Chemical contamination of our ecosystems is regarded as one of the planet’s greatest threats. Regulatory agencies and businesses are tasked with managing these chemicals but face significant challenges due to the sheer number of chemicals for which toxicity data are required. In fact, over 100,000 chemicals require evaluation worldwide. It has become increasingly apparent that current risk assessment strategies that rely heavily on animal testing and are prohibitively time-consuming and expensive are not able to address these regulatory mandates. This project will develop, test, validate, and commercialize quantitative PCRbased arrays (EcoToxChips) to address an urgent worldwide need for advanced toxicity testing tools that are accessible, affordable, consistent, and reliable. To facilitate their adoption into standard practices we will produce and leverage social science knowledge, as well as provide a user-friendly bioinformatics portal https://www.ecotoxxplorer.ca/ and an end-user vetted technical guidance document.
We anticipate important socioeconomic benefits associated with the adoption of our deliverables, namely more focused animal testing, improved regulatory decision-making, and cost-efficiencies. For example, with respect to the Government of Canada’s Chemicals Management Plan, we estimate our work will translate to cost-efficiencies of $27.3M/yr, time savings of 7-fold, and a 90% reduction in the numbers of animals tested. Our proposed work is driven by a diverse set of key end-users (e.g. multiple Canadian and international regulators, businesses), and it builds upon long-standing and highly productive collaborations among individuals and institutions spanning multiple disciplines within academia, government (scientists, regulators), and industry. These relationships will ensure that benefits are realized, and demonstrate that EcoToxChips and EcoToxXplorer.ca can help transform ecological and chemical risk assessment into a process that is significantly more cost-effective, timely, informative, ethical, and predictive.