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Adv Pharm Bull. 2017;7(3): 409-418.
doi: 10.15171/apb.2017.049
PMID: 29071223
PMCID: PMC5651062
Scopus ID: 85030089731
  Abstract View: 1715
  PDF Download: 1668

Research Article

An Alignment-Independent 3D-QSAR Study of FGFR2 Tyrosine Kinase Inhibitors

Behzad Jafari 1,2,3, Maryam Hamzeh-Mivehroud 1,2, Ali Akbar Alizadeh 1, Mehdi Sharifi 1,2, Siavoush Dastmalchi 1,2*

1 Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
2 School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.
3 Students Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.
*Corresponding Author: Email: siavoush11@yahoo.com

Abstract

Purpose: Receptor tyrosine kinase (RTK) inhibitors are widely used pharmaceuticals in cancer therapy. Fibroblast growth factor receptors (FGFRs) are members of RTK superfamily which are highly expressed on the surface of carcinoma associate fibroblasts (CAFs). The involvement of FGFRs in different types of cancer makes them promising target in cancer therapy and hence, the identification of novel FGFR inhibitors is of great interest. In the current study we aimed to develop an alignment independent three dimensional quantitative structure-activity relationship (3D-QSAR) model for a set of 26 FGFR2 kinase inhibitors allowing the prediction of activity and identification of important structural features for these inhibitors. Methods: Pentacle software was used to calculate grid independent descriptors (GRIND) for the active conformers generated by docking followed by the selection of significant variables using fractional factorial design (FFD). The partial least squares (PLS) model generated based on the remaining descriptors was assessed by internal and external validation methods. Results: Six variables were identified as the most important probes-interacting descriptors with high impact on the biological activity of the compounds. Internal and external validations were lead to good statistical parameters (r2 values of 0.93 and 0.665, respectively). Conclusion: The results showed that the model has good predictive power and may be used for designing novel FGFR2 inhibitors.

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Submitted: 04 Jul 2017
Revision: 08 Aug 2017
Accepted: 12 Aug 2017
ePublished: 25 Sep 2017
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