Integrated computational and Drosophila cancer model platform captures previously unappreciated chemicals perturbing a kinase network

Abstract Drosophila provides an inexpensive and quantitative platform for measuring whole animal drug response. A complementary approach is virtual screening, where chemical libraries can be efficiently screened against protein target(s). Here, we present a unique discovery platform integrating structure-based modeling with Drosophila biology and organic synthesis. We demonstrate this platform by developing chemicals targeting a Drosophila model of Medullary Thyroid Cancer (MTC) with disease-promoting kinase network activated by mutant dRetM955T. Structural models for kinases relevant to MTC were generated for virtually screening to identify initial hits that were dissimilar to known kinase inhibitors yet suppressed dRetM955T-induced oncogenicity. We then combined features from the hits and known inhibitors to create a ‘hybrid’ molecule with improved dRetM955T phenotypic outcome. Our platform provides a framework to efficiently explore novel chemical spaces, develop compounds outside of the current inhibitor chemical space, and ‘correct’ cancer-causing signaling networks to improve disease prognosis while minimizing whole body toxicity.