Rationale: Impurities refer to any substances that affect the purity of pharmaceuticals. Controlling impurities has always been a significant concern during drug development. Impurities impact the drug’s purity, diminish the efficacy of pharmaceuticals, and alter their appearance, physical, and chemical properties. Additionally, impurities can compromise the stability of pharmaceuticals and elevate their toxic and side effects. Methods: Impurity source analysis is the basis of drug impurity control. To clarify the source of impurity can optimize the synthesis process, prescription process, packaging and storage conditions of the pharmaceuticals, and control the impurity within a reasonable limit to achieve the ultimate goal of impurity research. Results: Analysis method is a means to obtain impurity information, and diversified analysis methods are also possible for effective control of different types of impurities. At present, there are relatively many quantitative studies on impurities, but there are still some challenges for the structure analysis of impurities, especially trace impurities. Conclusion: The research progress of drug impurity control and evaluation from the sources of various drug impurities and impurity analysis techniques was reviewed in this article, with the aim of providing references for the related research.

Rationale: There are significant differences in Sinomenii Caulis (SC) obtained from different geographical regions and medicinal plant parts. This study aims to explore potential quality markers that are correlated with clinical efficacy in SC by a comprehensive strategy that integrates chemical profiling, chemometrics, and network pharmacology. Methods: First, an alkaloid database was created through the utilization of the UNIFI system to qualitatively analyze of alkaloids in SC. Then, differential compounds in SC collected from various geographic regions were screened by applying multivariate data analysis. Subsequently, the support vector machine (SVM) and random forest (RF) algorithms are adopted to calculate the grouping accuracy of different components. Finally, network pharmacology was conducted to analyze the pharmacological properties and potential associations of these target compounds. Results: A total of 81 alkaloids were identified from SC samples, including 13 aporphine alkaloids, 18 protoberberine alkaloids, 32 morphine alkaloids, 10 benzylisoquinoline alkaloids, and 8 other types of alkaloids. Notably, palmatine, sinoracutine, and magnoflorine are active ingredients with the ability to differentiate the different regions of SC samples. And thus should be prioritized when selecting quality markers. Additionally, it was observed that the RF algorithms demonstrated higher classification accuracy than the SVM model. Conclusion: This comprehensive strategy may prove to be a powerful technique for screening the quality markers components, which could be used for the quality control of SC, and can serve as reference for design of quality control of other herbs.