Unsymmetrical urea is a ubiquitous moiety in pharmaceuticals, providing a linkage between pharmacophores. The assembly of an unsymmetrical urea bridge in various therapeutic agents can be accomplished through several approaches. Conventional methods involving hazardous compounds such as phosgene and isolated isocyanates pose safety concerns; a safe surrogate of phosgene, namely CDI, is popularly employed for the construction of both symmetrical and unsymmetrical urea. While the use of CDI for the small-scale synthesis of NCEs is a popular strategy, translation of the same chemistry to the large-scale manufacture of unsymmetrical urea containing APIs often encounters certain challenges such as symmetrical urea formation, solubility, and purification issues. Consequently, alternate approaches involving the intermediacy of a stable alkyl/aryl carbamate are typically adopted in manufacturing scenarios. Herein, we describe an effective supervised ML approach involving minimal data sets of flow chemistry parameters to accelerate the process optimization of CDI-based unsymmetrical urea construction for the anticancer drug Larotrectinib. A series of multi-output regression and ensemble models were evaluated to identify the best one that can be employed for rapid and effective reaction optimization. Using this approach, we were able to arrive at the optimal experimental conditions that can be potentially applied for Larotrectinib scale-up with good product purity and yield.

中文翻译：

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使用低数据集的监督机器学习算法：Larotrectinib 中关键尿素部分构建的流式化学优化


不对称尿素是药物中普遍存在的部分，提供药效基团之间的联系。各种治疗剂中不对称尿素桥的组装可以通过几种方法来完成。传统方法涉及光气和分离异氰酸酯等危险化合物，存在安全隐患；光气的安全替代品，即 CDI，广泛用于构建对称和不对称尿素。虽然使用 CDI 小规模合成 NCE 是一种流行的策略，但将相同的化学转化为大规模生产含有不对称尿素的 API 通常会遇到某些挑战，例如对称尿素形成、溶解度和纯化问题。因此，在制造场景中通常采用涉及稳定的烷基/芳基氨基甲酸酯中介的替代方法。在此，我们描述了一种有效的监督机器学习方法，涉及最少的流化学参数数据集，以加速抗癌药物 Larotrectinib 的基于 CDI 的不对称尿素构建的过程优化。对一系列多输出回归和集成模型进行了评估，以确定可用于快速有效的反应优化的最佳模型。使用这种方法，我们能够获得可用于 Larotrectinib 放大的最佳实验条件，并具有良好的产品纯度和产量。