1
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Armstrong GB, Shah V, Sanches P, Patel M, Casey R, Jamieson C, Burley GA, Lewis W, Rattray Z. A framework for the biophysical screening of antibody mutations targeting solvent-accessible hydrophobic and electrostatic patches for enhanced viscosity profiles. Comput Struct Biotechnol J 2024; 23:2345-2357. [PMID: 38867721 PMCID: PMC11167247 DOI: 10.1016/j.csbj.2024.05.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 06/14/2024] Open
Abstract
The formulation of high-concentration monoclonal antibody (mAb) solutions in low dose volumes for autoinjector devices poses challenges in manufacturability and patient administration due to elevated solution viscosity. Often many therapeutically potent mAbs are discovered, but their commercial development is stalled by unfavourable developability challenges. In this work, we present a systematic experimental framework for the computational screening of molecular descriptors to guide the design of 24 mutants with modified viscosity profiles accompanied by experimental evaluation. Our experimental observations using a model anti-IL8 mAb and eight engineered mutant variants reveal that viscosity reduction is influenced by the location of hydrophobic interactions, while targeting positively charged patches significantly increases viscosity in comparison to wild-type anti-IL-8 mAb. We conclude that most predicted in silico physicochemical properties exhibit poor correlation with measured experimental parameters for antibodies with suboptimal developability characteristics, emphasizing the need for comprehensive case-by-case evaluation of mAbs. This framework combining molecular design and triage via computational predictions with experimental evaluation aids the agile and rational design of mAbs with tailored solution viscosities, ensuring improved manufacturability and patient convenience in self-administration scenarios.
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Affiliation(s)
- Georgina B. Armstrong
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Vidhi Shah
- Large Molecule Discovery, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Paula Sanches
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Mitul Patel
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Ricky Casey
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Craig Jamieson
- Department of Pure and Applied Chemistry, University of Strathclyde, Glasgow, UK
| | - Glenn A. Burley
- Department of Pure and Applied Chemistry, University of Strathclyde, Glasgow, UK
| | - William Lewis
- Drug Substance Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, UK
| | - Zahra Rattray
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
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2
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Hao X, Fan L. ProtT5 and random forests-based viscosity prediction method for therapeutic mAbs. Eur J Pharm Sci 2024; 194:106705. [PMID: 38246432 DOI: 10.1016/j.ejps.2024.106705] [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: 11/09/2023] [Revised: 01/01/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024]
Abstract
Viscosity is a key characteristic of therapeutic antibodies for subcutaneous administration which requires low volume and high concentration formulations. It would be highly beneficial to accurately predict the viscosity of newly developed therapeutic antibodies in the early stages of development. In this work, a ProtT5-XL-UniRef50 (ProtT5) and Random Forests (RF)-based prediction method was proposed for accurately predicting the viscosity of monoclonal antibodies, with only corresponding sequences needed. Starting from the given heavy and light chain V-region sequences, corresponding features were first extracted from the ProtT5 pretrained model. Kernel principal analysis (Kernel-PCA) was then used for reducing the extracted 2048-D (1024-D for each sequence) feature vector to a reasonable level for efficient training of the RF-regressor. Then, the RF model was constructed on 40 commercially available therapeutic antibodies and tested with 3-folds cross-validation. Test results show that the model could reproduce the viscosity value at a high level (Pearson correlation coefficient (PCC) = 0.928). Performance on classifying high (>30 cP) and low (<30 cP) viscosity is much more satisfactory, the Accuracy (ACC) and the area under precision-recall curve (AUC) of the classification model from validation tests are 0.975 and 1.000, respectively. Compared to 5 existing state-of-the-art viscosity prediction methods, the proposed method performs best which would facilitate high concentration antibody viscosity high-throughput screening.
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Affiliation(s)
- Xiaohu Hao
- Production and R&D Center I of LSS (Life Science Service), GenScript Biotech Corporation, No. 28, Yongxi Rd., Nanjing, 211110, Jiangsu, China
| | - Long Fan
- Production and R&D Center I of LSS (Life Science Service), GenScript Biotech Corporation, No. 28, Yongxi Rd., Nanjing, 211110, Jiangsu, China; Production and R&D Center I of LSS (Life Science Service), GenScript (Shanghai) Biotech Corporation, No. 186, Hedan Rd., Shanghai, 200100, China.
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3
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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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4
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Haddad S, Oktay L, Erol I, Şahin K, Durdagi S. Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers. ACS OMEGA 2023; 8:40864-40877. [PMID: 37929100 PMCID: PMC10620895 DOI: 10.1021/acsomega.3c06074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 09/13/2023] [Indexed: 11/07/2023]
Abstract
The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure-activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds.
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Affiliation(s)
- Safa Haddad
- Computational
Biology and Molecular Simulations Laboratory, Department of Biophysics,
School of Medicine, Bahçeşehir
University, Istanbul 34353, Turkey
- Computational
Drug Design Center (HITMER), Bahçeşehir
University, Istanbul 34353, Turkey
| | - Lalehan Oktay
- Computational
Biology and Molecular Simulations Laboratory, Department of Biophysics,
School of Medicine, Bahçeşehir
University, Istanbul 34353, Turkey
- Computational
Drug Design Center (HITMER), Bahçeşehir
University, Istanbul 34353, Turkey
| | - Ismail Erol
- Computational
Biology and Molecular Simulations Laboratory, Department of Biophysics,
School of Medicine, Bahçeşehir
University, Istanbul 34353, Turkey
- Computational
Drug Design Center (HITMER), Bahçeşehir
University, Istanbul 34353, Turkey
| | - Kader Şahin
- Department
of Analytical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul 34734, Turkey
| | - Serdar Durdagi
- Computational
Biology and Molecular Simulations Laboratory, Department of Biophysics,
School of Medicine, Bahçeşehir
University, Istanbul 34353, Turkey
- Computational
Drug Design Center (HITMER), Bahçeşehir
University, Istanbul 34353, Turkey
- Molecular
Therapy Lab, Department of Pharmaceutical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul 34353, Turkey
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5
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Pang KT, Yang YS, Zhang W, Ho YS, Sormanni P, Michaels TCT, Walsh I, Chia S. Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics. Biotechnol Adv 2023; 67:108192. [PMID: 37290583 DOI: 10.1016/j.biotechadv.2023.108192] [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: 02/16/2023] [Revised: 05/09/2023] [Accepted: 06/01/2023] [Indexed: 06/10/2023]
Abstract
In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.
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Affiliation(s)
- Kuin Tian Pang
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technology University, Singapore
| | - Yuan Sheng Yang
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Wei Zhang
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Ying Swan Ho
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Pietro Sormanni
- Chemistry of Health, Yusuf Hamied Department of Chemistry, University of Cambridge, United Kingdom
| | - Thomas C T Michaels
- Department of Biology, Institute of Biochemistry, ETH Zurich, Otto-Stern-Weg 3, 8093 Zurich, Switzerland; Bringing Materials to Life Initiative, ETH Zurich, Switzerland
| | - Ian Walsh
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.
| | - Sean Chia
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.
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6
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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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7
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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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8
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Condado-Morales I, Sokolova V, Wahlund PO, Heding KE, Auclair S, Kingsbury JS, Arosio P, Lorenzen N. AF4 and PEG Precipitation as Predictive Assays for Antibody Self-Association. Mol Pharm 2023; 20:1323-1330. [PMID: 36668814 DOI: 10.1021/acs.molpharmaceut.2c00946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Monoclonal antibodies (mAbs) are often formulated as high-protein-concentration solutions, which in some cases can exhibit physical stability issues such as high viscosity and opalescence. To ensure that mAb-based drugs can meet their manufacturing, stability, and delivery requirements, it is advantageous to screen for and select mAbs during discovery that are not prone to such behaviors. It has been recently shown that both these macroscopic properties can be predicted to a certain extent from the diffusion interaction parameter (kD), which is a measure of self-association under dilute conditions.1 However, kD can be challenging to measure at the early stage of discovery, where a relatively large amount of a high-purity material, which is required by traditional methods, is often not available. In this study, we demonstrate asymmetric field-flow fractionation (AF4) as a tool to measure self-association and therefore identify antibodies with problematic issues at high concentrations. The principle lies on the ability to concentrate the sample close to the membrane during the injection mode, which can reach formulation-relevant concentrations (>100 mg/mL).2 By analyzing a well-characterized library of commercial antibodies, we show that the measured retention time of the antibodies allows us to pinpoint molecules that exhibit issues at high concentrations. Remarkably, our AF4 assay requires very little (30 μg) sample under dilute conditions and does not need extensive sample purification. Furthermore, we show that a polyethylene glycol (PEG) precipitation assay provides results consistent with AF4 and moreover can further differentiate molecules with issues of opalescence or high viscosity. Overall, our results delineate a two-step strategy for the identification of problematic variants at high concentrations, with AF4 for early developability screening, followed by a PEG assay to validate the problematic molecules and further discriminate between opalescence or high-viscosity issues. This two-step antibody selection strategy enables us to select antibodies early in the discovery process, which are compatible with high-concentration formulations.
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Affiliation(s)
- Itzel Condado-Morales
- Global Research Technology, Novo Nordisk A/S, Maaloev 2760, Denmark.,Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zürich, Zürich 8093, Switzerland
| | - Viktoria Sokolova
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zürich, Zürich 8093, Switzerland
| | - Per-Olof Wahlund
- Global Research Technology, Novo Nordisk A/S, Maaloev 2760, Denmark
| | | | - Sarah Auclair
- Global CMC Development, Sanofi, Framingham, Massachusetts 02210, United States
| | | | - Paolo Arosio
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zürich, Zürich 8093, Switzerland
| | - Nikolai Lorenzen
- Global Research Technology, Novo Nordisk A/S, Maaloev 2760, Denmark
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9
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Svilenov HL, Arosio P, Menzen T, Tessier P, Sormanni P. Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties. MAbs 2023; 15:2164459. [PMID: 36629855 PMCID: PMC9839375 DOI: 10.1080/19420862.2022.2164459] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/22/2022] [Accepted: 12/29/2022] [Indexed: 01/12/2023] Open
Abstract
Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.
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Affiliation(s)
- Hristo L. Svilenov
- Laboratory of General Biochemistry and Physical Pharmacy, Faculty of Pharmaceutical Sciences, Ghent University, Gent, Belgium
| | - Paolo Arosio
- Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland
| | - Tim Menzen
- Coriolis Pharma Research GmbH, Martinsried, 82152, Germany
| | - Peter 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
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
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10
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Licari G, Martin KP, Crames M, Mozdzierz J, Marlow MS, Karow-Zwick AR, Kumar S, Bauer J. Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics. Mol Pharm 2022; 20:1096-1111. [PMID: 36573887 PMCID: PMC9906779 DOI: 10.1021/acs.molpharmaceut.2c00838] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Adequate stability, manufacturability, and safety are crucial to bringing an antibody-based biotherapeutic to the market. Following the concept of holistic in silico developability, we introduce a physicochemical description of 91 market-stage antibody-based biotherapeutics based on orthogonal molecular properties of variable regions (Fvs) embedded in different simulation environments, mimicking conditions experienced by antibodies during manufacturing, formulation, and in vivo. In this work, the evaluation of molecular properties includes conformational flexibility of the Fvs using molecular dynamics (MD) simulations. The comparison between static homology models and simulations shows that MD significantly affects certain molecular descriptors like surface molecular patches. Moreover, the structural stability of a subset of Fv regions is linked to changes in their specific molecular interactions with ions in different experimental conditions. This is supported by the observation of differences in protein melting temperatures upon addition of NaCl. A DEvelopability Navigator In Silico (DENIS) is proposed to compare mAb candidates for their similarity with market-stage biotherapeutics in terms of physicochemical properties and conformational stability. Expanding on our previous developability guidelines (Ahmed et al. Proc. Natl. Acad. Sci. 2021, 118 (37), e2020577118), the hydrodynamic radius and the protein strand ratio are introduced as two additional descriptors that enable a more comprehensive in silico characterization of biotherapeutic drug candidates. Test cases show how this approach can facilitate identification and optimization of intrinsically developable lead candidates. DENIS represents an advanced computational tool to progress biotherapeutic drug candidates from discovery into early development by predicting drug properties in different aqueous environments.
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Affiliation(s)
- Giuseppe Licari
- Early
Stage Pharmaceutical Development, Pharmaceutical Development Biologicals
& In silico Team, Boehringer Ingelheim
International GmbH & Co. KG, Biberach/Riss 88397, Germany
| | - Kyle P. Martin
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Maureen Crames
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Joseph Mozdzierz
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Michael S. Marlow
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States
| | - Anne R. Karow-Zwick
- Early
Stage Pharmaceutical Development, Pharmaceutical Development Biologicals
& In silico Team, Boehringer Ingelheim
International GmbH & Co. KG, Biberach/Riss 88397, Germany
| | - Sandeep Kumar
- Biotherapeutics
Discovery & In silico Team, Boehringer
Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut 06877, United States,
| | - Joschka Bauer
- Early
Stage Pharmaceutical Development, Pharmaceutical Development Biologicals
& In silico Team, Boehringer Ingelheim
International GmbH & Co. KG, Biberach/Riss 88397, Germany,
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Aguilar Rangel M, Bedwell A, Costanzi E, Taylor RJ, Russo R, Bernardes GJL, Ricagno S, Frydman J, Vendruscolo M, Sormanni P. Fragment-based computational design of antibodies targeting structured epitopes. SCIENCE ADVANCES 2022; 8:eabp9540. [PMID: 36367941 PMCID: PMC9651861 DOI: 10.1126/sciadv.abp9540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterization showed that all designs are stable and bind their intended targets with affinities in the nanomolar range without in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as similar predictions are obtained when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to preselected epitopes.
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Affiliation(s)
- Mauricio Aguilar Rangel
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Alice Bedwell
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Elisa Costanzi
- Department of Bioscience, Università degli Studi di Milano, Milano 20133, Italy
| | - Ross J. Taylor
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Rosaria Russo
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milano 20122, Italy
| | - Gonçalo J. L. Bernardes
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Stefano Ricagno
- Department of Bioscience, Università degli Studi di Milano, Milano 20133, Italy
- Institute of Molecular and Translational Cardiology, IRCCS Policlinico San Donato, Milan 20097, Italy
| | - Judith Frydman
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
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Löhr T, Sormanni P, Vendruscolo M. Conformational Entropy as a Potential Liability of Computationally Designed Antibodies. Biomolecules 2022; 12:718. [PMID: 35625644 PMCID: PMC9138470 DOI: 10.3390/biom12050718] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 05/06/2022] [Accepted: 05/08/2022] [Indexed: 01/28/2023] Open
Abstract
In silico antibody discovery is emerging as a viable alternative to traditional in vivo and in vitro approaches. Many challenges, however, remain open to enabling the properties of designed antibodies to match those produced by the immune system. A major question concerns the structural features of computer-designed complementarity determining regions (CDRs), including the role of conformational entropy in determining the stability and binding affinity of the designed antibodies. To address this problem, we used enhanced-sampling molecular dynamics simulations to compare the free energy landscapes of single-domain antibodies (sdAbs) designed using structure-based (DesAb-HSA-D3) and sequence-based approaches (DesAbO), with that of a nanobody derived from llama immunization (Nb10). Our results indicate that the CDR3 of DesAbO is more conformationally heterogeneous than those of both DesAb-HSA-D3 and Nb10, and the CDR3 of DesAb-HSA-D3 is slightly more dynamic than that of Nb10, which is the original scaffold used for the design of DesAb-HSA-D3. These differences underline the challenges in the rational design of antibodies by revealing the presence of conformational substates likely to have different binding properties and to generate a high entropic cost upon binding.
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Affiliation(s)
| | | | - Michele Vendruscolo
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK; (T.L.); (P.S.)
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Lim SW, Tan KJ, Azuraidi OM, Sathiya M, Lim EC, Lai KS, Yap WS, Afizan NARNM. Functional and structural analysis of non-synonymous single nucleotide polymorphisms (nsSNPs) in the MYB oncoproteins associated with human cancer. Sci Rep 2021; 11:24206. [PMID: 34921182 PMCID: PMC8683427 DOI: 10.1038/s41598-021-03624-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/26/2021] [Indexed: 12/17/2022] Open
Abstract
MYB proteins are highly conserved DNA-binding domains (DBD) and mutations in MYB oncoproteins have been reported to cause aberrant and augmented cancer progression. Identification of MYB molecular biomarkers predictive of cancer progression can be used for improving cancer management. To address this, a biomarker discovery pipeline was employed in investigating deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in predicting damaging and potential alterations on the properties of proteins. The nsSNP of the MYB family; MYB, MYBL1, and MYBL2 was extracted from the NCBI database. Five in silico tools (PROVEAN, SIFT, PolyPhen-2, SNPs&GO and PhD-SNP) were utilized to investigate the outcomes of nsSNPs. A total of 45 nsSNPs were predicted as high-risk and damaging, and were subjected to PMut and I-Mutant 2.0 for protein stability analysis. This resulted in 32 nsSNPs with decreased stability with a DDG score lower than - 0.5, indicating damaging effect. G111S, N183S, G122S, and S178C located within the helix-turn-helix (HTH) domain were predicted to be conserved, further posttranslational modifications and 3-D protein analysis indicated these nsSNPs to shift DNA-binding specificity of the protein thus altering the protein function. Findings from this study would help in the field of pharmacogenomic and cancer therapy towards better intervention and management of cancer.
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Affiliation(s)
- Shu Wen Lim
- Faculty of Applied Sciences, UCSI University, No. 1, Jalan Menara Gading UCSI Height, 56000, Cheras, Kuala Lumpur, Malaysia
| | - Kennet JunKai Tan
- Faculty of Applied Sciences, UCSI University, No. 1, Jalan Menara Gading UCSI Height, 56000, Cheras, Kuala Lumpur, Malaysia
| | - Osman Mohd Azuraidi
- Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, 43400, Serdang, Selangor, Malaysia
| | - Maran Sathiya
- School of Pharmacy, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia
| | - Ee Chen Lim
- Faculty of Applied Sciences, UCSI University, No. 1, Jalan Menara Gading UCSI Height, 56000, Cheras, Kuala Lumpur, Malaysia
| | - Kok Song Lai
- Health Sciences Division, Abu Dhabi Women's College, Higher Colleges of Technology, 41012, Abu Dhabi, United Arab Emirates
| | - Wai-Sum Yap
- Faculty of Applied Sciences, UCSI University, No. 1, Jalan Menara Gading UCSI Height, 56000, Cheras, Kuala Lumpur, Malaysia.
| | - Nik Abd Rahman Nik Mohd Afizan
- Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, 43400, Serdang, Selangor, Malaysia.
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