title: Anti-cancer drug development: computational strategies to identify and target proteins involved in cancer metabolism.
abstract: Cancer remains a fundamental burden to public health despite substantial efforts aimed at developing effective chemotherapeutics and significant advances in chemotherapeutic regimens. The major challenge in anti-cancer drug design is to selectively target cancer cells with high specificity. Research into treating malignancies by targeting altered metabolism in cancer cells is supported by computational approaches, which can take a leading role in identifying candidate targets for anti-cancer therapy as well as assist in the discovery and optimisation of anti-cancer agents. Natural products appear to have privileged structures for anti-cancer drug development and the bulk of this particularly valuable chemical space still remains to be explored. In this review we aim to provide a comprehensive overview of current strategies for computer-guided anti-cancer drug development. We start with a discussion of state-of-the art bioinformatics methods applied to the identification of novel anti-cancer targets, including machine learning techniques, the Connectivity Map and biological network analysis. This is followed by an extensive survey of molecular modelling and cheminformatics techniques employed to develop agents targeting proteins involved in the glycolytic, lipid, NAD+, mitochondrial (TCA cycle), amino acid and nucleic acid metabolism of cancer cells. A dedicated section highlights the most promising strategies to develop anti-cancer therapeutics from natural products and the role of metabolism and some of the many targets which are under investigation are reviewed. Recent success stories are reported for all the areas covered in this review. We conclude with a brief summary of the most interesting strategies identified and with an outlook on future directions in anti-cancer drug development.
authors.affiliation: Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom.
authors.forename: Lora
authors.initials: L
authors.lastname: Mak
authors.forename: Sonia
authors.initials: S
authors.lastname: Liggi
authors.forename: Lu
authors.initials: L
authors.lastname: Tan
authors.forename: Kanthida
authors.initials: K
authors.lastname: Kusonmano
authors.forename: Judith M
authors.initials: JM
authors.lastname: Rollinger
authors.forename: Alexios
authors.initials: A
authors.lastname: Koutsoukas
authors.forename: Robert C
authors.initials: RC
authors.lastname: Glen
authors.forename: Johannes
authors.initials: J
authors.lastname: Kirchmair
mesh_terms: D000818:Animals; D000970:Antineoplastic Agents / Q000494:pharmacology*; D001688:Biological Products / Q000494:pharmacology; D019295:Computational Biology / Q000379:methods; D017076:Computer-Aided Design; D015195:Drug Design; D006801:Humans; D008958:Models, Molecular; D058990:Molecular Targeted Therapy; D009369:Neoplasms / Q000188:drug therapy* / Q000378:metabolism / Q000473:pathology
chemical_list: D000970:Antineoplastic Agents; D001688:Biological Products