Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs

Han, Koeun; Zhang, Lei; Miao, Wang; Zhang, Rui; Wang, Chunyu; Zhang, Chengzhi

doi:10.3390/molecules23092303

Cited by 24 publications

(9 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These tools can more effectively and accurately screen out the active ingredients and targets from EOs, and predict the mechanism of action. 71 In recent years, the mechanisms of traditional herbal medicines, including EOs, in the treatment of various serious diseases have been successfully predicted, including depression, 72 arthritis, 73 diabetes 74 and other diseases, 75,76 and achieved certain results.…”

Section: Application Of Network Pharmacology In Essential Oil Researchesmentioning

confidence: 99%

See 1 more Smart Citation

Biological, Functional and Network Pharmacological Exploration of Essential Oils in Treatment and Healthcare of Human Diseases

Zhang¹,

Tang²,

Liu³

et al. 2022

Future Integr Med

View full text Add to dashboard Cite

Essential oils (EOs) are natural products with bioactive functions that are obtained from various plant species, including Lavandula angustifolia and plant parts, through extraction methods, such as hydro-distillation, steam distillation and cold pressing, which can be dated back to ancient Egyptian and Greek times. Although various EOs are effective for disease treatment, such as human infectious diseases and mental disorders, the specific pharmacological mechanisms remain unclear due to its complex composition. Previous studies have attempted to recruit pharmaceutical analysis techniques, such as HPLC and MALDI-TOF, in order to elucidate the compositions of EOs. However, these have provided limited information on the mechanism of the bioactive functions of EOs. In recent years, network pharmacology has emerged as a convenient and appropriate approach to study the molecular mechanism of traditional medicines. To date, there is a lack of updated reviews on the recent progress of network pharmacology in the field of interactions between EOs and human diseases. Therefore, the present study scrutinized recent and important literatures in the field of network pharmacology and EOs, aiming to provide a timely yet brief overview of EOs as a potential treatment for diseases via network pharmacology, and facilitating the application of EOs as a complementary medicine and therapy for human diseases.

show abstract

Section: Application Of Network Pharmacology In Essential Oil Researchesmentioning

confidence: 99%

“…95 In summary, the application of network pharmacology and molecular docking technology can effectively clarify the pharmacodynamic material basis of complex chemical substance systems, and improve the efficiency in drug discovery and development via the EO screening process. 71,96…”

Section: Application Of Network Pharmacology In Essential Oil Researchesmentioning

confidence: 99%

Biological, Functional and Network Pharmacological Exploration of Essential Oils in Treatment and Healthcare of Human Diseases

Zhang¹,

Tang²,

Liu³

et al. 2022

Future Integr Med

View full text Add to dashboard Cite

show abstract

“…Implemented with that, many strategies have already been adopted and obtained success in the research of disease analysis [26], medical imaging [27], [28]. In this sense, introducing AI into TCM research has attracted extreme attention as well, and hereby, has resulted in many achievements [29]- [33]. Especially, some novel concepts, such as using the convolutional neural network to classify the herbs [34] and exploring the relationship between the treatment action and herbal property by deep learning [35], have paved the way to extend the research of SE prediction of TCM CP.…”

Section: Introductionmentioning

confidence: 99%

Approaching High-Accuracy Side Effect Prediction of Traditional Chinese Medicine Compound Prescription Using Network Embedding and Deep Learning

Wang

Yan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In this paper, we realize high-accuracy side-effect prediction of Traditional Chinese Medicine Compound Prescription by introducing network embedding and deep learning. A random walk network that could efficiently interpret the information in the prescription is established from a conventional Bag-of-Word network. After the validation of this random walk network, the highest prediction accuracy reaches 0.908 where a simple five-layer artificial neural network is implemented, rendering this method is promising for Traditional Chinese Medicine side-effect prediction and other medicines with a similar structure such as the compound drugs. INDEX TERMS Side effect, network embedding, deep learning, traditional Chinese medicine.

show abstract

“…The in silico approach can be used from ADMET property predication to clinical trial simulation. It was used to predict the drug-like properties of Chinese herbal compounds include the virtual screening method, pharmacophore model method and machine learning method [ 15 ]. For a metabolism prediction, in silico tools are most commonly used for predicting substrates and inhibitors of metabolic enzymes, sites of metabolism, and the structures of probable metabolites.…”

Section: Introductionmentioning

confidence: 99%

In vitro investigation of metabolic fate of α-mangostin and gartanin via skin permeation by LC-MS/MS and in silico evaluation of the metabolites by ADMET predictor™

Rukthong

Sereesongsang

Kulsirirat

et al. 2020

BMC Complement Med Ther

View full text Add to dashboard Cite

Background Mangosteen, Garciniam angostana L., is a juicy fruit commonly found in Thailand. The rinds of Garciniam angostana L.have been used as a traditional medicine for the treatment of trauma, diarrhea and skin infection. It is also used in dermatological product such as in cosmetics. The mangosteen pericarp can be used to extract valuable bioactive xanthone compounds such as α-mangostin and gartanin. This study is aimed to predict the metabolism of α-mangostin and gartanin using in silico and in vitro skin permeation strategies. Methods Based on their 2D molecular structures, metabolites of those compounds were predicted in silico using ADMET Predictor™. The Km and Vmax, for 5 important recombinant CYP isozymes 1A2, 2C9, 2C19, 2D6 and 3A4 were predicted. Moreover, the in vitro investigation of metabolites produced during skin permeation using human epidermal keratinocyte cells, neonatal (HEKn cells) was performed by LC-MS/MS. Results It was found that the results derived from in silico were in excellent alignment with those obtained from in vitro studies for both compounds. The prediction referred that gartanin and α-mangostin were the substrate of CYP1A2, 2C9, 2C19 and 3A. In the investigation of α-mangostin metabolites by LC-MS/MS system, the MW of the parent compound was increased from 411.200 to 459.185 Da. Therefore, α-mangostin might be metabolized via tri-oxidation process. The increased molecular weight of parent compound (397.200 to 477.157 Da) illustrated that gartanin might be conjugated to sulfated derivatives. Conclusions In all the studies, α-mangostin and gartanin were predicted to be. metabolized via phase I and phase II metabolism (sulfation), respectively.

show abstract

Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs

Cited by 24 publications

References 82 publications

Biological, Functional and Network Pharmacological Exploration of Essential Oils in Treatment and Healthcare of Human Diseases

Biological, Functional and Network Pharmacological Exploration of Essential Oils in Treatment and Healthcare of Human Diseases

Approaching High-Accuracy Side Effect Prediction of Traditional Chinese Medicine Compound Prescription Using Network Embedding and Deep Learning

In vitro investigation of metabolic fate of α-mangostin and gartanin via skin permeation by LC-MS/MS and in silico evaluation of the metabolites by ADMET predictor™

Contact Info

Product

Resources

About