AI-driven hazard prioritization of plastic additives using Tox21 bioassays and self-supervised graph transformers

초록

As plastics degrade into micro- and nano-sized particles, they can leach additive chemicals into the environment, potentially exerting greater toxicity than the polymer matrix itself. The European Chemicals Agency Plastic Additives Initiative has compiled a list of more than 400 plastic additives that are used in high volumes. This study aimed to screen the potential toxicity of these chemicals using Tox21 bioassays and deep learning models. To this end, we collected the Tox21 dataset, which provides extensive bioactivity profiles for more than 7,000 chemicals across various endpoints, including human nuclear receptor signaling and stress response pathways. We then trained deep learning models using experimental data from Tox21 bioassays. Specifically, we employed the GROVER algorithm, which was designed to overcome typical limitations of traditional graph neural networks by leveraging transformers and self-supervised pretraining. We fine-tuned the model on twelve Tox21 bioassay datasets, using the F1 score as the primary evaluation metric. As a result, the GROVER model outperformed baseline algorithms, including graph convolutional networks, random forest, support vector machines, and logistic regression. Using the fine-tuned GROVER models, we identified 78 highly active chemicals among 171 additives. For these active plastic additive chemicals, we also investigated existing hazard information (minimal oral point-of-departure) from the CompTox Chemical Dashboard and their Globally Harmonized System of Classification and Labelling of Chemicals information from PubChem database. This approach revealed significant data gaps for plastic additive chemicals with potential toxicity and can support regulatory decision-making. Collectively, this study provides a practical use case for applying cutting-edge AI models as new approach methodologies to modernize hazard assessment, in alignment with the 3Rs (Replacement, Reduction, Refinement) principle for animal testing.

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