Generic placeholder image

Anti-Cancer Agents in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1871-5206
ISSN (Online): 1875-5992

Research Article

Discovery of Potent Bruton’s Tyrosine Kinase Inhibitors Using Ligand Based Modeling

Author(s): Wafa A. Mera, Malek Alzihlif, Mutasem O. Taha and Mohammad A. Khanfar

Volume 17, Issue 2, 2017

Page: [265 - 275] Pages: 11

DOI: 10.2174/1871520616666160926114416

Price: $65

Abstract

Background: Bruton’s Tyrosine Kinase (BTK) is a one of the Tec tyrosine kinase family. It has an essential role in B-cell development and function. Activation of BTK has been associated with the pathogenesis of many types of lymphomas and leukemia, and involved in non-life threatening autoimmune diseases.

Objective: In this study, exhaustive pharmacophore modeling was combined with QSAR analyses to examine the structural requirements for anti-BTK activities.

Method: Genetic function algorithm (GFA) was coupled with multiple linear regression (MLR) analysis to select the best combinations of physicochemical descriptors and pharmacophoric hypothesis capable of generating predictive and self-consistent QSAR models. The optimum pharmacophores were decorated with exclusion volumes to improve their receiver operating characteristic (ROC) curve properties. The best predictive QSAR model and its corresponding pharmacophore models were validated by discovering of novel promising BTK inhibitors retrieved from the National Cancer Institute (NCI) database.

Results: Several potent hits exhibited anti-proliferative activities on U-937 cell-line in low micromolar IC50, and one active compound showed nontoxic activities on normal fibroblast cell line.

Conclusion: Our efforts culminated in the identification of potent BTK ligands having desired inhibitory activities and structurally distinct from known active reference compounds (i.e., training compounds) and represent new chemotypes.

Keywords: BTK, QSAR, Pharmacophore, anticancer, multiple linear regression.

Graphical Abstract

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy