Prostate Cancer (PCa), leading cause of cancer death worldwide (www.cancer.gov), is a complex malignancy where a spectrum of targets lies behind many PCa forms.We applied Computer-aided Drug Design (CADD) methods to assist the identification of inhibitors of individual targets in Prostate Cancer (PCa): one is the Androgen Receptor (AR), and the other is the Signal Transducer and Activator of Transcription5a/b (Stat5).With the aim of generating a binding mode hypothesis for a recently developed class of dual AR/CYP17 inhibitors (Handratta et al., 2005 ) against the AR (CYP17 is a key enzyme for androgens biosynthesis), we have successfully implemented a Structure-Based strategy founded on flexible-receptor docking (Induced-Fit Docking, www.schr�dinger.com.).Recently, Nevalainen identified small-molecule inhibitors of Stat5a/b (proprietary work, Thomas Jefferson University). We generated a model of pY-Stat5 receptor and implemented both ligand- and receptor-based strategies to finally obtain either more potent analogs of the lead-compound or novel chemical classes.

Computer-aided drug design (CADD) to discover novel inhibitors of protein targets in prostate cancer

244th National Meeting (2012)

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