4.7 Review

Advances in Fingerprint Analysis for Standardization and Quality Control of Herbal Medicines

Journal

FRONTIERS IN PHARMACOLOGY
Volume 13, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fphar.2022.853023

Keywords

herbal medicines; fingerprint analysis; chemometrics; chemical fingerprint; DNA fingerprint

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This article evaluates various fingerprinting techniques applicable to the standardization and quality control of herbal medicines, and discusses the application of multivariate data analysis or chemometrics in analyzing complex datasets for herbal medicine determination. Fingerprint analysis, as a useful technique, provides a method for assessing the quality of herbal materials and formulations, particularly considering the complexity of herbal drugs.
Herbal drugs or herbal medicines (HMs) have a long-standing history as natural remedies for preventing and curing diseases. HMs have garnered greater interest during the past decades due to their broad, synergistic actions on the physiological systems and relatively lower incidence of adverse events, compared to synthetic drugs. However, assuring reproducible quality, efficacy, and safety from herbal drugs remains a challenging task. HMs typically consist of many constituents whose presence and quantity may vary among different sources of materials. Fingerprint analysis has emerged as a very useful technique to assess the quality of herbal drug materials and formulations for establishing standardized herbal products. Rather than using a single or two marker(s), fingerprinting techniques take great consideration of the complexity of herbal drugs by evaluating the whole chemical profile and extracting a common pattern to be set as a criterion for assessing the individual material or formulation. In this review, we described and assessed various fingerprinting techniques reported to date, which are applicable to the standardization and quality control of HMs. We also evaluated the application of multivariate data analysis or chemometrics in assisting the analysis of the complex datasets from the determination of HMs. To ensure that these methods yield reliable results, we reviewed the validation status of the methods and provided perspectives on those. Finally, we concluded by highlighting major accomplishments and presenting a gap analysis between the existing techniques and what is needed to continue moving forward.

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