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Computer‐Assisted Selective Optimization of Side‐Activities—from Cinalukast to a PPARα Modulator
Author(s) -
Pollinger Julius,
Schierle Simone,
Neumann Sebastian,
Ohrndorf Julia,
Kaiser Astrid,
Merk Daniel
Publication year - 2019
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.201900286
Subject(s) - antagonism , antagonist , peroxisome proliferator activated receptor , receptor , in vitro , computational biology , pharmacology , chemistry , combinatorial chemistry , biology , biochemistry
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 (CysLT 1 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 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 PPARα activation and diminished CysLT 1 R antagonism. Thereby, this prospective application study highlights the potential of automating SOSA.