Computer-Aided Design And Synthesis Of Novel Human Aromatase(Cyp19)Inhibitors

In many countries, breast cancer is one of the most common carcinomas among women living. Nearly two-thirds of breast cancers are hormone-dependent and estrogens play a critical role for these cancers’cell proliferation. Aromatase converts androgens to estrogens and is a particularly attractive target in the treatment of estrogen receptor positive breast cancer. Inhibitors of this enzyme are potential therapeutics for estrogen dependant breast cancers. However, the current aromatase inhibitors can suffer from drug related toxicity, severer drug resistance and serious drug-drug interactions. Therefore, there is an emergent need to develop novel aromatase inhibitors with higher activity, high selectivity and lower toxicity.In search of potent aromatase inhibitors, molecular docking studies by using CDocker of receptor-ligand interactions protocol section of Discovery Studio2.1have been used. The results obtained in the docking study indicate all the compounds in the higher activity range from one or two hydrogen bond（s） with amide backbone of Met374. On the base of docked conformation, three-dimensional quantitative structure-activity relationship （3D-QSAR） and two-dimensional relationship（2D-QSAR） studies using molecular shape, spatial, electronic, structural and thermodynamic descriptors have been performed on a diverse set of compounds having human aromatase inhibitory activities. This paper mainly uses genetic function approximation （GFA） to study the QSAR of steroidal and non-steroidal compounds aromatase inhibitors, and finally we established the high correlation and predictable QSAR model. In the study of steroidal aromatase inhibitors QSAR model obtained indicates that the aromatase inhibitory activity can be enhanced by increasing SIC, SC_3C, Jurs_WNSA₁, Jurs_WPSA₁and decreasing CDocker interaction energy （ECD）, IAC_Total and Shadow_XZfrac. The model built by this method showed satisfactory statistical results whose proper predictability was validated by independent test set（train set:R2=0.790, test set:R2=0.730）. The predicted results shows that this model has a comparatively good predictive power which can be used in prediction of activity of new steroidal aromatase inhibitors.In the study of non-steroidal aromatase inhibitors QSAR model, the molecular docking study shows that one or more hydrogen bond formation with Met374is one of the essential requirements for the ligands for optimum aromatase inhibition. The binding is further stabilized by van der Waals interactions with a few non-polar amino acid residues in the active site. The QSAR model obtained indicates that the aromatase inhibitory activity can be enhanced by increasing Num_ChainAssemblies, V_DIST_mag and CHI_3C and decreasing Molecular Volume, E_DIST_equ, Jurs_RPCS and Shadow_XYfrac. The model built by this method showed satisfactory statistical results whose proper predictability was validated by independent test set（train set:R2=0.776, test set:R2=0.712）. The predicted results shows that this model has a comparatively good predictive power which can be used in prediction of activity of new non-steroidal aromatase inhibitors.In this paper, based on the established model of non-steroidal aromatase inhibitors QSAR,13non-steroidal compounds were designed and synthesized. The structures of all compounds and the intermediate compounds have been confirmed by1HNMR and have been carried on the inhibitory activity test. The test results shows that have good inhibition activity compound are compound2-3（IC50=1.6μg/mL）, compound2-6（IC50=1.7μg/mL）,2-7（IC50=2.4μg/mL） and compound2-14（IC50=0.7μg/mL）. The inhibitory activity of compound2-14is better than Aminoblastin（IC50=7.7μg/mL） and Tamoxifen （IC50=1.2μg/mL）.The success rate of model design is69.23%, and its prediction was coincident with our experiment results.