Computer‐Assisted Selective Optimization of Side‐Activities—from Cinalukast to a PPARα Modulator. (27th June 2019)

• Record Type: Journal Article
• Authors: Pollinger, Julius; Schierle, Simone; Neumann, Sebastian; Ohrndorf, Julia; Kaiser, Astrid; Merk, Daniel
• Is Part Of: ChemMedChem. Volume 14:Number 14(2019)
• Page Start: 1343
• Page End: 1348
• Publication Date: 2019-06-27
• Subjects: automation -- nuclear receptors -- peroxisome proliferator-activated receptors -- virtual combinatorial library; Pharmaceutical chemistry -- Periodicals; 615.19005
• DOI: 10.1002/cmdc.201900286
• Languages: English
• ISSNs: 1860-7179

Abstract

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. Abstract : Selective optimization of side‐activities (SOSA) aims to invert the activity profiles of drug molecules. We have automated this approach using the CysLT1 R antagonist cinalukast as lead, which has weak side‐activity on PPARα. Automated analogue design and scoring produced a descendant of the drug that was experimentally confirmed as more active and markedly more selective toward the side‐target, corroborating the concept of computer‐assisted SOSA.