Dan Griffin, Amgen

"Opportunities for Machine Learning and Artificial Intelligence to Advance Synthetic & Hybrid Drug Substance Process Development"

Abstract: The application of machine learning (ML) and artificial intelligence (AI) to chemical synthesis of medicinal compounds has been widely discussed, leading to the development of numerous computer-aided synthesis planning (CASP) tools by both academic groups and commercial enterprises. To date, much of this work has focused on retrosynthetic analysis from the perspective of medicinal chemists. That emphasis has left significant opportunities untapped in applying ML and AI to support process engineers in clinical and commercial drug substance process development. In this talk, I will outline the role of process development, underscore its importance, highlight recent areas of research, and propose new opportunities where AI can further advance the field.

Bio:

Dan Griffin is the Director of Process Development, Drug Substance Technologies – Synthetics at Amgen, where he leads a team of engineers and process chemists in developing clinical and commercial manufacturing processes for Amgen’s small molecule and hybrid drug substance portfolio. He earned his Ph.D. in Chemical & Biomolecular Engineering from Georgia Tech under the guidance of Professors Kawajiri, Grover, and Rousseau. His past research has focused on crystallization, process controls, continuous manufacturing, and the application of machine learning and artificial intelligence to advance chemical process development.