Computer-assisted selective optimization of side-activities - from cinalukast to a PPARα modulator.

Automated computational analogue design and scoring can speed up hit-to-lead optimization and appears particularly promising in selective optimization of side-activities (SOSA) where possible analogue diversity is confined. Probing this concept, we employed the cysteinyl leukotriene receptor 1 (CysLT(1)R) antagonist cinalukast as lead for which we discovered peroxisome proliferator-activated receptor alpha (PPAR alpha) modulatory activity. We automatically generated a virtual library of close analogues and classified these roughly 8000 compounds for PPAR alpha agonism and CysLT(1)R antagonism using automated affinity scoring and machine learning. A computationally preferred analogue for SOSA was synthesized, and in vitro characterization indeed revealed a marked activity shift toward enhanced PPAR alpha activation and diminished CysLT(1)R antagonism. Thereby, this prospective application study highlights the potential of automating SOSA.