(untitled) (International Journal of Pharmaceutical Sciences and Drug Research, 2023)
We performed a Relation Computed-Aided design based on the structure of benzo[d]isoxazol derivatives inhibitors (BDIO) derivatives, new potent inhibitors of the BRD4 protein. By using in situ modifications of the three Dimensional (3D) models of BRD4-BDIOx complex (Protein Data Bank (PDB) entry code: 5Y8Z) were prepared for the training and validation sets compounds of 29 BDIOx with observed inhibitory potencies (). We first built a Quantitative Structure Activity Relationship (QSAR) model in the gas phase, linearly correlating the calculated enthalpies of the BRD4-BDIOx complex formation with ( ; = 0,80) first and then a superior QSAR model was brought forth, correlating computed relative Gibbs’ free energies of complexation and ( = -0.1205 + 6.9374 ; = 0.96) which was then validated by a 3D-QSAR pharmacophore generation model (PH4) ( = 0.996 + 0.0554 ; = 0.95). The structural information of the active conformation of the training set BDIOs from the models guided us in the design of a virtual combinatorial library (VCL) of 99 225 analogs. We then filtered the VCL by applying Lipinski’s rule-of-five, in order to identify new BDIOs drug likely analogs. The Pharmacophore (PH4)-based screening retained 106 new and potent BDIOs with predicted inhibitory potencies up to 158 times more active than the most active traing set BDIO1 ( ). Finally, the predicted pharmacokinetic profiles of the best potent of these new analogs ( ) were compared to current orally administered anticancer drugs. This computational approach, which combines molecular mechanics and the Poisson–Boltzmann (PB) implicit solvation theory, the pharmacophore model, the analysis of BRD4-BDIOs interaction energies, the in silico screening of VCL compounds, and the inference of ADME properties resulted in a set of new suggested BRD4 inhibitors for the fight against CRPC.