Structure-based pharmacophore mapping and virtual screening of natural products to identify polypharmacological inhibitor against c-MET/EGFR/VEGFR-2

Abstract

Three receptor tyrosine kinases (RTKs), c-MET, EGFR, and VEGFR-2 have been identified as potential oncogenic targets involved in tumor development, metastasis, and invasion. Designing inhibitors that can simultaneously interact with multiple targets is a promising approach, therefore, inhibiting these three RTKs with a single chemical component might give an effective chemotherapeutic strategy for addressing the disease while limiting adverse effects. The in-silico methods have been developed to identify the polypharmacological inhibitors particularly for drug repurposing and multitarget drug design. Here, to find a viable inhibitor from natural source against these three RTKs, structure–based pharmacophore mapping and virtual screening of SN-II database were carried out. The filtered compound SN00020821, identified as Cedeodarin, from different computational approaches, demonstrated good interactions with all the three targets, c-MET/EGFR/VEGFR-2, with interaction energies of −42.35 kcal/mol, −49.32 kcal/mol and −44.83 kcal/mol, respectively. SN00020821displayed stable key interactions with critical amino acids of all the three receptors' kinase catalytic domains including “DFG motif” explored through the MD simulations. Furthermore, it also met the ADMET requirements and was determined to be drug-like as predicted from the Lipinski’s rule of five and Veber’s rule. Finally, SN00020821 provides a novel molecular scaffold that could be investigated further as a polypharmacological anticancer therapeutic candidate that targets the three RTKs.

Communicated by Ramaswamy H. Sarma

Keywords:

• Cancer
• Pharmacophore modeling
• c-MET
• EGFR
• VEGFR
• Virtual screening
• Cedeodarin

Acknowledgement

This work was done utilizing the bioinformatics facility of Dr. B. R Ambedkar center for Biomedical Research, New Delhi and Translational Health Science and Technology Institute (THSTI), Faridabad. Authors thank ACBR and BIOVIA Accelrys (Bioinformatics facility), Bangalore for all the technical support offered. M. Singh is thankful to Council of Scientific & Industrial Research (CSIR), New Delhi for awarding Senior Research Fellowship (Grant no. 09/1049(0023)/017/EMR-1).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Funding

The author(s) reported there is no funding associated with the work featured in this article.