Predicting Antibody Developability Profiles Through Early Stage Discovery Screening

• Published in: mAbs Vol. 12; no. 1; p. 1743053
• Main Authors: Bailly, Marc, Mieczkowski, Carl, Juan, Veronica, Metwally, Essam, Tomazela, Daniela, Baker, Jeanne, Uchida, Makiko, Kofman, Ester, Raoufi, Fahimeh, Motlagh, Soha, Yu, Yao, Park, Jihea, Raghava, Smita, Welsh, John, Rauscher, Michael, Raghunathan, Gopalan, Hsieh, Mark, Chen, Yi-Ling, Nguyen, Hang Thu, Nguyen, Nhung, Cipriano, Dan, Fayadat-Dilman, Laurence
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 01-01-2020; Taylor & Francis Group
• Subjects: Antibodies, Monoclonal; antibody discovery; biophysical properties; developability; Drug Discovery - methods; Humans; Monoclonal antibodies; protein analytics; protein engineering; Protein Engineering - methods; Workflow; protein engineering; biophysical properties; developability; antibody discovery; CMC; Monoclonal antibodies; antibody screening; protein analytics; manufacturability
• ISSN: 1942-0862; 1942-0870
• DOI: 10.1080/19420862.2020.1743053

Monoclonal antibodies play an increasingly important role for the development of new drugs across multiple therapy areas. The term 'developability' encompasses the feasibility of molecules to successfully progress from discovery to development via evaluation of their physicochemical properties. These properties include the tendency for self-interaction and aggregation, thermal stability, colloidal stability, and optimization of their properties through sequence engineering. Selection of the best antibody molecule based on biological function, efficacy, safety, and developability allows for a streamlined and successful CMC phase. An efficient and practical high-throughput developability workflow (100 s-1,000 s of molecules) implemented during early antibody generation and screening is crucial to select the best lead candidates. This involves careful assessment of critical developability parameters, combined with binding affinity and biological properties evaluation using small amounts of purified material (<1 mg), as well as an efficient data management and database system. Herein, a panel of 152 various human or humanized monoclonal antibodies was analyzed in biophysical property assays. Correlations between assays for different sets of properties were established. We demonstrated in two case studies that physicochemical properties and key assay endpoints correlate with key downstream process parameters. The workflow allows the elimination of antibodies with suboptimal properties and a rank ordering of molecules for further evaluation early in the candidate selection process. This enables any further engineering for problematic sequence attributes without affecting program timelines.