Computational Models for 5αR Inhibitors for Treatment of Prostate Cancer: Review of Previous Works and Screening of Natural Inhibitors of 5αR2
Rajamani M. Jayadeepa,
Taking into consideration the high importance of the drug target 5-α-reductase (5αR) in prostate cancer in this work we are going first to review previous works and discuss works related to the computer aided drug design of 5αR inhibitors. We report new results in the in silico screening of natural 5αR inhibitors. Traditionally, drugs were discovered by testing compounds synthesized in time consuming multi-step processes against a battery of in vivo biological screens. Promising compounds were then further studied in development, where their pharmacokinetic properties, metabolism and potential toxicity were investigated. Here we present a study on herbal lead compounds and their potential binding affinity to the effectors molecules of major disease like Prostate Cancer. Clinical studies demonstrate a positive correlation between the extent of 5αR type 2 (5αR2) and malignant progression of precancerous lesions in prostate. Therefore, identification of effective, well-tolerated 5αR inhibitors represents a rational chemo preventive strategy. This study has investigated the effects of naturally occurring non-protein compounds berberine and monocaffeyltartaric acid that inhibits 5αR type2. Our results reveal that these compounds use less energy to bind to 5αR and inhibit its activity. Their high ligand binding affinity to 5αR introduce the prospect for their use in chemopreventive applications; in addition they are freely available natural compounds that can be safely used to prevent prostate cancer.
Keywords: 5αR2, prostate cancer, berberine, monocaffeyltartaric acid, docking, ADME, QSAR, homology modeling, in silico screening, CoMFA
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