This poster illustrates how high performance computing and machine learning may support inference of toxicokinetic-toxicodynamic models to make emerge chemical interactions within mixtures.
Toxicokinetics-Toxicodynamics (TKTD) models are increasingly used for inference of toxicity indices of interest in Environmental Risk Assessment (ERA) thanks to their clear description of numerous mechanisms, from the kinetics of compounds inside organisms (Toxicokinetics, TK) to their related damages and effect dynamics at the individual level (Toxicodynamics, TD) . TKTD models offer the advantage of accounting for temporal aspects of both exposure and toxicity, considering data points all along the time course of experiments. In addition, TKTD models allow predictions under untested situations from time-variable exposure profiles either measured in the field or simulated in risk assessment scenarios. Although ERA can follow a compound-by-compound approach, in practice, ecosystems are exposed to many chemical products, from agricultural, industrial and domestic sources. Using TKTD models to describe such mixture effects over time requires making assumptions a priori on potential interactions of involved products . These assumptions are then tested and evaluated based on fitting TKTD models to observed data under exposure to mixtures. This poster illustrates how high performance computing  and machine learning  may be of particular help for the inference of TKTD models without a priori knowledge on emerging chemical interactions that leads to cocktail effects.