Molecular modeling and ADMET predictions of flavonoids as prospective aromatase inhibitors

192-200With the advent of a myriad of treatment possibilities for breast cancer, enzyme inhibition turns out to be the prevailing strategy for inhibiting estrogen biosynthesis. Aromatization of ring A of androstenedione, testosterone and 16-hydroxytestosterone results in increased estrogen level, which embraces the risk for breast cancer. In this present research, we have targeted human placental aromatase complexed with HDDG046 (PDB ID: 4GL7) for its inhibition by several inhibitors of flavonoid derivatives and further screening those molecules for ADMET properties for assessing its credibility for acceptance in successive steps of drug discovery. Novel flavonoid derivative molecules have been designed using Maestro 10.4, based on the literature review. Further, their molecular modeling studies have been performed against the imported target PDB ID: 4GL7 using the GLIDE platform and have been subjected to ADMET assessment using the QikProp and pkCSM program. From all the series exposed to molecular modeling; 2K, 4K, 6K, 8W and 10K molecules have been subjected to ADMET study based on their interaction profile. Successively screening of these molecules led to selection of 8W molecule for further validation by pkCSM. The results obtained have been compared with the reported molecule HDDG046 which presents substantially positive outcomes for 8W in terms of CaCo2 permeability, water solubility, P- glycoprotein; hERG I, II and CYP interactions, hepatotoxicity, LD50 value and so forth. Juxtaposing the results of all the designed molecules under study, we have established that these prospective molecules especially 8W of flavonoid derivatives have the potency to inhibit the target under study, which can be useful in the treatment of breast cancer. This has been estimated based on the in silico approaches performed using Molecular Modeling which utilizes the integral function of Molecular Mechanics and Quantum Mechanics. In addition, the ADMET predictions validate their integrity for being the lead molecules in drug discovery stages in the near future

Molecular modeling and ADMET predictions of flavonoids as prospective aromatase inhibitors

With the advent of a myriad of treatment possibilities for breast cancer, enzyme inhibition turns out to be the prevailing strategy for inhibiting estrogen biosynthesis. Aromatization of ring A of androstenedione, testosterone and 16a-hydroxytestosterone results in increased estrogen level, which embraces the risk for breast cancer. In this present research, we have targeted human placental aromatase complexed with HDDG046 (PDB ID: 4GL7) for its inhibition by several inhibitors of flavonoid derivatives and further screening those molecules for ADMET properties for assessing its credibility for acceptance in successive steps of drug discovery. Novel flavonoid derivative molecules have been designed using Maestro 10.4, based on the literature review. Further, their molecular modeling studies have been performed against the imported target PDB ID: 4GL7 using the GLIDE platform and have been subjected to ADMET assessment using the QikProp and pkCSM program. From all the series exposed to molecular modeling; 2K, 4K, 6K, 8W and 10K molecules have been subjected to ADMET study based on their interaction profile. Successively screening of these molecules led to selection of 8W molecule for further validation by pkCSM. The results obtained have been compared with the reported molecule HDDG046 which presents substantially positive outcomes for 8W in terms of CaCo2 permeability, water solubility, P- glycoprotein; hERG I, II and CYP interactions, hepatotoxicity, LD50 value and so forth. Juxtaposing the results of all the designed molecules under study, we have established that these prospective molecules especially 8W of flavonoid derivatives have the potency to inhibit the target under study, which can be useful in the treatment of breast cancer. This has been estimated based on the in silico approaches performed using Molecular Modeling which utilizes the integral function of Molecular Mechanics and Quantum Mechanics. In addition, the ADMET predictions validate their integrity for being the lead molecules in drug discovery stages in the near future