QSAR STUDIES ON BICALUTAMIDE DRUG FOR PROSTATE CANCER TREATMENT

Abstract

Prostate Cancer (PC) is a dangerous and deadliest type of cancer it is the main reason of male death globally. The development of new and stronger anti-prostate cancer composites is a constant requirement.  It can also be associated with alterations in AR functions. Indeed, androgen blockade by drugs that prevent the production of androgens and/or block the action of the AR inhibits prostate cancer growth. For its treatment, a very effective drug is used named as Bicalutamide. This drug is used to block the androgen action. It works by preventing testosterone from binding to the androgen receptors in prostate cancer cells. As a means to improve the effectiveness of the Bicalutamide drug, and in order to exploit the well-established potential of the fluorine atom in enhancing the pharmacological properties and drug-like physicochemical characteristics of candidate compounds, a wide array of diverse new structures has been designed and synthesized, through the introduction of fluoro-, trifluoromethyl- and trifluoromethoxy groups in diverse positions of both aromatic rings of the parent scaffolds. We have employed 2D and 3D QSAR approaches to identify the best descriptor to design better active compounds. In the 2D QSAR method, different types of descriptors existed from which some were eliminated due to their same or zero value. Overall 192 descriptors from which 142 were eliminated and 50 were used for further analysis. In LNCAP and VCaP cell lines, the correlation coefficient (R2) values 0.99 were after pruning. In 3D QSAR, The generated model against the 22Rv1 cell line (by CoMFA and CoMSIA) gave the best results. The q2 value for CoMFA is 0.365 and for CoMSIA is 0.430 against the 22Rv1 cell line. We conclude that 2D QSAR in LNCaP and VCaP had better results compared to other cell lines whereas in 3D QSAR, dataset compounds yielded better results against the 22Rv1 cell line.