Computer-Assisted Selective Optimization of Side-Activities-from Cinalukast to a PPARα Modulator

Abstract

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 (CysLT₁ R) antagonist cinalukast as lead for which we discovered peroxisome proliferator-activated receptor α (PPARα) modulatory activity. We automatically generated a virtual library of close analogues and classified these roughly 8000 compounds for PPARα agonism and CysLT₁ 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 PPARα activation and diminished CysLT₁ R antagonism. Thereby, this prospective application study highlights the potential of automating SOSA.

Keywords: automation; nuclear receptors; peroxisome proliferator-activated receptors; virtual combinatorial library.