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Integrating Multi-Omics and Machine Learning to Predict Microplastic Cytotoxicity Under Leave-One-Polymer-Out Validation.
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Scientists used computer models to predict how toxic different microplastic types are to human lung cells, combining particle size, electrical charge, and biological data. Particle size turned out to be the strongest warning sign of harm, though the model needs testing on more plastic types before it can reliably guide safety regulations or waste policies.
Microplastic (MP) cytotoxicity prediction remains challenging because available experimental datasets are limited and biological responses vary substantially among polymers. This study integrated multi-omics, physicochemical descriptors, and machine learning to predict MP cytotoxicity in BEAS-2B (a human bronchial epithelial cell line) cells. Biological descriptors were derived from multi-omics screening and concentration-dependent validation, while Z-Ave (hydrodynamic average particle size), zeta potential, and concentration were used as physicochemical descriptors. Using 50 original polymer-concentration observations from five polymers, 18 combinations of six machine-learning algorithms and three descriptor sets (QSAR, QBAR, and QSBAR) were evaluated by nested leave-one-polymer-out (LOPO) cross-validation. Gaussian perturbation, applicability-domain analysis, and SHAP (SHapley Additive exPlanations) were used for sensitivity and model interpretation. RF-QSBAR achieved the best overall out-of-fold performance (R=0.794, RMSE=0.072, and CCC=0.858), followed by GBDT-QSBAR (R=0.783, RMSE=0.074, and CCC=0.865). The polymer-level bootstrap 95% CI for the RF-QSBAR R was 0.428-0.818, reflecting uncertainty associated with the limited number of polymers. RF-QSBAR showed stable performance under training-set Gaussian perturbation, with R values ranging from 0.809 to 0.860. Applicability-domain analysis identified extrapolation risk for some held-out polymers, and SHAP analysis identified Z-Ave as the most influential predictive feature, followed by PLPP4 (phospholipid phosphatase 4) and GPD2 (mitochondrial glycerol-3-phosphate dehydrogenase 2). Integrating physicochemical and biological descriptors showed potential for cross-polymer MP cytotoxicity prediction; however, Z-Ave should be interpreted as a proxy for correlated between-polymer differences rather than an independent causal size effect, and further external validation is required. This framework may provide a potential tool for supporting risk assessment, waste management, and future regulatory decision-making.
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