Multiple information contents derived from the chromatograms and their application to the modeling of quantitative profile–efficacy relationship

Identifying the active ingredients of carbonized Typhae Pollen by spectrum-effect relationship combined with MBPLS, PLS, and SVM algorithms

Typhae Pollen (TP) and its carbonized product (carbonized Typhae Pollen, CTP), as cut-and-dried herbal drugs, have been widely used in the form of slices in clinical settings. However, the two drugs exhibit a great difference in terms of their clinical efficacy, for TP boasts an effect of removing blood stasis and promoting blood circulation, while CTP typically presents a hemostatic function. Since the active ingredients of CTP, so far, still remain unclear, this study aimed at identifying the active ingredients of CTP by spectrum-effect relationship approach coupled with multi-block partial least squares (MBPLS), partial least squares (PLS), and support vector machine (SVM) algorithms. In this study, the chemical profiles of a series of CTP samples which were stir-fried for different duration (denoted as CTP0∼CTP9) were firstly characterized by UHPLC-QE-Orbitrap MS. Then the hemostatic effect of the CTP samples was evaluated from the perspective of multiple parameters-APTT, PT, TT, FIB, TXB2, 6-keto-PGF1α, PAI-1 and t-PA-using established rat models with functional uterine bleeding. Subsequently, MBPLS, PLS and SVM were combined to perform spectrum-effect relationship analysis to identify the active ingredients of CTP, followed by an in vitro hemostatic bioactivity test for verification. As a result, a total of 77 chemical ingredients were preliminarily identified from the CTP samples, and the variations occurred in these ingredients were also analyzed during the carbonizing process. The study revealed that all the CTP samples, to a varying degree, showed a hemostatic effect, among which CTP6 and CTP7 were superior to the others in terms of the hemostatic effect. The block importance in the projection (BIP) indexes of MBPLS model indicated that flavonoids and organic acids made more contributions to the hemostatic effect of CTP in comparison to other ingredients. Consequently, 9 bioactive ingredients, including quercetin-3-O-glucoside, kaempferol-3-O-rutinoside, quercetin, kaempferol, isorhamnetin, 2-methylenebutanedioic acid, pentanedioic acid, benzoic acid and 3-hydroxybenzoic acid, were further identified as the potential active ingredients based on PLS and SVM models as well as the in vitro verification. This study successfully revealed the bioactive ingredients of CTP associated with its hemostatic effect, and also provided a scientific basis for further understanding the mechanism of TP processing. In addition, it proposed a novel path to identify the active ingredients for Chinese herbal medicines.

Combining ultra-high-performance liquid chromatography quadruple exactive orbitrap mass spectrometry with chemometrics to identify and verify the blood-activating components of hawthorn

Hawthorn, one of the widely-used Chinese herbal medicines, has been used to treat blood stasis syndrome in the clinic, but its blood-activating components are unclear. This study combined the ultra-high-performance liquid chromatography-quadruple exactive-orbitrap mass spectrometry with chemometrics to identify the blood-activating components of hawthorn. Different polar fractions of hawthorn aqueous extracts were extracted and mixed to prepare 14 samples. The contents of 25 chemical components for 14 samples were determined by the proposed quantitative method which was validated in terms of linearity, precision, stability, repeatability, and recovery, while the blood-activating effect was evaluated by measuring the whole blood viscosity, plasma viscosity, and plasma fibrinogen levels. Then the partial least squares model was established on the spectrum-effect relationship. The result showed that vitexin-2″-O-rhamnoside, rutin, citric acid, malic acid, gallic acid, and fumaric acid could reduce the whole blood viscosity, plasma viscosity, and plasma fibrinogen levels in blood stasis model rats, and these components were the blood-activating components of hawthorn. This study provided a scientific basis for clarifying the blood-activating components of hawthorn, and the spectrum-effect approach proved to be an effective approach to discovering the bioactive components of Chinese herbal medicines.

The relevance of pharmacognosy in pharmacological research on herbal medicinal products

As all medicines, herbal medicinal products are expected to be safe, effective, and of appropriate quality. However, regulations on herbal medicinal products vary from country to country, and herbal preparations do occur not only in the form of medicinal products but also as less strictly regulated product groups like dietary supplements. Therefore, it is not always easy for the consumers to discriminate high-quality products from low-quality products. On the other hand, herbal medicines have many special features that distinguish them from conventional medicinal products. Plants are complex multicomponent mixtures; in addition, their phytochemical composition is not constant because of inherent variability and a plethora of external influences. Therefore, the production process of an herbal medicinal product needs to be strictly monitored. First of all, the starting materials need to be correctly authenticated and free of adulterants and contaminants. During plant growth, many factors like harvest season and time, developmental stage, temperature, and humidity have a strong impact on plant metabolite production. Also, postharvest processing steps like drying and storage can significantly alter the phytochemical composition of herbal material. As the production of many phytopharmaceuticals includes an extraction step, the extraction solvent and conditions need to be optimized in order to enrich the bioactive constituents in the extract. The quality of finished preparations needs to be determined either on the basis of marker constituents or on the basis of analytical fingerprints. Thus, all production stages should be accompanied by appropriate quality assessment measures. Depending on the particular task, different methods need to be applied, ranging from macroscopic, microscopic, and DNA-based authentication methods to spectroscopic methods like vibrational spectroscopy and chromatographic and hyphenated methods like HPLC, GC-MS and LC-MS. Also, when performing pharmacological and toxicological studies, many features inherent in herbal medicinal products need to be considered in order to guarantee valid results: concerning in vitro studies, difficulties are often related to lacking knowledge of ADME characteristics of the bioactive constituents, nuisance compounds producing false positive and false negative results, and solubility problems. In in vivo animal studies, the route of administration is a very important issue. Clinical trials on herbal medicinal products in humans very often suffer from a poor reporting quality. This often hampers or precludes the pooling of clinical data for systematic reviews. In order to overcome this problem, appropriate documentation standards for clinical trials on herbal medicinal products have been defined in an extension of the CONSORT checklist. This article is part of a Special Issue entitled "Botanicals for Epilepsy".

Ensemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints

Background

Recent quality control of complex mixtures, including herbal medicines, is not limited to chemical chromatographic definition of one or two selected compounds; multivariate linear regression methods with dimension reduction or regularisation have been used to predict the bioactivity capacity from the chromatographic fingerprints of the herbal extracts. The challenge of this type of analysis requires a multi-dimensional approach at two levels: firstly each herb comprises complex mixtures of active and non-active chemical components; and secondly there are many factors relating to the growth, production, and processing of the herbal products. All these factors result in the significantly diverse concentrations of bioactive compounds in the herbal products. Therefore, it is imminent to have a predictive model with better generalisation that can accurately predict the bioactivity capacity of samples when only the chemical fingerprints data are available.

Results

In this study, the algorithm of Stacking Multivariate Linear Regression (SMLR) and a few other commonly used chemometric approaches were evaluated. They were to predict the Cluster of Differentiation 80 (CD80) expression bioactivity of a commonly used herb, Astragali Radix (AR), from the corresponding chemical chromatographic fingerprints. SMLR provides a superior prediction accuracy in comparison with the other multivariate linear regression methods of PCR, PLSR, OPLS and EN in terms of MSE_test and the goodness of prediction of test samples.

Conclusions

SMLR is a better platform than some multivariate linear regression methods. The first advantage of SMLR is that it has better generalisation to predict the bioactivity capacity of herbal medicines from their chromatographic fingerprints. Future studies should aim to further improve the SMLR algorithm. The second advantage of SMLR is that single chemical compounds can be effectively identified as highly bioactive components which demands further CD80 bioactivity confirmation..

Chemometrics-based approach to feature selection of chromatographic profiles and its application to search active fraction of herbal medicine

In our previous report (J Pharmaceut Biomed 56 (2011) 443-447), a support vector machine (SVM)-based pharmacodynamic model was established for predicting active fractions of herbal medicines (HMs), where information contents embedded in the chromatograms of the fractions were represented with the peak areas. However, in this representation the global characteristics of the chromatograms were completely missed, which is definitely contrary to the global and holistic views in theories of HMs and undoubtedly reduce the success rate of this model. To deal with the challenge, two chemometrics methods, that is, minimum redundancy maximum relevance (mRMR) and particle swarm optimizer (PSO), were applied in this article for feature selection of the whole chromatograms, and the PSO was also used to tune the SVM parameters. As a case, a sample HM, that is, Xiangdan injection, was investigated. The predictive accuracy was fully evaluated and compared with those by other popular and reported methods. Furthermore, the confirmation on the independent predicting set exhibited that the predicted bioactivities were well consistent with the experimental values. The important potential application of the present model is to be extended to help search active fractions of other HMs.

A novel approach to active compounds identification based on support vector regression model and mean impact value

Traditionally, active compounds were discovered from natural product extracts by bioassay-guided fractionation, which was with high cost and low efficiency. A well-trained support vector regression model based on mean impact value was used to identify lead active compounds on inhibiting the proliferation of the HeLa cells in curcuminoids from Curcuma longa L. Eight constituents possessing the high absolute mean impact value were identified to have significant cytotoxicity, and the cytotoxic effect of these constituents was partly confirmed by subsequent MTT (3-(4, 5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assays and previous reports. In the dosage range of 0.2-211.2, 0.1-140.2, 0.2-149.9 μm, 50% inhibiting concentrations (IC50 ) of curcumin, demethoxycurcumin, and bisdemethoxycurcumin were 26.99 ± 1.11, 19.90 ± 1.22, and 35.51 ± 7.29 μm, respectively. It was demonstrated that our method could successfully identify lead active compounds in curcuminoids from Curcuma longa L. prior to bioassay-guided separation. The use of a support vector regression model combined with mean impact value analysis could provide an efficient and economical approach for drug discovery from natural products.