A novel critical control point and chemical marker identification method for the multi-step process control of herbal medicines via NMR spectroscopy and chemometrics

Abstract

Herbal medicines have played a vital role in maintaining the health of the world population in the past thousands of years, and have proved to be an effective therapy. It is important to improve our understanding of the effects of the multi-step processing in herbal medicines on the chemical changes to ensure product quality. A proton nuclear paramagnetic resonance (¹H NMR)-based evaluation strategy was developed for an efficient process variation exploration and diversified metabolite identification. In this study, 48 process intermediates from 6 commercial batches of the multi-step manufacturing chain of Danshen processing were obtained. Hierarchical classification analysis (HCA) tree based on ¹H NMR spectra clustered the samples according to the processing steps, which indicates that ¹H NMR has the potential capability for critical control point identification based on its adequate information of the organic compounds. Then, principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied to distinguish the major metabolite differences between the intermediates before and after the critical control point. In this case, the alkali-isolation and acid-dissolution method was recognized as the most critical process in the multi-step chain of Danshen extract manufacturing. Potential metabolites with the larger amplitude of variation and contributing the most to the discrimination were found to be potential quality markers by S-plot, including several previously undetected amino acids. The results in this study are consistent with previous research studies and reference experiments conducted with other analytical tools. Taken together, they prove that ¹H NMR with chemometrics is a very effective process quality control tool to provide comprehensive information on the chemical changes during the processing of herbal medicines, and help with the identification of critical control points and potential critical quality markers.

Fig. 1. ¹H NMR spectra of Danshen extracts and expansions of a representative spectrum: (A) are preprocessed ¹H NMR spectra of the process intermediates collected from production batch 1, colored by the intermediate number A–G, (B) is the enlarged organic and amino acid region, (C) is the enlarged fatty hydrocarbon region and (D) is the enlarged aromatic region. (B)–(D) are all parts of the ¹H NMR spectrum of intermediate G collected from production batch 1.

Fig. 2. Multivariate analysis results based on the ¹H NMR data of intermediates: (A) PLS-DA score plot, (B) scores scatter plot t1 vs. u1, (C) permutation test model validation plot, (D) dendrogram of the hierarchical clustering (sample names are identified by the intermediate name accompanying the batch number, such as A-1, which represents the intermediate A collected from the production batch 1.).

Fig. 3. (A) PCA results based on ¹H NMR data (0.6–9.9 ppm) of intermediates A and B, (B) OPLS-DA score plot, (C) permutation test model validation plot of OPLS-DA model, (D) S-plot of the OPLS-DA model.

Fig. 4. (A) PCA results based on ¹H NMR data (5.60–9.90 ppm) of intermediates A and B, (B) OPLS-DA score plot, (C) permutation test model validation plot of OPLS-DA model, (D) S-plot of OPLS-DA model.

Fig. 5. (A) PCA results based on ¹H NMR data (0.60–3.30 ppm) of intermediates A and B, (B) OPLS-DA score plot, (C) validation of the corresponding partial least squares discriminant analysis model via a permutation test, (D) S-plot of OPLS-DA model.