GOLIATH addresses the work programme topic ‘SC1-BHC-27-2018: New testing and screening methods to identify endocrine disrupting chemicals (EDCs)’ by focussing on one of the most urgent regulatory needs, namely the lack of methods for testing EDCs that disrupt metabolism – chemicals collectively referred to as ‘metabolism disrupting chemicals’ (MDCs). MDCs are natural and anthropogenic chemicals that have the ability to promote metabolic changes that can ultimately result in obesity, diabetes and/or fatty liver in humans. GOLIATH will generate the world’s first integrated approach to testing and assessment (IATA) specifically tailored to MDCs. With a focus on the main cellular targets of metabolic disruption – hepatocytes, pancreatic endocrine cells, myocytes and adipocytes - GOLIATH will develop new methods and optimise existing methods that span the entire adverse outcome pathway (AOP) spectrum, using in silico predictive modelling and high throughput screening, (pre-)validated ready-to-use in vitro assays and optimised in vivo toxicity testing guidelines. GOLIATH will provide key information on the endocrine mode of action by which MDCs disrupt metabolic pathways and induce adverse effects on human health by incorporating multi-omics technologies, and translating results from in vitro and in vivo assays to adverse metabolic health outcomes in humans at real life exposures. Given the importance of international acceptance of the developed test methods for regulatory use, GOLIATH will link with ongoing initiatives of the OECD for test method (pre-)validation, IATA and AOP development. With a consortium comprised of world-leading experts in endocrinology, molecular biology, toxicology, epidemiology, test method development, validation and chemical regulation, GOLIATH will be pivotal in the development of an internationally harmonised strategy for testing MDCs, and help to reduce the worldwide rise in metabolic disorders that have reached ‘Goliathan’ proportions.
Melanie Schneider, Jean-Luc Pons, William Bourguet, Gilles Labesse Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity published pages: 160-168, ISSN: 1367-4803, DOI: 10.1093/bioinformatics/btz538
Bioinformatics 36/1
2020-02-25
2019
Melanie Schneider, Jean-Luc Pons, Gilles Labesse, William Bourguet In Silico Predictions of Endocrine Disruptors Properties published pages: 2709-2716, ISSN: 1945-7170, DOI: 10.1210/en.2019-00382
Endocrinology 160/11
2020-02-25
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