Method and system for developing chromatography protocol

Abstract

A method of purifying a target molecule can include introducing a filler comprising a high molecular weight species concentration (% HMW) into a chromatography apparatus comprising a sartbind phenol chromatography medium. A method of generating a chromatography protocol may include identifying chromatography fill parameters, identifying chromatography performance criteria. A method of generating the chromatography scheme may include selecting a combination of test values for the fill parameter, and performing a chromatography run for each combination of the set of test value combinations, thereby generating an actual performance criterion value corresponding to each combination of the set of test value combinations.

Description

Interactive reference for related applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 583,474, filed September 18, 2023, which is hereby incorporated by reference in its entirety. Technical Field

[0002] This invention relates to systems and methods for developing and implementing chromatographic protocols to produce biopharmaceutical products. Some aspects of this disclosure relate to systems and methods for developing hydrophobic interaction (HIC) chromatographic protocols. Background Technology

[0003] Biopharmaceutical products (e.g., antibodies, antibody-drug conjugates, fusion proteins, adeno-associated virus (AAV), proteins, tissues, cells, peptides, or other biologically derived therapeutic products) are increasingly being used to treat and prevent infectious diseases, genetic diseases, autoimmune diseases, and other illnesses. The production of biopharmaceutical products requires chromatographic techniques to purify, characterize, and validate the products.

[0004] The production of biopharmaceutical products may include, for example, affinity chromatography (e.g., protein A or protein L), ion exchange chromatography, size exclusion chromatography, reversed-phase chromatography, rapid protein liquid chromatography, high-performance liquid chromatography, countercurrent chromatography, periodic countercurrent chromatography, chiral chromatography and mixed-mode chromatography, and hydrophobic interaction chromatography.

[0005] Conventional methods for developing and optimizing chromatographic protocols are time-consuming, labor-intensive, and wasteful of reagents and products. The time and effort required also contribute significantly to the costs associated with improving existing chromatographic protocols. Using chromatographic protocols that are not optimized can also lead to waste of reagents and / or products, increasing costs associated with the production of biopharmaceutical products. Summary of the Invention

[0006] This disclosure may relate to a method for generating a chromatographic scheme for a target molecule. The target molecule may be an antibody-drug conjugate. The method may include identifying chromatographic filling parameters, identifying chromatographic performance standards, generating a potential predictive model domain that associates the chromatographic filling parameters with the chromatographic performance standards, selecting combinations of test values ​​for the filling parameters, wherein the selected combinations of test values ​​form a set of test value combinations, performing chromatographic runs for each combination in the set of test value combinations to generate actual performance standard values ​​corresponding to each combination in the set of test value combinations, and ranking each predictive model in the predictive model domain based on the correlation between the performance standard values ​​predicted by the model and the actual performance standard values.

[0007] In some aspects of this disclosure, the chromatographic packing parameters may include packing buffer salt concentration, packing density, the content of high molecular weight species in the packing material, or a combination thereof. The chromatographic performance criteria may include reduction in the content of high molecular weight species, target molecule yield, or a combination thereof.

[0008] In some aspects of this disclosure, the method may further include selecting a prediction model with the highest ranking, and using the selected model to determine chromatographic packing parameter values ​​for the chromatographic protocol. The selected model can predict that the chromatographic packing parameter values ​​for the chromatographic protocol correspond to one or more target performance standard values.

[0009] In some aspects of this disclosure, the method may further include developing a desirability metric, which includes a quantitative relationship between performance criteria. The desirability metric may be calculated as a combination of two or more performance criteria, wherein each performance criterion is assigned a weight that contributes to the desirability metric. The two or more performance criteria may include a reduction in the content of high molecular weight species and the yield of target molecules, wherein each performance criterion has an equal weight.

[0010] This disclosure may also relate to a method for generating a chromatographic scheme for a target molecule. The target molecule may be an antibody-drug conjugate. The method may include identifying a first chromatographic parameter and a second chromatographic parameter; identifying a first performance standard and a second performance standard; selecting a first test value for the first chromatographic parameter; selecting a second test value for the second chromatographic parameter; identifying the chromatographic medium; generating a first performance standard value, wherein each first performance standard value corresponds to a combination of the first test value and the second test value; generating a second performance standard value, wherein each second performance standard value corresponds to a combination of the first test value and the second test value; generating a first pool of multivariate models, wherein each multivariate model in the first pool of multivariate models correlates the first chromatographic parameter and the second chromatographic parameter with the first performance standard; and generating a second pool of multivariate models. The process includes: each multivariate model in the second multivariate model pool associating the first chromatographic parameter and the second chromatographic parameter with the second performance standard; using each multivariate model in the first multivariate model pool to generate a first predicted performance standard value, wherein each first predicted performance standard value corresponds to a combination of a first test value and a second test value; using each multivariate model in the second multivariate model pool to generate a second predicted performance standard value, wherein each second predicted performance standard value corresponds to a combination of a first test value and a second test value; determining the multivariate model determination coefficients in the first multivariate model pool; and determining the multivariate model determination coefficients in the second multivariate model pool.

[0011] In some aspects of this disclosure, identifying the chromatographic medium may include performing a first chromatographic run on a first chromatographic medium to produce a first desired value corresponding to the first chromatographic medium, performing a second chromatographic run on a second chromatographic medium to produce a second desired value corresponding to the second chromatographic medium, and selecting a chromatographic medium corresponding to the maximum desired value. The first and second chromatographic runs may be performed using the same medium density and packing buffer composition. The first desired value may be calculated based on one or more performance criteria of the first chromatographic run, and the second desired value may be calculated based on one or more performance criteria of the second chromatographic run. The first chromatographic parameter may be the column packing amount (g / L) of the HIC medium, the second chromatographic parameter may be the citrate concentration of the packing buffer, the first performance criterion may be yield, and the second performance criterion may be the quantification of impurity reduction. Generating the first multivariate model pool may include identifying potential multivariate model domains, calculating the variance inflation factor for each potential multivariate model in the potential multivariate model domains, and selecting all potential multivariate models having a variance inflation factor less than or equal to a collinearity threshold to generate the first multivariate model pool.

[0012] This disclosure may also relate to a method for purifying a target molecule. The target molecule may be an antibody-drug conjugate. The method may include introducing a packing material comprising a high molecular weight species concentration (%HMW) of about 3% to about 20% into a chromatographic apparatus containing sartobind phenyl chromatographic media, wherein the packing material is introduced at a density of about 10 g packing material / L total chromatographic apparatus volume to about 40 g / L, and wherein the packing material comprises the target molecule and about 5 mM to about 200 mM citrate, and eluing an eluent containing the target molecule out of the chromatographic apparatus, wherein the yield of the target molecule in the eluent is at least about 70%, and wherein the difference between the %HMW of the packing material and the %HMW of the eluent is at least about 2%.

[0013] In some aspects of this disclosure, the antibody-drug conjugate may include a drug bound to an antibody via lysine conjugation. The %HMW of the filler may be from about 7% to about 15%. The filler may be introduced at a density of from about 20 g / L to about 30 g / L. The filler may contain from about 110 mM to about 175 mM of citrate. The yield of the target protein in the eluent may be from about 75% to about 95%. The difference between the %HMW of the filler and the %HMW of the eluent may be from about 3% to about 8%. The concentration of the target protein in the eluent may be from about 70% to about 85% of the concentration of the target protein in the filler. The antibody-drug conjugate may include a cleavable maytansinoid. The antibody-drug conjugate may include an IgG4 antibody. Attached Figure Description

[0014] The accompanying drawings, incorporated in and constituting a part of this specification, illustrate various exemplary embodiments of the present disclosure and, together with the description herein, serve to explain the principles of the disclosed embodiments. Any feature of the embodiments or examples described herein (e.g., composition, formulation, method, etc.) may be combined with any other embodiments or examples, and all such combinations are covered in this disclosure. Furthermore, the systems and methods described are not limited to any single aspect or embodiment thereof, nor to any combination or arrangement of such aspects and embodiments. For the sake of brevity, certain arrangements and combinations are not discussed and / or illustrated separately herein.

[0015] Figure 1 An exemplary method for developing chromatographic protocols according to aspects of this disclosure is depicted in flowchart form; Figure 2 An exemplary method for developing chromatographic protocols according to aspects of this disclosure is depicted in flowchart form; Figure 3 Performance standard diagrams related to different chromatographic media were depicted; Figure 4 A series of performance standards and chromatographic packing parameters according to aspects of this disclosure are depicted in graphs; Figure 5 Chromatograms of exemplary chromatographic schemes according to aspects of this disclosure are depicted; Figure 6A A mass spectrum of a sample prior to a chromatographic process according to aspects of this disclosure is depicted; Figure 6B The chromatographic process following the aspects of this disclosure is described. Figure 6A The mass spectrum of the sample; Figure 7A A particle size distribution map of the sample prior to the chromatographic process according to aspects of this disclosure is depicted; Figure 7B The chromatographic process following the aspects of this disclosure is described. Figure 7A The sample particle size distribution diagram; Figure 8 Bioassay diagrams are depicted according to aspects of this disclosure; and Figure 9 A diagram depicting the combination of aspects according to this disclosure is provided. Detailed Implementation

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. While any suitable methods and materials (e.g., similar to or equivalent to those described herein) may be used in the practice or testing of this disclosure, specific example methods are described hereafter. All publications mentioned are hereby incorporated by reference.

[0017] As used herein, the terms “comprises,” “comprising,” or any other variation thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but may also include other elements not expressly listed or elements inherent to such a process, method, article, or apparatus. The term “exemplary” is used in the sense of “example” rather than “ideal.” The terms “for example” and “such as,” and their grammatically equivalent words, should be understood to follow the phrase “and not limited to” unless otherwise expressly stated.

[0018] As used herein, the term “about” is intended to describe the variation due to experimental error. When applied to numerical values, the term “about” may indicate a difference of + / - 5% from a published numerical value, unless a different variation has been specified. As used herein, the singular forms “a,” “an,” and “described” include plural indicators unless the context clearly specifies otherwise. Furthermore, all ranges should be understood to include endpoints; for example, 1 mm to 5 mm would include all distances or lengths between 1 mm and 5 mm, as well as between 1 mm and 5 mm.

[0019] It should be noted that, unless a different variation has been specified, all values ​​disclosed or claimed herein (including all disclosed values, limitations and ranges) may have a variation of + / - 5% from the disclosed values.

[0020] As used herein, the term "protein" includes biotherapeutic proteins, recombinant proteins used for research or treatment, trap proteins and other Fc fusion proteins, chimeric proteins, antibodies, monoclonal antibodies, human antibodies, bispecific antibodies, antibody fragments, antibody-like molecules, nanobodies, recombinant antibody chimeras, cytokines, chemokines, peptide hormones, etc. The term "peptide" refers to any amino acid polymer having more than about 20 amino acids covalently linked via amide bonds. Proteins contain one or more amino acid polymer chains (e.g., peptides). Therefore, peptides can be proteins, and proteins can contain multiple peptides to form a single functional biomolecule.

[0021] Target molecules may include any polypeptide, protein, or other molecule that needs to be isolated, purified, characterized, or otherwise prepared. Target molecules may include cell-derived polypeptides, including antibodies. Target molecules may include antibodies that have undergone post-translational modifications, such as, for example, lysine-based conjugates. Lysine-based conjugates can be used to covalently link cell-derived antibodies to one or more other pharmaceutically active compounds. Antibodies covalently linked to one or more other pharmaceutically active compounds may be referred to as antibody-drug conjugates and are discussed in further detail below.

[0022] Target molecules (e.g., peptides or antibodies) can be manufactured using recombinant cell-based production systems, such as insect baculovirus systems, yeast systems (e.g., Pichia sp.), or mammalian systems (e.g., CHO cells and CHO derivatives, such as CHO-K1 cells). The term "cell" includes any cell suitable for expressing recombinant nucleic acid sequences. Cells include prokaryotes and eukaryotes (unicellular or multicellular), bacterial cells (e.g., *Escherichia coli*, *Bacillus* spp., *Streptomyces* spp., etc.), mycobacterial cells, fungal cells, yeast cells (e.g., *Saccharomyces cerevisiae*, *Saccharomyces pombe*, *Pichia pastoris*, *Pichia methanolica*, etc.), plant cells, insect cells (e.g., SF-9, SF-21, baculovirus-infected insect cells, *Trichoplusiani*, etc.), non-human animal cells, human cells, or cell fusions, such as, for example, hybridomas or tetravalent tumors. In some embodiments, the cells may be human, monkey, ape, hamster, rat, or mouse cells. In some embodiments, the cells may be eukaryotic cells and selected from the following: CHO (e.g., CHO K1, DXB-11 CHO, Veggie-CHO), COS (e.g., COS-7), retinal cells, Vero, CV1, kidney cells (e.g., HEK293, 293 EBNA, MSR 293, MDCK, HaK, BHK), HeLa, HepG2, WI38, MRC 5, Colo205, HB 8065, HL-60 (e.g., BHK21), Jurkat, Daudi, A431 (epidermal), CV-1, U937, 3T3, L cells, C127 cells, SP2 / 0, NS-0, MMT060562, Sertoli cells, BRL 3A cells, HT1080 cells, myeloma cells, tumor cells, and cell lines derived from the above cells. In some implementations, the cell contains one or more viral genes, such as retinal cells expressing viral genes (e.g., PER.C6™ cells).

[0023] As used herein, the term "antibody" refers to an immunoglobulin composed of four polypeptide chains: two heavy (H) chains and two light (L) chains linked together by disulfide bonds. Typically, antibodies have a molecular weight exceeding 100 kDa, such as those between 130 kDa and 200 kDa, or approximately 140 kDa, 145 kDa, 150 kDa, 155 kDa, or 160 kDa. Each heavy chain contains a heavy chain variable region (abbreviated as HCVR or VH herein) and a heavy chain constant region. The heavy chain constant region contains three domains: CH1, CH2, and CH3. Each light chain contains a light chain variable region (abbreviated as LCVR or VL herein) and a light chain constant region. The light chain constant region contains one domain: CL. The VH and VL regions can be further subdivided into hypervariable regions called complementarity-determining regions (CDRs), interspersed with more conserved regions called framework regions (FRs). Each VH and VL consists of 3 CDRs and 4 FRs, arranged in the following order from the amino terminus to the carboxyl terminus: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4 (heavy chain CDRs can be abbreviated as HCDR1, HCDR2 and HCDR3; light chain CDRs can be abbreviated as LCDR1, LCDR2 and LCDR3).

[0024] One class of immunoglobulins, called immunoglobulin G (IgG), is commonly found in human serum and consists of four polypeptide chains (two light chains and two heavy chains). Each light chain is linked to one heavy chain via a cystine disulfide bond, and the two heavy chains are linked together by two cystine disulfide bonds. Other classes of human immunoglobulins include IgA, IgM, IgD, and IgE. Regarding IgG, there are four subclasses: IgG1, IgG2, IgG3, and IgG4. Each subclass has a different constant region and therefore can have different effector functions. In some embodiments described herein, the target molecule may comprise a polypeptide, including IgG, such as, for example, IgG4.

[0025] As used herein, the term "antibody" also includes the antigen-binding fragment of a complete antibody molecule. As used herein, the terms "antigen-binding portion" of an antibody, "antigen-binding fragment" of an antibody, etc., include any naturally occurring, enzymatically obtained, synthetic, or genetically engineered polypeptide or glycoprotein that specifically binds to an antigen to form a complex. The antigen-binding fragment of an antibody can be derived from, for example, a complete antibody molecule using any suitable standard technique, such as proteolytic digestion or recombinant genetic engineering, involving the manipulation and expression of DNA encoding variable and optionally constant domains of the antibody. Such DNA is known and / or readily available from, for example, commercial sources, DNA libraries (including, for example, phage-antibody libraries), or can be synthesized. The DNA can be sequenced and chemically manipulated, or by using molecular biotechniques such as arranging one or more variable and / or constant domains into a suitable conformation, or by introducing codons, generating cysteine ​​residues, modifying, adding, or deleting amino acids, etc.

[0026] As used herein, the term "drug" can refer to any compound having the desired biological activity. The desired biological activity can include activities that can be used to diagnose, cure, alleviate, treat, or prevent diseases in humans or other animals. As used herein, a drug can include available reactive functional groups that allow the drug to conjugate with an antibody. In some embodiments, the antibody is linked to the drug via a linker, and the drug has functional groups that can form bonds with the linker. For example, the drug may have reactive groups, such as amino, carboxyl, thiol, hydroxyl, or ketone groups, which can form bonds with the linker. The drug can be any cytotoxic drug that inhibits cell growth or suppresses the immune system. For example, drugs may include anti-tubulin agents, DNA minor groove binding agents, DNA replication inhibitors, alkylating agents, antibiotics, folic acid antagonists, antimetabolites, chemosensitizers, topoisomerase inhibitors, vinca alkaloids, steroids, vitamins, agonists, antagonists, ligands, signal transduction molecules, checkpoint inhibitors, oligonucleotides (including siRNA), peptides, immune system stimulants / inhibitors, hormones, antiviral agents, antifungal agents, radioactive nuclei, chelating agents, oligosaccharides, ligands, or other suitable compounds that inhibit cell growth. Examples of particularly useful cytotoxic agents include DNA minor groove binding agents, DNA alkylating agents, tubulin inhibitors, auristatin, camptothecin, docamycin / duocarmycin, etoposide, maytansine, maytansinoids (e.g., DM1, DM4, M114), taxane, benzodiazepines, or drugs containing benzodiazepines (e.g., pyrrolo[1,4]benzodiazepine (PBD), indolinobenzodiazepine, and oxazolidinobenzodiazepine), and vinca alkaloids. In one or more embodiments, the antibody-drug conjugate may include a cleavable maytansine-like substance.

[0027] An antibody-drug conjugate can refer to any suitable substance containing an antibody chemically linked to a drug (e.g., an antibody covalently bonded to a drug). Antibodies can be conjugated to drugs via any suitable means (e.g., random lysine conjugation) to form antibody-drug conjugates. Other suitable means of conjugating antibodies to drugs include, but are not limited to, cysteine ​​(e.g., engineered cysteine), enzyme conjugation (e.g., sortase), transglutaminase, formylglycine synthase, non-covalent binding, carbonates, non-natural amino acids, engineered azides, and inter-chain cysteine ​​rebridging linkers.

[0028] Antibody-drug conjugates may contain a linker between the drug and the antibody. The linker may be degradable or non-degradable. Degradable linkers may include, for example, enzyme-degradable linkers, including peptide-containing linkers that can be degraded by cellular proteases (e.g., lysosomal proteases or endosomal proteases), or sugar linkers, such as glucuronidate-containing linkers that can be degraded by glucuronidase. Peptide-containing linkers may include, for example, dipeptides such as valine-citrulline, phenylalanine-lysine, or valine-alanine. Other suitable degradable linkers include, for example, pH-sensitive linkers (e.g., linkers that hydrolyze at pH below 5.5, such as hydrazone linkers) and linkers that degrade under reducing conditions (e.g., disulfide linkers), glycosidase-cleavable linkers, phosphatase-cleavable linkers, and light-responsive linkers. Non-degradable linkers typically release the drug under conditions where the antibody is hydrolyzed by proteases.

[0029] The antibody-drug conjugates disclosed herein may include one or more types of antibodies, such as, for example, human antibodies, humanized antibodies, chimeric antibodies, monoclonal antibodies, multispecific antibodies, bispecific antibodies, antigen-binding antibody fragments, single-chain antibodies, diabody, triabody, or tetrabody, Fab fragments or F(ab')2 fragments, IgD antibodies, IgE antibodies, IgM antibodies, IgG antibodies, IgG1 antibodies, IgG2 antibodies, IgG3 antibodies, or IgG4 antibodies, or antibody-drug conjugates. In one aspect, the antibody is an IgG1 antibody. In one aspect, the antibody is an IgG2 antibody. In one aspect, the antibody is an IgG4 antibody. In one aspect, the antibody is a chimeric IgG2 / IgG4 antibody. In one aspect, the antibody is a chimeric IgG2 / IgG1 antibody. In one aspect, the antibody is a chimeric IgG2 / IgG1 / IgG4 antibody. In one aspect, the antibody-drug conjugate includes an IgG4 antibody.

[0030] In some aspects, the antibody-drug conjugates of this disclosure may comprise one or more types of antibodies selected from the group consisting of: anti-programmed cell death 1 antibodies (e.g., anti-PD1 antibodies, as described in U.S. Patent Application Publication No. US2015 / 0203579A1), anti-programmed cell death ligand-1 antibodies (e.g., anti-PD-L1 antibodies, as described in U.S. Patent Application Publication No. US2015 / 0203580A1), anti-Dll4 antibodies, anti-angiogenic-2 antibodies (e.g., anti-ANG2 antibodies, as described in U.S. Patent No. 9,402,898), and anti-angiogenic antibodies. Antibodies against platelet-derived growth factor receptors (e.g., anti-AngPtl3 antibodies, as described in U.S. Patent No. 9,018,356), anti-platelet-derived growth factor receptor antibodies (e.g., anti-PDGFR antibodies, as described in U.S. Patent No. 9,265,827), anti-prolactin receptor antibodies (e.g., anti-PRLR antibodies, as described in U.S. Patent No. 9,302,015), anti-complement 5 antibodies (e.g., anti-C5 antibodies, as described in U.S. Patent Application Publication No. US2015 / 0313194A1), anti-TNF antibodies, and anti-epidermal growth factor receptor antibodies (e.g., anti-EGFR antibodies, as described in U.S. Patent No. 9,018,356), as well as anti-platelet-derived growth factor receptor antibodies (e.g., anti-EGFR antibodies, as described in U.S. Patent No. 9,018,356), and anti-PDGFR antibodies (e.g., anti-PDG ... As described in 132,192, or anti-EGFRvIII antibodies, as described in U.S. Patent Application Publication No. US2015 / 0259423A1, anti-proprotein convertase subtilisin-9 antibodies (e.g., anti-PCSK9 antibodies, as described in U.S. Patent No. 8,062,640 or U.S. Patent Application Publication No. US2014 / 0044730A1), anti-growth and differentiation factor-8 antibodies (e.g., anti-GDF8 antibodies, also known as anti-myosin antibodies, as described in U.S. Patent No. 8,871,209 or 9,260,515), anti-glucagon receptor antibodies ( For example, anti-GCGR antibodies, as described in U.S. Patent Application Publication No. US2015 / 0337045A1 or US2016 / 0075778A1, anti-VEGF antibodies, anti-IL1R antibodies, interleukin-4 receptor antibodies (e.g., anti-IL4R antibodies, as described in U.S. Patent Application Publication No. US2014 / 0271681A1 or U.S. Patent Nos. 8,735,095 or 8,945,559), and anti-interleukin-6 receptor antibodies (e.g., anti-IL6R antibodies, as described in U.S. Patent Nos. 7,582,298, 8,043,617 or 9,173).Anti-interleukin 33 (e.g., anti-IL33 antibodies, as described in U.S. Patent Application Publication No. US2014 / 0271658A1 or US2014 / 0271642A1), anti-respiratory syncytial virus antibodies (e.g., anti-RSV antibodies, as described in U.S. Patent Application Publication No. US2014 / 0271653A1), and anti-differentiation cluster 3 antibodies (e.g., anti-CD3 antibodies, as described in U.S. Patent Application Publication No. US2014 / 0088295A1 and US201...). Anti-differentiation cluster 20 antibodies (e.g., anti-CD20 antibodies, as described in U.S. Patent Application Publication Nos. US2014 / 0088295A1 and US20150266966A1 and U.S. Patent No. 7,879,984), anti-differentiation cluster 48 antibodies (e.g., anti-CD48 antibodies, as described in U.S. Patent No. 9,228,014), and anti-Fel antibodies. Antibodies against d1 (e.g., as described in U.S. Patent No. 9,079,948), anti-Middle East Respiratory Syndrome Virus (e.g., anti-MERS antibody), anti-Ebola virus antibody (e.g., Regeneron's REGN-EB3), anti-CD19 antibody, anti-CD28 antibody, anti-IL1 antibody, anti-IL2 antibody, anti-IL3 antibody, anti-IL4 antibody, anti-IL5 antibody, anti-IL6 antibody, anti-IL7 antibody, anti-Erb3 antibody, anti-Zika virus antibody, anti-lymphocyte activation gene 3 antibody (e.g., anti-LAG3 antibody or anti-CD223 antibody), and anti-activin A antibody. Each U.S. patent and U.S. patent publication mentioned in this paragraph is incorporated herein by reference in its entirety.

[0031] In some aspects, the antibody-drug conjugates of this disclosure may include one or more types of antibodies selected from the group consisting of: anti-CD3 x anti-CD20 bispecific antibodies, anti-CD3 x anti-mucin 16 bispecific antibodies, and anti-CD3 x anti-prostate-specific membrane antigen bispecific antibodies. In some aspects, the target molecule includes antibodies selected from the group consisting of: alirocumab, sarilumab, fasinumab, nesvacumab, dupilumab, trevogrumab, evinacumab, and rinucumab.

[0032] As used herein, the term "impurity" refers to foreign or harmful molecules, including nucleic acids, proteins, and other compounds that are not target molecules for the chromatographic operation. Exemplary impurities include host cell proteins, variants of target molecules (e.g., aggregates, deamidated species, and / or misconjugated antibody-drug conjugates), low molecular weight species and fragments, proteins (e.g., protein A or protein L) that are part of an absorbent used in affinity chromatography, excess free linker-payloads (e.g., drug components of antibody-drug conjugates that do not bind to antibodies), excess reagents (e.g., organic solvents) from the conjugation method, endotoxins, and viruses.

[0033] As described above, chromatographic techniques can be used to separate different components of a mixture to purify target molecules (e.g., antibody-drug conjugates) and / or characterize the content of the mixture. However, in some cases, high molecular weight species (e.g., host cell proteins, aggregates, other impurities) not included in the biopharmaceutical product may co-elute with it. Furthermore, the percentage of high molecular weight species in a drug product (including biopharmaceutical products and other high molecular weight species) may increase due to stress (e.g., time, or extreme temperatures). Therefore, drug product formulations with a lower percentage of high molecular weight species (e.g., formulations of biopharmaceutical products administered to subjects) may exhibit improved shelf life compared to other formulations.

[0034] Therefore, while some chromatographic protocols (such as HIC protocols) may produce drug products with acceptable levels of high molecular weight species, there is still a need for improved chromatographic protocols to further reduce the number of high molecular weight species co-eluted with biopharmaceutical products. Improved chromatographic protocols can result in reduced impurities in drug product formulations compared to formulations developed using conventional chromatography. The concentration of impurities (e.g., high molecular weight species) in the sample can be reduced. Alternatively, the chromatographic protocols of this disclosure can increase the concentration of target molecules.

[0035] Various parameters of a chromatographic protocol can be adjusted to improve its performance. For example, the mode and / or media used in the chromatographic protocol can be adjusted to increase its efficiency and / or effectiveness. Alternatively, parameters regarding how the sample (e.g., chromatographic packing material) is introduced into the chromatographic system (e.g., column) can be adjusted to improve the protocol's performance. Such parameters may include, for example, packing density, packing buffer composition, the amount of high molecular weight molecular weight species within the packing material, the dimensions of the chromatographic apparatus (e.g., overall volume, bed height, inner diameter, membrane thickness), media composition, chromatographic media density, one or more buffer compositions, the concentration of high molecular weight species in the packing material, flow rate, pH, temperature, conductivity, salt concentration, antibody-drug conjugate concentration in the packing material, and / or the drug-to-antibody ratio of the packing material.

[0036] Conventional methods for developing and optimizing chromatographic protocols are time-consuming and labor-intensive. The time and labor costs associated with developing chromatographic protocols can be prohibitive, leading to the use of non-optimized protocols in production processes. The use of non-optimized protocols also increases costs associated with the production of biopharmaceutical products. Therefore, a high-throughput system for developing and evaluating chromatographic protocols is needed. The methods disclosed herein can be used to generate, develop, and / or validate improved chromatographic protocols.

[0037] In one aspect, the method of this disclosure produces an eluent, wherein the impurity concentration in the eluent of the chromatographic protocol is lower than the impurity concentration in the packing material. In another aspect, the method of this disclosure produces a protocol for preparing a chromatographic process targeting a packing material (including target molecules and impurities), and the prepared chromatographic process produces an eluent, wherein the impurity concentration in the eluent is lower than the impurity concentration in the packing material.

[0038] Chromatographic protocols can be implemented at various stages of the manufacture of biopharmaceutical products. In some aspects, the target molecule may undergo one or more chromatographic processes. For example, a solution containing the target molecule (i.e., packing material) may be introduced into the chromatographic system to produce an eluent containing the target molecule. The eluent may have fewer species (e.g., fewer host cell proteins, fewer high molecular weight species, and fewer unwanted target molecule variants) than the solution introduced into the chromatographic system. Chromatographic protocols may include chromatographic operating parameters to be performed, including but not limited to chromatographic mode, column dimensions (e.g., total volume, bed height, inner diameter, membrane thickness), media composition, packing density, chromatographic media density, composition of one or more buffers, concentration of high molecular weight species in the packing material, flow rate, pH, temperature, conductivity, salt concentration, concentration of antibody-drug conjugate in the packing material, and / or drug to antibody ratio in the packing material.

[0039] The chromatographic protocols disclosed herein can be used with any suitable chromatographic system column configured to perform such protocols, such as a system comprising one or more chromatographic columns.

[0040] As previously mentioned, certain impurities may co-elute with the target molecules during chromatographic operations. It is necessary to monitor the concentration of certain impurities that ultimately appear in the drug product formulation to ensure that the manufactured formulation meets internal quality assurance standards and applicable regulatory agency standards.

[0041] When developing improved chromatographic protocols, one or more performance criteria of the potential new chromatographic protocol can be compared with the performance criteria of existing chromatographic protocols. Performance criteria may include the concentration of one or more impurities, such as, as non-limiting examples, high molecular weight species composition (e.g., %HMW), total protein content, particle size distribution, residual free linker-payload content, residual organic solvent content, or unconjugated antibody content.

[0042] In some aspects, %HMW can be measured by size exclusion HPLC, microchip capillary electrophoresis, analytical ultracentrifugation, dynamic light scattering, mass spectrometry, SDS-PAGE, analytical hydrophobic interaction chromatography, or combinations thereof. Total protein content can be measured by ultraviolet spectrophotometry or other suitable methods (e.g., analytical chromatography, SDS-PAGE). In some aspects, analytical chromatography can be used to measure residual free linker-load content. Residual organic solvent content can be measured, for example, by gas chromatography-mass spectrometry. Particle size distribution can be determined, for example, by dynamic light scattering. Particle size distribution can be used to quantify the amount of aggregates or other large-particle species present in a sample.

[0043] In some aspects, analytical chromatography (e.g., hydrophobic interaction chromatography) can be used to determine the content of unconjugated antibodies. For example, a packing sample can be used as the substrate for a hydrophobic interaction chromatographic operation, whereby the operation separates unconjugated antibodies from conjugated antibodies. A chromatogram can be generated during the chromatographic operation, wherein one or more peaks of the chromatogram correspond to conjugated antibodies, and one or more peaks of the chromatogram correspond to unconjugated antibodies. The percentage of unconjugated antibodies can be determined by comparing the relative peak areas corresponding to conjugated and unconjugated antibodies.

[0044] As mentioned above, some impurities have permissible thresholds (e.g., maximum limits) in the final pharmaceutical product formulation. These thresholds may be based on internal quality assurance metrics and / or applicable regulatory agency standards. For example, the maximum permissible concentration (%HMW) of high molecular weight species may be about 9%, about 8%, about 7%, about 6%, about 5%, about 4%, or about 3%. The maximum permissible concentration of residual free linker-load may be about 5% by weight, about 4% by weight, about 3% by weight, about 2% by weight, about 1% by weight, about 0.5% by weight, or about 0.1% by weight. The maximum permissible residual organic solvent concentration may depend on the solvent. In some aspects, the maximum permissible residual organic solvent concentration may be about 10,000 parts per million (ppm), about 8,000 ppm, about 5,000 ppm, about 3,000 ppm, about 2,000 ppm, or about 1,000 ppm. The maximum permissible concentration of unconjugated antibodies may be about 15%, about 12%, about 10%, about 8%, or about 5%. Eluents with monodisperse particle size distributions may have acceptable levels of aggregates and / or other large-particle species. In some aspects, chromatographic operations may be designed such that the eluent of the operation has a monodisperse particle size distribution, including, for example, an average particle size of less than or equal to about 15 nm, less than or equal to about 12 nm, less than or equal to about 10 nm, less than or equal to about 8 nm, or less than or equal to about 5 nm.

[0045] The development goals of chromatographic protocols may include producing an eluent with a certain amount of high molecular weight species (e.g., an eluent with less than or equal to 3% HMW). The performance criterion for a chromatographic protocol may be the reduction of high molecular weight species. The HMW reduction (%HMWΔ) of a chromatographic protocol can be defined as subtracting the %HMW of the eluent from the %HMW of the packing material, as shown in Equation 1.

[0046] The methods disclosed herein may include developing a chromatographic scheme in which the %HMWΔ of the chromatographic scheme is at least about 2%, such as, for example, at least about 3%, at least about 4%, at least about 5%, about 1% to about 15%, about 1% to about 12%, about 1% to about 10%, about 1% to about 8%, about 2% to about 8%, or about 2% to about 5%.

[0047] In some respects, performance metrics may include efficiency and / or yield. For example, performance metrics may include productivity and / or total protein yield. Productivity may be defined as the amount of purified material produced per unit time (e.g., the amount of antibody-drug conjugate eluted during a chromatographic operation), such as, for example, grams per hour. The total protein yield of a chromatographic operation may be calculated as the total protein concentration (TP) of the eluent. 洗脱液 ) and the total protein concentration (TP) of the filler 填充物 The ratio of ) is shown in Equation 2.

[0048] Protein concentration (e.g., the total protein concentration of the packing or eluent) can be measured by ultraviolet spectrophotometry or other suitable methods for determining protein concentration. In some aspects, the total protein yield may correspond to (e.g., equal to) the yield of the drug-antibody conjugate. The methods disclosed herein may include developing a chromatographic protocol in which the yield of the target molecule in the eluent is at least about 70%, such as, for example, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, about 75% to about 95%, about 75% to about 90%, about 80% to about 95%, or about 80% to about 90%.

[0049] Performance criteria may include one or more metrics that quantify the yield of drug-antibody conjugates. For example, performance criteria may include the drug-to-antibody ratio. Alternatively or concurrently, performance criteria may include one or more metrics of the chromatographic operation that compare the drug-to-antibody ratio of the packing material to the drug-to-antibody ratio of the eluent. For example, performance criteria may include the difference between the drug-to-antibody ratio of the packing material and the eluent, or the ratio of the drug-to-antibody ratio of the packing material to the eluent.

[0050] Alternatively or concurrently, performance criteria may include an expectancy index and / or another combination of performance criteria. In some aspects, the expectancy index may be calculated as a combination of two or more performance criteria. Based on the favorability of performance criterion values, a corresponding expected value within a 0-1 scale may be assigned to the performance criterion values. For example, in aspects requiring higher yields, based on a function (e.g., a linear function), an expected value of 1 may be assigned to performance criterion values ​​with yields greater than or equal to 90%, an expected value of 0 may be assigned to yield values ​​less than or equal to 70%, and a value between 0 and 1 may be assigned to yield values ​​between 70% and 90%.

[0051] An expectancy index can be calculated as a combination (e.g., an average) of the expected values ​​of the components corresponding to a performance criterion. An expectancy index can be constructed such that each performance criterion in the combination is equally weighted. In some aspects, an expectancy index can be constructed such that each performance criterion in the combination is assigned a weight that increases or decreases its contribution to the expectancy index. For example, an expectancy index can be constructed such that the expected value corresponding to %HMW or drug-to-antibody ratio performance criterion has a heavier weight than other performance criters. As another example, the expected value corresponding to %HMW or drug-to-antibody ratio may be given the highest weight, the expected value corresponding to particle distribution may be given the second highest weight, and the expected value corresponding to yield may be given the lowest weight.

[0052] In the case of antibody-drug conjugates as target molecules, the development of chromatographic protocols (e.g., HIC protocols) may present additional challenges. For example, antibody-drug conjugates may exhibit increased heterogeneity compared to other biopharmaceutical products due to the multiplicity of sites on the antibody available for conjugation (e.g., reactive lysine attachment sites). Variations in drug-antibody conjugation can result in many different species generated during the production of biopharmaceutical products. Various antibody-drug conjugate species may have different drug-to-antibody ratios, hydrophobicities, and aggregation tendencies. Therefore, in some embodiments, impurities (such as aggregates) may have similar surface hydrophobicities and different drug-to-antibody ratios compared to the target molecule. Consequently, there is a need to design improved chromatographic protocols for target molecules, including antibody-drug conjugates. The high-throughput methods for developing and evaluating chromatographic protocols described herein can be used to develop chromatographic protocols for antibody-drug conjugates as target molecules.

[0053] The systems and methods disclosed herein provide a development process for improving chromatographic protocols. This improved development process can lead to improvements in chromatographic protocols. In one or more embodiments, the systems and methods of this disclosure allow for the development of predictive models of chromatographic protocols capable of high-throughput evaluation. Predictive models can be generated that quantify one or more performance criteria based on a set of chromatographic parameters (e.g., packing parameters).

[0054] Certain chromatographic modes or media types can be selected for specific operations. For example, in chromatographic operations requiring the reduction of high molecular weight species and / or aggregates, hydrophobic interaction chromatography can be used to separate high molecular weight species and / or aggregates from target molecules (e.g., antibody-drug conjugates). After selecting the chromatographic mode based on the target of the chromatographic operation, the development of the chromatographic protocol may include determining the type of chromatographic media used in the protocol.

[0055] Throughout this disclosure, exemplary chromatographic protocols, including hydrophobic interaction chromatography (HIC) protocols, are referenced. It should be noted that one or more HIC protocols may be referenced in describing the systems and methods of this disclosure; such systems and methods are not necessarily limited to HIC and are applicable to a variety of chromatographic modes (e.g., affinity chromatography (e.g., protein A or protein L), ion exchange chromatography, size exclusion chromatography, reversed-phase chromatography, rapid protein liquid chromatography, high-performance liquid chromatography, countercurrent chromatography, periodic countercurrent chromatography, chiral chromatography, and / or mixed-mode chromatography).

[0056] refer to Figure 1The method 100 for developing a chromatographic protocol may include identifying filling parameters (step 101), identifying performance criteria (step 102), performing chromatographic runs using the filling parameters to generate performance criterion values ​​(step 103), ranking potential predictive models (step 104), selecting and validating predictive models (step 105), and using the validated predictive models to identify filling parameter values ​​for the chromatographic protocol (step 106).

[0057] Identifying packing parameters may include identifying, selecting, and / or determining parameters of the chromatographic protocol that will change during protocol development. For example, packing parameters may include “input variables” or adjustable, changeable, and controllable aspects of the chromatographic protocol, while simultaneously monitoring and / or recording the impact of packing parameters on performance criteria. As described above, examples of packing parameters include, but are not limited to, chromatographic mode, column size (e.g., total volume (e.g., column volume or membrane volume), bed height, membrane thickness, inner diameter, etc.), media composition, packing density, composition of one or more buffer solutions, concentration of high molecular weight species in the packing material, and flow rate.

[0058] As mentioned above, various chromatographic modes may include, but are not limited to, hydrophobic interaction chromatography, affinity chromatography (e.g., protein A or protein L), ion exchange chromatography, size exclusion chromatography, reversed-phase chromatography, rapid protein liquid chromatography, high-performance liquid chromatography, countercurrent chromatography, periodic countercurrent chromatography, chiral chromatography, and / or mixed-mode chromatography. The selection of a specific chromatographic mode may take into account various factors, including the type of packing material, preferred separation method, and program size. The selected chromatographic mode may subsequently affect chromatographic operating parameters, including but not limited to the type of chromatographic medium, i.e., the static phase, the mobile phase, and the buffer composition.

[0059] Column dimensions may include total volume (e.g., column volume or membrane volume), bed height, membrane thickness, and / or inner diameter. An exemplary column volume may be from about 0.2 mL to about 4600 mL. An exemplary bed height may be from about 1 cm to about 30 cm. An exemplary inner diameter may be from about 1 cm to about 14 cm. An exemplary membrane volume may be from about 0.08 mL to about 5 liters. An exemplary membrane thickness may be from about 4 mm to about 8 mm.

[0060] A chromatographic column may contain a type of chromatographic medium. For example, a column may contain amino acid media, ligand-specific media, immunoaffinity media, ion affinity media, hydrophobic interaction media, and / or charged media. The medium may be in the form of resin, beads, particles bound in a packed bed column, membranes, etc., or in any form capable of containing mixtures of biopharmaceutical products or other liquids. The medium may contain a support structure, such as, for example, agarose beads (e.g., sepharose), silica beads, cellulose membranes, cellulose beads, hydrophilic polymer beads, or other compressible synthetic structures. The selection of the chromatographic medium can be based on many different factors, including, for example, the chromatographic mode and the type of analyte, i.e., the species to be separated during chromatography. Chromatographic media can be selected to optimize the interaction between the medium and the analyte.

[0061] Packing density can be quantified as the mass (e.g., grams) of the antibody-drug conjugate packed per volume of chromatographic equipment (e.g., column volume or membrane volume). Chromatographic protocols with higher packing densities may result in greater interaction between the medium and the mobile phase compared to protocols using lower packing densities. Increased packing density can lead to improved conditions where high molecular weight species are reduced. Using protocols with higher packing densities also results in lower yields compared to protocols using lower packing densities.

[0062] In some aspects, the packing density of the chromatographic operation may be from about 10 g / L to about 40 g / L, such as, for example, about 20 g / L to about 30 g / L, about 10 g / L to about 25 g / L, about 15 g / L to about 30 g / L, about 15 g / L to about 25 g / L, about 25 g / L to about 30 g / L, or about 20 g / L to about 25 g / L.

[0063] Packing parameters may include a composition of one or more buffers for the chromatographic protocol, such as a packing buffer or a composition of other buffers constituting the mobile phase of the chromatographic protocol. The composition of one or more buffers can affect how the components of the packing interact with each other and / or with the chromatographic medium. Packing parameters associated with the buffer composition may include the presence and / or concentration of one or more salts or other compounds in the buffer. Alternatively or additionally, packing parameters associated with the buffer composition may include the pH of the buffer.

[0064] For example, one or more buffer solutions in a chromatographic protocol may include one or more salts (e.g., citrate). The packing parameter may be a binary parameter, meaning that one or more salts are present. In some aspects, the packing parameter may include the concentration of one or more salts (e.g., expressed in millimoles [mM]). For example, the chromatographic protocol may include a citrate concentration in the packing buffer, such as, for example, citrate concentrations of about 5 mM to about 200 mM, 5 mM to about 150 mM, 5 mM to about 100 mM, 25 mM to about 200 mM, 50 mM to about 200 mM, about 100 mM to about 200 mM, about 100 mM to about 150 mM, about 150 mM to about 200 mM, about 110 mM to about 175 mM, about 50 mM to about 125 mM, about 110 mM to about 200 mM, about 140 mM to about 155 mM, about 110 mM to about 155 mM, or about 140 mM to about 175 mM.

[0065] The packing parameters may include the content of high molecular weight species in the chromatographic packing material. For example, the packing parameters may include the %HMW of the packing material. The %HMW of the packing material may be from about 3% to about 20%, such as, for example, from about 3% to about 15%, from about 3% to about 10%, from about 5% to about 20%, from about 5% to about 15%, from about 5% to about 10%, from about 7% to about 20%, from about 7% to about 15%, or from about 7% to about 10%.

[0066] Packing parameters may also include one or more other properties of the packing material, such as viscosity and / or density. In some embodiments, the properties of the packing material (e.g., the content of high molecular weight species, viscosity, and / or density) may be adjusted during or after production and before performing the chromatographic protocol.

[0067] Refer again Figure 1 The method 100 for developing chromatographic protocols may include identifying performance criteria (step 102). Indicators quantifying the performance, efficiency, and / or effectiveness of the chromatographic protocol are used. Performance criteria may include “output variables” or aspects of the chromatographic protocol, depending on the values ​​selected for the filling parameters. Identifying performance criteria may include identifying, selecting, and / or determining the performance of chromatographic protocols that will be used to evaluate potential chromatographic protocols during development.

[0068] Examples of performance criteria include, but are not limited to, the composition of high molecular weight species (e.g., %HMW), the reduction in the content of high molecular weight species (e.g., %HMWΔ), productivity, total protein yield, drug-to-antibody ratio, drug-to-antibody ratio difference, drug-to-antibody ratio, total protein content, particle size distribution, residual free linker-payload content, residual organic solvent content, unconjugated antibody content, or expectation index.

[0069] Refer again Figure 1 Method 100 for developing a chromatographic protocol may include performing chromatographic runs to generate performance standard values ​​(step 103). Chromatographic runs may be performed by varying the identified packing parameters. Other packing parameters may remain constant while the identified packing parameters may be varied. For example, if flow rate and packing density are identified as packing parameters, all other parameters of the chromatographic protocol (e.g., chromatographic mode, column size, media composition, buffer composition, and concentration of high molecular weight species in the packing material) may remain constant during the chromatographic run. Continuing with this example, where flow rate and packing density are identified as packing parameters, performing chromatographic runs may include performing chromatographic runs according to different flow rates and media densities of the chromatographic protocol. Each chromatographic run may generate one value for each identified performance standard. For example, when four performance standards are identified, each chromatographic run may generate four values, one value for each performance standard.

[0070] Refer again Figure 1 The method 100 for developing a chromatographic protocol may include ranking potential predictive models (step 104). As described in more detail herein, a predictive model domain and / or a pool of candidate predictive models may be generated. Each predictive model may include a mathematical relationship between the identified filling parameters and the identified performance criteria. For example, a predictive model may be able to predict the value of the identified performance criterion based on a given set of values ​​for the identified filling parameters. In the aspect of identifying multiple performance criteria, the potential predictive model may include a function corresponding to each identified performance criterion.

[0071] As described in more detail herein, a latent predictive model can be used to generate predicted performance standard values. These predicted performance standard values ​​can be compared with performance standard values ​​generated by a chromatographic run (step 103). This comparison leads to the quantification of each predictive model corresponding to the correlation between the predictive model and the data generated by the chromatographic run. For example, based on the correlation between the predicted performance standard values ​​generated from the model and the performance standard values ​​generated by the chromatographic run, the coefficient of determination (R²) can be... 2 This can be assigned to each potential predictive model. Each potential predictive model can be ranked based on the correlation between the predictive model's quantification (e.g., coefficient of determination) and the data generated by the chromatographic run.

[0072] Refer again Figure 1 Based on the correlation between the predicted performance standard values ​​and the performance standards generated by the chromatographic run, after ranking the predictive models, a predictive model can be selected and validated (step 105). For example, in one or more embodiments, the predictive model with the highest ranking can be selected. After selecting a predictive model, it can be validated via another chromatographic run. Validating the selected predictive model may include further studies to ensure that the predictive model is relevant to phenomena observed over a wide range of filling parameter values.

[0073] Furthermore, the chromatographic protocols developed using the methods of this disclosure can be validated through further studies to ensure that biopharmaceutical products prepared using the developed chromatographic protocols meet internal quality assurance criteria and applicable regulatory agency standards. Further studies may include characterizing protein concentrations, the content of high molecular weight species, drug-to-antibody ratios, drug-packet distribution (e.g., using mass spectrometry), and / or particle size distribution (e.g., using dynamic light scattering). Alternatively or additionally, further studies may include peptide mapping, binding assays, and other bioassays of species in the eluent produced by the chromatographic protocol. In some aspects, validating the chromatographic protocol may include conducting one or more in vivo studies. For example, in vivo studies using suitable similar organisms may be performed to determine the pharmacokinetics, pharmacodynamics, efficacy, and / or toxicity of species produced using the developed chromatographic protocol.

[0074] Also refer to Figure 1 After validating the selected prediction model, it can be used to evaluate thousands of chromatographic protocols in a high-throughput manner. The optimal filling parameter values ​​can be solved using the rates of the chromatographic protocols evaluated by the prediction model, thereby identifying improved chromatographic protocols. For example, the validated prediction model can be used to solve for filling parameter values ​​that provide optimal values ​​for one or more performance criteria, such as expectancy metrics.

[0075] As more predictive models emerge and the chromatographic run database grows, more accurate predictive models will be generated for each identified performance criterion. All performance criteria for all filling parameters can be evaluated using the high-throughput methods described above to determine which filling parameters result in sufficient performance criteria. Advantageously, high-throughput methods for evaluating chromatographic protocols can reduce the time, labor, and costs associated with developing chromatographic protocols suitable for the production of biopharmaceutical products.

[0076] refer to Figure 2 The method 200 for developing a chromatographic scheme may include identifying filling parameters (step 201), identifying performance criteria (step 202), identifying chromatographic media (step 203), selecting filling parameter test values ​​(step 204), performing chromatographic runs on the chromatographic media using the filling parameter test values ​​to generate performance criterion values ​​(step 205), identifying prediction model domains (step 206), generating expected performance criterion values ​​for each prediction model in the prediction model domain, comparing the expected performance criterion values ​​with the performance criterion values ​​generated by the chromatographic runs based on the combination of test values ​​used in the chromatographic runs (step 207), assigning a ranking to each prediction model in the prediction model domain based on the comparison (step 209), selecting and validating prediction models (step 210), and using the validated prediction models to identify filling parameter values ​​(step 211).

[0077] Some steps of method 200, such as, for example, identifying filling parameters (step 201), identifying performance criteria (step 202), performing chromatographic runs on the chromatographic medium using the filling parameter test values ​​to generate performance criterion values ​​(step 205), assigning a ranking to each predictive model in the predictive model domain (step 209), selecting and validating predictive models (step 210), and using the validated predictive models to identify filling parameter values ​​(step 211), may be similar to the steps of method 100 for developing a chromatographic protocol, as described above. In some aspects, method 200 for developing a chromatographic protocol may include additional steps that are not required in method 100.

[0078] Also refer to Figure 2 The method 200 for developing a chromatographic protocol may further include identifying the chromatographic medium (step 203). For example, for a given chromatographic mode, there may be several candidate chromatographic media that can be used in the chromatographic protocol. Chromatographic media may include amino acid media, ligand-specific media, immunoaffinity media, ion affinity media, hydrophobic interaction media, and / or charged media. Chromatographic media may be in the form of resins, beads, particles bound in a packed bed column, membranes, etc., or in any form capable of containing a mixture containing a biopharmaceutical product or other liquids. Chromatographic media may include a supporting structure, such as, for example, agarose beads (e.g., sepharose), silica beads, cellulose membranes, cellulose beads, hydrophilic polymer beads, or other compressible synthetic structures.

[0079] The suitability of a given medium for a chromatographic protocol can depend on the properties of the target molecules and other species within the mixture constituting the chromatographic packing. In some aspects, other factors may affect the suitability of a given medium for a chromatographic protocol, such as, for example, column size or other packing parameters of the chromatographic protocol. Therefore, some chromatographic media exhibit superior performance for certain chromatographic protocols compared to other chromatographic media (e.g., as measured by one or more performance criteria). Any change in the target molecules or the chromatographic protocol can lead to a change in the suitability of the selected chromatographic medium. Therefore, in some aspects, the method 200 for developing a chromatographic protocol may include identifying or determining a chromatographic medium suitable for the chromatographic protocol.

[0080] Identifying chromatographic media may involve performing chromatographic runs using different types of chromatographic media, which may generate one or more performance criteria corresponding to each type of chromatographic media used in the run. When identifying chromatographic media, the same chromatographic protocol (except for the chromatographic media used) may be used for the chromatographic runs to better understand the impact of the chromatographic media on the performance criteria. Identifying chromatographic media may further include monitoring the performance criteria corresponding to different types of chromatographic media and ranking the chromatographic media according to their corresponding performance criteria. The chromatographic media with the highest ranking may be selected for use in the chromatographic protocol.

[0081] As an illustrative and non-limiting example, identifying chromatographic media may include performing chromatographic runs using eight different types of chromatographic media, for a total of eight runs. Each chromatographic run may produce one or more performance standard values ​​(e.g., expectation index) corresponding to each chromatographic media type. The eight media may be ranked according to the performance standards corresponding to the eight media, and the media with the highest ranking (e.g., maximum expectation index) may be selected. In some aspects, multiple chromatographic runs may be performed for each chromatographic media type. The performance standard values ​​generated during multiple runs for a chromatographic media type may be averaged to produce a performance standard value corresponding to the chromatographic media type.

[0082] As described above regarding method 100, a combination of test values ​​can be used to perform chromatographic runs to generate performance standards. (Reference) Figure 2 Method 200 for developing a chromatographic protocol may include selecting test values ​​for the packing parameters of the chromatographic run (step 204). In one or more embodiments, a test value is selected for each packing parameter. Combinations of test values ​​may include every possible combination of the test values ​​selected for each packing parameter. For example, if packing density, packing buffer sulfate concentration, and high molecular weight species content are selected as packing parameters, selecting test values ​​may include selecting test values ​​for packing density (e.g., 20 g / L, 35 g / L, and 50 g / L), packing buffer citrate concentration (e.g., 130 mM, 150 mM, and 170 mM), and high molecular weight species content (e.g., 12% HMW, 8% HMW, and 4% HMW). In this example, combinations of test values ​​(including every possible combination of test values ​​selected for each packing parameter) may include combinations of test values ​​as shown in Table 1 below.

[0083] Table 1

[0084] Selecting test values ​​for fill parameters may include choosing two or more test values ​​for each fill parameter; for example, selecting test values ​​may include choosing two, three, four, five, six, seven, eight, or nine test values ​​for each fill parameter. Selecting test values ​​for fill parameters may also include choosing less than or equal to ten test values, less than or equal to eight test values, less than or equal to six test values, or less than or equal to five test values. In some aspects, the number of test values ​​selected for one fill parameter may be the same as the number of test values ​​selected for each of the other fill parameters. In other aspects, the number of test values ​​selected for the first fill parameter may differ from the number of test values ​​selected for the second fill parameter. For example, more test values ​​may be selected for fill parameters that are considered to produce more variability and / or have a wider operating range. Including more test values ​​for fill parameters may increase the amount of time required for chromatographic runs based on combinations of test values, but it provides a more robust dataset for developing predictive models.

[0085] In some aspects, selecting the test values ​​for the packing parameters for a chromatographic run may include selecting a combination of selectable test values. In other words, rather than selecting individual test values ​​for each packing parameter and using every possible combination of the selected test values, a combination of packing parameter test values ​​may be selected. For example, if packing density, packing buffer sulfate concentration, and the content of high molecular weight species are selected as packing parameters, selecting test values ​​may include selecting combinations of test values ​​as shown in Table 2.

[0086] Table 2

[0087] Advantageously, by selecting combinations of test values ​​instead of using each selected combination of test values, more test values ​​can be tested for each filler parameter while using fewer chromatographic runs. Using more test values ​​during chromatographic runs improves the robustness of the predictive model developed using the methods described herein. Furthermore, performing chromatographic runs can be time-consuming and laborious, as previously mentioned, increasing associated costs. Therefore, reducing the number of chromatographic runs in step 205 reduces the overall cost associated with method 200 for developing the chromatographic protocol.

[0088] Refer again Figure 2A method for developing a chromatographic protocol may include performing a chromatographic run on an identified chromatographic medium using combinations of test values ​​to generate performance standard values ​​(step 205). For example, a single chromatographic run may be performed for each selected combination of test values, thereby generating each identified performance standard value corresponding to the selected combination of test values. In some embodiments, multiple chromatographic runs (e.g., three or more) may be performed for each selected combination of test values. The performance standards generated from the multiple chromatographic runs may be averaged to generate performance standard values. In some embodiments, the chromatographic run of step 205 may be performed using the chromatographic medium identified in step 203.

[0089] refer to Figure 2 The method 200 for developing a chromatographic protocol may further include identifying a prediction model domain. Identifying the prediction model domain may include identifying a prediction model domain for each identified performance criterion. For each identified performance criterion, the prediction model may include a mathematical relationship between one or more identified filling parameters and the identified performance criterion. For example, if two performance criteria are identified, step 206 may include identifying a first prediction model domain that associates one or more filling parameters with a first performance criterion and identifying a second prediction model domain that associates one or more filling parameters with a second performance criterion.

[0090] The predictive model domain can include univariate, bivariate, trivariate, or other multivariate models. For example, products, quotients, exponents, and other multivariate relationships with filled parameters can be considered. The latent model domain can also include known mechanical or empirical relationships.

[0091] In some aspects, the architecture of a predictive model can be determined by the number of identified imputation parameters, such that the number of independent variables in the predictive model is less than or equal to the number of identified imputation parameters. For example, in an implementation where two imputation parameters are identified, the potential predictive model may include univariate and bivariate models. In an aspect where three imputation parameters are identified, the potential predictive model may include univariate, bivariate, and trivariate models.

[0092] A predictive model domain can include tens of thousands of models, such as, for example, over 50,000 potential predictive models. In some aspects, additional steps can be used to reduce the number of potential predictive models in the predictive model domain. For example, models with duplicate parameters can be removed, so that the predictive model domain does not include repeating models. When developing algebraic expressions that associate evaluation criteria with filler parameters, equivalent expressions can be established. These equivalent expressions may be functionally repetitive and can be removed from the domain.

[0093] In some aspects, a variance inflation factor (VIF) can be calculated for each potential predictive model, and models with a VIF greater than or equal to a collinearity threshold can be excluded from the predictive model domain. In some aspects, the collinearity threshold is 4 or less, such as, for example, 2, 3, or 4. After removing models with duplicate parameters and models with a VIF greater than or equal to the collinearity threshold, the predictive model domain can include hundreds of models. For example, for each identified performance criterion, the remaining predictive model domain can include fewer than or equal to 500 models.

[0094] refer to Figure 2 The method 200 for developing a chromatographic protocol may include generating a predicted performance standard value for each predictive model in a predictive model domain (step 207). Predicted performance standard values ​​can be generated using combinations of test values, such that for each predictive model, one predicted performance standard value is generated corresponding to each combination of test values. For example, if the chromatographic run of step 205 is performed with 27 combinations of test values, each predictive model in the predictive model domain can be used to generate 27 predicted performance standard values. In this example, if two performance standards are identified, where a first predictive model domain for a first performance standard includes 500 models and a second predictive model domain for a second performance standard includes 600 models, generating predicted performance standard values ​​may include generating 29,700 predicted performance standard values.

[0095] refer to Figure 2 The method 200 for developing a chromatographic protocol may include comparing a predicted performance standard value with a performance standard value generated by the chromatographic run in step 205 (step 208). Comparing the predicted performance standard value with the performance standard value generated by the chromatographic run may include generating a statistical index for each predictive model in the predictive model domain, the statistical index relating to the degree to which the predictive model is correlated with the observed phenomenon. For example, based on the comparison of the predicted performance standard value with the performance standard value generated by the chromatographic run, a coefficient of determination (R²) may be calculated for each predictive model in the predictive model domain. 2 ).

[0096] Also refer to Figure 2 The method 200 for developing chromatographic protocols may include assigning a ranking to each prediction model in a prediction model domain based on a comparison of expected performance standard values ​​with performance standard values ​​(step 209). For example, the prediction model domain may be ranked based on statistical indicators relating the correlation between prediction models within the domain. In some aspects, a coefficient of determination is calculated for each prediction model, and the domain is ranked in descending order based on the coefficient of determination.

[0097] As described above regarding method 100, method 200 for developing a chromatographic protocol may include selecting and validating a prediction model (step 210) and using the validated prediction model to identify the packing parameter values ​​for the chromatographic protocol (step 211).

[0098] Example Example 1 Six chromatographic runs were performed using six different hydrophobic interaction chromatography (HIC) media, based on the target molecule (including antibody-drug conjugate) chromatographic protocol. Specifically, the antibody-drug conjugate comprised an IgG4 antibody attached to a cleavable levothyroxine (M114) via random lysine conjugation. The chromatographic protocol specified a packing buffer containing 175 mM citrate (pH 8). The protocol further specified a packing density of 20 g / L and a packing volume %HMW of approximately 8% to approximately 9.5%. The six HIC media included capto phenyl, phenyl sepharose 6 fast flow, toyopearl phenyl-650C, capto butyl impres, sartobind phenyl, and octyl sepharose 4 fast flow, each with different hydrophobicities. The relative hydrophobicities of these media are as follows: Figure 3 As shown.

[0099] For each of the six chromatographic runs, two performance metrics were recorded: target molecule yield and high molecular weight species reduction rate (%HMWΔ). These performance metrics were plotted and displayed. Figure 3 (FIG. 3) in Figure 300 (Plot 300). Figure 300 includes lines 302 and 304, which identify the desired performance standard values ​​for the developed chromatographic protocol. For example, line 302 corresponds to approximately 70% yield, and line 304 corresponds to approximately 3% %HMWΔ. As shown in Figure 300, yield is inversely proportional to the hydrophobicity of the medium, and %HMWΔ is inversely proportional to the hydrophobicity of the medium.

[0100] In addition, the mass balance of total protein, high molecular weight species, and free linker-payload associated with the 6 chromatographic runs was calculated according to Equation 3, as shown below.

[0101] Table 3 below summarizes the calculated total protein mass balance, high molecular weight species mass balance, and free linker-payload mass balance.

[0102] Table 3

[0103] Based on the data in Figure 300 and Table 3, sartobind phenyl medium is shown to be the preferred medium, providing an optimal balance between HMW removal, target molecule yield, and recovery of proteins, aggregates, and linker-payloads. Therefore, sartobind phenyl can be a suitable starting point when developing new chromatographic protocols (e.g., HIC protocols), particularly in the case of target molecules (including lysine-conjugated antibody-drug conjugates).

[0104] Example 2 In one embodiment of the chromatographic protocol development, packing density, packing buffer citrate concentration, and packing %HMW were identified as three packing parameters. Yield and eluent %HMW were identified as performance standards. Additionally, a performance index was constructed, which is a combination of yield and eluent %HMW, with both yield and eluent %HMW being equally weighted. Test values ​​for the packing parameters were selected, and chromatographic runs were performed using combinations of these test values ​​to generate performance standard values, including yield and eluent %HMW values. The performance index was calculated based on the resulting yield and eluent %HMW values. The chromatographic runs were performed on a chromatographic system comprising a column (including a sartobind phenyl hydrophobic interaction medium).

[0105] A first predictive model domain was identified, wherein each model includes a mathematical relationship of yield as a function of one or more of the fill density, fill buffer citrate concentration, and filler %HMW. A second predictive model domain was identified, wherein each model includes a mathematical relationship of elution %HMW as a function of one or more of the fill density, fill buffer citrate concentration, and filler %HMW.

[0106] For each model in the first prediction model domain, a projected yield corresponding to each combination of test values ​​is generated. The projected yield is compared with the generated yield values, and a coefficient of determination is calculated for each prediction model in the first prediction model domain based on the comparison. The prediction models in the first prediction model domain are ranked in descending order according to their coefficients of determination, and the prediction model with the highest coefficient of determination is selected as the first prediction model.

[0107] The first prediction model can be described according to Equation 4 shown below.

[0108] Refer to Equation 4, x Represents the filling density in grams per liter. y The filling buffer represents the millimolectic acid concentration. zThe filler represents %HMW, and A, B, C, D, E, F, G, and H are constants. In some aspects, the first predictive model may include a value of A of about 0.01 L / g to about 1.00 L / g, such as, for example, about 0.354 L / g. The first predictive model may include a value of B of about -1.00 mL / mol to about -0.01 mL / mol, such as, for example, about -0.155 mL / mol. The first predictive model may include a value of C of about -10.0 to about -0.01, such as, for example, about -2.04. The first predictive model may include a value of D of about -0.1 (mL / mol). 2 To approximately 0.1 (mL / mol) 2 For example, approximately -0.005 (mL / molar) 2 The first prediction model may include an E value of about -500 to about 500, such as, for example, about -150. The first prediction model may include an F value of about -15 to about -0.01, such as, for example, about -8.814. The first prediction model may include a G value of about 0.01 to about 1.0, such as, for example, about 0.473. The first prediction model may include an H value of about 10 to about 200, such as, for example, about 102.326.

[0109] For each model in the second prediction model domain, a projected eluent %HMW value corresponding to each combination of test values ​​is generated. The eluent %HMW value is compared with the generated eluent %HMW value, and a determination coefficient for each prediction model in the second prediction model domain is calculated based on the comparison. The prediction models in the second prediction model domain are ranked in descending order according to their determination coefficients, and the prediction model with the highest determination coefficient is selected as the second prediction model.

[0110] The second prediction model can be described according to Equation 5 shown below.

[0111] Refer to Equation 5, x Represents the filling density in grams per liter. y The filling buffer represents the millimolectic acid concentration. z The filler represents %HMW, and A, B, C, D, E, F, G, and H are constants. In some aspects, the second predictive model may include a value of A of about 0.01 L / g to about 1.00 L / g, such as, for example, about 0.078 L / g. The second predictive model may include a value of B of about -1.00 mL / mol to about 1.00 mL / mol, such as, for example, about -0.025 mL / mol. The second predictive model may include a value of C of about 0.01 to about 1.0, such as, for example, about 0.433. The second predictive model may include a value of D of about -0.1 (L / g). 2 To approximately 0.1 (L / g)2 For example, approximately -0.001 (L / g) 2 The second prediction model may include an E value of about -200 to about 200, such as, for example, about -33.75. The second prediction model may include an F value of about -0.1 L / g to about 0.1 L / g, such as, for example, about 0.008 L / g. The second prediction model may include a G value of about -15.0 to about 15.0, such as, for example, about -8.814. The second prediction model may include an H value of about 0.01 to about 10.0, such as, for example, about 1.985.

[0112] The first and second prediction models are plotted together dynamically to visualize the impact of adjusting the fill parameter on the performance metric. An expectation index combining other performance metric values ​​is also plotted. The dynamic plot based on the first and second prediction models illustrates the predicted performance metric based on the choice of fill parameter. The dynamic plot also shows how adjusting the fill parameter affects the performance metric. Figure 4 The static screenshot of the dynamic graph is shown, where the fill density selected as the fill parameter is approximately 25 g / L, the citrate concentration of the fill buffer is approximately 148 mM, and the filler %HMW is approximately 6%. In the dynamic graph, if one of the fill parameter selections is changed, the curves shown in Figures 401, 402, 404, 411, 412, 413, 414, 421, 422, and 423 will also change.

[0113] refer to Figure 4 The dynamic graphs include a first set of graphs 401, 402, and 403 generated using a first prediction model, and a second set of graphs 411, 412, and 413 generated using a second prediction model. The dynamic graphs may also include a third set of graphs 421, 422, and 423 generated using the first and second prediction models combined according to an expectancy index. The dynamic graphs may also include graphs 404 and 414 representing expected values ​​associated with each performance criterion as part of the expectancy index. For example, as described herein, the values ​​associated with a performance criterion (e.g., yield, %HMW) can be assigned expected values ​​within a scale of 0 to 1. Based on the weights associated with each performance criterion, the expected values ​​of each performance criterion can be combined into an expectancy index. Figure 404 shows the expected values ​​associated with different yield values. Figure 414 shows the expected values ​​associated with different eluent %HMW values. Figure 4 In the illustrated embodiments, the expected values ​​associated with yield and eluent %HMW are assigned the same weight and used to generate the expectedness indexes plotted in Figures 421, 422 and 423.

[0114] The vertical dashed lines shown in Figures 401, 402, 403, 411, 412, 413, 421, 422, and 423 represent the currently selected values ​​for the corresponding filling parameters. For example, Figures 401, 411, and 421 include vertical dashed lines at a filling density of approximately 25 g / L; Figures 402, 412, and 422 include vertical dashed lines at a filling buffer citrate concentration of approximately 148 mM; and Figures 403, 413, and 423 show vertical dashed lines at a filling %HMW of approximately 6%. The horizontal dashed lines shown in Figures 401, 402, 403, 411, 412, 413, 421, 422, and 423 represent standard values ​​for target performance. For example, Figures 401, 402, 403 and 404 include horizontal dashed lines corresponding to a yield of approximately 80%; Figures 411, 412, 413 and 414 include horizontal dashed lines corresponding to an eluent %HMW of approximately 3%; and Figures 421, 422 and 423 include horizontal dashed lines corresponding to an expectation index of 0.9.

[0115] Thousands of chromatographic protocols were evaluated in a high-throughput manner using dynamic plots. The dynamic plots (especially expectancy plots 421, 422, and 423) can also be used to easily visualize the filling parameter values ​​that optimize the expectancy of chromatographic protocols. For example, based on... Figure 4 The dynamic graph shown illustrates a chromatographic protocol developed with the following packing parameters: packing density of about 20 to about 30 g / L, packing buffer citrate concentration of about 140 mM to about 155 mM, and packing %HMW of about 7% to about 15%.

[0116] Example 3 Full-scale chromatographic runs were used to validate the predictive models developed in Example 2 (e.g., including the first and second predictive models). Full-scale chromatographic runs were performed on sartobind phenyl membranes at a packing density of 26.7 g / L. The packing buffer had a citrate concentration of 150 mM. The packing material used for the full-scale chromatographic runs comprised 9.44% %HMW.

[0117] use Figure 5 The chromatogram shown represents an exemplary chromatographic protocol used for full-scale chromatography operation, plotting the absorbance measured at the outlet of the chromatographic apparatus against the volume passing through the apparatus. Reference Figure 5At T0, a buffer solution containing 0.1 N sodium hydroxide is introduced into the chromatography apparatus. At T1, water (e.g., reverse osmosis deionized water) is introduced into the apparatus. At T2, an equilibration buffer is introduced into the apparatus. The equilibration buffer consists of 50 mM Tris and 150 mM sodium citrate, and is pH 8.0. At T3, the packing material is introduced into the chromatography apparatus. Pool collection begins when the absorbance of the eluent increases after the introduction of the packing material (e.g., around T4). At T5, a stripping buffer is introduced into the chromatography apparatus. The stripping buffer contains reverse osmosis deionized water. At T6, a buffer solution containing 0.5 N sodium hydroxide is introduced into the chromatography apparatus. The eluent collected between T5 and T6 is referred to as the stripping fraction. At T7, a storage buffer is introduced into the chromatography apparatus. The storage buffer contains 20% by weight ethanol.

[0118] Record the total protein yield and eluent %HMW of the full-scale chromatography run and compare them with the predicted performance benchmarks generated using a predictive model. Table 4 summarizes the actual and predicted performance benchmarks.

[0119] Table 4

[0120] Example 4 Further studies were conducted to validate the chromatographic protocol developed in Example 2. Mass spectra were generated from the packing material and eluent of the chromatographic protocol from Example 3 to confirm the drug distribution of the target molecules before and after the chromatographic protocol. The mass spectra of the packing material are shown below. Figure 5 As shown in Figure A, and the mass spectrum of the eluent is as follows: Figure 5 As shown in B, each peak in the mass spectrometry corresponds to a species of target molecule. For example, while each peak corresponds to an antibody-drug conjugate target molecule, each peak may correspond to a species of target molecules with different drug-to-antibody ratios. Table 5 provides a summary of the mass spectrometry peaks.

[0121] Table 5

[0122] The drug-to-antibody ratio of a sample can be determined by comparing the relative abundance of various target molecular species. In this embodiment, mass spectrometry was used to determine the drug-to-antibody ratio of the packing material and the eluent. The drug-to-antibody ratio of the packing material was 3.9, and the drug-to-antibody ratio of the eluent was 2.9.

[0123] Compared to the filler, the eluent exhibits a lower drug-to-antibody ratio, evidence that species with higher drug-to-antibody ratios are retained on the membrane. Removal of species with higher drug-to-antibody ratios yields a more homogeneous drug product. Furthermore, the presence of some species with high drug-to-antibody ratios may affect the stability of the resulting drug product and / or may alter the immunogenicity response to the resulting drug product. Therefore, according to aspects of this disclosure, removal of species with high drug-to-antibody ratios can produce a drug product with reduced risk characteristics.

[0124] Example 5 Dynamic light scattering was used to determine the particle size distribution of the packing material and eluent in the chromatographic protocol of Example 3. The measured particle size distribution of the packing material is shown below. Figure 6A As shown, the particle size distribution of the eluent is as follows. Figure 6B As shown in Table 6. Figure 6A and Figure 6B The peak values ​​shown in each of the graphs.

[0125] Table 6

[0126] like Figure 6A , Figure 6B As shown in Table 6, the developed chromatographic scheme improves particle size distribution and average particle size by removing larger molecules, confirming the ability to reduce aggregates through chromatographic schemes. The 4.11% eluent %HMW shown in Table 4 further supports this view, representing a %HMWΔ of 5.33% compared to a packing %HMW of 9.44%.

[0127] Example 6 As part of the validation of the chromatographic protocol developed in Example 2, the packing material and eluent of the chromatographic protocol of Example 3 were subjected to bioassays.

[0128] For the assay, 1300 EBC-1 human cells (endogenously expressing human MET) were seeded in opaque, white-bottomed 96-well plates in minimum essential medium (1x Earle's salt supplemented with 10% fetal bovine serum and 1% penicillin / streptomycin / glutamine) free of non-essential amino acids. Each well was incubated overnight at 37°C and 5% CO2. Following incubation, as a pretreatment step, 20 μL serial dilutions of the packing material from the chromatographic protocol of Example 3, the eluent from the protocol, and conjugated reference standards were prepared in medium, with concentrations ranging from 1.0 pM to 1.0 nM. These serial dilutions were added to the 96-well plates and incubated (i.e., a second incubation) for 72 hours. After the second incubation, 100 μL of a commercially available cell viability assay kit was added to each plate, and luminescence was recorded using an ENVISION multi-mode plate reader. Figure 8The graph shows the results of the bioassay, where line 702 represents the measurement from the reference standard, line 704 represents the measurement from the packing material of the chromatographic protocol described in Example 3, and line 706 represents the measurement from the eluent of the chromatographic protocol. Figure 8 The figure shows that the cytotoxic properties of the antibody-drug conjugates from the eluent of the chromatographic protocol are consistent with those of the batches produced using previously validated methods.

[0129] Using GraphPad Prism software, data from the bioassay were analyzed on an 11-point response curve using a 4-parameter logistic equation. The half-maximal inhibitory concentration (IC50) was calculated, and the relative potency was determined using Equation 6, as shown below.

[0130] Refer to Equation 6, IC 50 Reference standard refers to the IC of the reference standard. 50 And IC 50 Test item refers to the IC of the sample to be tested. 50 (e.g., the packing material or eluent for chromatographic operations). Table 7 summarizes the relative power of the bioassays in Example 6.

[0131] Example 7 As part of the validation of the chromatographic protocol developed in Example 2, the packing material and eluent of the chromatographic protocol in Example 3 were subjected to enzyme-linked immunosorbent assay (ELISA) binding assay.

[0132] ELISA plates coated with unconjugated anti-human MET antibodies were incubated overnight at 4°C. After overnight incubation, serially diluted buffers of the antibody-drug conjugate described in Example 1 and serially diluted buffers of the reference standard were pre-bound with 50 pM human MET myc-myc-hexahistidine for one hour at room temperature. The reference standard was a batch of antibody-drug conjugate manufactured using a previously validated method.

[0133] Then, each pre-conjugated mixture was transferred in duplicate to an ELISA plate coated with unconjugated anti-human MET antibody (i.e., the same antibody used in antibody-drug conjugates) and incubated for one hour. After incubation, the mixture was analyzed by reacting with an antihistamine (such as commercially available Qiagen). TMThose (catalog number 34460) conjugated with horseradish peroxidase (HRP) were used to detect plate-bound human MET myc-myc-hexahistidine, and colorimetric analysis was performed using 3,3',5,5'-tetramethylbenzidine (TMB) (a colorimetric HRP substrate). The absorbance of each well of the ELISA plate at 450 nm was recorded on an ENVISION multimode plate reader, and antibody concentration functions were plotted. Figure 9 The graph shows the results of the bioassay, where line 802 represents the measurement from the reference standard, line 804 represents the measurement from the packing material of the chromatographic protocol described in Example 3, and line 806 represents the measurement from the eluent of the chromatographic protocol described in Example 3. Figure 9 The figure shows that the relative binding power of the conjugates from the chromatographic protocol's packing and eluent is not significantly different from that of the reference standard (e.g., a batch manufactured using a previously validated method).

[0134] Using GraphPad Prism software, data from the bioassay were analyzed on an 11-point reaction curve using a 4-parameter logic equation. The half-maximal inhibitory concentration (IC50) was derived from the analysis results. The IC50 value is defined as the concentration of the antibody-drug conjugate required to block 50% binding of human MET myc-myc-hexahistine to a plate coated with unconjugated antibody, and was used to calculate the relative potency using Equation 6.

[0135] Table 7 summarizes the relative potency of the bioassay of Example 6 and the combination assay of Example 7.

[0136] Table 7

[0137] This disclosure is further described by the following non-restrictive items.

[0138] Project 1. A method for generating a chromatographic scheme for a target molecule, the method comprising: Identify chromatographic packing parameters; Identification of chromatographic performance standards; Generate a potential predictive model domain that associates the chromatographic packing parameters with the chromatographic performance criteria; Select a combination of test values ​​for the fill parameter, wherein the selected combination of test values ​​forms a set of test value combinations; Chromatographic runs are performed for each combination of the test value set to generate actual performance standard values ​​corresponding to each combination of the test value set; and Based on the correlation between the performance standard value predicted by the model and the actual performance standard value, each prediction model in the prediction model domain is ranked. The target molecule mentioned therein is an antibody-drug conjugate.

[0139] Project 2. The method as described in Project 1, wherein the chromatographic packing parameters include packing buffer salt concentration, chromatographic medium density, content of high molecular weight species of the packing material, or combinations thereof.

[0140] Project 3. The method of any one of Project 1 or 2, wherein the chromatographic performance criteria include reduction in the content of high molecular weight species, target molecule yield, or a combination thereof.

[0141] Project 4. The method of any one of Projects 1 to 3, further comprising: selecting the prediction model with the highest ranking; and using the selected model to determine the chromatographic packing parameter values ​​of the chromatographic protocol.

[0142] Project 5. The method as described in Project 4, wherein the selected model predicts that the chromatographic packing parameter values ​​of the chromatographic protocol correspond to one or more target performance standard values.

[0143] Project 6. The method of any one of Projects 1 to 5, further comprising developing an expectancy index that includes a quantitative relationship between performance criteria.

[0144] Project 7. The method of Project 6, wherein the expectancy index is calculated as a combination of two or more performance criteria, and wherein each performance criterion is assigned a weight that contributes to the expectancy index.

[0145] Project 8. The method as described in Project 7, wherein the two or more performance criteria include a reduction in the content of high molecular weight species and a target molecule yield, and wherein each performance criterion has an equal weight.

[0146] Project 9. A method for generating a chromatographic scheme for a target molecule, the method comprising: Identify the first and second chromatographic parameters; Identify the primary and secondary performance standards; Select the first test value of the first chromatographic parameter; Select the second test value for the second chromatographic parameter; Identify the chromatographic medium; Generate first performance standard values, wherein each first performance standard value corresponds to a combination of a first test value and a second test value; Generate second performance standard values, wherein each second performance standard value corresponds to a combination of a first test value and a second test value; A first multivariate model pool is generated, wherein each multivariate model in the first multivariate model pool associates the first chromatographic parameter and the second chromatographic parameter with the first performance standard; A second multivariate model pool is generated, wherein each multivariate model in the second multivariate model pool associates the first chromatographic parameter and the second chromatographic parameter with the second performance standard; Each multivariate model in the first multivariate model pool is used to generate a first predicted performance standard value, wherein each first predicted performance standard value corresponds to a combination of a first test value and a second test value; Each multivariate model in the second multivariate model pool is used to generate a second predicted performance standard value, wherein each second predicted performance standard value corresponds to a combination of a first test value and a second test value; Determine the coefficients of determination for the multivariate models in the first multivariate model pool; and Determine the determination coefficients of the multivariate models in the second multivariate model pool; The target molecule mentioned therein is an antibody-drug conjugate.

[0147] Item 10. The method as described in Item 9, wherein identifying the chromatographic medium comprises: A first chromatographic run is performed on a first chromatographic medium to produce a first desired value corresponding to the first chromatographic medium; A second chromatographic run is performed on a second chromatographic medium to produce a second desired value corresponding to the second chromatographic medium; and Select the chromatographic medium corresponding to the maximum expected value.

[0148] Item 11. The method as described in Item 10, wherein the same medium density and filling buffer composition are used for the first chromatographic run and the second chromatographic run.

[0149] Item 12. The method of Item 11, wherein the first expected value is calculated based on one or more performance standard values ​​of the first chromatographic run, and the second expected value is calculated based on one or more performance standard values ​​of the second chromatographic run.

[0150] Item 13. The method of any one of Items 9 to 12, wherein the first chromatographic parameter includes the column packing amount (g / L) of HIC medium, the second chromatographic parameter includes the citrate concentration of the packing buffer, the first performance criterion is yield, and the second performance criterion is the quantification of impurity reduction.

[0151] Item 14. The method of any one of items 9 to 13, wherein generating the first multivariate model pool comprises: Identify potential multivariate model domains; Calculate the variance inflation factor for each latent multivariate model in the latent multivariate model domain; All potential multivariate models with variance inflation factors less than or equal to the collinearity threshold are selected to generate the first multivariate model pool.

[0152] Project 15. A method for purifying a target molecule, the method comprising: A packing material comprising a high molecular weight species concentration (%HMW) of about 3% to about 20% is introduced into a chromatographic apparatus containing sartobindphenyl chromatographic media, wherein the packing material is introduced at a density of about 10 g packing material / L total chromatographic volume to about 40 g / L, and wherein the packing material comprises the target molecule and a citrate of about 5 mM to about 200 mM; and The eluent containing the target molecule is eluted from the chromatographic apparatus, wherein the yield of the target molecule in the eluent is at least about 70%, and wherein the difference between the %HMW of the packing material and the %HMW of the eluent is at least about 2%. The target molecule mentioned therein is an antibody-drug conjugate.

[0153] Item 16. The method of Item 15, wherein the filler is introduced at a density of about 20 g / L to about 30 g / L.

[0154] Item 17. The method of Item 15, wherein the antibody-drug conjugate comprises a drug that binds to an antibody via lysine conjugation.

[0155] Item 18. The method as described in any one of Items 15 to 17, wherein: The %HMW of the filler is about 7% to about 15%; The filler contains approximately 110 mM to approximately 175 mM of citrate; The yield of the target molecule in the eluent is approximately 75% to approximately 95%; and / or The difference between the %HMW of the packing material and the %HMW of the eluent is approximately 3% to approximately 8%.

[0156] Item 19. The method of Item 15, wherein the antibody-drug conjugate comprises cleavable leptin.

[0157] Item 20. The method of Item 15, wherein the antibody-drug conjugate comprises an IgG4 antibody.

[0158] Those skilled in the art will understand that the concepts upon which this disclosure is based can readily be used as a basis for designing other methods and systems for performing some of the purposes of this disclosure. Therefore, the claims should not be considered as limited to the foregoing description.

Claims

1. A method for generating a chromatographic scheme targeting a target molecule, the method comprising: Identify chromatographic packing parameters; Identification of chromatographic performance standards; Generate a potential predictive model domain that associates the chromatographic packing parameters with the chromatographic performance criteria; Select a combination of test values ​​for the fill parameter, wherein the selected combination of test values ​​forms a set of test value combinations; Chromatographic runs are performed for each combination of the test value set to generate actual performance standard values ​​corresponding to each combination of the test value set. as well as Based on the correlation between the performance standard value predicted by the model and the actual performance standard value, each prediction model in the prediction model domain is ranked. The target molecule mentioned therein is an antibody-drug conjugate.

2. The method of claim 1, wherein the chromatographic packing parameters include packing buffer salt concentration, chromatographic medium density, content of high molecular weight species of the packing material, or combinations thereof.

3. The method of claim 1, wherein the chromatographic performance criteria include a reduction in the content of high molecular weight species, the yield of target molecules, or a combination thereof.

4. The method of claim 1, further comprising: Select the prediction model with the highest ranking; as well as The selected model is used to determine the chromatographic packing parameter values ​​for the chromatographic protocol.

5. The method of claim 4, wherein the selected model predicts that the chromatographic packing parameter values ​​of the chromatographic scheme correspond to one or more target performance standard values.

6. The method of claim 1, further comprising developing an expectation index, the expectation index comprising a quantitative relationship between performance standards.

7. The method of claim 6, wherein the expectation index is calculated as a combination of two or more performance criteria, and wherein each performance criterion is assigned a weight that contributes to the expectation index.

8. The method of claim 7, wherein the two or more performance criteria include a reduction in the content of high molecular weight species and a target molecule yield, and wherein each performance criterion has an equal weight.

9. A method for generating a target molecular chromatography scheme, the method comprising: Identify the first and second chromatographic parameters; Identify the primary and secondary performance standards; Select the first test value of the first chromatographic parameter; Select the second test value for the second chromatographic parameter; Identify the chromatographic medium; Generate first performance standard values, wherein each first performance standard value corresponds to a combination of a first test value and a second test value; Generate second performance standard values, wherein each second performance standard value corresponds to a combination of a first test value and a second test value; A first multivariate model pool is generated, wherein each multivariate model in the first multivariate model pool associates the first chromatographic parameter and the second chromatographic parameter with the first performance standard; A second multivariate model pool is generated, wherein each multivariate model in the second multivariate model pool associates the first chromatographic parameter and the second chromatographic parameter with the second performance standard; Each multivariate model in the first multivariate model pool is used to generate a first predicted performance standard value, wherein each first predicted performance standard value corresponds to a combination of a first test value and a second test value; Each multivariate model in the second multivariate model pool is used to generate a second predicted performance standard value, wherein each second predicted performance standard value corresponds to a combination of a first test value and a second test value; Determine the determination coefficients of the multivariate models in the first multivariate model pool; as well as Determine the determination coefficients of the multivariate models in the second multivariate model pool; The target molecule mentioned therein is an antibody-drug conjugate.

10. The method of claim 9, wherein identifying the chromatographic medium comprises: A first chromatographic run is performed on a first chromatographic medium to produce a first desired value corresponding to the first chromatographic medium; A second chromatographic run is performed on a second chromatographic medium to produce a second desired value corresponding to the second chromatographic medium; as well as Select the chromatographic medium corresponding to the maximum expected value.

11. The method of claim 10, wherein the first chromatographic run and the second chromatographic run are performed using the same media density and filling buffer composition.

12. The method of claim 11, wherein the first expected value is calculated based on one or more performance standard values ​​of the first chromatographic run, and the second expected value is calculated based on one or more performance standard values ​​of the second chromatographic run.

13. The method of claim 9, wherein the first chromatographic parameter includes the column packing amount (g / L) of HIC medium, the second chromatographic parameter includes the citrate concentration of the packing buffer, the first performance criterion is yield, and the second performance criterion is the quantification of impurity reduction.

14. The method of claim 9, wherein generating the first multivariate model pool comprises: Identify potential multivariate model domains; Calculate the variance inflation factor for each latent multivariate model in the latent multivariate model domain; All potential multivariate models with variance inflation factors less than or equal to the collinearity threshold are selected to generate the first multivariate model pool.

15. A method for purifying a target molecule, the method comprising: A packing material comprising a high molecular weight species concentration (%HMW) of about 3% to about 20% is introduced into a chromatographic apparatus containing sartobind phenyl chromatographic media, wherein the packing material is introduced at a density of about 10 g packing material / L total chromatographic volume to about 40 g / L, and wherein the packing material comprises the target molecule and a citrate of about 5 mM to about 200 mM; and The eluent containing the target molecule is eluted from the chromatographic apparatus, wherein the yield of the target molecule in the eluent is at least about 70%, and wherein the difference between the %HMW of the packing material and the %HMW of the eluent is at least about 2%. The target molecule mentioned therein is an antibody-drug conjugate.

16. The method of claim 15, wherein the filler is introduced at a density of about 20 g / L to about 30 g / L.

17. The method of claim 15, wherein the antibody-drug conjugate comprises a drug conjugated to an antibody via lysine conjugation.

18. The method of claim 15, wherein: The %HMW of the filler is about 7% to about 15%; The filler contains approximately 110 mM to approximately 175 mM of citrate; The yield of the target molecule in the eluent is approximately 75% to approximately 95%; and / or The difference between the %HMW of the packing material and the %HMW of the eluent is approximately 3% to approximately 8%.

19. The method of claim 15, wherein the antibody-drug conjugate comprises cleavable leptin.

20. The method of claim 15, wherein the antibody-drug conjugate comprises an IgG4 antibody.

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