Qiu-Shuang Sheng, Bin Liu, Xiao Wang, Lei Hua, Shou-Cheng Zhao, Xiao-Zhong Sun, Mu-Yang Li, Xiang-Yu Zhang, Jia-Xu Wang, Pei-Li Hu
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Computational toxicology, leveraging machine learning and QSAR modeling, enables high-throughput hazard prioritization and risk prediction. While NAMs offer human-relevant, efficient alternatives for chemical safety evaluation, critical bottlenecks remain. These involve insufficient physiological complexity in current in vitro models, interpretability limitations of AI-driven approaches, challenges in quantifying mixture toxicity and low-dose effects, and a lag in regulatory adoption. Emerging strategies like probabilistic risk assessment, AI-driven exposomics, and tiered testing paradigms hold promise for addressing chemical mixture risks and personalized exposures. Future progress requires interdisciplinary collaboration to refine microphysiological systems, harmonize regulatory frameworks with scientific innovation, and establish open-access data repositories, paving the way for precision toxicology and sustainable chemical risk management.</p>","PeriodicalId":8329,"journal":{"name":"Archives of Toxicology","volume":" ","pages":""},"PeriodicalIF":6.9000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Revolutionizing toxicological risk assessment: integrative advances in new approach methodologies (NAMs) and precision toxicology.\",\"authors\":\"Qiu-Shuang Sheng, Bin Liu, Xiao Wang, Lei Hua, Shou-Cheng Zhao, Xiao-Zhong Sun, Mu-Yang Li, Xiang-Yu Zhang, Jia-Xu Wang, Pei-Li Hu\",\"doi\":\"10.1007/s00204-025-04169-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Traditional toxicological paradigms, reliant on animal testing and simplistic in vitro models, face significant limitations, including prolonged timelines, high costs, and poor translational predictability due to interspecies differences. This review highlights the transformative potential of New Approach Methodologies (NAMs) in overcoming these challenges. Key advancements include Organ-on-a-Chip (OoC) platforms that emulate human organ physiology and multi-organ crosstalk, significantly improving predictive accuracy. Integration of multi-omics technologies (genomics, proteomics, metabolomics) provides unprecedented mechanistic insights into toxicity pathways. Computational toxicology, leveraging machine learning and QSAR modeling, enables high-throughput hazard prioritization and risk prediction. While NAMs offer human-relevant, efficient alternatives for chemical safety evaluation, critical bottlenecks remain. These involve insufficient physiological complexity in current in vitro models, interpretability limitations of AI-driven approaches, challenges in quantifying mixture toxicity and low-dose effects, and a lag in regulatory adoption. Emerging strategies like probabilistic risk assessment, AI-driven exposomics, and tiered testing paradigms hold promise for addressing chemical mixture risks and personalized exposures. Future progress requires interdisciplinary collaboration to refine microphysiological systems, harmonize regulatory frameworks with scientific innovation, and establish open-access data repositories, paving the way for precision toxicology and sustainable chemical risk management.</p>\",\"PeriodicalId\":8329,\"journal\":{\"name\":\"Archives of Toxicology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of Toxicology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00204-025-04169-y\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"TOXICOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Toxicology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00204-025-04169-y","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TOXICOLOGY","Score":null,"Total":0}
Revolutionizing toxicological risk assessment: integrative advances in new approach methodologies (NAMs) and precision toxicology.
Traditional toxicological paradigms, reliant on animal testing and simplistic in vitro models, face significant limitations, including prolonged timelines, high costs, and poor translational predictability due to interspecies differences. This review highlights the transformative potential of New Approach Methodologies (NAMs) in overcoming these challenges. Key advancements include Organ-on-a-Chip (OoC) platforms that emulate human organ physiology and multi-organ crosstalk, significantly improving predictive accuracy. Integration of multi-omics technologies (genomics, proteomics, metabolomics) provides unprecedented mechanistic insights into toxicity pathways. Computational toxicology, leveraging machine learning and QSAR modeling, enables high-throughput hazard prioritization and risk prediction. While NAMs offer human-relevant, efficient alternatives for chemical safety evaluation, critical bottlenecks remain. These involve insufficient physiological complexity in current in vitro models, interpretability limitations of AI-driven approaches, challenges in quantifying mixture toxicity and low-dose effects, and a lag in regulatory adoption. Emerging strategies like probabilistic risk assessment, AI-driven exposomics, and tiered testing paradigms hold promise for addressing chemical mixture risks and personalized exposures. Future progress requires interdisciplinary collaboration to refine microphysiological systems, harmonize regulatory frameworks with scientific innovation, and establish open-access data repositories, paving the way for precision toxicology and sustainable chemical risk management.
期刊介绍:
Archives of Toxicology provides up-to-date information on the latest advances in toxicology. The journal places particular emphasis on studies relating to defined effects of chemicals and mechanisms of toxicity, including toxic activities at the molecular level, in humans and experimental animals. Coverage includes new insights into analysis and toxicokinetics and into forensic toxicology. Review articles of general interest to toxicologists are an additional important feature of the journal.