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Wittkopp F, Welsh J, Todd R, Staby A, Roush D, Lyall J, Karkov S, Hunt S, Griesbach J, Bertran MO, Babi D. Current state of implementation of in silico tools in the biopharmaceutical industry-Proceedings of the 5th modeling workshop. Biotechnol Bioeng 2024; 121:2952-2973. [PMID: 38853778 DOI: 10.1002/bit.28768] [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: 03/20/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 06/11/2024]
Abstract
The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICH Q8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for "mechanistic modeling." Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community.
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Affiliation(s)
- Felix Wittkopp
- Roche Diagnostics GmbH, Gene Therapy Technical Development, Penzberg, Germany
| | - John Welsh
- Rivanna Bioprocess Solutions, Charlottesville, Virginia, USA
| | - Robert Todd
- Digital Process Design, Boulder, Colorado, USA
| | - Arne Staby
- CMC Development, Novo Nordisk, Bagsværd, Denmark
| | - David Roush
- Roush Biopharma Panacea, Colts Neck, New Jersey, USA
| | - Jessica Lyall
- Purification Development, Genentech, South San Francisco, California, USA
| | - Sophie Karkov
- Purification Research, Global Research Technologies, Novo Nordisk, Måløv, Denmark
| | - Stephen Hunt
- Allogene Therapeutics, Inc., South San Francisco, California, USA
| | | | - Maria-Ona Bertran
- Product Supply API Manufacturing Development, Novo Nordisk, Bagsværd, Denmark
| | - Deenesh Babi
- Product Supply API Manufacturing Development, Novo Nordisk, Bagsværd, Denmark
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Saleh D, Hess R, Ahlers-Hesse M, Rischawy F, Wang G, Grosch JH, Schwab T, Kluters S, Studts J, Hubbuch J. A multiscale modeling method for therapeutic antibodies in ion exchange chromatography. Biotechnol Bioeng 2023; 120:125-138. [PMID: 36226467 DOI: 10.1002/bit.28258] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 09/09/2022] [Accepted: 10/08/2022] [Indexed: 11/10/2022]
Abstract
The development of biopharmaceutical downstream processes relies on exhaustive experimental studies. The root cause is the poorly understood relationship between the protein structure of monoclonal antibodies (mAbs) and their macroscopic process behavior. Especially the development of preparative chromatography processes is challenged by the increasing structural complexity of novel antibody formats and accelerated development timelines. This study introduces a multiscale in silico model consisting of homology modeling, quantitative structure-property relationships (QSPR), and mechanistic chromatography modeling leading from the amino acid sequence of a mAb to the digital representation of its cation exchange chromatography (CEX) process. The model leverages the mAbs' structural characteristics and experimental data of a diverse set of 21 therapeutic antibodies to predict elution profiles of two mAbs that were removed from the training data set. QSPR modeling identified mAb-specific protein descriptors relevant for the prediction of the thermodynamic equilibrium and the stoichiometric coefficient of the adsorption reaction. The consideration of two discrete conformational states of IgG4 mAbs enabled prediction of split-peak elution profiles. Starting from the sequence, the presented multiscale model allows in silico development of chromatography processes before protein material is available for experimental studies.
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Affiliation(s)
- David Saleh
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.,Early Stage Bioprocess Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Rudger Hess
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.,Early Stage Bioprocess Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Michelle Ahlers-Hesse
- Late Stage DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Federico Rischawy
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.,Late Stage DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Gang Wang
- Late Stage DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Jan-Hendrik Grosch
- Early Stage Bioprocess Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Thomas Schwab
- Early Stage Bioprocess Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Simon Kluters
- Late Stage DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Joey Studts
- Late Stage DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Jürgen Hubbuch
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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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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Sankar K, Trainor K, Blazer L, Adams J, Sidhu S, Day T, Meiering E, Maier J. A Descriptor set for Quantitative Structure-Property Relationship Prediction in Biologics. Mol Inform 2022; 41:e2100240. [PMID: 35277930 DOI: 10.1002/minf.202100240] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 03/11/2022] [Indexed: 11/12/2022]
Abstract
In addition to attaining the desired binding to their targets, a crucial aspect in the development of biotherapeutics is 'developability', which includes several desirable properties such as high solubility, low viscosity and aggregation, physico-chemical stability and low immunogenicity. The lack of any of these properties can lead to significant obstacles in advancing them to clinic; thus in silico methods capable of raising warning flags in earlier stages of development are highly beneficial. We have developed a computational framework based on a large and diverse set of protein specific descriptors ideal for making liability predictions using a machine-learning approach. This set offers a high degree of feature diversity classifiable by sequence, structure and surface patches. We assess the sensitivity and applicability of these descriptors in four dedicated case studies that are believed to be representative of biophysical characterizations commonly employed during the development process. In addition to data sets obtained from public sources, we have validated the descriptors on novel experimental data sets in order to address antibody developability and to generate prospective predictions on Adnectins. The results demonstrate that the descriptors are well suited to assist in the improvement of properties of systems that exhibit poor solubility or aggregation.
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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: 121] [Impact Index Per Article: 40.3] [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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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: 34] [Impact Index Per Article: 11.3] [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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Karlberg M, de Souza JV, Fan L, Kizhedath A, Bronowska AK, Glassey J. QSAR Implementation for HIC Retention Time Prediction of mAbs Using Fab Structure: A Comparison between Structural Representations. Int J Mol Sci 2020; 21:ijms21218037. [PMID: 33126648 PMCID: PMC7663183 DOI: 10.3390/ijms21218037] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 10/22/2020] [Accepted: 10/27/2020] [Indexed: 12/19/2022] Open
Abstract
Monoclonal antibodies (mAbs) constitute a rapidly growing biopharmaceutical sector. However, their growth is impeded by high failure rates originating from failed clinical trials and developability issues in process development. There is, therefore, a growing need for better in silico tools to aid in risk assessment of mAb candidates to promote early-stage screening of potentially problematic mAb candidates. In this study, a quantitative structure–activity relationship (QSAR) modelling workflow was designed for the prediction of hydrophobic interaction chromatography (HIC) retention times of mAbs. Three novel descriptor sets derived from primary sequence, homology modelling, and atomistic molecular dynamics (MD) simulations were developed and assessed to determine the necessary level of structural resolution needed to accurately capture the relationship between mAb structures and HIC retention times. The results showed that descriptors derived from 3D structures obtained after MD simulations were the most suitable for HIC retention time prediction with a R2 = 0.63 in an external test set. It was found that when using homology modelling, the resulting 3D structures became biased towards the used structural template. Performing an MD simulation therefore proved to be a necessary post-processing step for the mAb structures in order to relax the structures and allow them to attain a more natural conformation. Based on the results, the proposed workflow in this paper could therefore potentially contribute to aid in risk assessment of mAb candidates in early development.
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Affiliation(s)
- Micael Karlberg
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; (M.K.); (L.F.); (A.K.)
| | - João Victor de Souza
- Chemistry—School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; (J.V.d.S.); (A.K.B.)
| | - Lanyu Fan
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; (M.K.); (L.F.); (A.K.)
- Chemistry—School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; (J.V.d.S.); (A.K.B.)
| | - Arathi Kizhedath
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; (M.K.); (L.F.); (A.K.)
| | - Agnieszka K. Bronowska
- Chemistry—School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; (J.V.d.S.); (A.K.B.)
| | - Jarka Glassey
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; (M.K.); (L.F.); (A.K.)
- Correspondence:
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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: 22] [Impact Index Per Article: 4.4] [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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