In Silico Modelling of Hepatocellular Carcinoma Linked PARP-1 Protein and Screening of Potential Inhibitors

Abstract

Hepatocellular carcinoma (HCC) is one among the hard-treating cancer with a high mortality rate and there is an urgent demand of more effective therapeutics for it. So many complications and side effects are associated with present available treatment modalities. There is a scarcity of drug molecules for HCC and most of them are (viz. Sorafenib) under clinical trials. Few proteins are reported which are linked with hepatocellular carcinoma. The HCC-linked protein Poly[ADP-ribose]polymerase-1 (PARP-1) is identified as a druggable target. We have used a computational method to find novel inhibitor as potential drug candidate molecule to treat HCC. Ninety-one conformations of PARP-1 inhibitors were designed for standard precision in silico docking. The different physical parameter scores, viz. docking score and glide score, were used to identify the suitable inhibitors. The best-docked inhibitors were further characterized by ADMET analysis, including Lipinski’s rule of five, Jorgensen’s rule of three, blood–brain barrier penetration index, skin permeability index, human intestinal absorption index and oral absorption index. C₁₅H₁₉N₃O₂ ⁺ (PJ3410) was identified as the best inhibitor of PARP-1 among all the selected 91 inhibitors conformers with best ADMET properties. PJ3410 may be a better drug molecule for the inhibition of HCC-linked PARP-1 protein. Present investigation reveals the importance of structure-based computational drug design methodology which is time and cost-effective.