Computational Approaches for Optimizing the Developability of Biotherapeutics
mAb candidates identified from high-throughput screening or binding affinity optimization often present liabilities for developability, such as aggregation-prone regions or poor solution behavior. In this work, we optimized an integrin α11 binding mAb for developability using homology modeling and rational design where reducing hydrophobic surface patches improved HIC behavior. A retrospective data analysis demonstrates that 3D descriptors, conformational sampling, stochastic titration, and multi-parameter models can screen candidates and enrich libraries with favorable developability properties for a range of biotherapeutics.
Airdate: July 25, 2019
