Establishing Drosophila melanogaster as part of the pre-clinical pipeline for anti-metastatic cancer drug discovery

Colorectal cancer (CRC) is the second most frequent cause of cancer-related mortality, which in 75% of cases is due to metastasis. There is therefore an urgent need for new anti-metastatic therapies.

Drug discovery has traditionally been based on the identification of a disease-causing protein and search for chemical compounds able to alter its function using in vitro cell culture and biochemical assays. However, the number of new drugs that reach the market has been dropping over the last decades. A key issue is that most small molecules identified in vitro, lack the desirable characteristics for absorption, distribution, metabolism, excretion and toxicity required in vivo. Given these issues, and the complexity of metastasis, the likelihood of molecules brought forward from in vitro stages being effective in an animal model is low. Therefore, it is expected that a large number of in vitro positive hits will not be effective when tested in mammalian models, primarily rodents, to fully understand the properties of new drugs.

The Berx and Goossens labs have developed a sophisticated in vitro screen that has identified several compounds able to block the epithelial-to-mesenchymal transition, a key step initiating metastasis. To reduce the number of rodents required to analyse which of positive hits may have beneficial effects in vivo, we propose to use our recently developed metastatic Drosophila melanogaster model for CRC as a first step to test the in vivo effects of anti-metastatic compounds identified in vitro. This model permits not only the assessment in vivo efficacy in a complex 3D environment, but also the assessment of drug absorption, distribution, metabolic stability and toxicity, reducing the possibilities of false positives.

While testing in mammalian models will still be required, by replacing mice with Drosophila as the first in vivo step in the pre-clinical pipeline we estimate that this will reduce the number of mice used an estimated 70%.

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University of Sheffield

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Sep 2021 - Aug 2024

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