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

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

Volume: 17 Issue: 2

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

Affiliation:

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

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.

Cite this article as:

Mera A. Wafa, Alzihlif Malek, Taha O. Mutasem and Khanfar A. Mohammad, Discovery of Potent Bruton’s Tyrosine Kinase Inhibitors Using Ligand Based Modeling, Anti-Cancer Agents in Medicinal Chemistry 2017; 17 (2) . https://dx.doi.org/10.2174/1871520616666160926114416