Laurence Fayadat-Dilman, PhD, Senior Director, Protein Sciences, Merck Research Labs
Monoclonal antibodies and antibody-derived molecules (e.g., bispecifics, multispecifics, fragments) represent a rapidly expanding therapeutic modality across multiple therapy areas. The term ‘developability’ encompasses the feasibility of molecules to successfully progress to development via evaluation of their physicochemical properties, tendency for self- interaction, aggregation, thermal stability, colloidal stability and optimization of their properties through sequence engineering. Selection of multi-parameter optimized antibody molecules, taking into consideration biological function, safety, and developability, allows for a streamlined and successful development. We developed an efficient and practical high-throughput developability workflow (100’s-1,000’s of molecules) implemented during the early phase of antibody generation and screening is critical to guide the selection process of the best lead candidates. This workflow allows elimination of antibodies with suboptimal properties early in the discovery process and rank ordering of molecules for further evaluation in the candidate selection process. Mining of our large high-quality datasets identified novel patterns and correlations between biophysical assays. These patterns and correlations represent the basis for training deep neural networks and establishing machine learning algorithms for in silico interrogation and prediction of developability profiles from a massive diversity of antibody sequences available in the discovery space.