State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing, China.
State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing, China.
Phytomedicine. 2023 Sep;118:154927. doi: 10.1016/j.phymed.2023.154927. Epub 2023 Jun 8.
The "one-to-multiple" phenomenon is prevalent in medicinal herbs. Accurate species identification is critical to ensure the safety and efficacy of herbal products but is extremely challenging due to their complex matrices and diverse compositions.
This study aimed to identify the determinable chemicalome of herbs and develop a reasonable strategy to track their relevant species from herbal products.
Take Astragali Radix-the typical "one to multiple" herb, as a case. An in-house database-driven identification of the potentially bioactive chemicalome (saponins and flavonoids) in AR was performed. Furthermore, a pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data. Then based on the data matrix, the random forest algorithm was trained to predict Astragali Radix species from commercial products.
The pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data (including 56 saponins and 49 flavonoids) from 26 batches of AR. Then the random forest algorithm was well-trained by importing the valid data matrix and showed high performance in predicting Astragalus species from ten commercial products.
This strategy could learn species-special combination features for accurate herbal species tracing and could be expected to promote the traceability of herbal materials in herbal products, contributing to manufacturing standardization.
“一药多源”现象在中药材中普遍存在。准确的物种鉴定对于确保草药产品的安全性和有效性至关重要，但由于其复杂的基质和多样的成分，这极具挑战性。
本研究旨在确定草药的可测定化学组，并制定合理的策略从草药产品中追踪其相关物种。
以黄芪为例。对 AR 中潜在生物活性化学组（皂苷和黄酮类化合物）进行了内部数据库驱动的鉴定。此外，首次开发并验证了伪靶向代谢组学方法，以获得高质量的半定量数据。然后，基于数据矩阵，利用随机森林算法从商业产品中预测黄芪的种类。
首次开发并验证了伪靶向代谢组学方法，以从 26 批黄芪中获得高质量的半定量数据（包括 56 种皂苷和 49 种黄酮类化合物）。然后，通过导入有效数据矩阵对随机森林算法进行了良好的训练，该算法在从十种商业产品中预测黄芪种类方面表现出了较高的性能。
该策略可以学习物种特有的组合特征，实现对草药的准确物种溯源，并有望促进草药产品中草药材料的可追溯性，为标准化生产做出贡献。
