Computer-Assisted Selective Optimization of Side-Activities-from Cinalukast to a PPARα Modulator.
ChemMedChem
; 14(14): 1343-1348, 2019 07 17.
Article
em En
| MEDLINE
| ID: mdl-31141287
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 receptorâ
1 (CysLT1 R) antagonist cinalukast as lead for which we discovered peroxisome proliferator-activated receptorâ
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 inâ
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
PPAR alfa
/
Bibliotecas de Moléculas Pequenas
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article