A support vector machine based pharmacodynamic prediction model for searching active fraction and ingredients of herbal medicine: Naodesheng prescription as an example

Chen, Chao; Li, Shuxian; Wang, Shumei; Liang, Shengwang

doi:10.1016/j.jpba.2011.05.010

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…One of the most important steps is to mine the information contents embedded in the chromatograms. Peak area and/or peak height of each independent peak without overlapping are mostly taken into consideration . However, in representing a chromatogram solely with its peak area and/or peak height, the global characteristic of the chromatogram is completely missed, which is definitely contrary to the global and holistic views in theories of HMs and undoubtedly reduce the success rate of the prediction model.…”

Section: Methodsmentioning

confidence: 99%

“…Jiang et al . proposed to use orthogonal PLS and canonical correlation analysis to identify antitumor constituents in curcuminoids from Curcuma longa L. In recent years, we reported a support vector machine (SVM)‐based model and a principle component regression (PCR)‐based model for estimating the pharmacological effect of HM from the chromatographic peak areas and the multiple information contents derived from the chromatograms .…”

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confidence: 99%

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Chemometrics‐Based Approach to Feature Selection of Chromatographic Profiles and its Application to Search Active Fraction of Herbal Medicine

Chao

Yuan

et al. 2013

Chem Biol Drug Des

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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.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Chemometrics‐Based Approach to Feature Selection of Chromatographic Profiles and its Application to Search Active Fraction of Herbal Medicine

Chao

Yuan

et al. 2013

Chem Biol Drug Des

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“…For instance, in China, Yan and his colleagues [7] studied 28 samples of Radix Tinosporae for analgesic bioactivities on mice. Chen and his colleagues used 32 combinations of 5-herb CHM mixture including RA and studied antiplatelet in SD rats [8]. Another research group of Jiang is based on 31 batches of curcuma volatile oil to study antitumor in vitro effects [9].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Radix Astragali Immunomodulatory Effect of CD80 Expression from Chromatograms by Quantitative Pattern-Activity Relationship

Lau

Fan

et al. 2017

BioMed Research International

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The current use of a single chemical component as the representative quality control marker of herbal food supplement is inadequate. In this CD80-Quantitative-Pattern-Activity-Relationship (QPAR) study, we built a bioactivity predictive model that can be applicable for complex mixtures. Through integrating the chemical fingerprinting profiles of the immunomodulating herb Radix Astragali (RA) extracts, and their related biological data of immunological marker CD80 expression on dendritic cells, a chemometric model using the Elastic Net Partial Least Square (EN-PLS) algorithm was established. The EN-PLS algorithm increased the biological predictive capability with lower value of RMSEP (11.66) and higher values of Rp2 (0.55) when compared to the standard PLS model. This CD80-QPAR platform provides a useful predictive model for unknown RA extract's bioactivities using the chemical fingerprint inputs. Furthermore, this bioactivity prediction platform facilitates identification of key bioactivity-related chemical components within complex mixtures for future drug discovery and understanding of the batch-to-batch consistency for quality clinical trials.

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“…Chen et al . introduced a support vector machine (SVM) pharmacodynamic prediction model to identify active fraction and constituents of Naodesheng prescription. Wang et al .…”

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confidence: 99%

“…Cheng et al (5) developed a stepwise causal adjacent relationship discovery algorithm to study relationship between composition and bioactivity and successfully identified active components from mixed extracts of Radix Salviae miltiorrhizae and Cortex Moutan. Chen et al (6) introduced a support vector machine (SVM) pharmacodynamic prediction model to identify active fraction and constituents of Naodesheng prescription. Wang et al (7) proposed composition-activity relationship models established by multiple linear regression, artificial neural networks, and support vector regression (SVR), respectively, to optimize the formulation of Qi-Xue-Bing-Zhi-Fang for decreasing cholesterol effect.…”

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confidence: 99%

A Novel Approach to Active Compounds Identification Based on Support Vector Regression Model and Mean Impact Value

Jiang

Zhang

et al. 2013

Chem Biol Drug Des

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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.

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A support vector machine based pharmacodynamic prediction model for searching active fraction and ingredients of herbal medicine: Naodesheng prescription as an example

Cited by 18 publications

References 15 publications

Chemometrics‐Based Approach to Feature Selection of Chromatographic Profiles and its Application to Search Active Fraction of Herbal Medicine

Chemometrics‐Based Approach to Feature Selection of Chromatographic Profiles and its Application to Search Active Fraction of Herbal Medicine

Prediction of Radix Astragali Immunomodulatory Effect of CD80 Expression from Chromatograms by Quantitative Pattern-Activity Relationship

A Novel Approach to Active Compounds Identification Based on Support Vector Regression Model and Mean Impact Value

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