Dataset Information

Evaluating the Traditional Chinese Medicine (TCM) Officially Recommended in China for COVID-19 Using Ontology-Based Side-Effect Prediction Framework (OSPF) and Deep Learning.

ABSTRACT:

Ethnopharmacological relevance

The novel coronavirus disease (COVID-19) outbreak in Wuhan has imposed a huge influence in terms of public health and economy on society. However, no effective drugs or vaccines have been developed so far. Traditional Chinese Medicine (TCM) has been considered as a promising supplementary treatment of this disease due to its clinically proven performance in many severe diseases, like severe acute respiratory syndrome (SARS). Meanwhile, many reports suggest that the side-effects (SE) of TCM prescriptions cannot be ignored in treating COVID-19 as it often leads to dramatic degradation of the patients' physical condition. Systematic evaluation of TCM regarding its latent SE becomes a burning issue.

Aim

In this study, we used an ontology-based side-effect prediction framework (OSPF) developed from our previous work and Artificial Neural Network (ANN)-based deep learning, to evaluate the TCM prescriptions officially recommended by China for the treatment of COVID-19.

Materials and methods

The OSPF developed from our previous work was implemented in this study, where an ontology-based model separated all ingredients in a TCM prescription into two categories: hot and cold. A database was created by converting each TCM prescription into a vector which contained ingredient dosages, corresponding hot/cold attribution and safe/unsafe labels. This allowed for training of the ANN model. A safety indicator (SI), as a complement to SE possibility, was then assigned to each TCM prescription. According to the proposed SI, from high to low, the recommended prescription list could be optimized. Furthermore, in interest of expanding the potential treatment options, SIs of other well-known TCM prescriptions, which are not included in the recommended list but are used traditionally to cure flu-like diseases, are also evaluated via this method.

Results

Based on SI, QFPD-T, HSBD-F, PMSP, GCT-CJ, SF-ZSY, and HSYF-F were the safest treatments in the recommended list, with SI scores over 0.8. PESP, QYLX-F, JHQG-KL, SFJD-JN, SHL-KFY, PESP1, XBJ-ZSY, HSZF-F, PSSP2, FFTS-W, and NHSQ-W were the prescriptions most likely to be unsafe, with SI scores below 0.1. In the additional lists of other TCM prescriptions, the indicators of XC-T, SQRS-S, CC-J, and XFBD-F were all above 0.8, while QF-Y, XZXS-S, BJ-S, KBD-CJ, and QWJD-T's indicators were all below 0.1.

Conclusions

In total, there were 10 TCM prescriptions with indicators over 0.8, suggesting that they could be considered in treating COVID-19, if suitable. We believe this work could provide reasonable suggestions for choosing proper TCM prescriptions as a supplementary treatment for COVID-19. Furthermore, this work introduces a novel and informative method which could help create recommendation list of TCM prescriptions for the treatment of other diseases.

SUBMITTER: Wang Z

PROVIDER: S-EPMC7899032 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Evaluating the Traditional Chinese Medicine (TCM) Officially Recommended in China for COVID-19 Using Ontology-Based Side-Effect Prediction Framework (OSPF) and Deep Learning.

Wang Zeheng Z Li Liang L Song Miao M Yan Jing J Shi Junjie J Yao Yuanzhe Y

Journal of ethnopharmacology 20210222

<h4>Ethnopharmacological relevance</h4>The novel coronavirus disease (COVID-19) outbreak in Wuhan has imposed a huge influence in terms of public health and economy on society. However, no effective drugs or vaccines have been developed so far. Traditional Chinese Medicine (TCM) has been considered as a promising supplementary treatment of this disease due to its clinically proven performance in many severe diseases, like severe acute respiratory syndrome (SARS). Meanwhile, many reports suggest ...[more]

PMID: 33631276

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Project description:BACKGROUND:Herbal medicine is widely used all over the world for treating various health disorders. It is employed either alone or in combination with synthetic drugs or plants to be more effective. OBJECTIVE:The assessment of the effect of both water and methanol extracts of 57 widely used plants from Traditional Chinese Medicine (TCM) against the main phase I metabolizing enzyme CYP3A4 in vitro for the first time. MATERIALS AND METHODS:The inhibition of cytochrome P450 activity was evaluated using a luminescence assay. The principal component analysis (PCA) was used to correlate the inhibitory activity with the main secondary metabolites present in the plant extracts. Molecular modeling studies on CYP3A4 (PDB ID 4NY4) were carried out with 38 major compounds present in the most active plant extracts to validate the observed inhibitory effect. RESULTS:Aqueous extracts of Acacia catechu, Andrographis paniculata, Arctium lappa, Areca catechu, Bupleurum marginatum, Chrysanthemum indicum, Dysosma versipellis, and Spatholobus suberectus inhibited CYP3A4 is more than 85% (at a dose of 100 ?g/mL). The corresponding methanol extracts of A. catechu, A. paniculata, A. catechu, Mahonia bealei, and Sanguisorba officinalis inhibited the enzyme by more than 50%. Molecular modeling studies revealed that two polyphenols, namely hesperidin and rutin, revealed the highest fitting scores in the active sites of the CYP3A4 with binding energies equal to -74.09 and -71.34 kcal/mol, respectively. CONCLUSION:These results provide evidence that many TCM plants can inhibit CYP3A4, which might cause a potential interference with the metabolism of other concomitantly administered herbs or drugs. SUMMARY:In this study, the inhibitory activity of the aqueous and methanol extracts of 57 widely used plants from Traditional Chinese Medicine (TCM) against the main phase I metabolizing enzyme CYP3A4 was tested in vitro for the first time.Aqueous extracts of Acacia catechu, Andrographis paniculata, Arctium lappa, Areca catechu, Bupleurum marginatum, Dysosma versipellis, and Spatholobus suberectus inhibited CYP3A4 by more than 85% (at a dose of 100 ?g/mL).The activity could be attributed to the presence of polyphenolics as revealed from the multivariate chemometric analysis and molecular modeling study.These results provide evidence that many TCM plants can inhibit CYP3A4, which might cause a potential interference with the metabolism of other concomitantly administered herbs or drugs. Abbreviation used: CHARMm: Chemistry at HARvard Macromolecular Mechanics, CYP: Cytochrome P450, DMSO: Dimethyl Sulfoxide, PCA: Principal Component Analysis, PDB: Protein Data Bank, TCM: Traditional Chinese Medicine.

Dataset Information

Evaluating the Traditional Chinese Medicine (TCM) Officially Recommended in China for COVID-19 Using Ontology-Based Side-Effect Prediction Framework (OSPF) and Deep Learning.

Ethnopharmacological relevance

Aim

Materials and methods

Results

Conclusions

Publications

Evaluating the Traditional Chinese Medicine (TCM) Officially Recommended in China for COVID-19 Using Ontology-Based Side-Effect Prediction Framework (OSPF) and Deep Learning.

Similar Datasets

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