TOXIFATE · Future Toxicology: Better predicting Toxicant-induced cell fate.

Future Toxicology: Better predicting Toxicant-induced cell fate.

Current global prosperity and modern health care rely upon the safe use of chemicals in industry, agriculture, and medicine. This prosperity is entirely dependent upon robust toxicology that identifies toxic chemicals so harm human health or the environment. The use of in vitro and computational approaches to predict chemical toxicity is revolutionizing toxicology right now. This revolution will have scientific, societal and ethical impact as it aims to improve toxicity assessment while reducing costs, increasing the number of chemicals that can be assessed and reducing animal use. A recent breakthrough was that computational approaches can sometimes outperform animal testing in predicting human toxicity, demonstrating the promise of this approach. However, our understanding of chemical-induced changes in cell and molecular biology is still rudimentary and we don’t have sufficient data to complete the revolution. The TOXIFATE project is contributing to the revolution by multi-disciplinary training in in vitro toxicology and computational approaches and generating a new and large dataset that will improve the prediction of muscle toxicity. TOXIFATE is: 1) Generating new datasets that show the quantitative relationships between cell stress and cell death responses using new in vitro biosensor assays. 2) Providing multi-disciplinary training of EU toxicologists in innovative in vitro and in silico technologies. 3) Contributing to society by improving chemical safety by providing new ways to quickly, ethically, and economically identify chemical hazards. Conclusions of the Action The successful completion of a PhD in Cheminformatics and Toxicology, focused on myotoxicity, represents a significant scientific and societal achievement. This research aimed to address the challenge of predicting myotoxicity—a form of muscle damage caused by chemical or drug exposure—by developing computational models that leverage both phenotypic and transcriptomics data. The research was part of an intersectoral program that integrated academic, industrial, and research institution collaborations, resulting in an approach that is both scientifically robust and practically applicable. The primary focus of the research was to develop predictive models capable of identifying myotoxicity—muscle damage caused by chemical or drug exposure—using phenotypic and transcriptomics data. Myotoxicity is a critical concern in drug development and toxicology, as it can result in severe side effects in patients, sometimes leading to the withdrawal of drugs from the market. The aim was to create robust models that could be used to predict myotoxic effects at early stages of development, thereby improving drug safety and reducing the likelihood of adverse outcomes in clinical settings.

Data: CORDIS, © European Union