Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low-volume optimization experiments are reported. A Buchwald–Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self-optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor.

中文翻译：

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具有多过程分析功能的段塞流平台有利于灵活的反应优化


流程处理提供了许多以快速和自动化的方式优化反应的机会，但通常需要相对大量的输入材料。为了解决这个问题，据报道使用配备两台分析仪器的灵活的段塞流反应器进行小批量优化实验。药物奥氮平的 Buchwald-Hartwig 胺化具有 6 个独立的可优化变量，使用三种不同的自动化方法进行优化：自我优化、实验设计和动力学建模。这些方法是互补的，并提供有关反应的不同信息：分别是帕累托最优操作​​点、响应面模型和机械模型。使用<标准流程操作所需材料的 10% 即可获得结果。最后，利用自动数据处理建立了化学计量模型，并且三个后续验证实验证明了段塞流反应器和标准（更大规模）流动反应器之间的良好一致性。