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

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

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 me...

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Bibliographic Details
Main Authors: Marissa Mock, Alex W. Jacobitz, Christopher James 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 Dykstra, Sudipa Ghimire-Rijal, Kevin Graham, Peter Grandsard, Joon Huh, John O. Hui, Mani Jain, Victoria Jann, Lei Jia, Sheree Johnstone, Neelam Khanal, Carl Kolvenbach, Linda Narhi, Rupa Padaki, Emma M. Pelegri-O’Day, Wei Qi, Vladimir Razinkov, Austin J. Rice, Richard Smith, Christopher Spahr, Jennitte Stevens, Yax Sun, Veena A. Thomas, Sarah van Driesche, Robert Vernon, Victoria Wagner, Kenneth W. Walker, Yangjie Wei, Dwight Winters, Melissa Yang, Iain D. G. Campuzano
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:mAbs
Subjects:
Developability
high throughput
in vitro assays
mab
machine learning
pharmacokinetics
Online Access:https://www.tandfonline.com/doi/10.1080/19420862.2023.2256745
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