Molecular modeling computations were utilized to generate pharmaceutical grade CYP3A4-enzyme inhibitors. In vitro metabolism of felodipine in human intestinal and liver microsomes (HLM and HIM) was optimized yielding a Michaelis-Menten plot from where the K(m) and V(max) values were estimated by nonlinear regression. The flavonoids, naringin, naringenin, and quercetin, were subsequently incubated with felodipine at the determined K(m) value in HLM. Comparing results obtained from a known CYP3A4 inhibitor, verapamil, the flavonoids inhibited felodipine metabolism. In-depth computational analysis of these flavonoids in terms of CYP3A4 binding, sequencing, and affinity, computational biomimetism was employed to validate the potential CYP3A4 inhibitors. The modeled compounds were comparatively evaluated by incubation with felodipine in both HLM and HIM. Results showed that the polymers 8-arm-PEG, o-(2-aminoethyl)-o-methyl-PEG, 4-arm-PEG (molecular weight = 10,000 g/mol and 20,000 g/mol, respectively), and poly(l-lysine) were able to inhibit the felodipine metabolism with the half maximal inhibitory concentration (IC(50)) values ranging from 7.22 to 30.0 μM (HLM) and 5.78 to 41.03 μM (HIM). Molecular docking confirmed drug-enzyme interactions by computing the free energies of binding (ΔE) and inhibition constants (K(i)) of the docked compounds utilizing a Lamarckian Genetic Algorithm. Comparative correlations between the computed and experimental K(i) values were obtained. Computational modeling of CYP3A4 inhibitors provided a suitable strategy to screen pharmaceutical grade compounds that may potentially inhibit presystemic CYP3A4-dependent drug metabolism with the prospect of improving oral drug bioavailability.
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