Application to Modeling of Aromatase Inhibitory Activity
Stephen J. Barigye*† , Matheus P. Freitas‡ , Priscila Ausina∥, Patricia Zancan§, Mauro Sola-Penna∥, and Juan A. Castillo-Garit⊥
† Department of Chemistry, McGill University, 801 Sherbrooke Street West, Montréal, QC H3A 0B8, Canada ‡ Department of Chemistry, Federal University of Lavras, P.O. Box 3037, 37200-000 Lavras-MG Brazil § Laboratório de Oncobiologia Molecular (LabOMol), Departamento de Biotecnologia Farmacêutica, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, 21941-902 Rio de Janeiro-RJ, Brazil ∥ Laboratório de Enzimologia e Controle do Metabolismo (LabECoM), Departamento de Biotecnologia Farmacêutica, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, 21941-902 Rio de Janeiro-RJ, Brazil ⊥ Unidad de Toxicología Experimental, Universidad de Ciencias Médicas “Serafín Ruiz de Zárate Ruiz”, Santa Clara, 50200 Villa Clara, Cuba
ACS Comb. Sci., 2018, 20 (2), pp 75–81 DOI: 10.1021/acscombsci.7b00155 Publication Date (Web): January 3, 2018 Copyright © 2018 American Chemical Society *Phone: 1-514-660-9351. E-mail: stephen.barigye@mcgill.ca.
Abstract
We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure–activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure–activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.