From random to rational: A discovery approach to selective subnanomolar inhibitors of human carbonic anhydrase IV based on the Castagnoli-Cushman multicomponent reaction.


Journal

European journal of medicinal chemistry
ISSN: 1768-3254
Titre abrégé: Eur J Med Chem
Pays: France
ID NLM: 0420510

Informations de publication

Date de publication:
15 Nov 2019
Historique:
received: 31 07 2019
revised: 21 08 2019
accepted: 22 08 2019
pubmed: 3 9 2019
medline: 31 12 2019
entrez: 3 9 2019
Statut: ppublish

Résumé

By exploiting the power of multicomponent chemistry, a relatively small, diverse set of primary sulfonamides was synthesized and screened against a panel of human carbonic anhydrases to reveal a low-nanomolar, albeit non-selective hCA IV lead inhibitor. Investigation of the docking poses of this compound identified a hydrophilic pocket unique to hCA IV and conveniently positioned near the carboxylate functionality of the initial lead. Various residues capable of forming hydrogen bonds as well as salt bridges were placed in this pocket via a carboxamides linkage, which led to drastic improvement of potency and selectivity towards hCA IV. This improvement of the desired inhibitory profile was rationalized by the new contacts as had been envisioned. These new tool compounds were shown to possess selective, dose-dependent cytotoxicity against human glioma T98G cell line. The latter showed a substantially increased hCA IV mRNA expression under hypoxic conditions.

Identifiants

pubmed: 31476557
pii: S0223-5234(19)30776-7
doi: 10.1016/j.ejmech.2019.111642
pii:
doi:

Substances chimiques

Antineoplastic Agents 0
Carbonic Anhydrase Inhibitors 0
RNA, Messenger 0
Carbonic Anhydrase IV EC 4.2.1.-
CA4 protein, human EC 4.2.1.1

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

111642

Informations de copyright

Copyright © 2019 Elsevier Masson SAS. All rights reserved.

Auteurs

Stanislav Kalinin (S)

Saint Petersburg State University, Saint Petersburg, 199034, Russian Federation.

Alessio Nocentini (A)

Department of NEUROFARBA, Section of Pharmaceutical and Nutraceutical Sciences, Laboratory of Molecular Modeling Cheminformatics & QSAR, University of Florence, Polo Scientifico, Via U. Schiff 6, 50019, Sesto Fiorentino, Firenze, Italy.

Alexander Kovalenko (A)

Saint Petersburg State University, Saint Petersburg, 199034, Russian Federation.

Vladimir Sharoyko (V)

Saint Petersburg State University, Saint Petersburg, 199034, Russian Federation.

Alessandro Bonardi (A)

Department of NEUROFARBA, Section of Pharmaceutical and Nutraceutical Sciences, Laboratory of Molecular Modeling Cheminformatics & QSAR, University of Florence, Polo Scientifico, Via U. Schiff 6, 50019, Sesto Fiorentino, Firenze, Italy.

Andrea Angeli (A)

Department of NEUROFARBA, Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Polo Scientifico, Via U. Schiff 6, 50019, Sesto Fiorentino, Firenze, Italy.

Paola Gratteri (P)

Department of NEUROFARBA, Section of Pharmaceutical and Nutraceutical Sciences, Laboratory of Molecular Modeling Cheminformatics & QSAR, University of Florence, Polo Scientifico, Via U. Schiff 6, 50019, Sesto Fiorentino, Firenze, Italy.

Tatiana B Tennikova (TB)

Saint Petersburg State University, Saint Petersburg, 199034, Russian Federation.

Claudiu T Supuran (CT)

Department of NEUROFARBA, Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Polo Scientifico, Via U. Schiff 6, 50019, Sesto Fiorentino, Firenze, Italy. Electronic address: claudiu.supuran@unifi.it.

Mikhail Krasavin (M)

Saint Petersburg State University, Saint Petersburg, 199034, Russian Federation. Electronic address: m.krasavin@spbu.ru.

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Classifications MeSH