Yiyu Cheng

Identification and determination of the major constituents in traditional Chinese medicine Si-Wu-Tang by HPLC coupled with DAD and ESI–MS

An HPLC/DAD/ESI/MS method was established for the qualitative and quantitative analysis of the major constituents in Si-Wu-Tang, a traditional Chinese medicine formula. Based on the baseline chromatographic separation of most constituents in Si-Wu-Tang on hypersil C18 column with water-acetonitrile-acetic acid as mobile phase, 12 compounds including phenolic acids, phthalides and terpene glycoside were identified by online ESI-MS and the comparison with literature data and standard samples. Most of these compounds derive from Paeonia lactiflora and Ligusticum chuanxiong. Seven of them were quantitated by HPLC coupled with DAD. The validation of the method, including sensitivity, linearity, repeatability, recovery, were examined. The linear calibration curve were acquired with R2 > 0.99 and LOD (S/N = 3) were between 0.75 and 5 ng. The repeatability was evaluated by intra- and inter-day assays and R.S.D. value were within +/- 2.38%. The recovery rates of selected compounds were in the range of 96.64-105.21% with R.S.D. less than 3.22%.

Rapid screening and identification of α-glucosidase inhibitors from mulberry leaves using enzyme-immobilized magnetic beads coupled with HPLC/MS and NMR.

α-Glucosidase plays important roles in the digestion and absorption of carbohydrates in the small intestine. The inhibition of α-glucosidase is regarded as a potential way to treat diabetes. We established an approach to screening α-glucosidase inhibitors from medicinal plants using enzyme-coated magnetic bead. Using 1-(3-dimethyl-aminopropyl)-3-ethylcarbodiimide and N-hydroxysuccinimide as reaction reagents, α-glucosidase was immobilized on the magnetic beads by covalent linkage. The conjugation of α-glucosidase to the magnetic beads was characterized using scanning electron microscope and X-ray diffractometer. The proposed approach was applied in fishing potential α-glucosidase inhibitors from extract of Morus alba, a Chinese medicinal plant. The structures of potential active compounds were identified via liquid chromatography-mass spectrometry and nuclear magnetic resonance. The results demonstrated that two flavonoids (isoquercitrin and astragalin) could bind to α-glucosidase, which was confirmed via conventional α-glucosidase inhibitory assay. Our findings suggested that enzyme-coated magnetic beads may be suitable for discovering active compounds from medicinal plants.

Identifying P-Glycoprotein Substrates Using a Support Vector Machine Optimized by a Particle Swarm

P-Glycoprotein (P-gp) contributes to extruding a structurally, chemically, and pharmacologically diverse range of substrates out of cells. This function may result in the failure of chemotherapy in cancer and influence pharmacokinetic properties of many drugs. Although a great deal of research has been devoted to the investigation of P-gp and its substrate specificity, still we do not have a clear understanding of the resolution of the three-dimensional structure of P-gp and its working role as a drug efflux pump at a molecular level. Hence to identify whether a compound is a P-gp substrate or not, computational methods are promising both in cancer treatment and the drug discovery processes. We have established more effective models for prediction of P-gp substrates with an average accuracy of >90% using a Particle Swarm (PS) algorithm and a Support Vector Machine (SVM) approach. The applied models yielded higher accuracies and contained fewer variables in comparison with previous studies. An analysis of P-gp substrate specificity based on the data set is also presented by a PS and a SVM. The aim of this study is 3-fold: (i) presentation of a modified PS algorithm that is applicable for selection of molecular descriptors in quantitative structure-activity relationship (QSAR) model construction, (ii) application of this modified PS algorithm as a wrapper to undertake feature selection in construction of a QSAR model to predict P-gp substrates with a multiple linear (ML) and SVM approach, and (iii) also finding factors (molecular descriptors) that most likely are associated with P-gp substrate specificity by using a PS and a SVM from the data set.

An ultrafiltration high-performance liquid chromatography coupled with diode array detector and mass spectrometry approach for screening and characterising tyrosinase inhibitors from mulberry leaves

Tyrosinase is a key enzyme in melanin synthesis. Its inhibitor may be used to efficiently treat hyperpigmentation and widely applied in cosmetic products and food supplements. In the present study, a new assay based on ultrafiltration high-performance liquid chromatography coupled with diode array detector and mass spectrometry (HPLC-DAD-MS) was developed for the rapid screening and identification of ligands for tyrosinase. Experiments were carried out to select the optimal binding conditions, tyrosinase concentration, and incubation time. Non-specific binding to the denatured tyrosinase was also investigated. Twelve compounds with tyrosinase binding activity were found in mulberry leaf extracts. The identities of these compounds were characterised by HPLC-DAD-MS(n). Particularly, two compounds, namely, quercetin-3-O-(6-O-malonyl)-β-D-glucopyranoside and kaempferol-3-O-(6-O-malonyl)-β-D-glucopyranoside, were identified as new tyrosinase inhibitors. The screening results were verified by tyrosinase inhibition assays. Experimental results proved that the proposed method could rapidly screen tyrosinase inhibitors in complex mixtures.

A quantitative system for pulse diagnosis in Traditional Chinese Medicine

The pulse diagnosis is one of the most important examinations in traditional Chinese medicine (TCM). Due to the subjectivity and fuzziness of pulse diagnosis in TCM, quantitative systems or methods are needed to modernize pulse diagnosis. But up to now, the effective models that can classify pulse types according to pulse waves automatically have not been reported, which undoubtedly limits the practical applications of pulse diagnosis in clinical medicines. In this article, a novel quantitative system for pulse diagnosis was constructed based on Bayesian networks (BNs) to build the mapping relationships between pulse waves and pulse types. The results show that the system obtains relative reliable predictions of pulse types, and its predictive accuracy rate reach 84%, which testifies that the model used in our system is feasible and effective and can be expected to facilitate popular applications of TCM

Rapid analysis of a Chinese herbal prescription by liquid chromatography–time-of-flight tandem mass spectrometry

A rapid method, using liquid chromatography coupled with time-of-flight tandem mass spectrometry was developed for the analysis of Chinese herbal prescription. The analysis was performed on a Waters UPLC BEH C18 column using gradient elution system. A hybrid quadrupole time-of-flight analyzer was used for the determination of accurate mass of the protonated or deprotonated molecule and fragment ion. The constituents of the prescription were identified and deduced according to the mass spectrometric fragmentation mechanisms, MS/MS data and relevant literatures. This method was rapid and reliable, which showed high sensitivity and resolution for identifying bioactive constituents in complex chemical system such as traditional Chinese medicine (TCM) prescriptions. With this method, the constituents of a Chinese herbal prescription Wen-Pi-Tang were identified. Mainly four types of constituents, phenolic compounds, alkaloids, triterpenoid saponins and flavonoid glycoside were characterized.

Quantitative analysis and chromatographic fingerprinting for the quality evaluation of Scutellaria baicalensis Georgi using capillary electrophoresis.

Abstract Quantitative analysis and chromatographic fingerprinting for the quality evaluation of a Chinese herb Scutellaria baicalensis Georgi using capillary electrophoresis (CE) technique was developed. The separation was performed with a 50.0cm (42.0cm to the detector window)×75μm i.d. fused-silica capillary, and the CE fingerprint condition was optimized using the combination of central composite design and multivariate analysis. The optimized buffer system containing 15mM borate, 40mM phosphate, 15mM SDS, 15% (v/v) acetonitrile and 7.5% (v/v) 2-propanol was employed for the method development, and the baseline separation was achieved within 15min. The determination of the major active components (Baicalin, Baicalein and Wogonin) was carried out using the optimized CE condition. Good linear relationships were provided over the investigated concentration ranges (the values of R 2: 0.9997 for Baicalin, 0.9992 for Baicalein, and 0.9983 for Wogonin, respectively). The average recoveries of these target components ranged between 96.1–105.6%, 98.6–105.2%, and 96.3–105.0%, respectively. CE fingerprints combined with the quantitative analysis can be used for the quality evaluation of S. baicalensis.

A computational approach to botanical drug design by modeling quantitative composition-activity relationship.

Herbal medicine has been successfully applied in clinical therapeutics throughout the world. Following the concept of quantitative composition–activity relationship, the presented study proposes a computational strategy to predict bioactivity of herbal medicine and design new botanical drug. As a case, the quantitative relationship between chemical composition and decreasing cholesterol effect of Qi‐Xue‐Bing‐Zhi‐Fang, a widely used herbal medicine in China, was investigated. Quantitative composition–activity relationship models generated by multiple linear regression, artificial neural networks, and support vector regression exhibited different capabilities of predictive accuracy. Moreover, the proportion of two active components of Qi‐Xue‐Bing‐Zhi‐Fang was optimized based on the quantitative composition–activity relationship model to obtain new formulation. Validation experiments showed that the optimized herbal medicine has greater activity. The results indicate that the presented method is an efficient approach to botanical drug design.

DNA Microarrays Are Predictive of Cancer Prognosis: A Re-evaluation

Purpose: The reliability of microarray-based cancer prognosis is questioned by Michiels et al. They reanalyzed seven studies published in the prominent journals as successful stories of microarray-based cancer prognosis and concluded that the originally reported assessments are overoptimistic. We set to investigate the reality of microarrays for predicting cancer prognosis by using the same data sets with commonly accepted data analysis approaches. Experiment Design: Michiels et al.s analysis protocol used a correlation-based feature selection method, split sample validation, and a nearest-centroid rule classifier. We examined their results through systematically replacing their analysis approaches with other commonly used methods as a parameter study. In addition, we applied a widely accepted permutation test in conjunction with 5-fold cross-validation to verify Michiels et al.s findings. Results: The stability of signature genes is likely obtained when a fold change–based feature selection method is applied. When cross-validation procedures are used to replace Michiels et al.s split sample validation, only one of the seven studies yielded uninformative classifiers. The permutation test reveals that the confidence interval based on the split sample used in the Michiels et al.s review is not a rigorous and robust approach to assess the validity of a classifier. Conclusions: We concluded that the use of DNA microarrays for cancer prognosis can be demonstrated. We also stressed that caution should be exercised when a general conclusion is withdrawn based on a single statistical practice without alternative validation, which can leave a false impression and pessimistic perspective for emerging biomarker methodologies to advance cancer research. Clin Cancer Res; 16(2); 629–36

Strategies and techniques for multi-component drug design from medicinal herbs and traditional Chinese medicine.

Many common diseases like diabetes, cardiovascular disease, and cancer are caused or exacerbated by disparate physiological, pathological, environmental, and lifestyle factors. However, the chief aim of current drug discovery approaches is to search for single-entity drugs that interact with well-defined molecular targets (a single receptor or enzyme). The concept of multi-target drugs or multi-component therapy is gaining increased attention with the discovery that many diseases (like hypertension) are best treated by multi-drug or multi-target therapies. Traditional medicines, such as traditional Chinese medicine (TCM) and Indian Ayurveda, have been re-evaluated and are becoming important resources for the discovery of bioactive molecules with therapeutic effects and for designing multi-targets drugs. This article provides an overview of new strategies and techniques to design therapeutic regimes that comprise more than one active ingredient to produce synergistic effects by simultaneously interacting with multiple molecular targets. Advances in phytochemistry, high throughput screening, DNA sequencing, systems biology, and bioinformatics can reveal the chemical composition and molecular mechanisms of TCM and together provide a new template for the early stages of drug discovery. Meanwhile, clinical knowledge of TCM provides a promising framework for multi-component drug design. A renaissance of multi-component drug discovery inspired by traditional medicine is possible.