1
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Armstrong GB, Shah V, Sanches P, Patel M, Casey R, Jamieson C, Burley GA, Lewis W, Rattray Z. A framework for the biophysical screening of antibody mutations targeting solvent-accessible hydrophobic and electrostatic patches for enhanced viscosity profiles. Comput Struct Biotechnol J 2024; 23:2345-2357. [PMID: 38867721 PMCID: PMC11167247 DOI: 10.1016/j.csbj.2024.05.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 06/14/2024] Open
Abstract
The formulation of high-concentration monoclonal antibody (mAb) solutions in low dose volumes for autoinjector devices poses challenges in manufacturability and patient administration due to elevated solution viscosity. Often many therapeutically potent mAbs are discovered, but their commercial development is stalled by unfavourable developability challenges. In this work, we present a systematic experimental framework for the computational screening of molecular descriptors to guide the design of 24 mutants with modified viscosity profiles accompanied by experimental evaluation. Our experimental observations using a model anti-IL8 mAb and eight engineered mutant variants reveal that viscosity reduction is influenced by the location of hydrophobic interactions, while targeting positively charged patches significantly increases viscosity in comparison to wild-type anti-IL-8 mAb. We conclude that most predicted in silico physicochemical properties exhibit poor correlation with measured experimental parameters for antibodies with suboptimal developability characteristics, emphasizing the need for comprehensive case-by-case evaluation of mAbs. This framework combining molecular design and triage via computational predictions with experimental evaluation aids the agile and rational design of mAbs with tailored solution viscosities, ensuring improved manufacturability and patient convenience in self-administration scenarios.
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Affiliation(s)
- Georgina B. Armstrong
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Vidhi Shah
- Large Molecule Discovery, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Paula Sanches
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Mitul Patel
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Ricky Casey
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Craig Jamieson
- Department of Pure and Applied Chemistry, University of Strathclyde, Glasgow, UK
| | - Glenn A. Burley
- Department of Pure and Applied Chemistry, University of Strathclyde, Glasgow, UK
| | - William Lewis
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Zahra Rattray
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
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2
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Heisler J, Kovner D, Izadi S, Zarzar J, Carter PJ. Modulation of the high concentration viscosity of IgG 1 antibodies using clinically validated Fc mutations. MAbs 2024; 16:2379560. [PMID: 39028186 PMCID: PMC11262234 DOI: 10.1080/19420862.2024.2379560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 07/09/2024] [Indexed: 07/20/2024] Open
Abstract
The self-association of therapeutic antibodies can result in elevated viscosity and create problems in manufacturing and formulation, as well as limit delivery by subcutaneous injection. The high concentration viscosity of some antibodies has been reduced by variable domain mutations or by the addition of formulation excipients. In contrast, the impact of Fc mutations on antibody viscosity has been minimally explored. Here, we studied the effect of a panel of common and clinically validated Fc mutations on the viscosity of two closely related humanized IgG1, κ antibodies, omalizumab (anti-IgE) and trastuzumab (anti-HER2). Data presented here suggest that both Fab-Fab and Fab-Fc interactions contribute to the high viscosity of omalizumab, in a four-contact model of self-association. Most strikingly, the high viscosity of omalizumab (176 cP) was reduced 10.7- and 2.2-fold by Fc modifications for half-life extension (M252Y:S254T:T256E) and aglycosylation (N297G), respectively. Related single mutations (S254T and T256E) each reduced the viscosity of omalizumab by ~6-fold. An alternative half-life extension Fc mutant (M428L:N434S) had the opposite effect in increasing the viscosity of omalizumab by 1.5-fold. The low viscosity of trastuzumab (8.6 cP) was unchanged or increased by ≤ 2-fold by the different Fc variants. Molecular dynamics simulations provided mechanistic insight into the impact of Fc mutations in modulating electrostatic and hydrophobic surface properties as well as conformational stability of the Fc. This study demonstrates that high viscosity of some IgG1 antibodies can be mitigated by Fc mutations, and thereby offers an additional tool to help design future antibody therapeutics potentially suitable for subcutaneous delivery.
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Affiliation(s)
- Joel Heisler
- Department of Antibody Engineering, Genentech, Inc, South San Francisco, CA, USA
| | - Daniel Kovner
- Department of Pharmaceutical Development, Genentech, Inc, South San Francisco, CA, USA
| | - Saeed Izadi
- Department of Pharmaceutical Development, Genentech, Inc, South San Francisco, CA, USA
| | - Jonathan Zarzar
- Department of Pharmaceutical Development, Genentech, Inc, South San Francisco, CA, USA
| | - Paul J. Carter
- Department of Antibody Engineering, Genentech, Inc, South San Francisco, CA, USA
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3
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Bauer J, Rajagopal N, Gupta P, Gupta P, Nixon AE, Kumar S. How can we discover developable antibody-based biotherapeutics? Front Mol Biosci 2023; 10:1221626. [PMID: 37609373 PMCID: PMC10441133 DOI: 10.3389/fmolb.2023.1221626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023] Open
Abstract
Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.
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Affiliation(s)
- Joschka Bauer
- Early Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, Germany
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
| | - Nandhini Rajagopal
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Priyanka Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Pankaj Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Andrew E. Nixon
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Sandeep Kumar
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
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4
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Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation. Sci Rep 2023; 13:2917. [PMID: 36806303 PMCID: PMC9941094 DOI: 10.1038/s41598-023-28841-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/25/2023] [Indexed: 02/22/2023] Open
Abstract
Deep learning, aided by the availability of big data sets, has led to substantial advances across many disciplines. However, many scientific problems of practical interest lack sufficiently large datasets amenable to deep learning. Prediction of antibody viscosity is one such problem where deep learning methods have not yet been explored due to the relative scarcity of relevant training data. In this work, we overcome this limitation using a biophysically meaningful representation that enables us to develop generalizable models even under limited training data. We present, PfAbNet-viscosity, a 3D convolutional neural network architecture, to predict high-concentration viscosity of therapeutic antibodies. We show that with the electrostatic potential surface of the antibody variable region as the only input to the network, the models trained on as few as couple dozen datapoints can generalize with high accuracy. Our feature attribution analysis shows that PfAbNet-viscosity has learned key biophysical drivers of viscosity. The applicability of our approach to other biological systems is discussed.
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5
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Schmitt J, Razvi A, Grapentin C. Predictive modeling of concentration-dependent viscosity behavior of monoclonal antibody solutions using artificial neural networks. MAbs 2023; 15:2169440. [PMID: 36705325 PMCID: PMC9888472 DOI: 10.1080/19420862.2023.2169440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Solutions of monoclonal antibodies (mAbs) can show increased viscosity at high concentration, which can be a disadvantage during protein purification, filling, and administration. The viscosity is determined by protein-protein-interactions, which are influenced by the antibody's sequence as well as solution conditions, like pH, buffer type, or the presence of salts and other excipients. To predict viscosity, experimental parameters, like the diffusion interaction parameter (kD), or computational tools harnessing information derived from primary sequence, are often used, but a reliable predictive tool is still missing. We present a modeling approach employing artificial neural networks (ANNs) using experimental factors combined with simulation-derived parameters plus viscosity data from 27 highly concentrated (180 mg/mL) mAbs. These ANNs can be used to predict if mAbs exhibit problematic viscosity at distinct concentrations or to model viscosity-concentration-curves.
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Affiliation(s)
| | - Abbas Razvi
- Lonza AG/Ltd, Drug Product Services, Basel, Switzerland
| | - Christoph Grapentin
- Lonza AG/Ltd, Drug Product Services, Basel, Switzerland,CONTACT Christoph Grapentin
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6
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Fernández-Quintero ML, Ljungars A, Waibl F, Greiff V, Andersen JT, Gjølberg TT, Jenkins TP, Voldborg BG, Grav LM, Kumar S, Georges G, Kettenberger H, Liedl KR, Tessier PM, McCafferty J, Laustsen AH. Assessing developability early in the discovery process for novel biologics. MAbs 2023; 15:2171248. [PMID: 36823021 PMCID: PMC9980699 DOI: 10.1080/19420862.2023.2171248] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 01/18/2023] [Indexed: 02/25/2023] Open
Abstract
Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.
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Affiliation(s)
- Monica L. Fernández-Quintero
- Center for Molecular Biosciences Innsbruck (CMBI), Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Anne Ljungars
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Franz Waibl
- Center for Molecular Biosciences Innsbruck (CMBI), Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | - Jan Terje Andersen
- Department of Immunology, University of Oslo, Oslo University Hospital Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine and Department of Pharmacology, University of Oslo, Oslo, Norway
| | | | - Timothy P. Jenkins
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Bjørn Gunnar Voldborg
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lise Marie Grav
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, CT, USA
| | - Guy Georges
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
| | - Hubert Kettenberger
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
| | - Klaus R. Liedl
- Center for Molecular Biosciences Innsbruck (CMBI), Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Peter M. Tessier
- Department of Chemical Engineering, Pharmaceutical Sciences and Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
| | - John McCafferty
- Department of Medicine, Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
- Maxion Therapeutics, Babraham Research Campus, Cambridge, UK
| | - Andreas H. Laustsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
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7
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Thorsteinson N, Comeau SR, Kumar S. Structure-Based Optimization of Antibody-Based Biotherapeutics for Improved Developability: A Practical Guide for Molecular Modelers. Methods Mol Biol 2023; 2552:219-235. [PMID: 36346594 DOI: 10.1007/978-1-0716-2609-2_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
A great effort to avoid known developability risks is now more often being made earlier during the lead candidate discovery and optimization phase of biotherapeutic drug development. Predictive computational strategies, used in the early stages of antibody discovery and development, to mitigate the risk of late-stage failure of antibody candidates, are highly valuable. Various structure-based methods exist for accurately predicting properties critical to developability, and, in this chapter, we discuss the history of their development and demonstrate how they can be used to filter large sets of candidates arising from target affinity screening and to optimize lead candidates for developability. Methods for modeling antibody structures from sequence and detecting post-translational modifications and chemical degradation liabilities are also discussed.
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Affiliation(s)
- Nels Thorsteinson
- Scientific Services Manager, Biologics, Chemical Computing Group ULC, Montreal, QC, Canada
| | - Stephen R Comeau
- Computational Biochemistry and Bioinformatics Group, Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT, USA
| | - Sandeep Kumar
- Computational Biochemistry and Bioinformatics Group, Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT, USA.
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8
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Ausserwöger H, Schneider MM, Herling TW, Arosio P, Invernizzi G, Knowles TPJ, Lorenzen N. Non-specificity as the sticky problem in therapeutic antibody development. Nat Rev Chem 2022; 6:844-861. [PMID: 37117703 DOI: 10.1038/s41570-022-00438-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 11/16/2022]
Abstract
Antibodies are highly potent therapeutic scaffolds with more than a hundred different products approved on the market. Successful development of antibody-based drugs requires a trade-off between high target specificity and target binding affinity. In order to better understand this problem, we here review non-specific interactions and explore their fundamental physicochemical origins. We discuss the role of surface patches - clusters of surface-exposed amino acid residues with similar physicochemical properties - as inducers of non-specific interactions. These patches collectively drive interactions including dipole-dipole, π-stacking and hydrophobic interactions to complementary moieties. We elucidate links between these supramolecular assembly processes and macroscopic development issues, such as decreased physical stability and poor in vivo half-life. Finally, we highlight challenges and opportunities for optimizing protein binding specificity and minimizing non-specificity for future generations of therapeutics.
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9
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Advanced Formulations/Drug Delivery Systems for Subcutaneous Delivery of Protein-Based Biotherapeutics. J Pharm Sci 2022; 111:2968-2982. [PMID: 36058255 DOI: 10.1016/j.xphs.2022.08.036] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/29/2022] [Accepted: 08/29/2022] [Indexed: 12/14/2022]
Abstract
Multiple advanced formulations and drug delivery systems (DDSs) have been developed to deliver protein-based biotherapeutics via the subcutaneous (SC) route. These formulations/DDSs include high-concentration solution, co-formulation of two or more proteins, large volume injection, protein cluster/complex, suspension, nanoparticle, microparticle, and hydrogel. These advanced systems provide clinical benefits related to efficacy and safety, but meanwhile, have more complicated formulations and manufacturing processes compared to conventional solution formulations. To develop a fit-for-purpose formulation/DDS for SC delivery, scientists need to consider multiple factors, such as the primary indication, targeted site, immunogenicity, compatibility, biopharmaceutics, patient compliance, etc. Next, they need to develop appropriate formulation (s) and manufacturing processes using the QbD principle and have a control strategy. This paper aims to provide a comprehensive review of advanced formulations/DDSs recently developed for SC delivery of proteins, as well as some knowledge gaps and potential strategies to narrow them through future research.
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10
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Masuda T, Okubo K, Baba S, Suzuki M, Tani F, Yamasaki M, Mikami B. Structure of thaumatin under acidic conditions: Structural insight into the conformations in lysine residues responsible for maintaining the sweetness after heat-treatment. Food Chem 2022; 389:132996. [PMID: 35500407 DOI: 10.1016/j.foodchem.2022.132996] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 11/19/2022]
Abstract
Thaumatin is an intensely sweet-tasting protein. Its sweetness persists when heated under acidic conditions, but disappears when heated at a pH above 7.0. To clarify how the structural features of thaumatin resist insoluble aggregation during heating under acidic conditions, we analysed its crystal structure obtained at pH 4.0, 6.0, and 8.0. Simultaneously, the melting temperature (Tm) at these pH levels was determined using differential scanning fluorimetry. At pH 4.0, the Tm of thaumatin was substantially lower and the overall B-factor value of its structure was higher than those at pH 6.0. Interestingly, the relative B-factor values for most lysine residues decreased as the pH reduced. These results suggest that the overall structure at pH 4.0 becomes flexible but the relative flexibility of some regions is lower than that at pH 6.0. Thus, the reduction in relative flexibility might play an important role in preventing thermal aggregation, thereby maintaining the sweetness.
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Affiliation(s)
- Tetsuya Masuda
- Laboratory of Food Chemistry, Department of Food Sciences and Human Nutrition, Faculty of Agriculture, Ryukoku University, 1-5 Yokotani Seta Oe-cho, Otsu, Shiga 520-2194, Japan; Laboratory of Food Chemistry, Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.
| | - Kyohei Okubo
- Laboratory of Food Chemistry, Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan
| | - Seiki Baba
- Structural Biology Division, Japan Synchrotron Radiation Research Institute, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5198, Japan
| | - Mamoru Suzuki
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Fumito Tani
- Laboratory of Food Chemistry, Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan
| | - Masayuki Yamasaki
- Laboratory of Food Chemistry, Department of Food Sciences and Human Nutrition, Faculty of Agriculture, Ryukoku University, 1-5 Yokotani Seta Oe-cho, Otsu, Shiga 520-2194, Japan
| | - Bunzo Mikami
- Research Institute for Sustainable Humanosphere, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan; Institute of Advanced Energy, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan
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11
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Effects of Monovalent Salt on Protein-Protein Interactions of Dilute and Concentrated Monoclonal Antibody Formulations. Antibodies (Basel) 2022; 11:antib11020024. [PMID: 35466277 PMCID: PMC9036246 DOI: 10.3390/antib11020024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/17/2022] [Accepted: 03/24/2022] [Indexed: 02/04/2023] Open
Abstract
In this study, we used sodium chloride (NaCl) to extensively modulate non-specific protein-protein interactions (PPI) of a humanized anti-streptavidin monoclonal antibody class 2 molecule (ASA-IgG2). The changes in PPI with varying NaCl (CNaCl) and monoclonal antibody (mAb) concentration (CmAb) were assessed using the diffusion interaction parameter kD and second virial coefficient B22 measured from solutions with low to moderate CmAb. The effective structure factor S(q)eff measured from concentrated mAb solutions using small-angle X-ray and neutron scattering (SAXS/SANS) was also used to characterize the PPI. Our results found that the nature of net PPI changed not only with CNaCl, but also with increasing CmAb. As a result, parameters measured from dilute and concentrated mAb samples could lead to different predictions on the stability of mAb formulations. We also compared experimentally determined viscosity results with those predicted from interaction parameters, including kD and S(q)eff. The lack of a clear correlation between interaction parameters and measured viscosity values indicates that the relationship between viscosity and PPI is concentration-dependent. Collectively, the behavior of flexible mAb molecules in concentrated solutions may not be correctly predicted using models where proteins are considered to be uniform colloid particles defined by parameters derived from low CmAb.
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12
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Blanco MA. Computational models for studying physical instabilities in high concentration biotherapeutic formulations. MAbs 2022; 14:2044744. [PMID: 35282775 PMCID: PMC8928847 DOI: 10.1080/19420862.2022.2044744] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.
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Affiliation(s)
- Marco A. Blanco
- Materials and Biophysical Characterization, Analytical R & D, Merck & Co., Inc, Kenilworth, NJ USA
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13
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Gupta P, Makowski EK, Kumar S, Zhang Y, Scheer JM, Tessier PM. Antibodies with Weakly Basic Isoelectric Points Minimize Trade-offs between Formulation and Physiological Colloidal Properties. Mol Pharm 2022; 19:775-787. [PMID: 35108018 PMCID: PMC9350878 DOI: 10.1021/acs.molpharmaceut.1c00373] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The widespread interest in antibody therapeutics has led to much focus on identifying antibody candidates with favorable developability properties. In particular, there is broad interest in identifying antibody candidates with highly repulsive self-interactions in standard formulations (e.g., low ionic strength buffers at pH 5-6) for high solubility and low viscosity. Likewise, there is also broad interest in identifying antibody candidates with low levels of non-specific interactions in physiological solution conditions (PBS, pH 7.4) to promote favorable pharmacokinetic properties. To what extent antibodies that possess both highly repulsive self-interactions in standard formulations and weak non-specific interactions in physiological solution conditions can be systematically identified remains unclear and is a potential impediment to successful therapeutic drug development. Here, we evaluate these two properties for 42 IgG1 variants based on the variable fragments (Fvs) from four clinical-stage antibodies and complementarity-determining regions from 10 clinical-stage antibodies. Interestingly, we find that antibodies with the strongest repulsive self-interactions in a standard formulation (pH 6 and 10 mM histidine) display the strongest non-specific interactions in physiological solution conditions. Conversely, antibodies with the weakest non-specific interactions under physiological conditions display the least repulsive self-interactions in standard formulations. This behavior can be largely explained by the antibody isoelectric point, as highly basic antibodies that are highly positively charged under standard formulation conditions (pH 5-6) promote repulsive self-interactions that mediate high colloidal stability but also mediate strong non-specific interactions with negatively charged biomolecules at physiological pH and vice versa for antibodies with negatively charged Fv regions. Therefore, IgG1s with weakly basic isoelectric points between 8 and 8.5 and Fv isoelectric points between 7.5 and 9 typically display the best combinations of strong repulsive self-interactions and weak non-specific interactions. We expect that these findings will improve the identification and engineering of antibody candidates with drug-like biophysical properties.
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Affiliation(s)
- Priyanka Gupta
- Biochemistry and Biophysics Department, Rensselaer Polytechnic Institute, Troy, New York 12180, United States.,Biotherapeutics Molecule Discovery Department, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Emily K Makowski
- Department of Pharmaceutical Sciences, Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Sandeep Kumar
- Biotherapeutics Molecule Discovery Department, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Yulei Zhang
- Department of Chemical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Justin M Scheer
- Biotherapeutics Molecule Discovery Department, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States.,Janssen R&D, South San Francisco, California 94080, United States
| | - Peter M Tessier
- Biochemistry and Biophysics Department, Rensselaer Polytechnic Institute, Troy, New York 12180, United States.,Department of Pharmaceutical Sciences, Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States.,Department of Chemical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States.,Department of Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States
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14
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Khetan R, Curtis R, Deane CM, Hadsund JT, Kar U, Krawczyk K, Kuroda D, Robinson SA, Sormanni P, Tsumoto K, Warwicker J, Martin ACR. Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics. MAbs 2022; 14:2020082. [PMID: 35104168 PMCID: PMC8812776 DOI: 10.1080/19420862.2021.2020082] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.
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Affiliation(s)
- Rahul Khetan
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Robin Curtis
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | | | | | - Uddipan Kar
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | | | - Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.,Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo, Japan.,Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
| | | | - Pietro Sormanni
- Chemistry of Health, Yusuf Hamied Department of Chemistry, University of Cambridge
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.,Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo, Japan.,Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.,The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Jim Warwicker
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Andrew C R Martin
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
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15
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Deciphering the high viscosity of a therapeutic monoclonal antibody in high concentration formulations by microdialysis-hydrogen/deuterium exchange mass spectrometry. J Pharm Sci 2022; 111:1335-1345. [PMID: 34999091 DOI: 10.1016/j.xphs.2021.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 12/30/2021] [Accepted: 12/31/2021] [Indexed: 11/22/2022]
Abstract
High concentration formulations of therapeutic monoclonal antibodies (mAbs) are highly desired for subcutaneous injection. However, high concentration formulations can exhibit unusual molecular behaviors, such as high viscosity or aggregation, that present challenges for manufacturing and administration. To understand the molecular mechanism of the high viscosity exhibited by high concentration protein formulations, we analyzed a human IgG4 (mAb1) at high protein concentrations using sedimentation velocity analytical ultracentrifugation (SV-AUC), X-ray crystallography, hydrogen/deuterium exchange mass spectrometry (HDX-MS), and protein surface patches analysis. Particularly, we developed a microdialysis HDX-MS method to determine intermolecular interactions at different protein concentrations. SV-AUC revealed that mAb1 displayed a propensity for self-association of Fab-Fab, Fab-Fc, and Fc-Fc. mAb1 crystal structure and HDX-MS results demonstrated self-association between complementarity-determining regions (CDRs) and Fc through electrostatic interactions. HDX-MS also indicated Fab-Fab interactions through hydrophobic surface patches constructed by mAb1 CDRs. Our multi-method approach, including fast screening of SV-AUC as well as interface analysis by X-ray crystallography and HDX-MS, helped to elucidate the high viscosity of mAb1 at high concentrations as induced by self-associations of Fab-Fc and Fab-Fab.
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16
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Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet TS, Flem-Karlsen K, Frank R, Mehta BB, Vu MH, Zengin T, Gutierrez-Marcos J, Lund-Johansen F, Andersen JT, Greiff V. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. MAbs 2022; 14:2008790. [PMID: 35293269 PMCID: PMC8928824 DOI: 10.1080/19420862.2021.2008790] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eva Smorodina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia
| | | | - Karine Flem-Karlsen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Norway
| | - Talip Zengin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Bioinformatics, Mugla Sitki Kocman University, Turkey
| | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
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17
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Heads JT, Kelm S, Tyson K, Lawson ADG. A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers. MAbs 2022; 14:2138092. [PMID: 36418193 PMCID: PMC9704409 DOI: 10.1080/19420862.2022.2138092] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
The propensity for some monoclonal antibodies (mAbs) to aggregate at physiological and manufacturing pH values can prevent their use as therapeutic molecules or delay time to market. Consequently, developability assessments are essential to select optimum candidates, or inform on mitigation strategies to avoid potential late-stage failures. These studies are typically performed in a range of buffer solutions because factors such as pH can dramatically alter the aggregation propensity of the test mAbs (up to 100-fold in extreme cases). A computational method capable of robustly predicting the aggregation propensity at the pH values of common storage buffers would have substantial value. Here, we describe a mAb aggregation prediction tool (MAPT) that builds on our previously published isotype-dependent, charge-based model of aggregation. We show that the addition of a homology model-derived hydrophobicity descriptor to our electrostatic aggregation model enabled the generation of a robust mAb developability indicator. To contextualize our aggregation scoring system, we analyzed 97 clinical-stage therapeutic mAbs. To further validate our approach, we focused on six mAbs (infliximab, tocilizumab, rituximab, CNTO607, MEDI1912 and MEDI1912_STT) which have been reported to cover a large range of aggregation propensities. The different aggregation propensities of the case study molecules at neutral and slightly acidic pH were correctly predicted, verifying the utility of our computational method.
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Affiliation(s)
- James T. Heads
- UCB Pharma, 208 Bath Road, SloughSL1 3WE, UK,CONTACT James T. Heads UCB Pharma, 208 Bath Road, SloughSL1 3WE, UK
| | | | - Kerry Tyson
- UCB Pharma, 208 Bath Road, SloughSL1 3WE, UK
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18
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Tomar DS, Licari G, Bauer J, Singh SK, Li L, Kumar S. Stress-dependent flexibility of a full-length human monoclonal antibody: Insights from molecular dynamics to support biopharmaceutical development. J Pharm Sci 2021; 111:628-637. [PMID: 34742728 DOI: 10.1016/j.xphs.2021.10.039] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/30/2021] [Accepted: 10/30/2021] [Indexed: 01/15/2023]
Abstract
After several decades of advancements in drug discovery, product development of biopharmaceuticals remains a time- and resource-consuming endeavor. One of the main reasons is associated to the lack of fundamental understanding of conformational dynamics of such biologic entities, and how they respond to various stresses encountered during manufacturing. In this work, we have studied the conformational dynamics of human IgG1κ b12 monoclonal antibody (mAb) using molecular dynamics simulations. The hundreds of nanoseconds long trajectories reveal that b12 mAb is highly flexible. Its variable domains show greater conformational fluctuations than the constant domains. Additionally, it collapses towards a more globular shape in response to thermal stress, leading to decrease in the total solvent exposed surface area and radius of gyration. This behavior is more pronounced for the deglycosylated b12 mAb, and it appears to correlate with increase in inter-domain contacts between specific regions of the antibody. Conformational fluctuations also cause temporary formation and disruption of hydrophobic and charged patches on the antibody surface, which is particularly important for the prediction of CMC properties during development phases of antibody-based biotherapeutics. The insights gained through these simulations may help the development of biologic drugs, especially with regards to manufacturing processes where antibodies may undergo significant thermal stress.
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Affiliation(s)
- Dheeraj S Tomar
- Biotherapeutics Pharmaceutical Sciences Research and Development, Pfizer Inc., 700 Chesterfield Parkway West, Chesterfield, MO, 63017, USA
| | - Giuseppe Licari
- Pharmaceuticals Development Biologicals, Boehringer Ingelheim Pharmaceuticals, Inc., D-88397 Biberach an der Riss, Germany
| | - Joschka Bauer
- Pharmaceuticals Development Biologicals, Boehringer Ingelheim Pharmaceuticals, Inc., D-88397 Biberach an der Riss, Germany
| | - Satish K Singh
- Biotherapeutics Pharmaceutical Sciences Research and Development, Pfizer Inc., 700 Chesterfield Parkway West, Chesterfield, MO, 63017, USA
| | - Li Li
- Biotherapeutics Pharmaceutical Sciences Research and Development, Pfizer Inc., 1 Burtt Road, Andover, Massachusetts, 01810, USA
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, CT 06877.
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19
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Qiao X, Qu L, Guo Y, Hoshino T. Secondary Structure and Conformational Stability of the Antigen Residues Making Contact with Antibodies. J Phys Chem B 2021; 125:11374-11385. [PMID: 34615354 DOI: 10.1021/acs.jpcb.1c05997] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Antibodies are crucial biomolecules that bring high therapeutic efficacy in medicine and accurate molecular detection in diagnosis. Many studies have been devoted to analyzing the antigen-antibody interaction from the importance of understanding the antibody recognition mechanism. However, most of the previous studies examined the characteristic of the antibody for interaction. It is also informative to clarify the significant antigen residues contributing to the binding. To characterize the molecular interaction of antigens, we computationally analyzed 350 antigen-antibody complex structures by molecular mechanics (MM) calculations and molecular dynamics (MD) simulations. Based on the MM calculations, the antigen residues contributing to the binding were extracted from all the 350 complexes. The extracted residues are located at the antigen-antibody interface and are responsible for making contact with the antibody. The appearances of the charged polar residues, Asp, Glu, Arg, and Lys, were noticeably large. In contrast, the populations of the hydrophobic residues, Leu, Val, and Ala, were relatively low. The appearance frequencies of the other amino acid residues were almost close to the abundance of general proteins of eukaryotes. The binding score indicated that the hydrophilic interaction was dominant at the antigen-antibody contact instead of the hydrophobic one. The positively charged residues, Arg and Lys, remarkably contributed to the binding compared to the negatively charged ones, Asp and Glu. Considerable contributions were also observed for the noncharged polar residues, Asn and Gln. The analysis of the secondary structures of the extracted antigen residues suggested that there was no marked difference in recognition by antibodies among helix, sheet, turn, and coil. A long helix of the antigen sometimes made contact with antibody complementarity-determining regions, and a large sheet also frequently covered the antibody heavy and light chains. The turn structure was the most popularly observed at the contact with antibody among 350 complexes. Three typical complexes were picked up for each of the four secondary structures. MD simulations were performed to examine the stability of the interfacial structures of the antigens for these 12 complex models. The alterations of secondary structures were monitored through the simulations. The structural fluctuations of the contact residues were low compared with the other domains of antigen molecules. No drastic conversion was observed for every model during the 100 ns simulation. The motions of the interfacial antigen residues were small compared to the other residues on the protein surface. Therefore, diverse molecular conformations are possible for antibody recognition as long as the target areas are polar, nonflexible, and protruding on the protein surface.
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Affiliation(s)
- Xinyue Qiao
- Graduate School of Pharmaceutical Sciences, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba 260-8675, Japan
| | - Liang Qu
- Graduate School of Pharmaceutical Sciences, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba 260-8675, Japan
| | - Yan Guo
- Graduate School of Pharmaceutical Sciences, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba 260-8675, Japan
| | - Tyuji Hoshino
- Graduate School of Pharmaceutical Sciences, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba 260-8675, Japan
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20
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Thorsteinson N, Gunn JR, Kelly K, Long W, Labute P. Structure-based charge calculations for predicting isoelectric point, viscosity, clearance, and profiling antibody therapeutics. MAbs 2021; 13:1981805. [PMID: 34632944 PMCID: PMC8510563 DOI: 10.1080/19420862.2021.1981805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The effect of hydrophobicity on antibody aggregation is well understood, and it has been shown that charge calculations can be useful for high-concentration viscosity and pharmacokinetic (PK) clearance predictions. In this work, structure-based charge descriptors are evaluated for their predictive performance on recently published antibody pI, viscosity, and clearance data. From this, we devised four rules for therapeutic antibody profiling which address developability issues arising from hydrophobicity and charged-based solution behavior, PK, and the ability to enrich for those that are approved by the U.S. Food and Drug Administration. Differences in strategy for optimizing the solution behavior of human IgG1 antibodies versus the IgG2 and IgG4 isotypes and the impact of pH alterations in formulation are discussed.
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Affiliation(s)
- Nels Thorsteinson
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - John R Gunn
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - Kenneth Kelly
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - Will Long
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - Paul Labute
- Research and Development, Chemical Computing Group ULC, Montreal, Quebec, Canada
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21
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Intrinsic physicochemical profile of marketed antibody-based biotherapeutics. Proc Natl Acad Sci U S A 2021; 118:2020577118. [PMID: 34504010 PMCID: PMC8449350 DOI: 10.1073/pnas.2020577118] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/03/2021] [Indexed: 01/28/2023] Open
Abstract
Successful biologic drug discovery and development involves finding functional as well as developable candidates. Once a candidate has been demonstrated to be functional, the next step is to determine whether it can be translated into a drug product. This requires that the candidate can withstand stresses encountered during manufacturing, shipping, and storage. Additionally, it must be safe, efficacious, and possess good pharmacology. In silico analyses of the variable regions of 77 marketed antibody-based biotherapeutics have revealed five nonredundant physicochemical descriptors. Distributions of these descriptors, observed for marketed biotherapeutics, can help prioritize a drug candidate for experimental testing at early discovery stages, guide engineering efforts to further optimize it, and help increase the productivity of biologic drug discovery and development. Feeding biopharma pipelines with biotherapeutic candidates that possess desirable developability profiles can help improve the productivity of biologic drug discovery and development. Here, we have derived an in silico profile by analyzing computed physicochemical descriptors for the variable regions (Fv) found in 77 marketed antibody-based biotherapeutics. Fv regions of these biotherapeutics demonstrate significant diversities in their germlines, complementarity determining region loop lengths, hydrophobicity, and charge distributions. Furthermore, an analysis of 24 physicochemical descriptors, calculated using homology-based molecular models, has yielded five nonredundant descriptors whose distributions represent stability, isoelectric point, and molecular surface characteristics of their Fv regions. Fv regions of candidates from our internal discovery campaigns, human next-generation sequencing repertoires, and those in clinical-stages (CST) were assessed for similarity with the physicochemical profile derived here. The Fv regions in 33% of CST antibodies show physicochemical properties that are dissimilar to currently marketed biotherapeutics. In comparison, physicochemical characteristics of ∼29% of the Fv regions in human antibodies and ∼27% of our internal hits deviated significantly from those of marketed biotherapeutics. The early availability of this information can help guide hit selection, lead identification, and optimization of biotherapeutic candidates. Insights from this work can also help support portfolio risk assessment, in-licensing, and biopharma collaborations.
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22
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Prabakaran R, Rawat P, Yasuo N, Sekijima M, Kumar S, Gromiha MM. Effect of charged mutation on aggregation of a pentapeptide: Insights from molecular dynamics simulations. Proteins 2021; 90:405-417. [PMID: 34460128 DOI: 10.1002/prot.26230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 06/30/2021] [Accepted: 08/24/2021] [Indexed: 12/14/2022]
Abstract
Aggregation of therapeutic monoclonal antibodies (mAbs) can negatively affect their chemistry, manufacturing, and control attributes and lead to undesirable immune responses in patients. Therefore, optimization of lead mAb drug candidates during discovery stages to mitigate aggregation is increasingly becoming an integral part of their developability assessments. The disruption of short sequence motifs called aggregation prone regions (APRs) found in amino acid sequences of mAb candidates can potentially mitigate their aggregation. In this work, we have performed molecular dynamics simulations to study the aggregation of an APR (VLVIY) found in λ light chains of human antibodies and its single point mutant KLVIY. Eighteen different multicopy peptide simulation systems of "VLVIY" and "KLVIY" were constructed by varying their concentrations, temperatures, termini capping, and flanking gate-keeper regions. Within 20 ns of the simulation, peptide "VLVIY" formed an aggregate of 100 peptides at ~0.1 M concentration with a 60% reduction in solvent accessible surface area (SASA). Furthermore, analysis of the SASA change, peptide cluster distribution, and water residence time demonstrated how Val ➔ Lys mutation resists aggregation and improves solubility. Presence of Lys slows down aggregation kinetics via charge-charge repulsions and by raising the kinetic barrier to formation of large oligomers. However, the effect of the Val ➔ Lys mutation is dependent on sequence and structural contexts around the APR. This mutation also alters the solvation shell around the peptide by favoring solute-solvent interactions, thereby increasing its solubility. This work has provided a detailed mechanistic explanation of how APR disruption can mitigate aggregation in biotherapeutics and improve their developability.
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Affiliation(s)
- R Prabakaran
- Protein Bioinformatics Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
| | - Puneet Rawat
- Protein Bioinformatics Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
| | - Nobuaki Yasuo
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - Masakazu Sekijima
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, Connecticut, USA
| | - M Michael Gromiha
- Protein Bioinformatics Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India.,Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
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23
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Bauer J, Mathias S, Kube S, Otte K, Garidel P, Gamer M, Blech M, Fischer S, Karow-Zwick AR. Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties along the entire pharmaceutical process chain. MAbs 2021; 12:1787121. [PMID: 32658605 PMCID: PMC7531517 DOI: 10.1080/19420862.2020.1787121] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The discovery of therapeutic monoclonal antibodies (mAbs) primarily focuses on their biological activity favoring the selection of highly potent drug candidates. These candidates, however, may have physical or chemical attributes that lead to unfavorable chemistry, manufacturing, and control (CMC) properties, such as low product titers, conformational and colloidal instabilities, or poor solubility, which can hamper or even prevent development and manufacturing. Hence, there is an urgent need to consider the developability of mAb candidates during lead identification and optimization. This work provides a comprehensive proof of concept study for the significantly improved developability of a mAb variant that was optimized with the help of sophisticated in silico tools relative to its difficult-to-develop parental counterpart. Interestingly, a single amino acid substitution in the variable domain of the light chain resulted in a three-fold increased product titer after stable expression in Chinese hamster ovary cells. Microscopic investigations revealed that wild type mAb-producing cells displayed potential antibody inclusions, while the in silico optimized variant-producing cells showed a rescued phenotype. Notably, the drug substance of the in silico optimized variant contained substantially reduced levels of aggregates and fragments after downstream process purification. Finally, formulation studies unraveled a significantly enhanced colloidal stability of the in silico optimized variant while its folding stability and potency were maintained. This study emphasizes that implementation of bioinformatics early in lead generation and optimization of biotherapeutics reduces failures during subsequent development activities and supports the reduction of project timelines and resources.
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Affiliation(s)
- Joschka Bauer
- Early Stage Pharmaceutical Development, Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Sven Mathias
- Institute of Applied Biotechnology, University of Applied Sciences Biberach , Biberach/Riss, Germany.,Early Stage Bioprocess Development, Bioprocess Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Sebastian Kube
- Early Stage Pharmaceutical Development, Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Kerstin Otte
- Institute of Applied Biotechnology, University of Applied Sciences Biberach , Biberach/Riss, Germany
| | - Patrick Garidel
- Early Stage Pharmaceutical Development, Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Martin Gamer
- Early Stage Bioprocess Development, Bioprocess Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Michaela Blech
- Early Stage Pharmaceutical Development, Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Simon Fischer
- Cell Line Development, Bioprocess Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Anne R Karow-Zwick
- Early Stage Pharmaceutical Development, Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
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24
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Prabakaran R, Rawat P, Kumar S, Gromiha MM. Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets. Brief Bioinform 2021; 22:6309925. [PMID: 34181000 DOI: 10.1093/bib/bbab240] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/18/2021] [Accepted: 06/02/2021] [Indexed: 01/09/2023] Open
Abstract
Several prediction algorithms and tools have been developed in the last two decades to predict protein and peptide aggregation. These in silico tools aid to predict the aggregation propensity and amyloidogenicity as well as the identification of aggregation-prone regions. Despite the immense interest in the field, it is of prime importance to systematically compare these algorithms for their performance. In this review, we have provided a rigorous performance analysis of nine prediction tools using a variety of assessments. The assessments were carried out on several non-redundant datasets ranging from hexapeptides to protein sequences as well as amyloidogenic antibody light chains to soluble protein sequences. Our analysis reveals the robustness of the current prediction tools and the scope for improvement in their predictive performances. Insights gained from this work provide critical guidance to the scientific community on advantages and limitations of different aggregation prediction methods and make informed decisions about their research needs.
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Affiliation(s)
| | | | - Sandeep Kumar
- Department of Biotherapeutics Discovery in Boehringer-Ingelheim Pharmaceutical Inc., Ridgefield, CT, USA
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25
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Grinshpun B, Thorsteinson N, Pereira JN, Rippmann F, Nannemann D, Sood VD, Fomekong Nanfack Y. Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies. MAbs 2021; 13:1932230. [PMID: 34116620 PMCID: PMC8204999 DOI: 10.1080/19420862.2021.1932230] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Understanding the pharmacokinetic (PK) properties of a drug, such as clearance, is a crucial step for evaluating efficacy. The PK of therapeutic antibodies can be complex and is influenced by interactions with the target, Fc-receptors, anti-drug antibodies, and antibody intrinsic factors. A growing body of literature has linked biophysical properties of antibodies, particularly nonspecific-binding propensity, hydrophobicity and charged regions to rapid clearance in preclinical species and selected human PK studies. A clear understanding of the connection between biophysical properties and their impact on PK would allow for early selection and optimization of antibodies and reduce costly attrition during clinical trials due to sub-optimal human clearance. Due to the difficulty in obtaining large and unbiased human PK data, previous studies have focused mostly on preclinical PK. For this study, we obtained and curated the most comprehensive clinical PK dataset to date and calculated accurate estimates of linear clearance for 64 monoclonal antibodies ranging from investigational candidates in Phase 2 trials to marketed products. This allows for the first time a deep analysis of the influence of biophysical and sequence-based in silico properties directly on human clearance. We use statistical analysis and a Random Forest classifier to identify properties that have the greatest influence in our dataset. Our findings indicate that in vitro poly-specificity assay and in silico estimated isoelectric point can discriminate fast and slow clearing antibodies, extending previous observations on preclinical clearance. This provides a simple yet powerful approach to select antibodies with desirable PK during early-stage screening.
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Affiliation(s)
- Boris Grinshpun
- Discovery & Development Technologies, Drug Disposition & Design, EMD Serono Research & Development Institute, Inc, Billerica, MA, USA
| | - Nels Thorsteinson
- Department of Scientific Services, Chemical Computing Group ULC, Montreal, Quebec, Canada
| | - Joao Ns Pereira
- Discovery & Development Technologies, Drug Disposition & Design, Merck Healthcare KGaA, Darmstadt, Germany
| | - Friedrich Rippmann
- Discovery & Development Technologies, Drug Disposition & Design, Merck Healthcare KGaA, Darmstadt, Germany
| | - David Nannemann
- Discovery & Development Technologies, Drug Disposition & Design, EMD Serono Research & Development Institute, Inc, Billerica, MA, USA
| | - Vanita D Sood
- Discovery & Development Technologies, Drug Disposition & Design, EMD Serono Research & Development Institute, Inc, Billerica, MA, USA
| | - Yves Fomekong Nanfack
- Discovery & Development Technologies, Drug Disposition & Design, EMD Serono Research & Development Institute, Inc, Billerica, MA, USA
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26
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Starr CG, Makowski EK, Wu L, Berg B, Kingsbury JS, Gokarn YR, Tessier PM. Ultradilute Measurements of Self-Association for the Identification of Antibodies with Favorable High-Concentration Solution Properties. Mol Pharm 2021; 18:2744-2753. [PMID: 34105965 DOI: 10.1021/acs.molpharmaceut.1c00280] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
There is significant interest in formulating antibody therapeutics as concentrated liquid solutions, but early identification of developable antibodies with optimal manufacturability, stability, and delivery attributes remains challenging. Traditional methods of identifying developable mAbs with low self-association in common antibody formulations require relatively concentrated protein solutions (>1 mg/mL), and this single challenge has frustrated early-stage and large-scale identification of antibody candidates with drug-like colloidal properties. Here, we describe charge-stabilized self-interaction nanoparticle spectroscopy (CS-SINS), an affinity-capture nanoparticle assay that measures colloidal self-interactions at ultradilute antibody concentrations (0.01 mg/mL), and is predictive of antibody developability issues of high viscosity and opalescence that manifest at four orders of magnitude higher concentrations (>100 mg/mL). CS-SINS enables large-scale, high-throughput selection of developable antibodies during early discovery.
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Affiliation(s)
- Charles G Starr
- Biologics Development, Sanofi, Framingham, Massachusetts 01701, United States
| | | | | | | | | | - Yatin R Gokarn
- Biologics Development, Sanofi, Framingham, Massachusetts 01701, United States
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27
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AbsoluRATE: An in-silico method to predict the aggregation kinetics of native proteins. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2021; 1869:140682. [PMID: 34102324 DOI: 10.1016/j.bbapap.2021.140682] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/12/2021] [Accepted: 06/04/2021] [Indexed: 12/12/2022]
Abstract
Protein aggregation has two aspects, namely, mechanistic and kinetics. Understanding protein aggregation kinetics is critical for prediction of progression of diseases caused by amyloidosis, accumulation of aggregates in biotherapeutics during storage and engineering commercial nano-biomaterials. In this work, we have collected experimentally determined absolute protein aggregation rates and developed an SVM based regression model to predict absolute rates of protein and peptide aggregation near-physiological conditions. The regression model achieved a correlation coefficient of 0.72 with MAE of 0.91 (natural log of kapp, where kapp is in hour-1) using leave-one-out cross-validation on a dataset of 82 non-redundant proteins/peptides. The model accounts for the experimental conditions (such as temperature, pH, ionic and protein concentration) and sequence-based properties. The amino acid sequence features revealed by this model as being important for aggregation kinetics, are also associated with the aggregation mechanism. In particular, inherent aggregation propensity of the protein/peptide sequence and number of aggregation prone regions (APRs) unpunctuated by the gatekeeping residues, were found to play important roles in the prediction of the absolute aggregation rates. This analysis shows that mechanism and kinetics of protein aggregation are coupled via common sequence attributes. The aggregation kinetic prediction method developed in this work is available at https://web.iitm.ac.in/bioinfo2/absolurate-pred/index.html.
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28
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Qu L, Qiao X, Qi F, Nishida N, Hoshino T. Analysis of Binding Modes of Antigen-Antibody Complexes by Molecular Mechanics Calculation. J Chem Inf Model 2021; 61:2396-2406. [PMID: 33934602 DOI: 10.1021/acs.jcim.1c00167] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Antibodies are one of the most important protein molecules in biopharmaceutics. Due to the recent advance in technology for producing monoclonal antibodies, many structural data are available on the antigen-antibody complexes. To characterize the molecular interaction in antigen-antibody recognition, we computationally analyzed 500 complex structures by molecular mechanics calculations. The presence of Ser and Tyr is markedly large in the complementarity-determining regions (CDRs). Although Ser is abundant in CDRs, its contribution to the binding score is not large. Instead, Tyr, Asp, Glu, and Arg significantly contribute to the molecular interaction from the viewpoint of the binding score. The decomposition of the binding score suggests that the hydrophilic interaction is predominant in all CDRs compared with the hydrophobic one. The contribution of the heavy chain is larger than that of the light chain. In particular, H2 and H3 largely contribute to the binding interaction. Tyr is a main contributing residue both in H2 and H3. The positively charged residue Arg also significantly contributes to the binding score in H3, while the contribution of Lys is small. The appearance of Ser is remarkable in H2, and Asp is abundant in H3. The non-charged polar residues, Thr, Asn, and Gln, appear much in H2, compared to appearing in H3. The negatively charged residues Asp and Glu significantly contribute to the binding score in H3. The contributions of Phe and Trp are not large in spite that the aromatic residues are capable of making the π-π or CH-π interaction. Gly is commonly abundant both in H2 and H3. The average distance of the shortest direct hydrogen bond between the antigen and antibody is longer than that of the hydrogen bonds observed in the complexes between compounds and their target proteins. Therefore, the antigen-antibody interface is not so tight as the compound-target protein interface. The calculation of shape complementarity is consistent with the result of the hydrogen bonds in that the fitness of the antigen-antibody contact is not so high as that of the compound-target protein contact. There exist many water molecules at the antigen-antibody interface. These findings suggest that Tyr, Asp, Glu, and Arg are rich in H3 and work as major contributors for the interaction with the antigen. Ser, Thr, Asn, and Gln are rich in H2 and support the interaction with enhancing molecular fitness. Gly is helpful in increasing flexibility and geometrical diversity. Because the antigen-antibody binding is fundamentally hydrophilic-driven, the non-polar residues are unfavorable for mediating the contact even for the aromatic residues such as Phe and Trp.
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Affiliation(s)
- Liang Qu
- Graduate School of Pharmaceutical Sciences, Chiba UniversityRINGGOLD, Inohana 1-8-1, Chuo-ku, Chiba 260-8675, Japan
| | - Xinyue Qiao
- Graduate School of Pharmaceutical Sciences, Chiba UniversityRINGGOLD, Inohana 1-8-1, Chuo-ku, Chiba 260-8675, Japan
| | - Fei Qi
- Graduate School of Pharmaceutical Sciences, Chiba UniversityRINGGOLD, Inohana 1-8-1, Chuo-ku, Chiba 260-8675, Japan
| | - Noritaka Nishida
- Graduate School of Pharmaceutical Sciences, Chiba UniversityRINGGOLD, Inohana 1-8-1, Chuo-ku, Chiba 260-8675, Japan
| | - Tyuji Hoshino
- Graduate School of Pharmaceutical Sciences, Chiba UniversityRINGGOLD, Inohana 1-8-1, Chuo-ku, Chiba 260-8675, Japan
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29
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Chi B, De Oliveira G, Gallagher T, Mitchell L, Knightley L, Gonzalez CC, Russell S, Germaschewski V, Pearce C, Sellick CA. Pragmatic mAb lead molecule engineering from a developability perspective. Biotechnol Bioeng 2021; 118:3733-3743. [PMID: 33913507 DOI: 10.1002/bit.27802] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/26/2021] [Accepted: 04/15/2021] [Indexed: 01/08/2023]
Abstract
As the number of antibody drugs being approved and marketed increases, our knowledge of what makes potential drug candidates a successful product has increased tremendously. One of the critical parameters that have become clear in the field is the importance of mAb "developability." Efforts are being increasingly focused on simultaneously selecting molecules that exhibit both desirable biological potencies and manufacturability attributes. In the current study mutations to improve the developability profile of a problematic antibody that inconsistently precipitates in a batch scale-dependent fashion using a standard platform purification process are described. Initial bioinformatic analysis showed the molecule has no obvious sequence or structural liabilities that might lead it to precipitate. Subsequent analysis of the molecule revealed the presence of two unusual positively charged mutations on the light chain at the interface of VH and VL domains, which were hypothesized to be the primary contributor to molecule precipitation during process development. To investigate this hypothesis, straightforward reversion to the germline of these residues was carried out. The resulting mutants have improved expression titers and recovered stability within a forced precipitation assay, without any change to biological activity. Given the time pressures of drug development in industry, process optimization of the lead molecule was carried out in parallel to the "retrospective" mutagenesis approach. Bespoke process optimization for large-scale manufacturing was successful. However, we propose that such context-dependent sequence liabilities should be included in the arsenal of in silico developability screening early in development; particularly since this specific issue can be efficiently mitigated without the requirement for extensive screening of lead molecule variants.
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Affiliation(s)
| | | | - Tom Gallagher
- Kymab Ltd., Cambridge, UK.,F-star Therapeutics Ltd., Cambridge, UK
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30
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Prabakaran R, Rawat P, Thangakani AM, Kumar S, Gromiha MM. Protein aggregation: in silico algorithms and applications. Biophys Rev 2021; 13:71-89. [PMID: 33747245 PMCID: PMC7930180 DOI: 10.1007/s12551-021-00778-w] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 01/01/2021] [Indexed: 01/08/2023] Open
Abstract
Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.
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Affiliation(s)
- R. Prabakaran
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
| | - Puneet Rawat
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
| | - A. Mary Thangakani
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT USA
| | - M. Michael Gromiha
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
- School of Computing, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Kanagawa Japan
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31
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Tilegenova C, Izadi S, Yin J, Huang CS, Wu J, Ellerman D, Hymowitz SG, Walters B, Salisbury C, Carter PJ. Dissecting the molecular basis of high viscosity of monospecific and bispecific IgG antibodies. MAbs 2021; 12:1692764. [PMID: 31779513 PMCID: PMC6927759 DOI: 10.1080/19420862.2019.1692764] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Some antibodies exhibit elevated viscosity at high concentrations, making them poorly suited for therapeutic applications requiring administration by injection such as subcutaneous or ocular delivery. Here we studied an anti-IL-13/IL-17 bispecific IgG4 antibody, which has anomalously high viscosity compared to its parent monospecific antibodies. The viscosity of the bispecific IgG4 in solution was decreased by only ~30% in the presence of NaCl, suggesting electrostatic interactions are insufficient to fully explain the drivers of viscosity. Intriguingly, addition of arginine-HCl reduced the viscosity of the bispecific IgG4 by ~50% to its parent IgG level. These data suggest that beyond electrostatics, additional types of interactions such as cation-π and/or π-π may contribute to high viscosity more significantly than previously understood. Molecular dynamics simulations of antibody fragments in the mixed solution of free arginine and explicit water were conducted to identify hotspots involved in self-interactions. Exposed surface aromatic amino acids displayed an increased number of contacts with arginine. Mutagenesis of the majority of aromatic residues pinpointed by molecular dynamics simulations effectively decreased the solution's viscosity when tested experimentally. This mutational method to reduce the viscosity of a bispecific antibody was extended to a monospecific anti-GCGR IgG1 antibody with elevated viscosity. In all cases, point mutants were readily identified that both reduced viscosity and retained antigen-binding affinity. These studies demonstrate a new approach to mitigate high viscosity of some antibodies by mutagenesis of surface-exposed aromatic residues on complementarity-determining regions that may facilitate some clinical applications.
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Affiliation(s)
| | - Saeed Izadi
- Early Stage Pharmaceutical Development, Genentech Inc., South San Francisco, CA, USA
| | - Jianping Yin
- Structural Biology, Genentech Inc., South San Francisco, CA, USA
| | | | - Jiansheng Wu
- Protein Chemistry, Genentech Inc., South San Francisco, CA, USA
| | - Diego Ellerman
- Protein Chemistry, Genentech Inc., South San Francisco, CA, USA
| | - Sarah G Hymowitz
- Structural Biology, Genentech Inc., South San Francisco, CA, USA
| | - Benjamin Walters
- Biochemical and Cellular Pharmacology, Genentech Inc., South San Francisco, CA, USA
| | - Cleo Salisbury
- Early Stage Pharmaceutical Development, Genentech Inc., South San Francisco, CA, USA
| | - Paul J Carter
- Antibody Engineering, Genentech Inc., South San Francisco, CA, USA
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32
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Makowski EK, Wu L, Gupta P, Tessier PM. Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods. MAbs 2021; 13:1895540. [PMID: 34313532 PMCID: PMC8346245 DOI: 10.1080/19420862.2021.1895540] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/05/2021] [Accepted: 02/22/2021] [Indexed: 11/30/2022] Open
Abstract
There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.
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Affiliation(s)
- Emily K. Makowski
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Lina Wu
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering
| | - Priyanka Gupta
- Department of Biochemistry and Biophysics, Rensselaer Polytechnic Institute, Troy, NY, USA
- Biotherapeutics Discovery Department, Boehringer Ingelheim, Ridgefield, CT, USA
| | - Peter M. Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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33
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Domnowski M, Lo Presti K, Binder J, Reindl J, Lehmann L, Kummer F, Wolber M, Satzger M, Dehling M, Jaehrling J, Frieß W. Generation of mAb Variants with Less Attractive Self-Interaction but Preserved Target Binding by Well-Directed Mutation. Mol Pharm 2020; 18:236-245. [PMID: 33331157 DOI: 10.1021/acs.molpharmaceut.0c00848] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Strongly attractive self-interaction of therapeutic protein candidates can impose challenges for manufacturing, filling, stability, and administration due to elevated viscosity or aggregation propensity. Suitable formulations can mitigate these issues to a certain extent. Understanding the self-interaction mechanism on a molecular basis and rational protein engineering provides a more fundamental approach, and it can save costs and efforts as well as alleviate risks at later stages of development. In this study, we used computational methods for the identification of aggregation-prone regions in a mAb and generated mutants based on these findings. We applied hydrogen-deuterium exchange mass spectrometry to identify distinct self-interaction hot spots. Ultimately, we generated mAb variants based on a combination of both approaches and identified mutants with low attractive self-interaction propensity, minimal off-target binding, and even improved target binding. Our data show that the introduction of arginine in spatial proximity to hydrophobic patches is highly beneficial on all these levels. For our mAb, variants that contain more than one aspartate residue flanking to the hydrophobic HCDR3 show decreased attractive self-interaction at unaffected off-target and target binding. The combined engineering strategy described here underlines the high potential of understanding self-interaction in the early stages of development to predict and reduce the risk of failure in subsequent development.
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Affiliation(s)
- Martin Domnowski
- Department of Pharmacy, Pharmaceutical Technology and Biopharmaceutics, Ludwig Maximilians-Universitaet, Munich 81377, Germany.,MorphoSys AG, Department of Protein Sciences (Research), Planegg 82152, Germany
| | - Ken Lo Presti
- Department of Pharmacy, Pharmaceutical Technology and Biopharmaceutics, Ludwig Maximilians-Universitaet, Munich 81377, Germany
| | - Jonas Binder
- Department of Pharmacy, Pharmaceutical Technology and Biopharmaceutics, Ludwig Maximilians-Universitaet, Munich 81377, Germany
| | - Josef Reindl
- MorphoSys AG, Department of Protein Sciences (Research), Planegg 82152, Germany
| | - Lucille Lehmann
- MorphoSys AG, Department of Protein Sciences (Research), Planegg 82152, Germany
| | - Felix Kummer
- MorphoSys AG, Department of Protein Sciences (Research), Planegg 82152, Germany
| | - Meike Wolber
- MorphoSys AG, Department of Protein Sciences (Research), Planegg 82152, Germany
| | - Marion Satzger
- MorphoSys AG, Department of Protein Sciences (Research), Planegg 82152, Germany
| | - Marco Dehling
- MorphoSys AG, Department of Protein Sciences (Research), Planegg 82152, Germany
| | - Jan Jaehrling
- MorphoSys AG, Department of Protein Sciences (Research), Planegg 82152, Germany
| | - Wolfgang Frieß
- Department of Pharmacy, Pharmaceutical Technology and Biopharmaceutics, Ludwig Maximilians-Universitaet, Munich 81377, Germany
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34
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Woldeyes MA, Qi W, Razinkov VI, Furst EM, Roberts CJ. Temperature Dependence of Protein Solution Viscosity and Protein-Protein Interactions: Insights into the Origins of High-Viscosity Protein Solutions. Mol Pharm 2020; 17:4473-4482. [PMID: 33170708 DOI: 10.1021/acs.molpharmaceut.0c00552] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Protein solution viscosity (η) as a function of temperature was measured at a series of protein concentrations under a range of formulation conditions for two monoclonal antibodies (MAbs) and a globular protein (aCgn). Based on theoretical arguments, a strong temperature dependence for protein-protein interactions (PPI) indicates highly anisotropic, short-ranged attractions that could lead to higher solution viscosities. The semi-empirical Ross-Minton model was used to determine the apparent intrinsic viscosity, shape, and "crowding" factors for each protein as a function of temperature and formulation conditions. The apparent intrinsic viscosity was independent of temperature for aCgn, while a slight decrease with increasing temperature was observed for the MAbs. The temperature dependence of solution viscosity was analyzed using the Andrade-Eyring equation to determine the effective activation energy of viscous flow (Ea,η). While Ea,η values were different for each protein, they were independent of formulation conditions for a given protein. PPI were quantified via the osmotic second virial coefficient (B22) and the protein diffusion interaction parameter (kD) as a function of temperature under the same formulation conditions as the viscosity measurements. Net interactions ranged from strongly attractive to repulsive by changing formulation pH and ionic strength for each protein. Overall, larger activation energies for PPI corresponded to larger activation energies for η, and those were predictive of the highest η values at higher protein concentrations.
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Affiliation(s)
- Mahlet A Woldeyes
- Department of Chemical & Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Wei Qi
- Drug Product Development, Amgen Inc., Thousand Oaks, California 91320, United States
| | - Vladimir I Razinkov
- Drug Product Development, Amgen Inc., Thousand Oaks, California 91320, United States
| | - Eric M Furst
- Department of Chemical & Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Christopher J Roberts
- Department of Chemical & Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
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35
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Waibl F, Fernández-Quintero ML, Kamenik AS, Kraml J, Hofer F, Kettenberger H, Georges G, Liedl KR. Conformational Ensembles of Antibodies Determine Their Hydrophobicity. Biophys J 2020; 120:143-157. [PMID: 33220303 PMCID: PMC7820740 DOI: 10.1016/j.bpj.2020.11.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 10/23/2020] [Accepted: 11/10/2020] [Indexed: 12/11/2022] Open
Abstract
A major challenge in the development of antibody biotherapeutics is their tendency to aggregate. One root cause for aggregation is exposure of hydrophobic surface regions to the solvent. Many current techniques predict the relative aggregation propensity of antibodies via precalculated scales for the hydrophobicity or aggregation propensity of single amino acids. However, those scales cannot describe the nonadditive effects of a residue’s surrounding on its hydrophobicity. Therefore, they are inherently limited in their ability to describe the impact of subtle differences in molecular structure on the overall hydrophobicity. Here, we introduce a physics-based approach to describe hydrophobicity in terms of the hydration free energy using grid inhomogeneous solvation theory (GIST). We apply this method to assess the effects of starting structures, conformational sampling, and protonation states on the hydrophobicity of antibodies. Our results reveal that high-quality starting structures, i.e., crystal structures, are crucial for the prediction of hydrophobicity and that conformational sampling can compensate errors introduced by the starting structure. On the other hand, sampling of protonation states only leads to good results when combined with high-quality structures, whereas it can even be detrimental otherwise. We conclude by pointing out that a single static homology model may not be adequate for predicting hydrophobicity.
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Affiliation(s)
- Franz Waibl
- Department of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria
| | - Monica L Fernández-Quintero
- Department of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria
| | - Anna S Kamenik
- Department of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria
| | - Johannes Kraml
- Department of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria
| | - Florian Hofer
- Department of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria
| | - Hubert Kettenberger
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
| | - Guy Georges
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
| | - Klaus R Liedl
- Department of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria.
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36
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Development of histidine-tagged cyclic peptide functionalized monolithic material for the affinity purification of antibodies in biological matrices. J Chromatogr A 2020; 1635:461707. [PMID: 33254002 DOI: 10.1016/j.chroma.2020.461707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 11/06/2020] [Accepted: 11/09/2020] [Indexed: 01/04/2023]
Abstract
The rapidly increasing applications of monoclonal antibodies (mAbs) in therapy have necessitated the development of mAb production and purification technologies for both academic and industrial usage. Herein, a histidine-tagged cyclic peptide (HHHHHHGSGSGSDC*AWHLGELVWC*T, the disulfide-bonded cysteines of which are indicated by asterisks, named HT25-cyclopeptide) functionalized monolithic material was developed by the metal ion chelation-based approach. The resulting material possessed suitable affinity and peptide ligand density (13.8 mg peptide ligand per mL of material), good porosity (67.1 %), acceptable specific surface area (52.95 m2/g), and lots of macropores (4.13 μm). Moreover, excellent antibody-specific selectivity, comparable or even better binding capacity (for dried material, maximum static binding capacity and dynamic binding capacity are about 119.3 mg/g and 17.05 mg/g, respectively) for antibody compared to previously developed affinity materials, acceptable resistance to trypsin digestion, and negligible nonspecific protein adsorption, were also achieved on this novel monolithic material. Compared with the corresponding cyclic peptide-based sepharose material, milder elution conditions were employed for the HT25-cyclopeptide-based monolithic material, which could effectively prevent the aggregation and denaturation of the enriched antibodies. This novel material was then successfully applied to the affinity enrichment and purification of mAbs (including infliximab and rituximab) in different cell culture media or IgG in human serum.
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37
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Somani S, Jo S, Thirumangalathu R, Rodrigues D, Tanenbaum LM, Amin K, MacKerell AD, Thakkar SV. Toward Biotherapeutics Formulation Composition Engineering using Site-Identification by Ligand Competitive Saturation (SILCS). J Pharm Sci 2020; 110:1103-1110. [PMID: 33137372 DOI: 10.1016/j.xphs.2020.10.051] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 10/23/2020] [Accepted: 10/26/2020] [Indexed: 10/23/2022]
Abstract
Formulation of protein-based therapeutics employ advanced formulation and analytical technologies for screening various parameters such as buffer, pH, and excipients. At a molecular level, physico-chemical properties of a protein formulation depend on self-interaction between protein molecules, protein-solvent and protein-excipient interactions. This work describes a novel in silico approach, SILCS-Biologics, for structure-based modeling of protein formulations. SILCS Biologics is based on the Site-Identification by Ligand Competitive Saturation (SILCS) technology and enables modeling of interactions among different components of a formulation at an atomistic level while accounting for protein flexibility. It predicts potential hotspot regions on the protein surface for protein-protein and protein-excipient interactions. Here we apply SILCS-Biologics on a Fab domain of a monoclonal antibody (mAbN) to model Fab-Fab interactions and interactions with three amino acid excipients, namely, arginine HCl, proline and lysine HCl. Experiments on 100 mg/ml formulations of mAbN showed that arginine increased, lysine reduced, and proline did not impact viscosity. We use SILCS-Biologics modeling to explore a structure-based hypothesis for the viscosity modulating effect of these excipients. Current efforts are aimed at further validation of this novel computational framework and expanding the scope to model full mAb and other protein therapeutics.
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Affiliation(s)
- Sandeep Somani
- Discovery Sciences, Janssen Research and Development (Janssen R&D), Spring House, PA 19477, USA
| | | | - Renuka Thirumangalathu
- BioTherapeutics Drug Product Development (BioTD DPD), Janssen Research and Development (Janssen R&D), Malvern, PA 19355, USA
| | - Danika Rodrigues
- BioTherapeutics Drug Product Development (BioTD DPD), Janssen Research and Development (Janssen R&D), Malvern, PA 19355, USA
| | - Laura M Tanenbaum
- BioTherapeutics Drug Product Development (BioTD DPD), Janssen Research and Development (Janssen R&D), Malvern, PA 19355, USA
| | - Ketan Amin
- BioTherapeutics Drug Product Development (BioTD DPD), Janssen Research and Development (Janssen R&D), Malvern, PA 19355, USA
| | - Alexander D MacKerell
- SilcsBio LLC, Baltimore, MD 21202, USA; Computer-Aided Drug Design Center, School of Pharmacy, University of Maryland, Baltimore, MD 21201, USA.
| | - Santosh V Thakkar
- BioTherapeutics Drug Product Development (BioTD DPD), Janssen Research and Development (Janssen R&D), Malvern, PA 19355, USA; BioTherapeutics Cell and Developability Sciences (BioTD CDS), Janssen Research and Development (Janssen R&D), Spring House, PA 19477, USA.
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38
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Matsuoka T, Miyauchi R, Nagaoka N, Hasegawa J. Mitigation of liquid-liquid phase separation of a monoclonal antibody by mutations of negative charges on the Fab surface. PLoS One 2020; 15:e0240673. [PMID: 33125371 PMCID: PMC7598502 DOI: 10.1371/journal.pone.0240673] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 09/30/2020] [Indexed: 01/02/2023] Open
Abstract
Some monoclonal antibodies undergo liquid-liquid phase separation owing to self-attractive associations involving electrostatic and other soft interactions, thereby rendering monoclonal antibodies unsuitable as therapeutics. To mitigate the phase separation, formulation optimization is often performed. However, this is sometimes unsuccessful because of the limited time for the development of therapeutic antibodies. Thus, protein mutations with appropriate design are required. In this report, we describe a case study involving the design of mutants of negatively charged surface residues to reduce liquid-liquid phase separation propensity. Physicochemical analysis of the resulting mutants demonstrated the mutual correlation between the sign of second virial coefficient B2, the Fab dipole moment, and the reduction of liquid-liquid phase separation propensity. Moreover, both the magnitude and direction of the dipole moment appeared to be essential for liquid-liquid phase separation propensity, where electrostatic interaction was the dominant mechanism. These findings could contribute to a better design of mutants with reduced liquid-liquid phase separation propensity and improved drug-like biophysical properties.
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Affiliation(s)
- Tatsuji Matsuoka
- Modality Research Laboratories, Daiichi Sankyo, Co., Ltd., Shinagawa, Tokyo, Japan
| | - Ryuki Miyauchi
- Modality Research Laboratories, Daiichi Sankyo, Co., Ltd., Shinagawa, Tokyo, Japan
| | - Nobumi Nagaoka
- Modality Research Laboratories, Daiichi Sankyo, Co., Ltd., Shinagawa, Tokyo, Japan
| | - Jun Hasegawa
- Modality Research Laboratories, Daiichi Sankyo, Co., Ltd., Shinagawa, Tokyo, Japan
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39
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Garripelli VK, Wu Z, Gupta S. Developability assessment for monoclonal antibody drug candidates: a case study. Pharm Dev Technol 2020; 26:11-20. [PMID: 32986499 DOI: 10.1080/10837450.2020.1829641] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Various screening approaches are used by industry to evaluate development risks associated with discovery candidates. This process has become more complicated with biological therapeutics, a class dominated by monoclonal antibodies (mAb), and, increasingly, their derivative constructs. Effective early assessment for drug-like properties (DLP) can save time and costs by allowing a more complete consideration of issues that could impact the desired end result of a stable drug product. Here we report a case study of four IgG1 mAbs, with sequence variations in the variable domain region, screened as a set of possible drug candidates. Our comprehensive, tiered approach used a battery of analytical tools to assess molecular characteristics, conformational stability, colloidal stability, and short-term storage stability. While most DLP for the four candidates were developmentally acceptable and comparable, mAb-2 was associated with adverse colloidal properties. Further investigation of mAb-2 in an expanded pH range revealed a propensity for phase separation, indicating a need for the additional product development effort. Our results support that comprehensive DLP assessments in an expanded pH range are beneficial in identifying development options for promising molecules that show challenging stability trends. This adaptable approach may be especially useful in the development of increasingly complex antibody constructs.
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Affiliation(s)
- Vivek Kumar Garripelli
- Pharmaceutical Development, Oncology Early Development CMC Biologics, AbbVie, Redwood City, CA, USA
| | - Zhen Wu
- ADC Therapeutics, San Mateo, CA, USA
| | - Supriya Gupta
- Pharmaceutical Development, Oncology Early Development CMC Biologics, AbbVie, Redwood City, CA, USA
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40
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Sawant MS, Streu CN, Wu L, Tessier PM. Toward Drug-Like Multispecific Antibodies by Design. Int J Mol Sci 2020; 21:E7496. [PMID: 33053650 PMCID: PMC7589779 DOI: 10.3390/ijms21207496] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/02/2020] [Accepted: 10/02/2020] [Indexed: 12/18/2022] Open
Abstract
The success of antibody therapeutics is strongly influenced by their multifunctional nature that couples antigen recognition mediated by their variable regions with effector functions and half-life extension mediated by a subset of their constant regions. Nevertheless, the monospecific IgG format is not optimal for many therapeutic applications, and this has led to the design of a vast number of unique multispecific antibody formats that enable targeting of multiple antigens or multiple epitopes on the same antigen. Despite the diversity of these formats, a common challenge in generating multispecific antibodies is that they display suboptimal physical and chemical properties relative to conventional IgGs and are more difficult to develop into therapeutics. Here we review advances in the design and engineering of multispecific antibodies with drug-like properties, including favorable stability, solubility, viscosity, specificity and pharmacokinetic properties. We also highlight emerging experimental and computational methods for improving the next generation of multispecific antibodies, as well as their constituent antibody fragments, with natural IgG-like properties. Finally, we identify several outstanding challenges that need to be addressed to increase the success of multispecific antibodies in the clinic.
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Affiliation(s)
- Manali S. Sawant
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; (M.S.S.); (C.N.S.)
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Craig N. Streu
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; (M.S.S.); (C.N.S.)
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA;
- Department of Chemistry, Albion College, Albion, MI 49224, USA
| | - Lina Wu
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA;
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter M. Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; (M.S.S.); (C.N.S.)
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA;
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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41
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Rawat P, Prabakaran R, Kumar S, Gromiha MM. AggreRATE-Pred: a mathematical model for the prediction of change in aggregation rate upon point mutation. Bioinformatics 2020; 36:1439-1444. [PMID: 31599925 DOI: 10.1093/bioinformatics/btz764] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 09/30/2019] [Accepted: 10/05/2019] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION Protein aggregation is a major unsolved problem in biochemistry with implications for several human diseases, biotechnology and biomaterial sciences. A majority of sequence-structural properties known for their mechanistic roles in protein aggregation do not correlate well with the aggregation kinetics. This limits the practical utility of predictive algorithms. RESULTS We analyzed experimental data on 183 unique single point mutations that lead to change in aggregation rates for 23 polypeptides and proteins. Our initial mathematical model obtained a correlation coefficient of 0.43 between predicted and experimental change in aggregation rate upon mutation (P-value <0.0001). However, when the dataset was classified based on protein length and conformation at the mutation sites, the average correlation coefficient almost doubled to 0.82 (range: 0.74-0.87; P-value <0.0001). We observed that distinct sequence and structure-based properties determine protein aggregation kinetics in each class. In conclusion, the protein aggregation kinetics are impacted by local factors and not by global ones, such as overall three-dimensional protein fold, or mechanistic factors such as the presence of aggregation-prone regions. AVAILABILITY AND IMPLEMENTATION The web server is available at http://www.iitm.ac.in/bioinfo/aggrerate-pred/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Puneet Rawat
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - R Prabakaran
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer-Ingelheim Pharmaceutical Inc. Ridgefield, CT, USA
| | - M Michael Gromiha
- Protein Bioinformatics Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India.,Advanced Computational Drug Discovery Unit (ACDD), Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Japan
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42
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Zhang Y, Wu L, Gupta P, Desai AA, Smith MD, Rabia LA, Ludwig SD, Tessier PM. Physicochemical Rules for Identifying Monoclonal Antibodies with Drug-like Specificity. Mol Pharm 2020; 17:2555-2569. [PMID: 32453957 PMCID: PMC7936472 DOI: 10.1021/acs.molpharmaceut.0c00257] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The ability of antibodies to recognize their target antigens with high specificity is fundamental to their natural function. Nevertheless, therapeutic antibodies display variable and difficult-to-predict levels of nonspecific and self-interactions that can lead to various drug development challenges, including antibody aggregation, abnormally high viscosity, and rapid antibody clearance. Here we report a method for predicting the overall specificity of antibodies in terms of their relative risk for displaying high levels of nonspecific or self-interactions at physiological conditions. We find that individual and combined sets of chemical rules that limit the maximum and minimum numbers of certain solvent-exposed amino acids in antibody variable regions are strong predictors of specificity for large panels of preclinical and clinical-stage antibodies. We also demonstrate how the chemical rules can be used to identify sites that mediate nonspecific interactions in suboptimal antibodies and guide the design of targeted sublibraries that yield variants with high antibody specificity. These findings can be readily used to improve the selection and engineering of antibodies with drug-like specificity.
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Affiliation(s)
- Yulei Zhang
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lina Wu
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Priyanka Gupta
- Department of Biochemistry and Biophysics, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Biotherapeutics Discovery Department, Boehringer Ingelheim, Ridgefield, CT 06877
| | - Alec A. Desai
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Matthew D. Smith
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lilia A. Rabia
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Isermann Department of Chemical & Biological Engineering, Troy, NY 12180, USA
| | - Seth D. Ludwig
- Isermann Department of Chemical & Biological Engineering, Troy, NY 12180, USA
| | - Peter M. Tessier
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Isermann Department of Chemical & Biological Engineering, Troy, NY 12180, USA
- Department of Biochemistry and Biophysics, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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43
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Apgar JR, Tam ASP, Sorm R, Moesta S, King AC, Yang H, Kelleher K, Murphy D, D’Antona AM, Yan G, Zhong X, Rodriguez L, Ma W, Ferguson DE, Carven GJ, Bennett EM, Lin L. Modeling and mitigation of high-concentration antibody viscosity through structure-based computer-aided protein design. PLoS One 2020; 15:e0232713. [PMID: 32379792 PMCID: PMC7205207 DOI: 10.1371/journal.pone.0232713] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 04/20/2020] [Indexed: 01/07/2023] Open
Abstract
For an antibody to be a successful therapeutic many competing factors require optimization, including binding affinity, biophysical characteristics, and immunogenicity risk. Additional constraints may arise from the need to formulate antibodies at high concentrations (>150 mg/ml) to enable subcutaneous dosing with reasonable volume (ideally <1.0 mL). Unfortunately, antibodies at high concentrations may exhibit high viscosities that place impractical constraints (such as multiple injections or large needle diameters) on delivery and impede efficient manufacturing. Here we describe the optimization of an anti-PDGF-BB antibody to reduce viscosity, enabling an increase in the formulated concentration from 80 mg/ml to greater than 160 mg/ml, while maintaining the binding affinity. We performed two rounds of structure guided rational design to optimize the surface electrostatic properties. Analysis of this set demonstrated that a net-positive charge change, and disruption of negative charge patches were associated with decreased viscosity, but the effect was greatly dependent on the local surface environment. Our work here provides a comprehensive study exploring a wide sampling of charge-changes in the Fv and CDR regions along with targeting multiple negative charge patches. In total, we generated viscosity measurements for 40 unique antibody variants with full sequence information which provides a significantly larger and more complete dataset than has previously been reported.
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Affiliation(s)
- James R. Apgar
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
- * E-mail:
| | - Amy S. P. Tam
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Rhady Sorm
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Sybille Moesta
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Amy C. King
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Han Yang
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Kerry Kelleher
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Denise Murphy
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Aaron M. D’Antona
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Guoying Yan
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Xiaotian Zhong
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Linette Rodriguez
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Weijun Ma
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Darren E. Ferguson
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Gregory J. Carven
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Eric M. Bennett
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
| | - Laura Lin
- BioMedicine Design, Pfizer Inc, Cambridge, Massachusetts, United States of America
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44
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Kuroda D, Tsumoto K. Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design. J Pharm Sci 2020; 109:1631-1651. [DOI: 10.1016/j.xphs.2020.01.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/25/2019] [Accepted: 01/10/2020] [Indexed: 12/18/2022]
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45
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Izadi S, Patapoff TW, Walters BT. Multiscale Coarse-Grained Approach to Investigate Self-Association of Antibodies. Biophys J 2020; 118:2741-2754. [PMID: 32416079 DOI: 10.1016/j.bpj.2020.04.022] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 03/12/2020] [Accepted: 04/13/2020] [Indexed: 11/20/2022] Open
Abstract
Self-association of therapeutic monoclonal antibodies (mabs) are thought to modulate the undesirably high viscosity observed in their concentrated solutions. Computational prediction of such a self-association behavior is advantageous early during mab drug candidate selection when material availability is limited. Here, we present a coarse-grained (CG) simulation method that enables microsecond molecular dynamics simulations of full-length antibodies at high concentrations. The proposed approach differs from others in two ways: first, charges are assigned to CG beads in an effort to reproduce molecular multipole moments and charge asymmetry of full-length antibodies instead of only localized charges. This leads to great improvements in the agreement between CG and all-atom electrostatic fields. Second, the distinctive hydrophobic character of each antibody is incorporated through empirical adjustments to the short-range van der Waals terms dictated by cosolvent all-atom molecular dynamics simulations of antibody variable regions. CG simulations performed on a set of 15 different mabs reveal that diffusion coefficients in crowded environments are markedly impacted by intermolecular interactions. Diffusion coefficients computed from the simulations are in correlation with experimentally measured observables, including viscosities at a high concentration. Further, we show that the evaluation of electrostatic and hydrophobic characters of the mabs is useful in predicting the nonuniform effect of salt on the viscosity of mab solutions. This CG modeling approach is particularly applicable as a material-free screening tool for selecting antibody candidates with desirable viscosity properties.
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Affiliation(s)
- Saeed Izadi
- Pharmaceutical Development, Genentech, South San Francisco, California.
| | - Thomas W Patapoff
- Pharmaceutical Development, Genentech, South San Francisco, California
| | - Benjamin T Walters
- Biochemical and Cellular Pharmacology, Genentech, South San Francisco, California.
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46
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Ferreira GM, Shahfar H, Sathish HA, Remmele RL, Roberts CJ. Identifying Key Residues That Drive Strong Electrostatic Attractions between Therapeutic Antibodies. J Phys Chem B 2019; 123:10642-10653. [PMID: 31739660 DOI: 10.1021/acs.jpcb.9b08355] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Attractive electrostatic protein-protein interactions (PPI) necessarily involve identifying oppositely charged regions of the protein surface that interact favorably. This cannot be done reliably if one only considers a single protein in isolation unless there are obvious charge "patches" that result in extreme molecular dipoles. Prior work [ J. Pharm. Sci. 2019 , 108 , 120 - 132 ] identified three monoclonal antibodies (MAbs) that displayed experimental behavior ranging from net repulsive to strongly attractive electrostatic interactions. The present work provides a systematic computational approach for identifying the origin of diverse PPI, in terms of which sets of amino acids or individual amino acids are most influential, and determining if there are different patterns of pairwise amino acid interaction "maps" that result in different behaviors. The charge was eliminated computationally, one by one, for each charged residue in the wild-type sequences, which resulted in predicted changes in the second osmotic virial coefficient. The results highlight interaction "maps" that correspond to cases with qualitatively different net electrostatic PPI for the different MAbs and solution conditions, as well as key sets of residues that contribute to strongly attractive PPI. A more computationally efficient method is also proposed to identify key amino acids based on Mayer-weighted interaction energies.
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Affiliation(s)
- Glenn M Ferreira
- Department of Chemical and Biomolecular Engineering , University of Delaware , Newark , Delaware 19716 , United States
| | - Hassan Shahfar
- Department of Chemical and Biomolecular Engineering , University of Delaware , Newark , Delaware 19716 , United States.,Department of Physics and Astronomy , University of Delaware , Newark , Delaware 19716 , United States
| | | | | | - Christopher J Roberts
- Department of Chemical and Biomolecular Engineering , University of Delaware , Newark , Delaware 19716 , United States
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47
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Chiu ML, Goulet DR, Teplyakov A, Gilliland GL. Antibody Structure and Function: The Basis for Engineering Therapeutics. Antibodies (Basel) 2019; 8:antib8040055. [PMID: 31816964 PMCID: PMC6963682 DOI: 10.3390/antib8040055] [Citation(s) in RCA: 229] [Impact Index Per Article: 45.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 11/25/2019] [Accepted: 11/28/2019] [Indexed: 12/11/2022] Open
Abstract
Antibodies and antibody-derived macromolecules have established themselves as the mainstay in protein-based therapeutic molecules (biologics). Our knowledge of the structure–function relationships of antibodies provides a platform for protein engineering that has been exploited to generate a wide range of biologics for a host of therapeutic indications. In this review, our basic understanding of the antibody structure is described along with how that knowledge has leveraged the engineering of antibody and antibody-related therapeutics having the appropriate antigen affinity, effector function, and biophysical properties. The platforms examined include the development of antibodies, antibody fragments, bispecific antibody, and antibody fusion products, whose efficacy and manufacturability can be improved via humanization, affinity modulation, and stability enhancement. We also review the design and selection of binding arms, and avidity modulation. Different strategies of preparing bispecific and multispecific molecules for an array of therapeutic applications are included.
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Affiliation(s)
- Mark L. Chiu
- Drug Product Development Science, Janssen Research & Development, LLC, Malvern, PA 19355, USA
- Correspondence:
| | - Dennis R. Goulet
- Department of Medicinal Chemistry, University of Washington, P.O. Box 357610, Seattle, WA 98195-7610, USA;
| | - Alexey Teplyakov
- Biologics Research, Janssen Research & Development, LLC, Spring House, PA 19477, USA; (A.T.); (G.L.G.)
| | - Gary L. Gilliland
- Biologics Research, Janssen Research & Development, LLC, Spring House, PA 19477, USA; (A.T.); (G.L.G.)
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48
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Sjuts H, Schreuder H, Engel CK, Bussemer T, Gokarn Y. Matching pH values for antibody stabilization and crystallization suggest rationale for accelerated development of biotherapeutic drugs. Drug Dev Res 2019; 81:329-337. [PMID: 31758731 DOI: 10.1002/ddr.21624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 11/07/2019] [Accepted: 11/08/2019] [Indexed: 11/11/2022]
Abstract
Monoclonal antibodies (mAbs) are currently leading products in the global biopharmaceutical market. Multiple mAbs are in clinical development and novel biotherapeutic protein scaffolds, based on the canonical immunoglobulin G (IgG) fold, are emerging as treatment options for various medical conditions. However, fast approvals for biotherapeutics are challenging to achieve, because of difficult scientific development procedures and complex regulatory processes. Selecting molecular entities with superior physicochemical properties that proceed into clinical trials and the identification of stable formulations are crucial developability aspects. It is widely accepted that the solution pH has critical influences on both the protein's colloidal stability and its crystallization behavior. Furthermore, proteins usually crystallize best at solution conditions that enable high protein solubility, purity, stability, and monodispersity. Therefore, we hypothesize that the solution pH value is a central parameter that is linking together protein formulation, protein crystallization, and thermal protein stability. In order to experimentally test this hypothesis, we have investigated the effect of the solution pH on the thermal stabilities and crystallizabilities for three different mAbs. Combining biophysical measurements with high throughput protein (HTP) crystallization trials we observed a correlation in the buffer pH values for eminent mAb stability and successful crystallization. Specifically, differential scanning fluorimetry (DSF) was used to determine pH values that exert highest thermal mAb stabilities and additionally led to the identification of unfolding temperatures of individual mAb domains. Independently performed crystallization trials with the same mAbs resulted in their successful crystallization at pH values that displayed highest thermal stabilities. In summary, the presented results suggest a strategy how protein crystallization could be used as a screening method for the development of biotherapeutic protein formulations with improved in vitro stabilities.
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Affiliation(s)
- Hanno Sjuts
- Biologics Research, Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany
| | - Herman Schreuder
- Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany
| | - Christian K Engel
- Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany
| | - Till Bussemer
- Biologics Development, Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany
| | - Yatin Gokarn
- Biologics Development, Sanofi US Services Inc., Framingham, Massachusetts, US
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49
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Singh P, Roche A, van der Walle CF, Uddin S, Du J, Warwicker J, Pluen A, Curtis R. Determination of Protein-Protein Interactions in a Mixture of Two Monoclonal Antibodies. Mol Pharm 2019; 16:4775-4786. [PMID: 31613625 DOI: 10.1021/acs.molpharmaceut.9b00430] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The coformulation of monoclonal antibody (mAb) mixtures provides an attractive route to achieving therapeutic efficacy where the targeting of multiple epitopes is necessary. Controlling and predicting the behavior of such mixtures requires elucidating the molecular basis for the self- and cross-protein-protein interactions and how they depend on solution variables. While self-interactions are now beginning to be well understood, systematic studies of cross-interactions between mAbs in solution do not exist. Here, we have used static light scattering to measure the set of self- and cross-osmotic second virial coefficients in a solution containing a mixture of two mAbs, mAbA and mAbB, as a function of ionic strength and pH. mAbB exhibits strong association at a low ionic strength, which is attributed to an electrostatic attraction that is enhanced by the presence of a strong short-ranged attraction of nonelectrostatic origin. Under all solution conditions, the measured cross-interactions are intermediate self-interactions and follow similar patterns of behavior. There is a strong electrostatic attraction at higher pH values, reflecting the behavior of mAbB. Protein-protein interactions become more attractive with an increasing pH due to reducing the overall protein net charges, an effect that is attenuated with an increasing ionic strength due to the screening of electrostatic interactions. Under moderate ionic strength conditions, the reduced cross-virial coefficient, which reflects only the energetic contribution to protein-protein interactions, is given by a geometric average of the corresponding self-coefficients. We show the relationship can be rationalized using a patchy sphere model, where the interaction energy between sites i and j is given by the arithmetic mean of the i-i and j-j interactions. The geometric mean does not necessarily apply to all mAb mixtures and is expected to break down at a lower ionic strength due to the nonadditivity of electrostatic interactions.
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Affiliation(s)
- Priyanka Singh
- Manchester Pharmacy School , University of Manchester , Manchester M13 9PL , United Kingdom
| | - Aisling Roche
- School of Chemical Engineering and Analytical Science , University of Manchester , Manchester M1 7DN , United Kingdom
| | - Christopher F van der Walle
- School of Chemical Engineering and Analytical Science , University of Manchester , Manchester M1 7DN , United Kingdom.,Dosage Form Design & Development , AstraZeneca , Granta Park , Cambridge CB21 6GH , United Kingdom
| | - Shahid Uddin
- Formulation Sciences CMC , Immunocore , Milton Park , Abingdon OX14 4RW , United Kingdom
| | - Jiali Du
- Dosage Form Design & Development , AstraZeneca , Gaithersburg MD20878 , United States
| | - Jim Warwicker
- School of Chemistry , University of Manchester , Manchester M1 7DN , United Kingdom
| | - Alain Pluen
- Manchester Pharmacy School , University of Manchester , Manchester M13 9PL , United Kingdom
| | - Robin Curtis
- School of Chemical Engineering and Analytical Science , University of Manchester , Manchester M1 7DN , United Kingdom
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50
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Rabia LA, Zhang Y, Ludwig SD, Julian MC, Tessier PM. Net charge of antibody complementarity-determining regions is a key predictor of specificity. Protein Eng Des Sel 2019; 31:409-418. [PMID: 30770934 DOI: 10.1093/protein/gzz002] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 12/23/2018] [Accepted: 01/18/2019] [Indexed: 11/14/2022] Open
Abstract
Specificity is one of the most important and complex properties that is central to both natural antibody function and therapeutic antibody efficacy. However, it has proven extremely challenging to define robust guidelines for predicting antibody specificity. Here we evaluated the physicochemical determinants of antibody specificity for multiple panels of antibodies, including >100 clinical-stage antibodies. Surprisingly, we find that the theoretical net charge of the complementarity-determining regions (CDRs) is a strong predictor of antibody specificity. Antibodies with positively charged CDRs have a much higher risk of low specificity than antibodies with negatively charged CDRs. Moreover, the charge of the entire set of six CDRs is a much better predictor of antibody specificity than the charge of individual CDRs, variable domains (VH or VL) or the entire variable fragment (Fv). The best indicators of antibody specificity in terms of CDR amino acid composition are reduced levels of arginine and lysine and increased levels of aspartic and glutamic acid. Interestingly, clinical-stage antibodies with negatively charged CDRs also have a lower risk for poor biophysical properties in general, including a reduced risk for high levels of self-association. These findings provide powerful guidelines for predicting antibody specificity and for identifying safe and potent antibody therapeutics.
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Affiliation(s)
- Lilia A Rabia
- Isermann Department of Chemical & Biological Engineering, Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA.,Department of Pharmaceutical Sciences.,Department of Chemical Engineering
| | | | - Seth D Ludwig
- Isermann Department of Chemical & Biological Engineering, Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Mark C Julian
- Isermann Department of Chemical & Biological Engineering, Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Peter M Tessier
- Isermann Department of Chemical & Biological Engineering, Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA.,Department of Pharmaceutical Sciences.,Department of Chemical Engineering.,Department of Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
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