Guo Ziyu, Li Junyao, Zeng Lina, Wang Ping, Li Meifang, Su Chang, Wang Shuhong
Shenzhen Institute for Drug Control, Shenzhen, China.
Front Pharmacol. 2025 Aug 14;16:1658241. doi: 10.3389/fphar.2025.1658241. eCollection 2025.
Exogenous contaminants in traditional Chinese medicine (TCM), including pesticide residues, heavy metals, mycotoxins, and sulfur dioxide residues, pose significant risks to human health and environmental safety. Conventional detection methods are limited by insufficient sensitivity, complex sample preparation, and challenges in multi-residue analysis, compromising accuracy and efficiency. To address these critical bottlenecks-particularly the escalating regulatory demands and trade barriers due to contamination incidents-this review establishes the first integrated 'dual track' quality control framework for TCM contaminants. We propose a novel risk stratified strategy synergizing laboratory grade accuracy with field deployable screening, overcoming the sensitivity portability trade-off. This work provides a roadmap for establishing globally harmonized standards. Future research should prioritize high-throughput methods, intelligent analytics, and green detection technologies. Integrating AI-driven automation with data traceability could establish unified systems for contaminant detection and degradation, enhancing TCM quality control and global competitiveness.
中药中的外源性污染物，包括农药残留、重金属、霉菌毒素和二氧化硫残留，对人类健康和环境安全构成重大风险。传统检测方法存在灵敏度不足、样品制备复杂以及多残留分析面临挑战等局限性，影响了准确性和效率。为应对这些关键瓶颈，特别是因污染事件导致的监管要求不断提高和贸易壁垒增加的问题，本综述建立了首个针对中药污染物的综合“双轨”质量控制框架。我们提出了一种新颖的风险分层策略，将实验室级别的准确性与可现场部署的筛查相结合，克服了灵敏度与便携性之间的权衡。这项工作为建立全球统一标准提供了路线图。未来的研究应优先考虑高通量方法、智能分析和绿色检测技术。将人工智能驱动的自动化与数据可追溯性相结合，可以建立污染物检测和降解的统一系统，提高中药质量控制和全球竞争力。
