Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies Article Swipe

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Marissa Mock , Alex W. Jacobitz , Christopher J. Langmead , Athena Sudom , Daniel Yoo , Sara C. Humphreys , Mai Alday , Larysa Alekseychyk , Nicolas Angell , Vivian Bi , Hannah Catterall , Chen-Chun Chen , Hui‐Ting Chou , Kip P. Conner , Kevin D. Cook , Ana R. Correia , Andrew B. Dykstra , Sudipa Ghimire-Rijal , Kevin C. Graham , Peter Grandsard , Joon H. Huh , John O. Hui , Mani Jain , Victoria Jann , Lei Jia , Sheree Johnstone , Neelam Khanal , Carl G. Kolvenbach , Linda O. Narhi , Rupa Padaki , Emma M. Pelegri-O’Day , Qi Wei , Vladimir I. Razinkov , Austin J. Rice , Richard Smith , Chris Spahr , Jennitte Stevens , Yax Sun , Veena A. Thomas , Sarah Van Driesche , Robert B. Vernon , Victoria Wagner , Kenneth W. Walker , Yangjie Wei , Dwight Winters , Melissa Yang , Iain D. G. Campuzano ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1080/19420862.2023.2256745

Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC_{0-672 h}) in normal mouse is above or below a threshold of 3.9 × 10⁶ h x ng/mL.

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In Silico

Computational Biology

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In silico Computational biology Drug discovery Monoclonal antibody Computer science In vivo Drug development Antibody Biology Biological system Bioinformatics Drug Biochemistry Immunology Pharmacology Gene Biotechnology

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Type: article
Language: en
Landing Page: https://doi.org/10.1080/19420862.2023.2256745
OA Status: gold
Cited By: 22
References: 75
Related Works: 10
OpenAlex ID: https://openalex.org/W4386691948

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DOI: https://doi.org/10.1080/19420862.2023.2256745

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Title: Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-09-12

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Authors: Marissa Mock, Alex W. Jacobitz, Christopher J. Langmead, Athena Sudom, Daniel Yoo, Sara C. Humphreys, Mai Alday, Larysa Alekseychyk, Nicolas Angell, Vivian Bi, Hannah Catterall, Chen-Chun Chen, Hui‐Ting Chou, Kip P. Conner, Kevin D. Cook, Ana R. Correia, Andrew B. Dykstra, Sudipa Ghimire-Rijal, Kevin C. Graham, Peter Grandsard, Joon H. Huh, John O. Hui, Mani Jain, Victoria Jann, Lei Jia, Sheree Johnstone, Neelam Khanal, Carl G. Kolvenbach, Linda O. Narhi, Rupa Padaki, Emma M. Pelegri-O’Day, Qi Wei, Vladimir I. Razinkov, Austin J. Rice, Richard Smith, Chris Spahr, Jennitte Stevens, Yax Sun, Veena A. Thomas, Sarah Van Driesche, Robert B. Vernon, Victoria Wagner, Kenneth W. Walker, Yangjie Wei, Dwight Winters, Melissa Yang, Iain D. G. Campuzano

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Landing page: https://doi.org/10.1080/19420862.2023.2256745

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OA URL: https://doi.org/10.1080/19420862.2023.2256745

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Concepts: In silico, Computational biology, Drug discovery, Monoclonal antibody, Computer science, In vivo, Drug development, Antibody, Biology, Biological system, Bioinformatics, Drug, Biochemistry, Immunology, Pharmacology, Gene, Biotechnology

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Cited by: 22

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Citations by year (recent): 2025: 10, 2024: 10, 2023: 2

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