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.