Automated Optimisation of Discrete and Continuous Parameters for Manufacture of Pharmaceuticals

自动化已被应用于流动反应器中的化学合成优化，使用算法来自动探索和微调反应条件。伯恩实验室(英国利兹)开发了一种可自我优化连续变量(如温度、试剂当量、停留时间)的系统，并成功地将这项技术转让给了工业合作伙伴。伯恩实验室与辉瑞的合作旨在探索液体处理机器人和数学技术，以将范围扩展到试剂、催化剂和溶剂选择等非连续变量，从而加速筛选和优化可流动的化学物质，并研究材料使用量的减少。目标：开发工作流程、仪器、控制软件和计算协议，以允许使用液体处理机器人进行非连续变量自优化。开发计算协议，以允许使用数学技术(如化学描述符)进行非连续变量自优化。研究在自优化工作流程中最大限度地减少材料使用

Automation has been applied to the optimization of chemical synthesis in flow reactors using algorithms to automatically explore and fine-tune reaction conditions. The Bourne lab (Leeds, UK) has developed a system which self-optimizes continuous variables (e.g. temperature, reagent equivalents, residence time) and has successfully transferred this technology to industrial partners. This Bourne lab-Pfizer collaboration aims to explore liquid handling robotics and mathematical techniques to extend the scope to non-continuous variables such as reagent, catalyst and solvent choice, thus accelerating the screening and optimization of flow-amenable chemistries, as well as investigating reductions in material usage. Objectives: Development of workflows, instrumentation, control software and computational protocols to allow non-continuous variable self-optimization using liquid handling robotics Development of computational protocols to allow non-continuous variable self-optimization using mathematical techniques such as chemical descriptors Studies into minimization of material usage in self-optimization workflows