Automated Optimisation of Discrete and Continuous Parameters for Manufacture of Pharmaceuticals

自动化已应用于流动反应器中化学合成的优化，使用算法自动探索和微调反应条件。伯恩实验室（英国利兹）开发了一种系统，可以自我优化连续变量（如温度、试剂当量、停留时间），并已成功地将这项技术转让给工业合作伙伴。伯恩实验室与辉瑞公司的合作旨在探索液体处理机器人技术和数学技术，将范围扩展到非连续变量，如试剂、催化剂和溶剂的选择，从而加速筛选和优化可流动的化学物质，并研究减少材料的使用。目标：开发工作流程、仪器、控制软件和计算协议，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，允许使用化学描述符等数学技术进行非连续变量自优化。研究自优化工作流程中材料使用的最小化

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