Identification of 1,2,4-Oxadiazoles-Based Novel EGFR Inhibitors: Molecular Dynamics Simulation-Guided Identification and in vitro ADME Studies

Abstract

Background In this work, we have identified heterocyclic derivatives with 1,2,4 oxadiazole scaffold mimicking the functions of tyrosine kinase inhibitors. Fourteen molecules that displayed the best fit were picked from the library of compounds and studied under in-silico and in-vitro conditions. Four compounds were selected for further cytotoxicity and ADME (Absorption, Distribution, Metabolism, Elimination) profiling showing IC 50 (from 8–13 µM) values against EGFR positive cancer cell line (MCF7). Methods A molecular dynamics simulation study was performed to understand the correlation of non-covalent binding energies with biological activity. The drug-like properties of the selected four compounds (7a, 7b, 7e, and 7m) were evaluated by in-vitro ADME studies. Compounds 7a, 7b , and 7m were the active compounds in the molecular dynamics simulations study. Further, EGFR binding activity was confirmed with EGFR WT and EGFR T790M kinase assay using a luminescence-based method. Results These compounds ( 7a, 7b , and 7m ) showed activity against EGFR WT and mutant EGFR T790M , exhibiting IC 50 values of <10 and <50 micromolar, respectively. These compounds also possess moderate aqueous solubility in 40–70 µg/mL at pH 7.4 and 30–100 µg/mL at pH 4.0. Further, 7a, 7b , and 7m showed balanced lipophilicity with Log D values ranging from 1–3. They demonstrated a good correlation in Caco-2 permeability with Apparent permeability (Papp) 1 to 5 × 10 −6 cm/s in comparison with 7e , which was found to be highly lipophilic (Log D >5) and showed high permeability (Papp 17 × 10 −6 cm/s). Lastly, all these compounds were moderately stable in liver microsomes at alkaline pH with a half-life of 30–60 min, while at a highly acidic pH (2.0), the compounds were stable up to 15–20 min. Conclusion Overall, in-vitro ADME results of these molecules showed good drug-like properties, which are well correlated with the in-silico ADME data, making them ideal for developing an oral drug delivery formulation.