Epidermal growth factor receptor (EGFR) structure-based bioactive pharmacophore models for identifying next-generation inhibitors against clinically relevant EGFR mutations

Abstract

Present work elucidates identification of next generation inhibitors for clinically relevant mutations of epidermal growth factor receptor (EGFR) using structure-based bioactive pharmacophore modeling followed by virtual screening (VS) techniques. Three-dimensional (3D) pharmacophore models of EGFR and its different mutants were generated. This includes seven 3D pharmacophoric points with three different chemical features (descriptors), that is, one hydrogen bond donor, three hydrogen bond acceptors and three aromatic rings. Pharmacophore models were validated using decoy dataset, Receiver operating characteristic plot, and external dataset compounds. The robust, bioactive 3D e-pharmacophore models were then used for VS of four different small compound databases: FDA approved, investigational, anticancer, and bioactive compounds collections of Selleck Chemicals. CUDC101 a multitargeted kinase inhibitor showed highest binding free energy and 3D pharmacophore fit value than the well known EGFR inhibitors, Gefitinib and Erlotinib. Further, we obtained ML167 as the second best hit on VS from bioactive database showing high binding energy and pharmacophore fit value with respect to EGFR receptor and its mutants. Optimistically, presented drug discovery based on the computational study serves as a foundation in identifying and designing of more potent EGFR next-generation kinase inhibitors and warrants further experimental studies to fight against lung cancer.

Keywords: computer-aided drug design; epidermal growth factor receptor; erlotinib; next-generation EGFR inhibitors; non-small cell lung cancer; pharmacophore; virtual screening.