Computational identification and exploration of novel FGFR tyrosine kinase inhibitors for the treatment of cholangiocarcinoma

Tyrosine kinase inhibitors are a specific drug class revolutionizing cancer treatment. FGFR (Fibroblast Growth Factor Receptor) is a member of the receptor tyrosine kinase family that has been involved in various alterations which have been increasingly recognized as critical molecular drivers in cholangiocarcinoma, a malignant tumor originating from bile duct epithelial cells. The paper focuses on stepwise computational investigations for the discovery of novel inhibitors of FGFR using pharmacophore modeling, virtual screening, docking, ADMET analysis, molecular dynamics, and knowledge-based structure-activity relationship. To begin with, we have considered a library of 120314868 compounds from the ZINC 15 database through pharmacophore modeling, which was narrowed down to 110 having binding affinity >-8.0 kcal mol(-1). The 110 compounds were analyzed using virtual screening and compared with the FDA-approved drug pemigatinib, which provided the 34 hits with binding affinities >-6.5 kcal mol(-1). Finally, the top 4 hits were considered for docking, and ADMET property analysis for drug-likeness. MD and MM-GBSA analysis were performed to predict the binding free energy of these chemicals and determine their stability. To gain insight into the structure and binding interactions of these compounds, knowledge-based SAR analyses were performed using their electrostatic potential maps computed with DFT. Several techniques were employed to build improved inhibitors based on these SAR, and they were then analyzed utilizing ADMET, MD studies, and MM-GBSA analyses. Finally, the results suggested that the identified four compounds and developed inhibitors from this current work can be employed effectively as prospective FGFR inhibitors for treating Cholangiocarcinoma.Communicated by Ramaswamy H. Sarma

Automated summary

This study focuses on computational investigations for the discovery of novel FGFR inhibitors. Through pharmacophore modeling, virtual screening, docking, ADMET analysis, molecular dynamics, and knowledge-based structure-activity relationship, four potential compounds were identified and analyzed for drug-likeness and stability.