Sheng Qiu-Shuang, Liu Bin, Wang Xiao, Hua Lei, Zhao Shou-Cheng, Sun Xiao-Zhong, Li Mu-Yang, Zhang Xiang-Yu, Wang Jia-Xu, Hu Pei-Li
Jilin Province Product Quality Supervision and Inspection Institute, Changchun, 130103, China.
National Institutes for Food and Drug Control, Beijing, 100050, China.
Arch Toxicol. 2025 Sep 2. doi: 10.1007/s00204-025-04169-y.
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.
传统毒理学范式依赖动物实验和简单的体外模型，面临着重大局限性，包括时间线长、成本高以及由于种间差异导致的翻译预测性差。本综述强调了新方法学（NAMs）在克服这些挑战方面的变革潜力。关键进展包括模拟人体器官生理学和多器官串扰的芯片上器官（OoC）平台，显著提高了预测准确性。多组学技术（基因组学、蛋白质组学、代谢组学）的整合为毒性途径提供了前所未有的机制见解。计算毒理学利用机器学习和定量构效关系（QSAR）建模，能够进行高通量危害优先级排序和风险预测。虽然NAMs为化学安全评估提供了与人类相关的高效替代方法，但关键瓶颈仍然存在。这些包括当前体外模型中生理复杂性不足、人工智能驱动方法的可解释性限制、量化混合物毒性和低剂量效应的挑战以及监管采用方面的滞后。概率风险评估、人工智能驱动的暴露组学和分层测试范式等新兴策略有望解决化学混合物风险和个性化暴露问题。未来的进展需要跨学科合作来完善微生理系统、使监管框架与科学创新相协调，并建立开放获取的数据存储库，为精准毒理学和可持续化学风险管理铺平道路。
