Computational methods for the design of potent aromatase inhibitors. (April 2013)

• Record Type: Journal Article
• Authors: Favia, Angelo Danilo; Nicolotti, Orazio; Stefanachi, Angela; Leonetti, Francesco; Carotti, Angelo
• Is Part Of: Expert opinion on drug discovery. Volume 8:Number 4(2013:Apr.)
• Page Start: 395
• Page End: 409
• Publication Date: 2013-04
• Subjects: 615.1
• DOI: 10.1517/17460441.2013.768983
• Languages: English
• ISSNs: 1746-0441

Abstract

<abstract> <title> <x xml:space="preserve">Abstract</x> </title> <p> <bold> <italic>Introduction:</italic> </bold> It has long been considered that the most significant risks for breast cancer are gender and age but, as many other tumors, this cancer has also been undeniably linked to gene mutations. The vast majority of breast cancers in postmenopausal women are estrogen-responsive, a hormone which is biosynthesized from blood-circulating androgens through an aromatization reaction, catalyzed by aromatase (AR). One strategy, therefore, to combat breast cancer, has been to find compounds that can inhibit the activity of aromatase to reduce estrogen levels.</p> <p> <bold> <italic>Areas covered:</italic> </bold> The authors provide a broad and updated overview of the general structure–activity relationships and on the latest ligand- and structure-based approaches applied to the discovery of potent, selective and safer breast cancer drugs. Specifically the authors review the most consolidated techniques, based on structure–activity relationships, pharmacophore mapping, rigid and flexible molecular docking, as well as sophisticated and reliable protocols simulating critical biological events.</p> <p> <bold> <italic>Expert opinion:</italic> </bold> The recently solved X-ray structures of aromatase represent solid milestones to breathe new life into the search of newer chemotypes with reduced risks of cross-reactivity toward other CYPs and safer pharmacological profiles. We anticipate that great benefits will arrive from the wealth of information obtained by integrating genomics, site-directed mutagenesis experiments with protein modeling. Furthermore, we welcome the advent of GPU technology that, in conjunction with dedicated algorithms, grants scientists an unprecedented point of view on physiologically relevant phenomena, occurring on the µs time scale, such as ligand binding/unbinding.</p> </abstract>