Jian-Lan Jiang

Identification of antitumor constituents in curcuminoids from Curcuma longa L. based on the composition-activity relationship.

Using orthogonal partial least squares (OPLS), based on the Simca-p11.5 software, and canonical correlation analysis (CCA), performed on MatLab r2010 software, the correlation between curcuminoids extracted from Curcuma longa L. and the antitumor activity on HeLa cells was investigated to identify the significantly active constituents. Fingerprints from 31 batches of curcuminoids from C. longa L. were established using high performance liquid chromatography-electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS), and a total of 26 selected characteristic peaks were quantitatively analyzed. Afterward, the antitumor activities of the curcuminoids on HeLa cells were measured using an MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. We found that 13 of the curcuminoids (peaks 9, 18, 14, 8, 16, 17, 24, 12, 4, 13, 10, 20 and 11) were significantly correlated with antitumor activity via a Loadings plot and VIP (variable importance in projection) in OPLS and a correlation coefficient in CCA. These results support a method for the discovery of antitumor active constituents.

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.

Feature Selection for the Identification of Antitumor Compounds in the Alcohol Total Extracts of Curcuma longa

Antitumor activity has been reported for turmeric, the dried rhizome of Curcuma longa. This study proposes a new feature selection method for the identification of the antitumor compounds in turmeric total extracts. The chemical composition of turmeric total extracts was analyzed by gas chromatography-mass spectrometry (21 ingredients) and high-performance liquid chromatography-mass spectrometry (22 ingredients), and their cytotoxicity was detected through an 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay against HeLa cells. A support vector machine for regression and a generalized regression neural network were used to research the composition-activity relationship and were later combined with the mean impact value to identify the antitumor compounds. The results showed that six volatile constituents (three terpenes and three ketones) and seven nonvolatile constituents (five curcuminoids and two unknown ingredients) with high absolute mean impact values exhibited a significant correlation with the cytotoxicity against HeLa cells. With the exception of the two unknown ingredients, the identified 11 constituents have been reported to exhibit cytotoxicity. This finding indicates that the feature selection method may be a supplementary tool for the identification of active compounds from herbs.

Antitumor compound identification from Zanthoxylum bungeanum essential oil based on composition-activity relationship

The chemical composition of Zanthoxylum bungeanum(Z. bungeanum) essential oil(39 batches) was analyzed by gas chromatography-mass spectrometry(GC-MS) analysis(23 ingredients), and the antitumor activity against HeLa cells was detected via the MTT[3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay. Two composition-activity relationship(CAR) models, generalized regression neural network(GRNN) and support vector regression(SVR) were respectively used to calculate the mean impact value(MIV) so as to identify bioactive compounds. Among them 9 ingredients(peaks 4, 15, 7, 8, 13, 3, 16, 9 and 11) were selected due to their high absolute MIVs. All of them have been confirmed with potential antitumor activity by previous researches. The CAR model combined with MIV was expected to be a credible tool for the active compound identification from herbs.

Composition–activity relationship modeling to predict the antitumor activity for quality control of curcuminoids from Curcuma longa L. (turmeric)

Composition–activity relationship (CAR) modeling is a novel and appropriate method to evaluate the quality of traditional Chinese medicines (TCMs) for it can correlate the chemical constituents of TCMs with their bioactivity. In this paper, we studied the relationship between the antitumor activity on HeLa cells and the curcuminoids from thirty one batches of Curcuma longa L. using support vector regression (SVR) models. Two types of SVR models (e-SVR and ν-SVR) combined with three kernel functions—a LKF (linear kernel function), a PKF (polynomial kernel function) or a RBKF (radial basis kernel function)—were employed. Three algorithms—a GA (genetic algorithm), a PSO (particle swarm optimization), or a GSA (grid search algorithm)—were adopted to determine the optimal parameters automatically. The results revealed that the e-SVR-RBKF-PSO model had the best model performance with a high correlation coefficient (Q = 0.9297) and a low mean square error (MSE = 0.0138) between the experimental and predicted values. This indicated that the model was able to predict the antitumor activity of curcuminoids from Curcuma longa L. with a high degree of accuracy. Therefore, CAR modeling could be a useful tool in the quality control of TCMs.

A Novel Approach to Evaluate the Quality and Identify the Active Compounds of the Essential Oil from Curcuma longa L.

Composition-Activity Relationship (CAR) modeling is a novel approach to evaluate the quality and identify active components of herbal medicine. In this study, Grid Search Method (GSM) and Heuristics algorithms, particularly Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), were adopted to determine the optimal parameters automatically. Then, support vector regression (SVR) combined with a linear kernel function or a radial basis kernel function (RBF) and back propagation artificial neural networks (BPANN) were employed to construct the model that correlated the main chemical components with the cytotoxicity of the essential oil from Curcuma longa L., respectively. Considering the robustness and predictive ability, the ν-SVR-RBF-PSO model had the best performance in various tests performed in this paper. Nine components were then identified to have significant cytotoxicity based on the superior model and Mean Impact Value (MIV) analysis. An optimal model can therefore be a useful tool to predict the bioactivity for quality evaluation and active components identification of herbal medicine.

Systematic, computer-assisted development of high performance liquid chromatography for multi-component analysis

The aim of this study was to develop a multi-component determination analytical method. The analytical Quality by Design (QbD) concept was used in the beginning of the establishment of a high performance liquid chromatography (HPLC) method for compound traditional Chinese medicine (TCM) preparations using a diode-array detector (DAD) and an evaporative light scattering detector (ELSD) in series. Herein, the QbD workflow is discussed and demonstrated with a systematic HPLC method development, including risk assessment, the design of experiments (DOEs), and assessment of the data to provide a method operable design space (DS). Modeling software Drylab was employed to set up experiments for the development of a simple and robust separation method and to visually achieve the required criteria as an initial DS of the analytical method based on simulation. To improve the method development and optimization step, the statistical software JMP@(SAS Institute) was applied to simultaneously optimize the chromatographic conditions such as the gradient time, the concentration of the aqueous phase, the column temperature, the flow rate and the ELSD parameters. Finally, a successful HPLC method was developed and validated to verify the robustness of the QbD system. The use of QbD workflows streamlines the development of methods as compared to traditional approaches. With the addition of systematic DOEs, the optimization resulted in critical resolution Rs, crit ≧ 1.5 for all the six compounds researched. As a result, a robust and reliable method operable design region was established. This method had fewer issues and failures throughout the lifecycle due to the knowledge gained via the QbD process.

The Application of SVR Model in the Improvement of QbD: A case study of the extraction of podophyllotoxin

Abstract In order to make a further optimization of process design via increasing the stability of design space, we brought in the model of Support Vector Regression (SVR). In this work, the extraction of podophyllotoxin was researched as a case study based on Quality by Design (QbD). We compared the fitting effect of SVR and the most used quadratic polynomial model (QPM) in QbD, and an analysis was made between the two design spaces obtained by SVR and QPM. As a result, the SVR stayed ahead of QPM in prediction accuracy, the stability of model and the generalization ability. The introduction of SVR into QbD made the extraction process of podophyllotoxin well designed and easier to control. The better fitting effect of SVR improved the application effect of QbD and the universal applicability of SVR, especially for non-linear, complicated and weak-regularity problems, widened the application field of QbD.

Anticancer component identification from the extract of Dysosma versipellis and Glycyrrhiza uralensis based on support vector regression and mean impact value

The good therapeutic effect of herbal medicines depends on their abundant components and its extremely necessary to find out the main bioactive ingredients. In this paper, the extract of Dysosma versipellis and Glycyrrhiza uralensis was studied for the first time by chemometrics. A HPLC-UV method was developed and validated to establish fingerprint spectra of 46 batches of different samples and a total of 45 common components of all samples were quantitatively and qualitatively analyzed using HPLC-UV and UPLC-Q-TOF-MS/MS, respectively. The anticancer effect of the extract was obtained by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay on HeLa cells. After that, a support vector regression (SVR) model optimized by particle swarm optimization (PSO) was constructed to depict the relationship between the chemical constituents and anticancer effect of the extract. Then the mean impact value (MIV) method was introduced to evaluate the bioactivity of the concerned components based on the optimal SVR–PSO model. The results showed that the developed model has an excellent fitting accuracy and generalization ability, and a ranking of the components for their anticancer activity was obtained. The employed strategy provides an efficient and convenient access to active anticancer constituents from the extract of D. versipellis and G. uralensis. The identified components provide explicit guidance for screening anticancer compounds and the developed model can be used for predicting the activity of new samples.

Development of a multi-component drug from turmeric using central composite design

A methodology to develop multi-component drugs based on traditional Chinese medicines has been developed using central composite design. Several active components from the traditional Chinese medicine turmeric were chosen for use in a multi-component antitumor drug. Response surface methodology based on a central composite design was applied to determine the quantitative composition-activity relationships in order to optimize the amount of each component in the drug. An MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay was used to measure the pharmacological activity as the response value. The experimental antitumor activity of the optimum combination was 92.85% in the MTT assay and superior to the activities of each single component. These results demonstrate that response surface methodology based on a central composite design is suitable for the design of multi-component drugs.