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Adv Pharm Bull. 2020;10(2): 271-277.
doi: 10.34172/apb.2020.032
PMID: 32373496
PMCID: PMC7191237
Scopus ID: 85088708880
  Abstract View: 421
  PDF Download: 315

Research Article

A QSAR Study on the 4-Substituted Coumarins as Potent Tubulin Polymerization Inhibitors

Leila Dinparast 1 ORCID logo, Siavoush Dastmalchi 2,3,4* ORCID logo

1 Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
2 Pharmaceutical Analysis Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
3 School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.
4 Faculty of Pharmacy, Near East University, POBOX: 99138, Nicosia, North Cyprus, Mersin 10, Turkey.
*Corresponding Author: Faculty of Pharmacy, Near East University, POBOX: 99138, Nicosia, North Cyprus, Mersin 10, Turkey. Email dastmalchi.s@tbzmed.ac.ir
*Corresponding Author: Siavoush Dastmalchi, Tel: +98 41 33364038, Email: dastmalchi. dastmalchi.s@tbzmed.ac.ir

Abstract

Purpose: Despite the discovery and synthesis of several anticancer drugs, cancer is still a major life threatening incident for human beings after cardiovascular diseases. Toxicity, severe side effects, and drug resistance are serious problems of available commercial anticancer drugs. Coumarins are synthetic and natural heterocycles that show promising antiproliferative activities against various tumors. The aim of this research is to computationally study the coumarin derivatives in order to develop reliable quantitative structure-activity relationship (QSAR) models for predicting their anticancer activities.

Methods: A data set of thirty one coumarin analogs with significant antiproliferative activities toward HepG2 cells were selected from the literature. The molecular descriptors for these compounds were calculated using Dragon, HyperChem, and ACD/Labs programs. Genetic algorithm (GA) accompanied by multiple linear regression (MLR) for simultaneous feature selection and model development was employed for generating the QSAR models.

Results: Based on the obtained results, the developed linear QSAR models with three and four descriptors showed good predictive power with r2 values of 0.670 and 0.692, respectively. Moreover, the calculated validation parameters for the models confirmed the reliability of the QSAR models.

Conclusion: The findings of the current study could be useful for the design and synthesis of novel anticancer drugs based on coumarin structure.

Keywords: Coumarin, Cancer, Antiproliferative, QSAR, GA-MLR
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Submitted: 05 Mar 2019
Revision: 13 Oct 2019
Accepted: 09 Nov 2019
ePublished: 18 Feb 2020
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