1
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Makowski EK, Chen HT, Wang T, Wu L, Huang J, Mock M, Underhill P, Pelegri-O’Day E, Maglalang E, Winters D, Tessier PM. Reduction of monoclonal antibody viscosity using interpretable machine learning. MAbs 2024; 16:2303781. [PMID: 38475982 PMCID: PMC10939158 DOI: 10.1080/19420862.2024.2303781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 01/05/2024] [Indexed: 03/14/2024] Open
Abstract
Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.
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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
| | - Hsin-Ting Chen
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Tiexin Wang
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Lina Wu
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jie Huang
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Marissa Mock
- Therapeutic Discovery, Research, Amgen Inc, Thousand Oaks, CA, USA
| | - Patrick Underhill
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | | | - Erick Maglalang
- Drug Product Technologies, Amgen Inc, Thousand Oaks, CA, USA
| | - Dwight Winters
- Therapeutic Discovery, Research, Amgen Inc, Thousand Oaks, CA, 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, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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2
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Dai J, Izadi S, Zarzar J, Wu P, Oh A, Carter PJ. Variable domain mutational analysis to probe the molecular mechanisms of high viscosity of an IgG 1 antibody. MAbs 2024; 16:2304282. [PMID: 38269489 PMCID: PMC10813588 DOI: 10.1080/19420862.2024.2304282] [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: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 01/26/2024] Open
Abstract
Subcutaneous injection is the preferred route of administration for many antibody therapeutics for reasons that include its speed and convenience. However, the small volume limit (typically ≤ 2 mL) for subcutaneous delivery often necessitates antibody formulations at high concentrations (commonly ≥100 mg/mL), which may lead to physicochemical problems. For example, antibodies with large hydrophobic or charged patches can be prone to self-interaction giving rise to high viscosity. Here, we combined X-ray crystallography with computational modeling to predict regions of an anti-glucagon receptor (GCGR) IgG1 antibody prone to self-interaction. An extensive mutational analysis was undertaken of the complementarity-determining region residues residing in hydrophobic surface patches predicted by spatial aggregation propensity, in conjunction with residue-level solvent accessibility, averaged over conformational ensembles from molecular dynamics simulations. Dynamic light scattering (DLS) was used as a medium throughput screen for self-interaction of ~ 200 anti-GCGR IgG1 variants. A negative correlation was found between the viscosity determined at high concentration (180 mg/mL) and the DLS interaction parameter measured at low concentration (2-10 mg/mL). Additionally, anti-GCGR variants were readily identified with reduced viscosity and antigen-binding affinity within a few fold of the parent antibody, with no identified impact on overall developability. The methods described here may be useful in the optimization of other antibodies to facilitate their therapeutic administration at high concentration.
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Affiliation(s)
- Jing Dai
- Department of Antibody Engineering, 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
| | - Patrick Wu
- Department of Bioanalytical Sciences, Genentech, Inc, South San Francisco, CA, USA
| | - Angela Oh
- Department of Structural Biology, 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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Billups M, Minervini M, Holstein M, Feroz H, Ranjan S, Hung J, Zydney AL. The role of intermolecular interactions on monoclonal antibody filtration through virus removal membranes. Biotechnol J 2023; 18:e2300265. [PMID: 37641433 DOI: 10.1002/biot.202300265] [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: 06/05/2023] [Revised: 07/31/2023] [Accepted: 08/23/2023] [Indexed: 08/31/2023]
Abstract
The removal of viruses by filtration is a critical unit operation to ensure the overall safety of monoclonal antibody (mAb) products. Many mAbs show very low filtrate flux during virus removal filtration, although there are still significant uncertainties regarding both the mechanisms and antibody properties that determine the filtration behavior. Experiments were performed with three highly purified mAbs through three different commercial virus filters (Viresolve Pro, Viresolve NFP, and Pegasus SV4) with different pore structures and chemistries. The flux decline observed during mAb filtration was largely reversible, even under conditions where the filtrate flux with the mAb was more than 100-fold smaller than the corresponding buffer flux. The extent of flux decline was highly correlated with the hydrodynamic diameter of the mAb as determined by dynamic light scattering (DLS). The mAb with the lowest filtrate flux for all three membranes showed the largest attractive intermolecular interactions and the greatest hydrophobicity, with the latter determined by binding to a butyl resin in an analytical hydrophobic interaction chromatography (HIC) column. These results strongly suggest that the flux behavior is dominated by reversible self-association of the mAbs, providing important insights into the design of more effective virus filtration processes and in the early identification of problematic mAbs/solution conditions.
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Affiliation(s)
- Matthew Billups
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Mirko Minervini
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Melissa Holstein
- Bristol Myers Squibb, Biologics Process Development, Global Product Development and Supply, Devens, Massachusetts, USA
| | - Hasin Feroz
- Bristol Myers Squibb, Biologics Process Development, Global Product Development and Supply, Devens, Massachusetts, USA
| | - Swarnim Ranjan
- Bristol Myers Squibb, Biologics Process Development, Global Product Development and Supply, Devens, Massachusetts, USA
| | - Jessica Hung
- Bristol Myers Squibb, Biologics Process Development, Global Product Development and Supply, Devens, Massachusetts, USA
| | - Andrew L Zydney
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA
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4
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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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5
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Rosace A, Bennett A, Oeller M, Mortensen MM, Sakhnini L, Lorenzen N, Poulsen C, Sormanni P. Automated optimisation of solubility and conformational stability of antibodies and proteins. Nat Commun 2023; 14:1937. [PMID: 37024501 PMCID: PMC10079162 DOI: 10.1038/s41467-023-37668-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/24/2023] [Indexed: 04/08/2023] Open
Abstract
Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.
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Affiliation(s)
- Angelo Rosace
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK
- Master in Bioinformatics for Health Sciences, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institute for Research in Biomedicine (IRB), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Anja Bennett
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK
- Department of Mammalian Expression, Global Research Technologies, Novo Nordisk A/S, Novo Nordisk Park 1, 2760, Måløv, Denmark
- BRIC, Faculty of Health and Medical Sciences, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Marc Oeller
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK
| | - Mie M Mortensen
- Department of Purification Technologies, Global Research Technologies, Novo Nordisk A/S, Novo Nordisk Park 1, 2760, Måløv, Denmark
- Faculty of Engineering and Science, Department of Biotechnology, Chemistry and Environmental Engineering, University of Aalborg, Fredrik Bajers Vej 7H, 9220, Aalborg, Denmark
| | - Laila Sakhnini
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK
- Department of Biophysics and Injectable Formulation 2, Global Research Technologies, Novo Nordisk A/S, Måløv, 2760, Denmark
| | - Nikolai Lorenzen
- Department of Biophysics and Injectable Formulation 2, Global Research Technologies, Novo Nordisk A/S, Måløv, 2760, Denmark
| | - Christian Poulsen
- Department of Mammalian Expression, Global Research Technologies, Novo Nordisk A/S, Novo Nordisk Park 1, 2760, Måløv, Denmark
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK.
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6
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Erkamp NA, Oeller M, Sneideris T, Ausserwoger H, Levin A, Welsh TJ, Qi R, Qian D, Lorenzen N, Zhu H, Sormanni P, Vendruscolo M, Knowles TPJ. Multidimensional Protein Solubility Optimization with an Ultrahigh-Throughput Microfluidic Platform. Anal Chem 2023; 95:5362-5368. [PMID: 36930285 PMCID: PMC10061369 DOI: 10.1021/acs.analchem.2c05495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Protein-based biologics are highly suitable for drug development as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to elevated concentrations, making insufficient solubility a critical bottleneck in the drug development pipeline. Here, we report an ultrahigh-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 μg of protein (>10-fold improvement), and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation excipients, such as sodium chloride, polysorbate, histidine, arginine, and sucrose. Additionally, we can measure how solubility is affected by the combinatorial effect of multiple additives, find a suitable pH for the formulation, and measure the impact of mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multidimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.
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Affiliation(s)
- Nadia A Erkamp
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Marc Oeller
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Tomas Sneideris
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Hannes Ausserwoger
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Aviad Levin
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Timothy J Welsh
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Runzhang Qi
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Daoyuan Qian
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Nikolai Lorenzen
- Biophysics and Injectable Formulation, Global Research Technology, Novo Nordisk A/S, 2760 Maaloev, Denmark
| | - Hongjia Zhu
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Pietro Sormanni
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Michele Vendruscolo
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Tuomas P J Knowles
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
- Cavendish Laboratory, Department of Physics, University of Cambridge, J J Thomson Ave, Cambridge CB3 0HE, U.K
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7
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Rodriguez-Aponte SA, Dalvie NC, Wong TY, Johnston RS, Naranjo CA, Bajoria S, Kumru OS, Kaur K, Russ BP, Lee KS, Cyphert HA, Barbier M, Rao HD, Rajurkar MP, Lothe RR, Shaligram US, Batwal S, Chandrasekaran R, Nagar G, Kleanthous H, Biswas S, Bevere JR, Joshi SB, Volkin DB, Damron FH, Love JC. Molecular engineering of a cryptic epitope in Spike RBD improves manufacturability and neutralizing breadth against SARS-CoV-2 variants. Vaccine 2023; 41:1108-1118. [PMID: 36610932 PMCID: PMC9797419 DOI: 10.1016/j.vaccine.2022.12.062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/22/2022] [Accepted: 12/25/2022] [Indexed: 12/30/2022]
Abstract
There is a continued need for sarbecovirus vaccines that can be manufactured and distributed in low- and middle-income countries (LMICs). Subunit protein vaccines are manufactured at large scales at low costs, have less stringent temperature requirements for distribution in LMICs, and several candidates have shown protection against SARS-CoV-2. We previously reported an engineered variant of the SARS-CoV-2 Spike protein receptor binding domain antigen (RBD-L452K-F490W; RBD-J) with enhanced manufacturability and immunogenicity compared to the ancestral RBD. Here, we report a second-generation engineered RBD antigen (RBD-J6) with two additional mutations to a hydrophobic cryptic epitope in the RBD core, S383D and L518D, that further improved expression titers and biophysical stability. RBD-J6 retained binding affinity to human convalescent sera and to all tested neutralizing antibodies except antibodies that target the class IV epitope on the RBD core. K18-hACE2 transgenic mice immunized with three doses of a Beta variant of RBD-J6 displayed on a virus-like particle (VLP) generated neutralizing antibodies (nAb) to nine SARS-CoV-2 variants of concern at similar levels as two doses of Comirnaty. The vaccinated mice were also protected from challenge with Alpha or Beta SARS-CoV-2. This engineered antigen could be useful for modular RBD-based subunit vaccines to enhance manufacturability and global access, or for further development of variant-specific or broadly acting booster vaccines.
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Affiliation(s)
- Sergio A Rodriguez-Aponte
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Neil C Dalvie
- The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ting Y Wong
- Department of Microbiology, Immunology, and Cell Biology, West Virginia University, Morgantown, WV 26506, USA; Vaccine Development Center at West Virginia University Health Sciences Center, Morgantown, WV 26506, USA
| | - Ryan S Johnston
- The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Christopher A Naranjo
- The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sakshi Bajoria
- Department of Pharmaceutical Chemistry, Vaccine Analytics and Formulation Center, University of Kansas, Lawrence, KS 66047, USA
| | - Ozan S Kumru
- Department of Pharmaceutical Chemistry, Vaccine Analytics and Formulation Center, University of Kansas, Lawrence, KS 66047, USA
| | - Kawaljit Kaur
- Department of Pharmaceutical Chemistry, Vaccine Analytics and Formulation Center, University of Kansas, Lawrence, KS 66047, USA
| | - Brynnan P Russ
- Department of Microbiology, Immunology, and Cell Biology, West Virginia University, Morgantown, WV 26506, USA; Vaccine Development Center at West Virginia University Health Sciences Center, Morgantown, WV 26506, USA
| | - Katherine S Lee
- Department of Microbiology, Immunology, and Cell Biology, West Virginia University, Morgantown, WV 26506, USA; Vaccine Development Center at West Virginia University Health Sciences Center, Morgantown, WV 26506, USA
| | - Holly A Cyphert
- Department of Biological Sciences, Marshall University, Huntington, WV 26506, USA
| | - Mariette Barbier
- Department of Microbiology, Immunology, and Cell Biology, West Virginia University, Morgantown, WV 26506, USA; Vaccine Development Center at West Virginia University Health Sciences Center, Morgantown, WV 26506, USA
| | - Harish D Rao
- Serum Institute of India Pvt. Ltd., Pune 411028, India
| | | | | | | | | | | | - Gaurav Nagar
- Serum Institute of India Pvt. Ltd., Pune 411028, India
| | | | - Sumi Biswas
- SpyBiotech Limited, Oxford Business Park North, Oxford OX4 2JZ, UK
| | - Justin R Bevere
- Department of Microbiology, Immunology, and Cell Biology, West Virginia University, Morgantown, WV 26506, USA; Vaccine Development Center at West Virginia University Health Sciences Center, Morgantown, WV 26506, USA
| | - Sangeeta B Joshi
- Department of Pharmaceutical Chemistry, Vaccine Analytics and Formulation Center, University of Kansas, Lawrence, KS 66047, USA
| | - David B Volkin
- Department of Pharmaceutical Chemistry, Vaccine Analytics and Formulation Center, University of Kansas, Lawrence, KS 66047, USA
| | - F Heath Damron
- Department of Microbiology, Immunology, and Cell Biology, West Virginia University, Morgantown, WV 26506, USA; Vaccine Development Center at West Virginia University Health Sciences Center, Morgantown, WV 26506, USA
| | - J Christopher Love
- The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA.
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8
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Bagert JD, Oganesyan V, Chiang CI, Iannotti M, Lin J, Yang C, Payne S, McMahon W, Edwards S, Dippel A, Hutchinson M, Huang F, Aleti V, Niu C, Qian C, Denham J, Ferreira S, Pradhan P, Penney M, Wang C, Liu W, Walseng E, Mazor Y. Robust production of monovalent bispecific IgG antibodies through novel electrostatic steering mutations at the C H1-C λ interface. MAbs 2023; 15:2273449. [PMID: 37930310 PMCID: PMC10629431 DOI: 10.1080/19420862.2023.2273449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 10/17/2023] [Indexed: 11/07/2023] Open
Abstract
Bispecific antibodies represent an increasingly large fraction of biologics in therapeutic development due to their expanded scope in functional capabilities. Asymmetric monovalent bispecific IgGs (bsIgGs) have the additional advantage of maintaining a native antibody-like structure, which can provide favorable pharmacology and pharmacokinetic profiles. The production of correctly assembled asymmetric monovalent bsIgGs, however, is a complex engineering endeavor due to the propensity for non-cognate heavy and light chains to mis-pair. Previously, we introduced the DuetMab platform as a general solution for the production of bsIgGs, which utilizes an engineered interchain disulfide bond in one of the CH1-CL domains to promote orthogonal chain pairing between heavy and light chains. While highly effective in promoting cognate heavy and light chain pairing, residual chain mispairing could be detected for specific combinations of Fv pairs. Here, we present enhancements to the DuetMab design that improve chain pairing and production through the introduction of novel electrostatic steering mutations at the CH1-CL interface with lambda light chains (CH1-Cλ). These mutations work together with previously established charge-pair mutations at the CH1-CL interface with kappa light chains (CH1-Cκ) and Fab disulfide engineering to promote cognate heavy and light chain pairing and enable the reliable production of bsIgGs. Importantly, these enhanced DuetMabs do not require engineering of the variable domains and are robust when applied to a panel of bsIgGs with diverse Fv sequences. We present a comprehensive biochemical, biophysical, and functional characterization of the resulting DuetMabs to demonstrate compatibility with industrial production benchmarks. Overall, this enhanced DuetMab platform substantially streamlines process development of these disruptive biotherapeutics.
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Affiliation(s)
- John D. Bagert
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | | | - Chi-I Chiang
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Mike Iannotti
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Jia Lin
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Chunning Yang
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Sterling Payne
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Will McMahon
- Process and Analytical Sciences, Biopharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Samuel Edwards
- Process and Analytical Sciences, Biopharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Andrew Dippel
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | | | - Fengying Huang
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Vineela Aleti
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Chendi Niu
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Chen Qian
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Jessica Denham
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Sofia Ferreira
- Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Pallab Pradhan
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Mark Penney
- Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Chunlei Wang
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Wenhai Liu
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Even Walseng
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
| | - Yariv Mazor
- Biologics Engineering, AstraZeneca, Gaithersburg, MD, USA
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9
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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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10
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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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11
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Lou H, Hageman MJ. Development of an In Vitro System To Emulate an In Vivo Subcutaneous Environment: Small Molecule Drug Assessment. Mol Pharm 2022; 19:4017-4025. [PMID: 36279508 DOI: 10.1021/acs.molpharmaceut.2c00490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A reliable in vitro system can support and guide the development of subcutaneous (SC) drug products. Although several in vitro systems have been developed, they have some limitations, which may hinder them from getting more engaged in SC drug product development. This study sought to develop a novel in vitro system, namely, Emulator of SubCutaneous Absorption and Release (ESCAR), to better emulate the in vivo SC environment and predict the fate of drugs in SC delivery. ESCAR was designed using computer-aided design (CAD) software and fabricated using the three-dimensional (3D) printing technique. ESCAR has a design of two acceptor chambers representing the blood uptake pathway and the lymphatic uptake pathway, respectively, although only the blood uptake pathway was investigated for small molecules in this study. Via conducting a DoE factor screening study using acetaminophen solution, the relationship of the output (drug release from the "SC" chamber to the "blood circulation" chamber) and the input parameters could be modeled using a variety of methods, including polynomial equations, machine learning methods, and Monte Carlo simulation-based methods. The results suggested that the hyaluronic acid (HA) concentration was a critical parameter, whereas the influence of the injection volume and injection position was not substantial. An in vitro-in vivo correlation (IVIVC) study was developed using griseofulvin suspension to explore the feasibility of applying ESCAR in formulation development and bioequivalence studies. The developed LEVEL A IVIVC model demonstrated that the in vivo PK profile could be correlated with the in vitro release profile. Therefore, using this model, for new formulations, only in vitro studies need to be conducted in ESCAR, and in vivo studies might be waived. In conclusion, ESCAR had important implications for research and development and quality control of SC drug products. Future work would be focused on further optimizing ESCAR and expanding its applications via assessing more types of molecules and formulations.
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Affiliation(s)
- Hao Lou
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas66047, United States
- Biopharmaceutical Innovation and Optimization Center, University of Kansas, Lawrence, Kansas66047, United States
| | - Michael J. Hageman
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas66047, United States
- Biopharmaceutical Innovation and Optimization Center, University of Kansas, Lawrence, Kansas66047, United States
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12
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Spassov VZ, Kemmish H, Yan L. Two physics‐based models for
pH
‐dependent calculations of protein solubility. Protein Sci 2022; 31:e4299. [PMID: 35481654 PMCID: PMC8996476 DOI: 10.1002/pro.4299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 02/01/2022] [Accepted: 02/28/2022] [Indexed: 11/11/2022]
Abstract
When engineering a protein for its biological function, many physicochemical properties are also optimized throughout the engineering process, and the protein's solubility is among the most important properties to consider. Here, we report two novel computational methods to calculate the pH-dependent protein solubility, and to rank the solubility of mutants. The first is an empirical method developed for fast ranking of the solubility of a large number of mutants of a protein. It takes into account electrostatic solvation energy term calculated using Generalized Born approximation, hydrophobic patches, protein charge, and charge asymmetry, as well as the changes of protein stability upon mutation. This method has been tested on over 100 mutations for 17 globular proteins, as well as on 44 variants of five different antibodies. The prediction rate is over 80%. The antibody tests showed a Pearson correlation coefficient, R, with experimental data from .83 to .91. The second method is based on a novel, completely force-field-based approach using CHARMm program modules to calculate the binding energy of the protein to a part of the crystal lattice, generated from X-ray structure. The method predicted with very high accuracy the solubility of Ribonuclease SA and its 3K and 5K mutants as a function of pH without any parameter adjustments of the existing BIOVIA Discovery Studio binding affinity model. Our methods can be used for rapid screening of large numbers of design candidates based on solubility, and to guide the design of solution conditions for antibody formulation.
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Affiliation(s)
- Velin Z. Spassov
- BIOVIA Dassault Systemes, 5005 Wateridge Vista Drive San Diego California USA
| | - Helen Kemmish
- BIOVIA Dassault Systemes, 5005 Wateridge Vista Drive San Diego California USA
| | - Lisa Yan
- BIOVIA Dassault Systemes, 5005 Wateridge Vista Drive San Diego California USA
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13
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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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14
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Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2313:57-113. [PMID: 34478132 DOI: 10.1007/978-1-0716-1450-1_4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.
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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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Dingfelder F, Henriksen A, Wahlund PO, Arosio P, Lorenzen N. Measuring Self-Association of Antibody Lead Candidates with Dynamic Light Scattering. Methods Mol Biol 2022; 2313:241-258. [PMID: 34478142 DOI: 10.1007/978-1-0716-1450-1_14] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
In this method chapter, we provide a brief overview of the key methods available to measure self-association of monoclonal antibodies (mAbs) and explain for which experimental throughputs they are usually applied. We then focus on dynamic light scattering (DLS) and describe experimental details on how to measure the diffusion interaction parameter (kD) which is occasionally referred to as the gold standard for measuring self-association of proteins. The kD is a well-established parameter to predict solution viscosity, which is one of the most critical developability parameters of mAbs. Finally, we present a pH and excipient screen that is designed to measure self-association with DLS under conditions that are relevant for bioprocessing and formulation of mAbs. The presented light scattering methods are well suited for lead candidate selections where it is essential to select mAbs with high developability potential for progression toward first human dose.
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Affiliation(s)
- Fabian Dingfelder
- Department of Biophysics and Injectable Formulation 2, Global Research Technologies, Novo Nordisk A/S, Måløv, Denmark.
| | - Anette Henriksen
- Department of Modelling and Predictive Technologies, Global Research Technologies, Novo Nordisk A/S, Måløv, Denmark
| | - Per-Olof Wahlund
- Department of Biophysics and Injectable Formulation 2, Global Research Technologies, Novo Nordisk A/S, Måløv, Denmark
| | - Paolo Arosio
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Nikolai Lorenzen
- Department of Biophysics and Injectable Formulation 2, Global Research Technologies, Novo Nordisk A/S, Måløv, Denmark.
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17
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Makowski EK, Schardt JS, Tessier PM. Improving antibody drug development using bionanotechnology. Curr Opin Biotechnol 2021; 74:137-145. [PMID: 34890875 DOI: 10.1016/j.copbio.2021.10.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 10/25/2021] [Accepted: 10/31/2021] [Indexed: 12/20/2022]
Abstract
Monoclonal antibodies are being used to treat a remarkable breadth of human disorders. Nevertheless, there are several key challenges at the earliest stages of antibody drug development that need to be addressed using simple and widely accessible methods, especially related to generating antibodies against membrane proteins and identifying antibody candidates with drug-like biophysical properties (high solubility and low viscosity). Here we highlight key bionanotechnologies for preparing functional and stable membrane proteins in diverse types of lipoparticles that are being used to improve antibody discovery and engineering efforts. We also highlight key bionanotechnologies for high-throughput and ultra-dilute screening of antibody biophysical properties during antibody discovery and optimization that are being used for identifying antibodies with superior combinations of in vitro (formulation) and in vivo (half-life) properties.
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Affiliation(s)
- Emily K Makowski
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - John S Schardt
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter M Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Departmant of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA.
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18
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Oeller M, Sormanni P, Vendruscolo M. An open-source automated PEG precipitation assay to measure the relative solubility of proteins with low material requirement. Sci Rep 2021; 11:21932. [PMID: 34753962 PMCID: PMC8578320 DOI: 10.1038/s41598-021-01126-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/18/2021] [Indexed: 02/02/2023] Open
Abstract
The solubility of proteins correlates with a variety of their properties, including function, production yield, pharmacokinetics, and formulation at high concentrations. High solubility is therefore a key requirement for the development of protein-based reagents for applications in life sciences, biotechnology, diagnostics, and therapeutics. Accurate solubility measurements, however, remain challenging and resource intensive, which limits their throughput and hence their applicability at the early stages of development pipelines, when long-lists of candidates are typically available in minute amounts. Here, we present an automated method based on the titration of a crowding agent (polyethylene glycol, PEG) to quantitatively assess relative solubility of proteins using about 200 µg of purified material. Our results demonstrate that this method is accurate and economical in material requirement and costs of reagents, which makes it suitable for high-throughput screening. This approach is freely-shared and based on a low cost, open-source liquid-handling robot. We anticipate that this method will facilitate the assessment of the developability of proteins and make it substantially more accessible.
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Affiliation(s)
- Marc Oeller
- grid.5335.00000000121885934Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK
| | - Pietro Sormanni
- Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK.
| | - Michele Vendruscolo
- Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK.
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19
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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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20
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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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21
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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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22
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Berner C, Menzen T, Winter G, Svilenov HL. Combining Unfolding Reversibility Studies and Molecular Dynamics Simulations to Select Aggregation-Resistant Antibodies. Mol Pharm 2021; 18:2242-2253. [PMID: 33928776 DOI: 10.1021/acs.molpharmaceut.1c00017] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The efficient development of new therapeutic antibodies relies on developability assessment with biophysical and computational methods to find molecules with drug-like properties such as resistance to aggregation. Despite the many novel approaches to select well-behaved proteins, antibody aggregation during storage is still challenging to predict. For this reason, there is a high demand for methods that can identify aggregation-resistant antibodies. Here, we show that three straightforward techniques can select the aggregation-resistant antibodies from a dataset with 13 molecules. The ReFOLD assay provided information about the ability of the antibodies to refold to monomers after unfolding with chemical denaturants. Modulated scanning fluorimetry (MSF) yielded the temperatures that start causing irreversible unfolding of the proteins. Aggregation was the main reason for poor unfolding reversibility in both ReFOLD and MSF experiments. We therefore performed temperature ramps in molecular dynamics (MD) simulations to obtain partially unfolded antibody domains in silico and used CamSol to assess their aggregation potential. We compared the information from ReFOLD, MSF, and MD to size-exclusion chromatography (SEC) data that shows whether the antibodies aggregated during storage at 4, 25, and 40 °C. Contrary to the aggregation-prone molecules, the antibodies that were resistant to aggregation during storage at 40 °C shared three common features: (i) higher tendency to refold to monomers after unfolding with chemical denaturants, (ii) higher onset temperature of nonreversible unfolding, and (iii) unfolding of regions containing aggregation-prone sequences at higher temperatures in MD simulations.
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Affiliation(s)
- Carolin Berner
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstr. 5, 81377 Munich, Germany
| | - Tim Menzen
- Coriolis Pharma Research GmbH, Fraunhoferstr. 18 b, 82152 Martinsried, Germany
| | - Gerhard Winter
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstr. 5, 81377 Munich, Germany
| | - Hristo L Svilenov
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstr. 5, 81377 Munich, Germany
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23
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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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24
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Mieczkowski C, Cheng A, Fischmann T, Hsieh M, Baker J, Uchida M, Raghunathan G, Strickland C, Fayadat-Dilman L. Characterization and Modeling of Reversible Antibody Self-Association Provide Insights into Behavior, Prediction, and Correction. Antibodies (Basel) 2021; 10:antib10010008. [PMID: 33671864 PMCID: PMC7931086 DOI: 10.3390/antib10010008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 12/24/2020] [Accepted: 02/01/2021] [Indexed: 12/20/2022] Open
Abstract
Reversible antibody self-association, while having major developability and therapeutic implications, is not fully understood or readily predictable and correctable. For a strongly self-associating humanized mAb variant, resulting in unacceptable viscosity, the monovalent affinity of self-interaction was measured in the low μM range, typical of many specific and biologically relevant protein-protein interactions. A face-to-face interaction model extending across both the heavy-chain (HC) and light-chain (LC) Complementary Determining Regions (CDRs) was apparent from biochemical and mutagenesis approaches as well as computational modeling. Light scattering experiments involving individual mAb, Fc, Fab, and Fab'2 domains revealed that Fabs self-interact to form dimers, while bivalent mAb/Fab'2 forms lead to significant oligomerization. Site-directed mutagenesis of aromatic residues identified by homology model patch analysis and self-docking dramatically affected self-association, demonstrating the utility of these predictive approaches, while revealing a highly specific and tunable nature of self-binding modulated by single point mutations. Mutagenesis at these same key HC/LC CDR positions that affect self-interaction also typically abolished target binding with notable exceptions, clearly demonstrating the difficulties yet possibility of correcting self-association through engineering. Clear correlations were also observed between different methods used to assess self-interaction, such as Dynamic Light Scattering (DLS) and Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS). Our findings advance our understanding of therapeutic protein and antibody self-association and offer insights into its prediction, evaluation and corrective mitigation to aid therapeutic development.
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Affiliation(s)
- Carl Mieczkowski
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, CA 94080, USA; (C.M.); (M.H.); (J.B.); (M.U.); (G.R.); (L.F.-D.)
| | - Alan Cheng
- Discovery Chemistry, Modeling and Informatics, Merck & Co., Inc., South San Francisco, CA 94080, USA
- Correspondence: ; Tel.: +1-650-496-4834
| | - Thierry Fischmann
- Department of Chemistry, Modeling and Informatics, Merck & Co., Inc., Kenilworth, NJ 07033, USA; (T.F.); (C.S.)
| | - Mark Hsieh
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, CA 94080, USA; (C.M.); (M.H.); (J.B.); (M.U.); (G.R.); (L.F.-D.)
| | - Jeanne Baker
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, CA 94080, USA; (C.M.); (M.H.); (J.B.); (M.U.); (G.R.); (L.F.-D.)
| | - Makiko Uchida
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, CA 94080, USA; (C.M.); (M.H.); (J.B.); (M.U.); (G.R.); (L.F.-D.)
| | - Gopalan Raghunathan
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, CA 94080, USA; (C.M.); (M.H.); (J.B.); (M.U.); (G.R.); (L.F.-D.)
| | - Corey Strickland
- Department of Chemistry, Modeling and Informatics, Merck & Co., Inc., Kenilworth, NJ 07033, USA; (T.F.); (C.S.)
| | - Laurence Fayadat-Dilman
- Discovery Biologics, Protein Sciences, Merck & Co., Inc., South San Francisco, CA 94080, USA; (C.M.); (M.H.); (J.B.); (M.U.); (G.R.); (L.F.-D.)
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25
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Bailly M, Mieczkowski C, Juan V, Metwally E, Tomazela D, Baker J, Uchida M, Kofman E, Raoufi F, Motlagh S, Yu Y, Park J, Raghava S, Welsh J, Rauscher M, Raghunathan G, Hsieh M, Chen YL, Nguyen HT, Nguyen N, Cipriano D, Fayadat-Dilman L. Predicting Antibody Developability Profiles Through Early Stage Discovery Screening. MAbs 2021; 12:1743053. [PMID: 32249670 PMCID: PMC7153844 DOI: 10.1080/19420862.2020.1743053] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Monoclonal antibodies play an increasingly important role for the development of new drugs across multiple therapy areas. The term 'developability' encompasses the feasibility of molecules to successfully progress from discovery to development via evaluation of their physicochemical properties. These properties include the tendency for self-interaction and aggregation, thermal stability, colloidal stability, and optimization of their properties through sequence engineering. Selection of the best antibody molecule based on biological function, efficacy, safety, and developability allows for a streamlined and successful CMC phase. An efficient and practical high-throughput developability workflow (100 s-1,000 s of molecules) implemented during early antibody generation and screening is crucial to select the best lead candidates. This involves careful assessment of critical developability parameters, combined with binding affinity and biological properties evaluation using small amounts of purified material (<1 mg), as well as an efficient data management and database system. Herein, a panel of 152 various human or humanized monoclonal antibodies was analyzed in biophysical property assays. Correlations between assays for different sets of properties were established. We demonstrated in two case studies that physicochemical properties and key assay endpoints correlate with key downstream process parameters. The workflow allows the elimination of antibodies with suboptimal properties and a rank ordering of molecules for further evaluation early in the candidate selection process. This enables any further engineering for problematic sequence attributes without affecting program timelines.
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Affiliation(s)
- Marc Bailly
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Carl Mieczkowski
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Veronica Juan
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Essam Metwally
- Computation and Structural Chemistry, South San Francisco, CA, USA
| | - Daniela Tomazela
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Jeanne Baker
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Makiko Uchida
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Ester Kofman
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Fahimeh Raoufi
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Soha Motlagh
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Yao Yu
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Jihea Park
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Smita Raghava
- Pharmaceutical Sciences, Sterile FormulationSciences, Kenilworth, NJ, USA
| | - John Welsh
- Downstream Process Development andEngineering, Kenilworth, NJ, USA
| | - Michael Rauscher
- Downstream Process Development andEngineering, Kenilworth, NJ, USA
| | | | - Mark Hsieh
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Yi-Ling Chen
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Hang Thu Nguyen
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Nhung Nguyen
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
| | - Dan Cipriano
- Discovery Biologics, Protein Sciences, South San Francisco, CA, USA
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26
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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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27
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Makowski EK, Wu L, Desai AA, Tessier PM. Highly sensitive detection of antibody nonspecific interactions using flow cytometry. MAbs 2021; 13:1951426. [PMID: 34313552 PMCID: PMC8317921 DOI: 10.1080/19420862.2021.1951426] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 06/29/2021] [Accepted: 06/30/2021] [Indexed: 11/12/2022] Open
Abstract
The rapidly evolving nature of antibody drug development has resulted in technologies that generate vast numbers (hundreds to thousands) of lead antibody candidates during early discovery. These candidates must be rapidly pared down to identify the most drug-like candidates for in-depth analysis of their safety and efficacy, which can only be performed on a limited number of antibodies due to time and resource requirements. One key biophysical property of successful antibody therapeutics is high specificity, defined as low levels of nonspecific binding or polyspecificity. Although there has been some progress in developing assays for detecting antibody polyspecificity, most of these assays are limited by poor sensitivity or assay formats that require proprietary antibody surface display methods, and some of these assays use complex and poorly defined polyspecificity reagents. Here we report the PolySpecificity Particle (PSP) assay, a sensitive flow cytometry assay for evaluating antibody nonspecific interactions that overcomes previous limitations and can be used for evaluating diverse types of IgGs, multispecific antibodies and Fc-fusion proteins. Our approach uses micron-sized magnetic beads coated with Protein A to capture antibodies at extremely dilute concentrations (<0.02 mg/mL). Flow cytometry analysis of polyspecificity reagent binding to these conjugates results in sensitive detection of differences in nonspecific interactions for clinical-stage antibodies. Our PSP assay strongly discriminates between antibodies with different levels of polyspecificity using previously reported polyspecificity reagents that are either well-defined proteins or highly complex protein mixtures. Moreover, we also find that a unique reagent, namely ovalbumin, results in the best assay sensitivity and specificity. Importantly, our assay is much more sensitive than standard assays such as ELISAs. We expect that our simple, sensitive, and high-throughput PSP assay will accelerate the development of safe and effective antibody therapeutics.
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Affiliation(s)
| | - Lina Wu
- Department of Chemical Engineering, University of Michigan
| | - Alec A. Desai
- Department of Chemical Engineering, University of Michigan
| | - Peter M. Tessier
- Department of Pharmaceutical Sciences, University of Michigan
- Department of Chemical Engineering, University of Michigan
- Department of Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, USA
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28
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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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29
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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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30
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Lobo SA, Bączyk P, Wyss B, Widmer JC, Jesus LP, Gomes J, Batista AP, Hartmann S, Wassmann P. Stability liabilities of biotherapeutic proteins: Early assessment as mitigation strategy. J Pharm Biomed Anal 2020; 192:113650. [PMID: 33065403 DOI: 10.1016/j.jpba.2020.113650] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 09/13/2020] [Accepted: 09/15/2020] [Indexed: 12/15/2022]
Abstract
Identification of molecular liabilities and implementation of mitigation strategies are key aspects that need to be considered by pharmaceutical companies developing therapeutic proteins. In the field of monoclonal antibodies, an efficient and streamlined process known as developability assessment is used for the selection of the "fittest" candidate. Other protein modalities, have in most cases only a limited number of possible candidates, requiring a paradigm change to a concept of candidate enabling. The assessment of liabilities at early project phases with the possibility to re-engineer candidates becomes essential for the success of these projects. Each protein possesses a unique stability profile resulting from the interplay of conformational, colloidal, chemical and physical stability attributes. All of these attributes strongly depend on external factors. Conformational and colloidal stability profiles of three non-immunoglobulin domain based proteins, namely Carbonic anhydrase, Ovalbumin and Thyroglobulin, and of two monoclonal antibodies were assessed in dependence of solution pH, ionic strength and varying buffering agents. The impact of screened external factors on proteins' stability attributes varied significantly, indicating presence of molecule specific liabilities. Screening of such a broad space of conditions at early project phases is only feasible using low-material consuming, high-throughput analytical methods as exemplified in this study.
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Affiliation(s)
- Susana A Lobo
- Instituto de Biologia Experimental e Tecnológica, Apartado 12, 2781-901 Oeiras, Portugal
| | | | | | | | - Lídia P Jesus
- Instituto de Biologia Experimental e Tecnológica, Apartado 12, 2781-901 Oeiras, Portugal
| | - Joana Gomes
- Instituto de Biologia Experimental e Tecnológica, Apartado 12, 2781-901 Oeiras, Portugal
| | - Ana P Batista
- Instituto de Biologia Experimental e Tecnológica, Apartado 12, 2781-901 Oeiras, Portugal
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31
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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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32
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Gentiluomo L, Svilenov HL, Augustijn D, El Bialy I, Greco ML, Kulakova A, Indrakumar S, Mahapatra S, Morales MM, Pohl C, Roche A, Tosstorff A, Curtis R, Derrick JP, Nørgaard A, Khan TA, Peters GHJ, Pluen A, Rinnan Å, Streicher WW, van der Walle CF, Uddin S, Winter G, Roessner D, Harris P, Frieß W. Advancing Therapeutic Protein Discovery and Development through Comprehensive Computational and Biophysical Characterization. Mol Pharm 2020; 17:426-440. [PMID: 31790599 DOI: 10.1021/acs.molpharmaceut.9b00852] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Therapeutic protein candidates should exhibit favorable properties that render them suitable to become drugs. Nevertheless, there are no well-established guidelines for the efficient selection of proteinaceous molecules with desired features during early stage development. Such guidelines can emerge only from a large body of published research that employs orthogonal techniques to characterize therapeutic proteins in different formulations. In this work, we share a study on a diverse group of proteins, including their primary sequences, purity data, and computational and biophysical characterization at different pH and ionic strength. We report weak linear correlations between many of the biophysical parameters. We suggest that a stability comparison of diverse therapeutic protein candidates should be based on a computational and biophysical characterization in multiple formulation conditions, as the latter can largely determine whether a protein is above or below a certain stability threshold. We use the presented data set to calculate several stability risk scores obtained with an increasing level of analytical effort and show how they correlate with protein aggregation during storage. Our work highlights the importance of developing combined risk scores that can be used for early stage developability assessment. We suggest that such scores can have high prediction accuracy only when they are based on protein stability characterization in different solution conditions.
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Affiliation(s)
- Lorenzo Gentiluomo
- Wyatt Technology Europe GmbH , Hochstrasse 18 , 56307 Dernbach , Germany.,Department of Pharmacy, Pharmaceutical Technology and Biopharmaceutics , Ludwig-Maximilians-Universitaet Muenchen , Butenandtstrasse 5 , 81377 Munich , Germany
| | - Hristo L Svilenov
- Department of Pharmacy, Pharmaceutical Technology and Biopharmaceutics , Ludwig-Maximilians-Universitaet Muenchen , Butenandtstrasse 5 , 81377 Munich , Germany
| | - Dillen Augustijn
- Department of Food Science, Faculty of Science , Copenhagen University , Rolighedsvej 26 , 1958 Frederiksberg , Denmark
| | - Inas El Bialy
- Department of Pharmacy, Pharmaceutical Technology and Biopharmaceutics , Ludwig-Maximilians-Universitaet Muenchen , Butenandtstrasse 5 , 81377 Munich , Germany
| | - Maria Laura Greco
- Dosage Form Design and Development , AstraZeneca , Sir Aaron Klug Building, Granta Park , Cambridge CB21 6GH , U.K
| | - Alina Kulakova
- Department of Chemistry , Technical University of Denmark , Kemitorvet 207 , 2800 Kongens Lyngby , Denmark
| | - Sowmya Indrakumar
- Department of Chemistry , Technical University of Denmark , Kemitorvet 207 , 2800 Kongens Lyngby , Denmark
| | | | - Marcello Martinez Morales
- Dosage Form Design and Development , AstraZeneca , Sir Aaron Klug Building, Granta Park , Cambridge CB21 6GH , U.K
| | - Christin Pohl
- Novozymes A/S , Krogshoejvej 36 , 2880 Bagsvaerd , Denmark
| | - Aisling Roche
- School of Chemical Engineering and Analytical Science, Manchester Institute of Biotechnology , The University of Manchester , 131 Princess Street , Manchester M1 7DN , U.K
| | - Andreas Tosstorff
- Department of Pharmacy, Pharmaceutical Technology and Biopharmaceutics , Ludwig-Maximilians-Universitaet Muenchen , Butenandtstrasse 5 , 81377 Munich , Germany
| | - Robin Curtis
- School of Chemical Engineering and Analytical Science, Manchester Institute of Biotechnology , The University of Manchester , 131 Princess Street , Manchester M1 7DN , U.K
| | - Jeremy P Derrick
- School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre , The University of Manchester , Oxford Road , Manchester M13 9PT , U.K
| | - Allan Nørgaard
- Novozymes A/S , Krogshoejvej 36 , 2880 Bagsvaerd , Denmark
| | - Tarik A Khan
- Pharmaceutical Development & Supplies, Pharma Technical Development Biologics Europe , F. Hoffmann-La Roche Ltd. , Grenzacherstrasse 124 , 4070 Basel , Switzerland
| | - Günther H J Peters
- Department of Chemistry , Technical University of Denmark , Kemitorvet 207 , 2800 Kongens Lyngby , Denmark
| | - Alain Pluen
- School of Chemical Engineering and Analytical Science, Manchester Institute of Biotechnology , The University of Manchester , 131 Princess Street , Manchester M1 7DN , U.K
| | - Åsmund Rinnan
- Department of Food Science, Faculty of Science , Copenhagen University , Rolighedsvej 26 , 1958 Frederiksberg , Denmark
| | | | - Christopher F van der Walle
- Dosage Form Design and Development , AstraZeneca , Sir Aaron Klug Building, Granta Park , Cambridge CB21 6GH , U.K
| | - Shahid Uddin
- Dosage Form Design and Development , AstraZeneca , Sir Aaron Klug Building, Granta Park , Cambridge CB21 6GH , U.K
| | - Gerhard Winter
- Department of Pharmacy, Pharmaceutical Technology and Biopharmaceutics , Ludwig-Maximilians-Universitaet Muenchen , Butenandtstrasse 5 , 81377 Munich , Germany
| | - Dierk Roessner
- Wyatt Technology Europe GmbH , Hochstrasse 18 , 56307 Dernbach , Germany
| | - Pernille Harris
- Department of Chemistry , Technical University of Denmark , Kemitorvet 207 , 2800 Kongens Lyngby , Denmark
| | - Wolfgang Frieß
- Department of Pharmacy, Pharmaceutical Technology and Biopharmaceutics , Ludwig-Maximilians-Universitaet Muenchen , Butenandtstrasse 5 , 81377 Munich , Germany
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33
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Hu Y, Arora J, Joshi SB, Esfandiary R, Middaugh CR, Weis DD, Volkin DB. Characterization of Excipient Effects on Reversible Self-Association, Backbone Flexibility, and Solution Properties of an IgG1 Monoclonal Antibody at High Concentrations: Part 1. J Pharm Sci 2020; 109:340-352. [DOI: 10.1016/j.xphs.2019.06.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 05/13/2019] [Accepted: 06/04/2019] [Indexed: 12/21/2022]
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34
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Hebditch M, Warwicker J. Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies. PeerJ 2019; 7:e8199. [PMID: 31976163 PMCID: PMC6967001 DOI: 10.7717/peerj.8199] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 11/13/2019] [Indexed: 01/05/2023] Open
Abstract
Improved understanding of properties that mediate protein solubility and resistance to aggregation are important for developing biopharmaceuticals, and more generally in biotechnology and synthetic biology. Recent acquisition of large datasets for antibody biophysical properties enables the search for predictive models. In this report, machine learning methods are used to derive models for 12 biophysical properties. A physicochemical perspective is maintained in analysing the models, leading to the observation that models cluster largely according to charge (cross-interaction measurements) and hydrophobicity (self-interaction methods). These two properties also overlap in some cases, for example in a new interpretation of variation in hydrophobic interaction chromatography. Since the models are developed from differences of antibody variable loops, the next stage is to extend models to more diverse protein sets. AVAILABILITY The web application for the sequence-based algorithms are available on the protein-sol webserver, at https://protein-sol.manchester.ac.uk/abpred, with models and virtualisation software available at https://protein-sol.manchester.ac.uk/software.
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Affiliation(s)
- Max Hebditch
- School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom
| | - Jim Warwicker
- School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom
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35
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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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36
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Sormanni P, Vendruscolo M. Protein Solubility Predictions Using the CamSol Method in the Study of Protein Homeostasis. Cold Spring Harb Perspect Biol 2019; 11:cshperspect.a033845. [PMID: 30833455 DOI: 10.1101/cshperspect.a033845] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
One of the major functions of the protein homeostasis system is to maintain proteins in their soluble states, and indeed several human disorders are associated with the aberrant aggregation of proteins. An active involvement of the protein homeostasis system is necessary to avoid aggregation because proteins are expressed at levels close to their solubility limits, hence being poorly soluble. The mechanisms by which the protein homeostasis system acts to control protein aggregation are, however, still not known in much detail. To facilitate systematic investigations of these mechanisms, we describe here the CamSol method of predicting protein solubility, and illustrate its initial applications. We anticipate that with the advent of powerful proteomics and transcriptomic methods, in combination with the use of the CamSol method and related approaches to predict the solubility and other biophysical properties of proteins, it will become possible to increase our understanding of the principles of protein homeostasis related to the maintenance of the proteome in its soluble form.
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Affiliation(s)
- Pietro Sormanni
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
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37
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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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38
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Sabetian S, Shamsir MS. Computer aided analysis of disease linked protein networks. Bioinformation 2019; 15:513-522. [PMID: 31485137 PMCID: PMC6704336 DOI: 10.6026/97320630015513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 04/16/2019] [Accepted: 04/17/2019] [Indexed: 12/26/2022] Open
Abstract
Proteins can interact in various ways, ranging from direct physical relationships to indirect interactions in a formation of protein-protein
interaction network. Diagnosis of the protein connections is critical to identify various cellular pathways. Today constructing and
analyzing the protein interaction network is being developed as a powerful approach to create network pharmacology toward detecting
unknown genes and proteins associated with diseases. Discovery drug targets regarding therapeutic decisions are exciting outcomes of
studying disease networks. Protein connections may be identified by experimental and recent new computational approaches. Due to
difficulties in analyzing in-vivo proteins interactions, many researchers have encouraged improving computational methods to design
protein interaction network. In this review, the experimental and computational approaches and also advantages and disadvantages of
these methods regarding the identification of new interactions in a molecular mechanism have been reviewed. Systematic analysis of
complex biological systems including network pharmacology and disease network has also been discussed in this review.
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Affiliation(s)
- Soudabeh Sabetian
- Department of Biological and Health Sciences, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia.,Infertility Research Center, Shiraz University, Shiraz 71454, Iran, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohd Shahir Shamsir
- Department of Biological and Health Sciences, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia
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39
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Schrag JD, Picard MÈ, Gaudreault F, Gagnon LP, Baardsnes J, Manenda MS, Sheff J, Deprez C, Baptista C, Hogues H, Kelly JF, Purisima EO, Shi R, Sulea T. Binding symmetry and surface flexibility mediate antibody self-association. MAbs 2019; 11:1300-1318. [PMID: 31318308 PMCID: PMC6748613 DOI: 10.1080/19420862.2019.1632114] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Solution stability is an important factor in the optimization of engineered biotherapeutic candidates such as monoclonal antibodies because of its possible effects on manufacturability, pharmacology, efficacy and safety. A detailed atomic understanding of the mechanisms governing self-association of natively folded protein monomers is required to devise predictive tools to guide screening and re-engineering along the drug development pipeline. We investigated pairs of affinity-matured full-size antibodies and observed drastically different propensities to aggregate from variants differing by a single amino-acid. Biophysical testing showed that antigen-binding fragments (Fabs) from the aggregating antibodies also reversibly associated with equilibrium dissociation constants in the low-micromolar range. Crystal structures (PDB accession codes 6MXR, 6MXS, 6MY4, 6MY5) and bottom-up hydrogen-exchange mass spectrometry revealed that Fab self-association occurs in a symmetric mode that involves the antigen complementarity-determining regions. Subtle local conformational changes incurred upon point mutation of monomeric variants foster formation of complementary polar interactions and hydrophobic contacts to generate a dimeric Fab interface. Testing of popular in silico tools generally indicated low reliabilities for predicting the aggregation propensities observed. A structure-aggregation data set is provided here in order to stimulate further improvements of in silico tools for prediction of native aggregation. Incorporation of intermolecular docking, conformational flexibility, and short-range packing interactions may all be necessary features of the ideal algorithm.
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Affiliation(s)
- Joseph D Schrag
- Human Health Therapeutics Research Centre, National Research Council Canada , Montreal , QC H4P 2R2 , Canada
| | - Marie-Ève Picard
- Département de Biochimie, de Microbiologie et de Bio-informatique, PROTEO, and Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Pavillon Charles-Eugène-Marchand , Québec City, QC G1V 0A6 , Canada
| | - Francis Gaudreault
- Human Health Therapeutics Research Centre, National Research Council Canada , Montreal , QC H4P 2R2 , Canada
| | - Louis-Patrick Gagnon
- Human Health Therapeutics Research Centre, National Research Council Canada , Montreal , QC H4P 2R2 , Canada
| | - Jason Baardsnes
- Human Health Therapeutics Research Centre, National Research Council Canada , Montreal , QC H4P 2R2 , Canada
| | - Mahder S Manenda
- Département de Biochimie, de Microbiologie et de Bio-informatique, PROTEO, and Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Pavillon Charles-Eugène-Marchand , Québec City, QC G1V 0A6 , Canada
| | - Joey Sheff
- Human Health Therapeutics Research Centre, National Research Council Canada , Ottawa , ON K1A 0R6 , Canada
| | - Christophe Deprez
- Human Health Therapeutics Research Centre, National Research Council Canada , Montreal , QC H4P 2R2 , Canada
| | - Cassio Baptista
- Human Health Therapeutics Research Centre, National Research Council Canada , Montreal , QC H4P 2R2 , Canada
| | - Hervé Hogues
- Human Health Therapeutics Research Centre, National Research Council Canada , Montreal , QC H4P 2R2 , Canada
| | - John F Kelly
- Human Health Therapeutics Research Centre, National Research Council Canada , Ottawa , ON K1A 0R6 , Canada
| | - Enrico O Purisima
- Human Health Therapeutics Research Centre, National Research Council Canada , Montreal , QC H4P 2R2 , Canada
| | - Rong Shi
- Département de Biochimie, de Microbiologie et de Bio-informatique, PROTEO, and Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Pavillon Charles-Eugène-Marchand , Québec City, QC G1V 0A6 , Canada
| | - Traian Sulea
- Human Health Therapeutics Research Centre, National Research Council Canada , Montreal , QC H4P 2R2 , Canada
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40
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Wang W, Ohtake S. Science and art of protein formulation development. Int J Pharm 2019; 568:118505. [PMID: 31306712 DOI: 10.1016/j.ijpharm.2019.118505] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 07/08/2019] [Accepted: 07/08/2019] [Indexed: 02/07/2023]
Abstract
Protein pharmaceuticals have become a significant class of marketed drug products and are expected to grow steadily over the next decade. Development of a commercial protein product is, however, a rather complex process. A critical step in this process is formulation development, enabling the final product configuration. A number of challenges still exist in the formulation development process. This review is intended to discuss these challenges, to illustrate the basic formulation development processes, and to compare the options and strategies in practical formulation development.
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Affiliation(s)
- Wei Wang
- Biological Development, Bayer USA, LLC, 800 Dwight Way, Berkeley, CA 94710, United States.
| | - Satoshi Ohtake
- Pharmaceutical Research and Development, Pfizer Biotherapeutics Pharmaceutical Sciences, Chesterfield, MO 63017, United States
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41
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Navarro S, Ventura S. Computational re-design of protein structures to improve solubility. Expert Opin Drug Discov 2019; 14:1077-1088. [DOI: 10.1080/17460441.2019.1637413] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Susanna Navarro
- Institut de Biotecnologia i de Biomedicina, Parc de Recerca UAB, Mòdul B, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Salvador Ventura
- Institut de Biotecnologia i de Biomedicina, Parc de Recerca UAB, Mòdul B, Universitat Autònoma de Barcelona, Barcelona, Spain
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