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An in vivo large-scale chemical screening platform using Drosophila for anti-cancer drug discovery
Author(s) -
Lee Willoughby,
Tanja Schlosser,
Samuel A. Manning,
John P. Parisot,
Ian P. Street,
Helena E. Richardson,
Patrick O. Humbert,
Anthony M. Brumby
Publication year - 2012
Publication title -
disease models and mechanisms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.327
H-Index - 83
eISSN - 1754-8411
pISSN - 1754-8403
DOI - 10.1242/dmm.009985
Subject(s) - in vivo , drug discovery , phenotypic screening , biology , computational biology , drug development , cancer , gene knockdown , pharmacology , cancer research , drug , bioinformatics , biochemistry , gene , phenotype , microbiology and biotechnology , genetics
Anti-cancer drug development involves enormous expenditure and risk. For rapid and economical identification of novel, bioavailable anti-tumour chemicals, the use of appropriate in vivo tumour models suitable for large-scale screening is key. Using a Drosophila Ras-driven tumour model, we demonstrate that tumour overgrowth can be curtailed by feeding larvae with chemicals that have the in vivo pharmacokinetics essential for drug development and known efficacy against human tumour cells. We then develop an in vivo 96-well plate chemical screening platform to carry out large-scale chemical screening with the tumour model. In a proof-of-principle pilot screen of 2000 compounds, we identify the glutamine analogue, acivicin, a chemical with known activity against human tumour cells, as a potent and specific inhibitor of Drosophila tumour formation. RNAi-mediated knockdown of candidate acivicin target genes implicates an enzyme involved in pyrimidine biosynthesis, CTP synthase, as a possible crucial target of acivicin-mediated inhibition. Thus, the pilot screen has revealed that Drosophila tumours are glutamine-dependent, which is an emerging feature of many human cancers, and has validated the platform as a powerful and economical tool for in vivo chemical screening. The platform can also be adapted for use with other disease models, thus offering widespread applications in drug development.

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