Quantitative Profiling of Polar Metabolites in Herbal Medicine Injections for Multivariate Statistical Evaluation Based on Independence Principal Component Analysis

<div><p>Botanical primary metabolites extensively exist in herbal medicine injections (HMIs), but often were ignored to control. With the limitation of bias towards hydrophilic substances, the primary metabolites with strong polarity, such as saccharides, amino acids and organic acids, are usually difficult to detect by the routinely applied reversed-phase chromatographic fingerprint technology. In this study, a proton nuclear magnetic resonance (¹H NMR) profiling method was developed for efficient identification and quantification of small polar molecules, mostly primary metabolites in HMIs. A commonly used medicine, Danhong injection (DHI), was employed as a model. With the developed method, 23 primary metabolites together with 7 polyphenolic acids were simultaneously identified, of which 13 metabolites with fully separated proton signals were quantified and employed for further multivariate quality control assay. The quantitative ¹H NMR method was validated with good linearity, precision, repeatability, stability and accuracy. Based on independence principal component analysis (IPCA), the contents of 13 metabolites were characterized and dimensionally reduced into the first two independence principal components (IPCs). IPC1 and IPC2 were then used to calculate the upper control limits (with 99% confidence ellipsoids) of χ² and Hotelling T² control charts. Through the constructed upper control limits, the proposed method was successfully applied to 36 batches of DHI to examine the out-of control sample with the perturbed levels of succinate, malonate, glucose, fructose, salvianic acid and protocatechuic aldehyde. The integrated strategy has provided a reliable approach to identify and quantify multiple polar metabolites of DHI in one fingerprinting spectrum, and it has also assisted in the establishment of IPCA models for the multivariate statistical evaluation of HMIs.</p></div

Proton spin-lattice relaxation time (<i>T</i>₁) values for 13 selected metabolites and the method validation parameters for ¹HNMR method.

<p>Proton spin-lattice relaxation time (<i>T</i>₁) values for 13 selected metabolites and the method validation parameters for ¹HNMR method.</p

Multivariate quality control charts.

<p>The χ² and Hotelling control charts of the first three principle components (PCs) in Phase I (A and B), and the first two independence principle components (IPCs) in Phase I (C and D) and Phase II (E and F). Red solid lines represented the upper control limits (UCL) of control charts with 99% confidence region.</p

Chemical shifts for 30 metabolites identified in the ¹H NMR spectra of Danhong injection.

<p>Chemical shifts for 30 metabolites identified in the ¹H NMR spectra of Danhong injection.</p

The loading plots and values of IPCA.

<p>(A) The IPCA loading plots for the first two IPCs in Phase II. The numbers represented the samples in Phase II listing in Table S2 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105412#pone.0105412.s001" target="_blank">File S1</a>. The influence of metabolites was showing in red arrow lines. (B) Bar diagram showed the loading values of metabolites in first two independence principle components.</p

The confidence ellipses for quality control.

<p>Scatterplots for the first two IPC scores with the confidence ellipses in Phase I (A) and Phase II (B). The χ² control ellipse used as process region was shown in green solid line, while the Hotelling T² control ellipse used as tolerance region was in red dashed line. The numbers represented the samples in Phase II listing in Table S2 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105412#pone.0105412.s001" target="_blank">File S1</a>, and the seventh sample fell outside the tolerance region.</p

Representative ¹H NMR spectra of Danhong injection.

<p>Peaks: 1, Isoleucine; 2, Leucine 3, Valine; 4, Threonine; 5, Alanine; 6, Acetate; 7, Proline; 8, Pyroglutamate; 9, Succinate; 10, Asparagine; 11, Malonate; 12, Glucose; 13, Galactose; 14, Arabinose; 15, Fructose 16, Rhamnose 17, Rutinose 18, Rutinulose; 19, Salvianic acid; 20, Salvianolic acid B; 21, Rosmarinic acid; 22, Lithospermic acid 23, Salvianolic acid A; 24, Procatechuic acid; 25, Procatechuic aldehyde; 26, 4-Hydroxybenzoic acid; 27, 4-Hydroxycinnamic acid; 28, Uridine; 29, Formate; 30, 5-(Hydroxymethyl)-2-furaldehyde.</p

Decomposition of the seventh sample in Phase II.

<p>Decomposition of the seventh sample in Phase II.</p

Component contributions of PCA and IPCA.

<p>(A) Pareto chart of principal components (PCs) in PCA indicated the first three PCs in Phase I explained 80.99% variances. (B) Kurtosis measurements of all extracted independence principal components (IPCs) in IPCA with Phase I data. The kurtosis of IPC8 was close to zero, so the first 7 components of IPCA were used to choose exact numbers of IPCs. (C) Kurtosis measurements of the first 7 IPCs showed that the kurtosis of IPC3 was close to zero, and the first 2 components were sufficient with IPCA.</p