Identification of PPARγ ligands with One-dimensional Drug Profile Matching
Authors Kovács D, Simon Z, Hári P, Málnási-Csizmadia A, Hegedűs C, Drimba L, Németh J, Sári R, Szilvássy Z, Peitl B
Received 24 April 2013
Accepted for publication 11 June 2013
Published 2 September 2013 Volume 2013:7 Pages 917—928
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 3
Diána Kovács,1 Zoltán Simon,2,3 Péter Hári,2,3 András Málnási-Csizmadia,2,4,5 Csaba Hegedus,6 László Drimba,1 József Németh,1 Réka Sári,1 Zoltán Szilvássy,1 Barna Peitl1
1Department of Pharmacology and Pharmacotherapy, University of Debrecen, Debrecen, Hungary; 2Drugmotif, Ltd, Veresegyház, Hungary; 3Printnet, Ltd, Budapest, Hungary; 4Department of Biochemistry, Institute of Biology, Eötvös Loránd University, Budapest, Hungary; 5Molecular Biophysics Research Group, Hungarian Academy of Sciences – Eötvös Loránd University, Budapest, Hungary; 6Cera-Med Ltd, Debrecen-Józsa, Hungary
Introduction: Computational molecular database screening helps to decrease the time and resources needed for drug development. Reintroduction of generic drugs by second medical use patents also contributes to cheaper and faster drug development processes. We screened, in silico, the Food and Drug Administration-approved generic drug database by means of the One-dimensional Drug Profile Matching (oDPM) method in order to find potential peroxisome proliferator-activated receptor gamma (PPARγ) agonists. The PPARγ action of the selected generics was also investigated by in vitro and in vivo experiments.
Materials and methods: The in silico oDPM method was used to determine the binding potency of 1,255 generics to 149 proteins collected. In vitro PPARγ activation was determined by measuring fatty acid-binding protein 4/adipocyte protein gene expression in a Mono Mac 6 cell line. The in vivo insulin sensitizing effect of the selected compound (nitazoxanide; 50–200 mg/kg/day over 8 days; n = 8) was established in type 2 diabetic rats by hyperinsulinemic euglycemic glucose clamping.
Results: After examining the closest neighbors of each of the reference set’s members and counting their most abundant neighbors, ten generic drugs were selected with oDPM. Among them, four enhanced fatty acid-binding protein/adipocyte protein gene expression in the Mono Mac 6 cell line, but only bromfenac and nitazoxanide showed dose-dependent actions. Induction by nitazoxanide was higher than by bromfenac. Nitazoxanide lowered fasting blood glucose levels and improved insulin sensitivity in type 2 diabetic rats.
Conclusion: We demonstrated that the oDPM method can predict previously unknown therapeutic effects of generic drugs. Nitazoxanide can be the prototype chemical structure of the new generation of insulin sensitizers.
Keywords: computer-aided prediction of receptor-ligand interaction, in silico lead selection, insulin sensitizers, one-dimensional drug profile matching, peroxisome proliferator activated receptor gamma, PPARγ, type two diabetes
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