化学
烷基化
序列(生物学)
水解
组合化学
化学选择性
有机化学
立体化学
质量(理念)
产量(工程)
催化作用
作者
Ijaz Ahmed,Russell F. Algera,Aaron F. Baldwin,Andrew Derrick,Nga M. Do,David F. Fernández,Steven J. Fussell,Danielle Green,Adam E. S. Gymer,Brian P. Jones,Sarah Karasik,Nahian Khan,Daniel Laity,Taegyo Lee,Jian Li,Chase Mack,Lesly Mejia,Ian Moses,Patrick O’Neill,Dylan Pedro
标识
DOI:10.1021/acs.oprd.6c00144
摘要
The target compound danuglipron belongs to a family of glucagon-like peptide-1 receptor agonist (GLP-1RA) candidates identified in the Pfizer laboratories for the treatment of type 2 diabetes mellitus and obesity. This article describes a development approach providing a data-centric, holistic methodology toward process design. The approach starts with rapid collection and generation of fundamental data through high-throughput experimentation (HTE), followed by lead evaluation, narrowing process options, and concluding with lead process confirmation. This systematic strategy identifies the most efficient process options, anticipates process sensitivities, removes unconscious bias in decision making, leading to fewer process iterations, and identifies an optimal process. By adopting this approach to danuglipron, a new 5-step process was developed, removing two unnecessary process steps, increasing the yield from 34 to 64%, and reducing PMI from 231 to 73 kg/kgA while being scaled at multiple facilities to generate metric tons of clinical-grade danuglipron.
科研通智能强力驱动
Strongly Powered by AbleSci AI