Development of AOP relevant to microplastics based on toxicity mechanisms of chemical additives using ToxCast™ and deep learning models combined approach.

Various additives are used in plastic products to improve the properties and the durability of the plastics. Their possible elution from the plastics when plastics are fragmented into micro- and nano-size in the environment is suspected to one of the major contributors to environmental and human toxicity of microplastics. In this context, to better understand the hazardous effect of microplastics, the toxicity of chemical additives was investigated. Fifty most common chemicals presented in plastics were selected as target additives. Their toxicity was systematically identified using apical and molecular toxicity databases, such as ChemIDplus and ToxCast™. Among the vast ToxCast assays, those having intended gene targets were selected for identification of the mechanism of toxicity of plastic additives. Deep learning artificial neural network models were further developed based on the ToxCast assays for the chemicals not tested in the ToxCast program. Using both the ToxCast database and deep learning models, active chemicals on each ToxCast assays were identified. Through correlation analysis between molecular targets from ToxCast and mammalian toxicity results from ChemIDplus, we identified the fifteen most relevant mechanisms of toxicity for the understanding mechanism of toxicity of plastic additives. They are neurotoxicity, inflammation, lipid metabolism, and cancer pathways. Based on these, along with, previously conducted systemic review on the mechanism of toxicity of microplastics, here we have proposed potential adverse outcome pathways (AOPs) relevant to microplastics pollution. This study also suggests in vivo and in vitro toxicity database and deep learning model combined approach is appropriate to provide insight into the toxicity mechanism of the broad range of environmental chemicals, such as plastic additives.