期刊
NATURE COMMUNICATIONS
卷 13, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-022-31609-5
关键词
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资金
- European Commission [309329]
- Academy of Finland [322761]
- EU [814572]
This study presents a predictive computational framework based on the molecular and phenotypic effects of engineered nanomaterials (ENMs) to predict their toxicity. The researchers identified mRNA-based toxicity markers and validated them in multiple independent datasets.
Nanomaterials have a range of potential applications, however, toxicity remains a concern, limiting application and requiring extensive testing. Here, the authors report on a predictive framework made using a range of tests linking materials properties with toxicity, allowing the prediction of toxicity from physiochemical and biological properties. There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone.
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