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Development of in Silico Models to Predict Viscosity and Mouse Clearance Using a Comprehensive Analytical Data Set Collected on 83 Scaffold-Consistent Monoclonal Antibodies.

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

MABS（2023）

Cited 9|Views35
Key words
Developability,high throughput,in vitro assays,mab,machine learning,pharmacokinetics,predictive model,therapeutic antibody,viscosity
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