Characterizing mechanisms of toxicological action for data-poor priority pollutants in Canada using novel model systems and transcriptomic methodologies

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There are thousands of chemicals in our environment that have never been subject to toxicological testing (referred to as `data-poor'). The root cause of this is reliance on traditional tests that are not high-throughput or economical. These tests also do not reveal toxicological mechanisms that inform species relevance and individual susceptibility. Transcriptomics (global gene expression profiling) provides a rich source of information to understand and predict toxicological effects. Transcriptomics has yet to be fully harnessed for use in regulatory decision-making and risk assessment. Objectives The overarching goal of my research and training program is to modernize regulatory evaluation of chemical toxicity testing. We aim to develop and apply pragmatic approaches to efficiently identify toxicological mechanisms and predict hazards from transcriptomic data. Specific objectives We will apply high-throughput transcriptomics and bioinformatics in human in vitro models to address knowledge gaps in data-poor chemicals of high concern. Projects will focus on two chemical families, per- and poly-fluorinated alkyl substances and bisphenols, because of their widespread use and the large number of data-poor chemicals within these families. The work will be broken into three phases: Phase 1a - Hypothesis generation. Deeply mine existing and new genomic data sets to decipher mechanisms of toxicity and identify physico-chemical drivers of toxicity; Phase 1b - Methods development. Apply/develop machine-learning tools to predict toxicological effects from transcriptomic profiles to increase the efficiency and reproducibility of data interpretation; Phase 2 - Hypothesis testing and methods validation. Conduct validation experiments to confirm molecular, biochemical and phenotypic predictions from Phase 1; Phase 3 - Discovery. Apply single-cell RNA-sequencing to identify cell type- and individual-specific mechanisms, potencies, and effects. The program supports 5 graduate students each year, for a total of 4 MSc, 3 PhD completed, and 2 MSc, 3 PhD initiated. The program provides a unique training ground focused on translation of research innovation into regulatory decision-making, policy and practice. Novelty and significance The work will provide critical information on the toxicity of data-poor chemicals and reveal how new models and artificial intelligence can be used to predict toxicological effects. The analytical approaches can be applied in green-chemistry development pipelines to reduce the probability of regrettable replacements. The work will impact policy related to priority chemical categories and use of genomic data in regulatory decision-making. The tools provide new approaches that increase analytical throughput and save cost/time for chemical evaluations, which impacts industry and regulatory agencies. Importantly, the training program will produce the next-generation of toxicologists and risk assessment specialists for Canada.