Discovery of discriminatory quality control markers for Chinese herbal medicines and related processed products by combination of chromatographic analysis and chemometrics methods: Radix Scutellariae as a case study.

The processing procedure of traditional Chinese herbal medicines (CHMs) plays an essential role in clinical applications. However, little progress has been made on the quality control of crude and processed products. The present work, taking Radix Scutellariae (RS), wine-processed RS and carbonized RS as a typical case, developed a comprehensive strategy integrating chromatographic analysis and chemometric methods for quality evaluation and discrimination of crude RS and its processed products. Chemical fingerprints were established by high-performance liquid chromatography coupled with photodiode array detector and quadrupole time-of-flight mass spectrometry, and similarity analyses were calculated based on eleven common characteristic peaks. Subsequently, four chemical markers were discovered by back propagation-artificial neural network (BP-ANN) modeling. The selected markers were quantified by the 'single standard to determine multi-components' (SSDMC) method, and then the quantitative data were subjected to principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). Furthermore, support vector machine (SVM) was employed to predict the different processed products of RS. Finally, a hotmap visualization was conducted for clarifying the distribution of major flavonoids among different drugs. Collectively, the proposed strategy might be well-acceptable for quality control of CHMs and their related processed products from the processing mechanism-based perspective.

From chemical consistency to effective consistency in precise quality discrimination of Sophora flower-bud and Sophora flower: Discovering efficacy-associated markers by fingerprint-activity relationship modeling.

Chromatographic fingerprint has been extensively used as a comprehensive approach for quality evaluation of herbal medicines (HMs). However, similar chemical profiles do not always mean similar efficacies. The present work, taking Sophora flower-bud and Sophora flower as a typical case, attempts to develop a rational strategy based on fingerprint-activity relationship modeling to realize quality evaluation from chemical consistency to effective consistency. A total of 57 batches of Sophora samples were collected and their antioxidant and hyaluronidase inhibitory activities were measured. Chemical fingerprints were established by high performance liquid chromatography (HPLC) coupled with photodiode array (PDA) detector and quadrupole time-of-flight mass spectrometry (Q-TOF MS), and similarity analyses were calculated based on eight common characteristic peaks. Subsequently, three principal bioactive markers were discovered by correlating biological effects with chemical fingerprints via partial least squares regression (PLSR) and back propagation-artificial neural network modeling (BP-ANN). The selected markers were quantified by the 'single standard to determine multi-components' method, and then the quantitative data as well as their bioactive properties were subjected to principal component analysis to generate two clear-cut groups. This study not only demonstrates the necessity of effective consistency besides chemical consistency in the quality evaluation of HMs, but also provides an applicable strategy to screen out efficacy-associated markers by fingerprint-activity relationship modeling.