In Silico Screening for Novel Tyrosine Kinase Inhibitors with Oxindole Scaffold as Anti-Cancer Agents: Design, QSAR Analysis, Molecular Docking and ADMET Studies

Abstract

Recently, anti-cancer targeting drugs are directed against specific molecules and signaling pathways. These targeting agents have reasonable specificity, efficacy and less side effects. Tyrosine kinases, which play an essential role in growth factor signaling regulation, are significant targets in this type of therapy. Synthesized numerous tyrosine-kinase inhibitors (TKIs), such as substituted indolin-2-ones, are effective as anti-tumor and anti-leukemia agents. In this study, a series of novel substituted indolin-2-ones were studied as kinase inhibitor analogs through quantitative structure-activity relationship (QSAR) analysis. Two chemometrics methods, such as multiple linear regression (MLR) and partial least squares combined with genetic algorithm for variable selection (GA-PLS), were employed to establish relationships between structural characteristics and kinase inhibitory activity of used oxindole analogs. The GA-PLS was developed as the best predictor and validated QSAR model. The data set compounds were also studied by molecular docking to investigate their binding mechanism in the active site of tyrosine kinase enzyme. According to the information obtained from QSAR models and molecular docking analysis, 40 new potent lead compounds with novel structural features were introduced. Molecular docking, drug-likeness rules, ADMET analysis, bioavailability, toxicity prediction and target identification were carried out on the newly designed oxindoles to elucidate fundamental structural properties that affect their inhibitory activity. Select biological data set (44 compounds) then developed QSAR models (MLR, GA-PLS) and molecular docking. Design new oxindole based structures (40 structures) and calculation of pred.pIC50 & Docking binding energy. Select 10 compounds and obtain in Silico Drug-Likeness, Bioavailability, Toxicology prediction and target identification parameters then 6 hits identified for next study.