc) region of mAbs (Chelius et al., Analytical Chemistry 2005;77:6004-11; Sinha et al., Protein Science: a publication of the Protein Society 2009;18:1573-84). Three common deamidation sites were measured in this study, namely, "NG" (Asn site 1) in the CH2 domain, "NG" (Asn site 2) and "NN" (Asn site 3) in the PENNY peptide in the CH3 domain. Three common deamidation sites were measured in this study, namely, "NG" (Asn site 1) in the CH2 domain, "NG" (Asn site 2) and "NN" (Asn site 3) in the CH3 domain. In the single-dose PK study, the relative abundance of deamidation at Asn site 1 remained almost unchanged at a low level of approximately 0.2% over 56 days (Fig. 3A). The relative abundance of deamidation at Asn site 2 and Asn site 3 increased from approximately 1% to 25.5% and 9.1%, respectively, over 56 days (Fig. 3A).The rate of deamidation depends on the primary amino acid sequence, protein structure, pH, temperature, and buffer composition (Chelius et al., Analytical Chemistry 2005;77:6004-11; Sinha et al., Protein Science: a publication of the Protein Society 2009;18:1573-84; Kossiakoff, Science 1988;240:191-4; Athmer et al., The Journal of Biological Chemistry 2002;277:30502-7; Xie et al., Journal of Pharmaceutical Sciences 1999;88:8-13; Wearne et al., Proteins 1989;5:8-12; Capasso et al., The Journal of Peptide Research: official journal of the American Peptide Society 1999;54:377-82; Song Y et al., The Journal of Peptide Research: official journal of the American Peptide Society 1999;54:377-82; Song Y et al., The Journal of Peptide Research: official journal of the American Peptide Society 1999;54:377-82; Song Y et al., The Journal of Peptide Research: official journal of the American Peptide Society 1999;54:377-82). al., Journal of Pharmaceutical Sciences 2001;90:141-56, Wakankar et al., Journal of Pharmaceutical Sciences 2006;95:2321-36, Zheng et al., International Journal of Pharmaceutics 2006;308:46-51, Pace et al., Journal of Pharmaceutical Sciences 2013;102:1712-23).Asn residues followed by glycine (Gly) or serine (Ser) in the primary amino acid sequence are particularly susceptible to deamidation (Patel et al., Pharmaceutical Research 1990;7:787-93; Stephenson et al., The Journal of Biological Chemistry 1989;264:6164-70; et al., Proceedings of the National Academy of Sciences of the United States of America 2001;98:944-9; Robinson et al., The Journal of Peptide Research: official journal of the American Peptide Society 2004;63:426-36). The "NG" and "NN" sites are completely exposed to solvent and are therefore more susceptible to deamidation than the "NG" site in the CH2 domain (DeLano et al., Science 2000;287:1279-83).

[0089] The rate of deamidation can be expressed using the first-order rate equation previously established (Geiger et al., The Journal of Biological Chemistry 1987;262:785-94). This rate equation is It can be represented as TIFF2025010564000028.tif5128, where P 脱アミド化 (t) is the percentage of deamidation as a function of time, P0 is the initial level of deamidation, and k 脱アミド化 R is the deamidation rate constant. This equation was nonlinearly fitted to the levels of deamidation measured by LC-MS in single-dose studies to identify the best fit parameters (Figure 3A and Table 1). The in vivo deamidation rate constants of Asn site 1, Asn site 2, and Asn site 3 were nonlinearly fitted to determine R, respectively. 2are 0.9946, 0.9866, and 0.8941, respectively, which is 0.003278% -1 , 0.5384% day -1 , and 0.1566% day -1 (Table 1).

[0090] Table 1. Best fit parameter values ​​in modified rate equation from single-dose PK study TIFF2025010564000029.tif59170 where P0 is the initial deamidation level on day 0 and k 脱アミド化 is the deamidation rate constant.

[0091] In the multiple-dose study, deamidation at Asn site 1 remained almost unchanged at a low level of approximately 0.5% after five biweekly doses over 56 days (Fig. 3B). The relative abundance of deamidation at Asn sites 2 and 3 accumulated within each dosing interval but dropped sharply after each new dose due to dilution by the newly administered non-deamidated MAB1 (Fig. 3B). Due to the accumulation of deamidated MAB1, pre- and post-dose deamidation levels gradually increased after each dose. Before the fifth dose on day 56, the pre-dose level of deamidation at Asn site 2 increased to 14.2%, whereas the post-dose level dropped to 7.9% (Fig. 3B). Before the fifth dose on day 56, the pre-dose level of deamidation at Asn site 3 increased to 6.2%, whereas the post-dose level dropped to 3.6% (Fig. 3B). As a result, the relative abundance of deamidation at Asn site 2 and Asn site 3 showed a sawtooth pattern with an increasing trend over time (Figure 3B). Figure 3B also suggests that both pre- and post-dose deamidation levels approach a steady state after a finite number of doses. To predict the steady-state level of deamination, we constructed a two-compartment model of MAB1 concentration, which is discussed in the modeling section below. Met oxidation is another common PTM of mAbs (Yin et al., Pharmaceutical research 2013;30:167-78; Li et al., mAbs 2016:0; Li et al., mAbs 2016:0; Liu et al., The Journal of biological chemistry 2011;286:11211-718). Met oxidation in the Fc region has been shown to affect Fc receptor binding (Pan et al., Protein Science: a publication of the Protein Society 2009;18:424-33). In this study, three common Met oxidation sites in the Fc region were evaluated: Met site 1 in the CH2 domain, and Met sites 2 and 3 in the CH3 domain. In a single-dose study, the relative abundance of oxidation at Met sites 1, 2, and 3 remained relatively unchanged over 56 days at levels of ∼8.0%, ∼2.0%, and ∼5.0%, respectively (Figure 4A).In the multiple-dose study, oxidation levels at Met site 1 showed a sawtooth pattern, decreasing slightly during each dosing interval and increasing slightly after each dose, fluctuating between approximately 7.0% and approximately 9.0%. Oxidation levels at Met sites 2 and 3 remained stable at levels of approximately 1.0% and 4.0%, respectively, in the multiple-dose study (Figure 4B).

[0092] N-terminal glutamine and glutamic acid at the N-terminus of mAbs tend to form pyroglutamic acid through chemical or enzymatic cyclization. The conversion of glutamine is much faster than that of glutamic acid (Yin et al., Pharmaceutical research 2013;30:167-78; Li et al., mAbs 2016:0; Li et al., mAbs 2016:0; Liu et al., The Journal of biological chemistry 2011;286:11211-718). N-terminally cyclized proteins are resistant to digestion by aminopeptidases, preventing degradation. The enzyme that catalyzes this conversion, glutaminyl cyclase, is found in human blood (Schilling et al., Biological Chemistry 2008;389:983-91), and the formation of pyroglutamate from glutamate in mAbs in vivo is a primarily pH-dependent nonenzymatic reaction with the same rate constant in PBS in vitro (Yin et al., Pharmaceutical Research 2013;30:167-78; Liu et al., The Journal of Biological Chemistry 2011;286:11211-7). In single-dose studies, N-terminal pyroglutamate increased from 0.7% to 11.7% over a 56-day period (Figure 5A). The rate of N-terminal pyroglutamate formation in vivo was calculated using a nonlinear fit to N-terminal pyroglutamate levels measured by LC-MS, R 2 is 0.9970 and 0.2201% -1In multiple-dose studies, N-terminal pyroglutamate formation resembled a sawtooth pattern similar to Asn deamidation (Figure 5B). Pyroglutamate levels rose during each dosing interval but were subject to dilution by newly administered unmodified MAB1 and dropped sharply after each new dose. Before the fifth dose on day 56, pyroglutamate levels rose to 7.6% and dropped to 4.2% after the fifth dose (Figure 5B).

[0093] The C-terminal lysine residue of the mAb heavy chain is susceptible to removal by basic carboxypeptidases during protein expression (Dick et al., Biotechnology and Bioengineering 2008;100:1132-43). Partial removal of the C-terminal lysine results in charge heterogeneity. Before dosing, the percentage of MAB1 formulations containing C-terminal lysine was approximately 2.0%. In both single- and multiple-dose studies, the C-terminal lysine was rapidly removed within 1 day after each dose (Figure 6). These observations are consistent with previously reported studies.

[0094] N-linked glycosylation in the Fc region is a commonly observed PQA of mAbs. The predominant structure of N-linked glycoforms of mAbs expressed in Chinese hamster ovary (CHO) usually consists of a core biantennary pentasaccharide structure with one or more additional monosaccharides attached. In addition to complex biantennary oligosaccharide structures, high mannose glycoforms (e.g., mannose 5) can also be observed in CHO-expressed antibodies. In a single-dose study, the relative abundance of the predominant glycoform (fucosylated complex biantennary) did not change over 56 days. However, the relative abundance of the mannose 5 glycoform decreased from 0.5% to undetectable within 6 weeks (Figure 6A). This finding is consistent with faster serum clearance of high mannose glycoforms through the high mannose receptor-mediated clearance pathway (Goetze et al., Glycobiology 2011;21:949-59; Alessandri et al., mAbs 2012;4:509-20). In multiple dose studies, the levels of mannose 5 declined during each dosing interval and showed a sawtooth pattern, assessed by mixing with MAB1 with freshly administered high levels of mannose 5 immediately after each new dose (Figure 6B). Before the fifth dose was administered, the levels of mannose 5 declined to 0.1%. After the fifth dose was administered, the levels of mannose 5 increased to 0.4%.

[0095] Modeling subject exposure to the PQA of MAB1 in a single-dose study Quantitative assessment of a subject's exposure to a quality attribute of a mAb is useful for assessing the impact of the quality attribute on drug safety and efficacy. As described in the Materials and Methods section, the in vivo serum concentration of a mAb with a particular PTM is determined by the C PTM (t) = C(t) P(t). PTMThe area under the curve (AUC) of (t) represents the subject's exposure to the mAb with PTM (Goetze et al., mAbs 2010;2:500-7). In this study, deamidation at Asn site 2 and Asn site 3 and N-terminal pyroglutamate formation were used as examples to demonstrate modeling of subject's exposure to PQA in a single dose study. The other PTMs discussed herein were not modeled because Met oxidation was unchanged in vivo (Figure 4), C-terminal lysine was rapidly cleared within hours after administration (Figure 6), and the levels of deamidation at mannose 5 and Asn site 1 were extremely low (less than 0.5%, Figures 3 and 7), making modeling of these PTMs potentially meaningless.

[0096] The serum concentration-time profile of MAB1 was calculated using a two-compartment pharmacokinetic model with the equation C(t) = Ae -αt +Be -βt where A and B are hybrid coefficients where α and β are hybrid linear constants. Experimental MAB1 serum concentrations from single-dose PK studies were determined using an ELISA assay. The modeling equation was expressed as R 2 The equation was solved by a nonlinear fit to the MAB1 serum concentrations measured by ELISA with a C(t) of 0.9916 (Figure 8A). The best-fit parameter values ​​of the modeled equation are shown in Table 2. The subject's exposure to total MAB1 over the 56-day period, represented by the AUC of C(t), was 4302.0 μg / mL·day-1 (Figure 8A).

[0097] The rate equation for deamidation at Asn site 2 is given by P 脱アミド化-Asn2 (t)=1-(1-0.01143)·e -0.005394t The serum concentration-time equation for MAB1 with deamidation at Asn site 2, C 脱アミド化-Asn2 (t)=C(t) P 脱アミド化-Asn2 (t) is therefore C(t) and P 脱アミド化-Asn2 This can be solved by multiplying (t). 脱アミド化-Asn2 The (t) curve is plotted in Figure 8B. 脱アミド化The subject's exposure to MAB1 with deamidation at Asn site 2 over 56 days, expressed as the AUC of (t), was calculated to be 426.7 μg / mL·day ( Fig. 8B ), accounting for 9.9% of the subject's exposure to total MAB1 over 56 days.

[0098] Similarly, the rate equation for deamidation at Asn site 3 is given by P 脱アミド化-Asn3 (t)=1-(1-0.008848)·e -0.001546t The serum concentration-time equation for MAB1 with deamidation at Asn site 3, C 脱アミド化-Asn3 (t)=C(t) P 脱アミド化-Asn3 (t) is therefore C(t) and P 脱アミド化-Asn3 This can be solved by multiplying (t). 脱アミド化-Asn3 The (t) curve is plotted in Figure 8B. 脱アミド化-Asn3 The subject's exposure to MAB1 with deamidation at Asn site 3 over 56 days, expressed as the AUC of (t), was calculated to be 152.5 μg / mL·day ( Fig. 8B ), accounting for 3.5% of the subject's exposure to total MAB1 over 56 days.

[0099] The rate equation for the formation of N-terminal pyroglutamic acid is given by P PyroE (t)=1-(1-0.006868)·e -0.002201t The serum concentration-time equation for MAB1 with N-terminal pyroglutamic acid, C PyroE (t)=C(t) P PyroE (t) is therefore C(t) and P PyroE This can be solved by multiplying (t). PyroE The (t) curve is plotted in Figure 8B. PyroE Subject exposure to MAB1 with N-terminal pyroglutamic acid over 56 days, expressed as AUC of (t), was 191.5 μg / mL·day ( Fig. 8B ), accounting for 4.5% of subject exposure to total MAB1 over 56 days.

[0100] Table 2. Best-fit parameter values ​​for the MAB1 serum concentration-time equation from the first dosing interval of the single-dose and multiple-dose PK studies TIFF2025010564000030.tif94166

[0101] Modeling subject exposure to the PQA of MAB1 in a multiple-dose study The subject's exposure to both total MAB1 and MAB1 with a particular property (e.g., Asn deamidation) can be calculated for any given time interval using the area under the concentration-time curve. The AUC of the predicted and experimental total MAB1 serum concentration-time curves represents the subject's exposure to total MAB1 over a defined time interval. Based on the predicted and experimental MAB1 serum concentration-time curves (Figure 9A), the predicted and experimentally determined subject's exposure to total MAB1 over five doses (56 days) was 5013 μg / mL·day and 4789 μg / mL·day, respectively. The difference between the predicted and experimental values ​​is 4.7%. Similarly, the AUC of the predicted and experimentally determined concentration-time curves of MAB1 with deamidation at Asn site 2 represents the subject's exposure to MAB1 with deamidation at Asn site 2 over a defined time interval. The predicted and experimentally determined subject exposures to MAB1 deamidated at Asn site 2 over five doses (56 days) were 381.5 μg / mL·day and 352.6 μg / mL·day, respectively (Figure ​(Figure9A). ...

[0102] The predicted and experimentally identified subject exposures to MAB1 deamidated at Asn site 3 over five doses (56 days) were 172.8 μg / mL·day and 158.8 μg / mL·day, respectively (Figure 9A). The predicted and experimentally identified subject exposures to MAB1 deamidated at Asn site 3 as a percentage of subject exposure to total MAB1 over 56 days were 3.4% and 3.3%, respectively.

[0103] The predicted and experimentally identified subject exposures to MAB1 with N-terminal pyroglutamic acid over five doses (56 days) were 218.5 μg / mL·day and 231.8 μg / mL·day, respectively (Figure S4A). The predicted and experimentally identified subject exposures to MAB1 with N-terminal pyroglutamic acid, as a percentage of subject exposure to total MAB1 over 56 days, were 4.3% and 4.8%, respectively.

[0104] Modeling the relative in vivo abundance of MAB1 PQAs in a multiple-dose study These models can also be used to predict the pre-dose and post-dose levels of PQA of the mAb at any given dose during the multiple dose study by using Equation 12 and Equation 13, respectively. For example, the predicted pre-dose and post-dose deamidation levels at Asn site 2 at day 56 are 14.2% and 7.9%, respectively. This prediction is consistent with the pre-dose and post-dose measured by LC / MS at day 56, which were 15.0% and 7.9%, respectively. All other predicted deamidation levels (Figure 9B, open circles) are in good agreement with the experimental measurements (Figure 9B, solid points). The pre-dose and post-dose PTM levels in the multiple dose study will reach a steady state, similar to the pre-dose and post-dose serum concentrations of the mAb reaching a steady state concentration (Figure 9B). The predicted pre-dose and post-dose steady state levels (i.e., plateau levels) of the PQA correspond to the maximum and minimum levels of this PQA in vivo in the multiple dose study. Using Equation 16 and Equation 17, the pre- and post-dose steady-state levels of MAB1 with deamidation at Asn site 2 were determined to be 17.9% and 9.7%, respectively.

[0105] Similarly, the predicted pre- and post-dose deamidation levels at Asn site 3 (FIG. 9B, open triangles) are in good agreement with the experimental measurements (FIG. 9B, solid triangles). The pre- and post-dose steady-state levels of MAB1 with deamidation at Asn site 3 were determined to be 8.4% and 4.6%, respectively.

[0106] The predicted pre- and post-treatment N-terminal pyroglutamic acid levels (FIG. 10B, open circles) are in good agreement with the experimental measurements (FIG. 10B, solid points). The pre- and post-treatment steady-state levels of MAB1 with N-terminal pyroglutamic acid were determined to be 8.1% and 4.5%, respectively.

[0107] Example of application of the model Quantitative assessment of in vivo PQAs of therapeutic proteins is important because it helps to identify potential critical quality attributes (CQAs) not identified in vitro, which impacts the risk assessment and control strategy of the product. The models described herein can be used for quantitative assessment of PQAs in many applications. First, the in vivo progression of PQAs and subject exposure to the PQAs in single and multiple dose regimens can be calculated using these models. Good agreement between model predictions and experimental measurements was demonstrated in the above sections (Figures 8, 9, and 10). Second, these models can be used to predict subject exposure with extended dosing period (t) and to identify pre- and post-dose steady-state concentrations / levels of PQAs (Figures 9 and 10), providing insights for preclinical and clinical trials. Third, these models can be used to evaluate the impact of changes in the initial levels of PQAs due to process changes or lot-to-lot variations by adjusting the initial levels (P0) of PQAs in these modeling equations. This is especially relevant when the PQA is a CDR modification. MAB1 used in this study does not have a CDR modification suitable for this modeling application, so -1 A hypothetical CDR deamidation rate of 2.5% in vivo per unit time was used as an example of using these models to assess the impact of initial levels of CDR deamidation on a subject's exposure to a PQA. There are three batches of a formulation with hypothetical initial levels of CDR deamidation of 0%, 10%, and 20%, respectively. The hypothetical in vivo CDR deamidation rate of these batches over time is 0%, 10%, and 20%, respectively. 脱アミド化 (t)=1-(1-0)·e -0.025t , P 脱アミド化 (t)=1-(1-0.1)·e-0.025t , and P 脱アミド化 (t)=1-(1-0.2)·e -0.025t Using the above model, in the single-dose study, subject exposure over 56 days to hypothetical CDR deamidation variants with 0%, 10%, and 20% initial deamidation was 1385 μg / mL·day, 1653 μg / mL·day, and 1921 μg / mL·day, respectively, accounting for 32.5%, 38.8%, and 45.1% of the subject exposure to the total mAb (Figure 11A). In the multiple-dose study, subject exposure over five doses (56 days) to hypothetical CDR deamidation variants with 0%, 10%, and 20% initial deamidation was 1086 μg / mL·day, 1478 μg / mL·day, and 1871 μg / mL·day, respectively, accounting for 21.7%, 29.5%, and 37.3% of the subject exposure to the total mAb (Figure 11B). Thus, if process controls yielded ±10% of this hypothetical initial level of CDR deamidation, subject exposure to this CDR deamidated mAb would vary by approximately 6-8% over a 56-day single or multiple dose study. The modeling shows that subject exposure to this hypothetical CDR deamidation is insensitive to the initial level of PQA, allowing the PQA to be broadened to allow justification for process risk assessment. These modeling results, in combination with the impact of CDR modifications on potency as measured by potency assays, can be used to assess subject exposure to effective drug when changes in initial CDR modifications are due to process controls or lot-to-lot variation, providing important information for product risk assessment.

[0108] Consideration The dynamic in vivo environment of circulating blood flow, which results in mAb clearance, elimination, and degradation, is much more meaningful for evaluating changes in PQA in patients compared to static incubation of mAbs in PBS or serum in vitro (Yin et al., Pharmaceutical research 2013;30:167-78). Thus, quantifying and modeling PQA in vivo provides important information not available from in vitro studies to evaluate the importance of PQA. For example, these results presented here (Figure 6) and previous studies (Li et al., mAbs 2016:0, Cai et al., Biotechnology and Bioengineering 2011;108:404-12) show that C-terminal lysines are rapidly removed in vivo within 1 day. Similarly, trisulfide bonds have been found to be rapidly converted to disulfide bonds in vivo (Li et al., mAbs 2016:0). The results presented here (Figure 4) and previous studies (Li et al., mAbs 2016:0, Li et al., mAbs 2016:0) demonstrated that the level of methionine oxidation is often unaffected after in vivo administration. Thus, C-terminal lysine removal, trisulfide bonds, and methionine oxidation are unlikely to be considered CQAs. In contrast, deamidation was found to accumulate in vivo in single and multiple dose regimens (Figure 3) as well as in previous studies (Li et al., mAbs 2016:0, Li et al., mAbs 2016:0). Deamidation that occurs and accumulates in the CDR regions may affect drug efficacy (Yan et al., Journal of pharmaceutical sciences 2009;98:3509-21, Haberger et al., mAbs 2014;6:327-39) or cause off-target binding. Thus, modeling of CDR deamidation in vivo using the formulas described herein allows for a quantitative assessment of the impact of CDR deamidation on subject exposure, as demonstrated above (Figure 11).

[0109] In this study, asparagine deamidation and N-terminal pyroglutamate formation were used as representative PTMs to demonstrate the validity of our model. Our model can also be applied to other PTMs that show either an increasing or decreasing trend. For example, for the enzyme-based clearance of mannose 5, the model was applied to evaluate the clearance of mannose 5 by employing third-order kinetics. Even though the relative abundance of mannose 5 decreased to less than 1.0% over time, the predicted values ​​were in good agreement with the experimental data (data not shown). Our model can be used to calculate other parameters to evaluate a given property or PTM in a multiple-dose study. These parameters include the rate of accumulation to steady state, the average level at steady state, and the rate of change of the PTM at steady state.

[0110] In summary, we characterized the general in vivo PTM changes of a therapeutic mAb (MAB1), modeled the in vivo behavior of PTMs for both single- and multiple-dose test regimens, and evaluated the impact of PTMs in vivo. Three asparagine residues in the Fc region of MAB1 showed different deamidation rates. The level of oxidation at three methionine residues in the Fc region of MAB1 showed no change over time in vivo. N-terminal pyroglutamic acid occurred rapidly in vivo. C-terminal lysine was completely removed within 1 day. MAB1 with high mannose glycosylation showed accelerated clearance. We used two Asn deamidations and N-terminal pyroglutamic acid formation as representative PQAs and constructed modeling equations to calculate serum concentrations of PQAs, subject exposure to PQAs, and relative abundance of PQAs in vivo for both single- and multiple-dose regimens. These model predictions were validated by experimental measurements and therefore can be used to simulate the progression of and subject exposure to PQAs in vivo in both single and multiple dose regimens, providing a quantitative approach to PQAs for criticality assessment in therapeutic mAbs.

[0111] The present invention is not limited in scope by the specific embodiments described herein. Indeed, various modifications of the invention in addition to those described herein will become apparent to those skilled in the art from the foregoing description and accompanying figures. Such modifications are intended to be included within the scope of the appended claims.

[0112] Sequence information SEQUENCE LISTING <110> Regeneron Pharmaceuticals, Inc. <120> QUANTITATION AND MODELING OF QUALITY ATTRIBUTES OF THERAPEUTIC MONOCLONAL ANTIBODIES <150> US 62 / 625,219 <151> 2018-02-01 <160> 1 <170> PatentIn version 3.5 <210> 1 <211> 7 <212> PRT <213> Artificial Sequence <220> <223> Met oxidized peptide <400> 1 Asp Thr Leu Met Ile Ser Arg 1 5

Claims

1. 1. A method for identifying acceptable ranges of product quality attributes (PQAs) of an antibody, comprising: (a) the in vivo serum concentration of an antibody with a PQA following a single dose of the antibody; (i) predicting the percentage of said antibody with a PQA using an in vivo rate constant identified for said PQA; and (ii) multiplying the predicted percentage of the antibody with a PQA by the total in vivo concentration of the antibody to identify the concentration of the antibody with a PQA, thereby generating a single dose model; Predict by (b) repeating step (a) at least once, thereby generating one or more additional single dose models; (c) superimposing a plurality of single-dose models of steps (a) and (b), thereby generating a multi-dose model; (d) using a multiple dose model to identify the relationship between initial PQA levels and PQA levels after multiple doses of the antibody; and (e) using said relationship to identify acceptable ranges of PQAs for the antibody; The method comprising:

2. The in vivo rate constant for the PQA is Quantifying the percentage of PQA as a function of time; and The quantified percentage of the PQA is calculated according to the equation To fit Identified by In the formula, P ＰＱＡ (t) is the rate of said PQA as a function of time, P 0 is the initial percentage of PQA, and k ＰＱＡ is the rate constant of the PQA; The method of claim 1.

3. 2. The method of claim 1, further comprising identifying exposure to the antibody with the PQA, wherein identifying exposure to the antibody with the PQA comprises identifying an area under the curve (AUC) of the concentration of the antibody with the PQA.

4. The serum concentration of the antibody is calculated according to the two-compartment pharmacokinetic model equation C(t) = Ae －αｔ +Be －βｔ 2. The method of claim 1 , wherein A and B are hybrid coefficients where α and β are hybrid linear constants, respectively.

5. 2. The method of claim 1, wherein the tolerance range of the antibody PQA comprises a range of PQA levels in which the PQA level after multiple administrations of the antibody is substantially insensitive to the initial PQA level.

6. 2. The method of claim 1, wherein the tolerance range of an antibody PQA comprises a range of PQA levels where exposure to the antibody with the PQA after multiple administrations of the antibody is substantially insensitive to the initial PQA level.

7. 2. The method of claim 1, wherein the acceptable range of PQA of the antibody is between about 0% and about 10%, between about 0% and about 8%, between about 0% and about 5%, between about 0% and about 4%, between about 0% and about 3%, between about 0% and about 2%, between about 0% and about 1%, between about 0% and about 0.5%, between about 0% and about 0.2%, or between about 0% and about 0.1%.

8. The method of claim 1 , wherein the antibody comprises a therapeutic recombinant monoclonal antibody.

9. The method of claim 1 , wherein the PQA may affect the stability, safety, and / or efficacy of a drug.

10. 2. The method of claim 1, wherein the PQA comprises one or more of deamidation, oxidation, glycation, disulfide formation, N-terminal pyroglutamic acid formation, C-terminal lysine removal, and high mannose glycosylation.

11. (i) modeling the correlation between pharmacokinetic concentrations and proportions of PQA variants; (ii) quantitatively assessing and predicting a subject's exposure to representative PQAs in either single-dose or multiple-dose regimens; (iii) predicting the maximum and minimum PQA levels observed in a multiple dose regimen; and / or (iv) determining the total in vivo concentration of the antibody in the subject following administration of the antibody to the subject; The method of claim 1 further comprising:

12. The method of claim 1, further comprising predicting a pre-administration in vivo concentration of the antibody with said PQA.

13. The method of claim 12, further comprising predicting a pre-dose in vivo concentration of the antibody with said PQA at steady state.

14. The method of claim 1, further comprising predicting the post-administration in vivo concentration of the antibody with said PQA.

15. 15. The method of claim 14, further comprising predicting the post-administration in vivo concentration of the antibody with said PQA at steady state.

Images