AI-based Antibody Therapeutics Developability Assessment and Its Engineering

Learn from an informative webinar about how AI-based platforms can facilitate the development process for antibody therapeutics and ensure candidates are not only therapeutically effective but also viable for large-scale production and clinical use.

Monoclonal antibodies (mAbs) have become essential in treating a wide spectrum of diseases, with over 100 therapeutic antibody products now approved and commercially available. Although mAbs represent the fastest-growing sector in therapeutics, their development is complex, with developability being a crucial consideration. Developability evaluates the potential of an antibody candidate to be produced as a safe and effective drug.

In the context of Contract Research and Manufacturing Organizations (CRMOs), the developability assessment focuses on ensuring that an antibody candidate can progress through the chemistry, manufacturing and control (CMC) processes efficiently, affordably and within an acceptable timeframe.

Register for this webinar today to learn how AI can be used to improve developability screening of antibody therapeutics and optimization of antibody candidates.

Speaker

Carter Hao, Senior Bioinformatics Scientist, GenScript

Carter Hao received his PhD in Control Science and Engineering from Zhejiang University of Technology. He is now a Bioinformatics Senior Scientist at GenScript Co. Ltd. and mainly takes charge of the research and development of antibody-related applications. His research interests include protein structure prediction, antibody developability prediction (viscosity, immunogenicity, aggregation and stability), antibody binding affinity maturation, antibody humanization, caninization, felinization and evolution computation. He has published 20+ papers in the above research fields in international journals such as the European Journal of Pharmaceutical Sciences, IEEE TCBB and Information Science.

Message Presenter