Anthropogenic chemicals and their transformation products are increasingly found in the environment, with persistence being a major driver of chemical risk. Methods for predicting biotransformation products and dissipation kinetics are needed to help regulators identify potentially persistent chemicals and prevent their release to the market and eventually to the environment. Leveraging machine learning and artificial intelligence is a promising avenue to tackle this problem. However, predictive models are only as good as the data used to train them, calling for large, high-quality data sets of biotransformation pathways and kinetics, which are currently lacking. The objectives of this Global Perspective are to (i) emphasize the importance of effectively communicating biotransformation data on chemical contaminants in the environment, (ii) describe specific components of reporting biotransformation pathways in a findable, accessible, interoperable, and reusable (FAIR) format, and (iii) provide a standardized tool for researchers to use for reporting their biotransformation data, with the intent to boost the quality and quantity of available biotransformation data. We demonstrate the application of our reporting tool for the case of perfluoroalkyl and polyfluoroalkyl substances (PFASs) as a means to develop a PFAS biotransformation database, thereby illustrating how the research community could profit from standard biotransformation data reporting.
Rich et al. (Wed,) studied this question.