Structure-based design and activity modeling of novel epidermal growth factor receptor kinase inhibitors; an in silico approach

The development of EGFR kinase inhibitors to treat non-small cell lung cancers is an important medical necessity due to persistent development of resistance by the mutations. In-view of this, a model with a very high predictive ability on thirty (30) EGFR kinase inhibitors was built using QSAR molecular modeling technique. Density Functional Theory method at B3LYP/6-311G* level of theory was used to identify the optimum conformation of the EGFR kinase inhibitors. A Multi-linear regression Genetic Function Algorithm method (MLR-GFA) was used to build five models. The described model among others was selected and reported since it has passed the requirements for good model validation with resulting parameters: R2 of 0.927451, R2adj of 0.901845, Q2CV of 0.869431R2test of 0.767984 and cRp2 of 0.625651. Molecular docking, drug-likeness and ADME studies were carried out to virtually screen these EGFR kinase inhibitors in order to identify a compound with the highest affinity toward the target protein (EGFR kinase enzyme with pdb entry 3IKA) with good pharmacokinetic profile. Compound 11 was identified as the lead compound with the highest binding affinity of −9.8 kcal/mol (and also with good ADME and drug-likeness properties) and was retained as template for designing new EGFR kinase inhibitors. Six new EGFR kinase inhibitors were designed using compound 11 as template. The newly designed compounds were found to have better affinity toward their target protein than the template and Gefitinib (positive control). The reported model predictive performance was further confirmed by predicting the pIC50 of these newly designed compounds.