Combination analysis of new drug discovery with "Xiaohe Silian" method and traditional Chinese medicine clinical pharmacy

2014 ◽  
Keyword(s):  
Drug Discovery ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clinical Pharmacy ◽  
New Drug

scholarly journals Traditional Chinese Medicine-Based Network Pharmacology Could Lead to New Multicompound Drug Discovery

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Jian Li ◽  
Cheng Lu ◽  
Miao Jiang ◽  
Xuyan Niu ◽  
Hongtao Guo ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Pattern Classification ◽  
Synergistic Effects ◽  
Molecular Networks ◽  
Network Pharmacology ◽  
Herbal Formula ◽  
Drug Candidates ◽  
New Drug

Current strategies for drug discovery have reached a bottleneck where the paradigm is generally “one gene, one drug, one disease.” However, using holistic and systemic views, network pharmacology may be the next paradigm in drug discovery. Based on network pharmacology, a combinational drug with two or more compounds could offer beneficial synergistic effects for complex diseases. Interestingly, traditional chinese medicine (TCM) has been practicing holistic views for over 3,000 years, and its distinguished feature is using herbal formulas to treat diseases based on the unique pattern classification. Though TCM herbal formulas are acknowledged as a great source for drug discovery, no drug discovery strategies compatible with the multidimensional complexities of TCM herbal formulas have been developed. In this paper, we highlighted some novel paradigms in TCM-based network pharmacology and new drug discovery. A multiple compound drug can be discovered by merging herbal formula-based pharmacological networks with TCM pattern-based disease molecular networks. Herbal formulas would be a source for multiple compound drug candidates, and the TCM pattern in the disease would be an indication for a new drug.

New development and update on China Bioanalysis Forum

Bioanalysis ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 199-201
Author(s):  
Fan Jin ◽  
Daniel Tang ◽  
Kelly Dong ◽  
Dafang Zhong
Keyword(s):  
Drug Discovery ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Drug Development ◽  
Research And Development ◽  
Continuous Growth ◽  
Contract Research Organization ◽  
Drug Discovery And Development ◽  
Medicine Research ◽  
New Development

This article provides an update on new development of China Bioanalysis Forum (CBF). CBF became a member association of Chinese Pharmaceutical Association (CPA) at the end of 2019. The official ceremony and first scientific symposium were held in Shanghai on 18 September 2020. The president of Chinese Pharmaceutical Association and representatives from industry, Contract Research Organization (CRO), hospitals and academic institutes attended the ceremony. Seven experts in the field gave presentations on various topics including Drug Metabolism and Pharmacokinetics (DMPK) and bioanalytical support in drug discovery and development as well as experience in Traditional Chinese Medicine research. With the continuous growth of research and development in China, it is well acknowledged that bioanalysis provides critical support for new innovative medicines and generic drug development in the region.

scholarly journals High content screening for drug discovery from traditional Chinese medicine

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Jing Wang ◽  
Ming-Yue Wu ◽  
Jie-Qiong Tan ◽  
Min Li ◽  
Jia-Hong Lu
Keyword(s):  
Drug Discovery ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
High Content Screening

scholarly journals Advancing Drug Discovery and Development from Active Constituents of Yinchenhao Tang, a Famous Traditional Chinese Medicine Formula

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Aihua Zhang ◽  
Hui Sun ◽  
Shi Qiu ◽  
Xijun Wang
Keyword(s):  
Systems Biology ◽  
Drug Discovery ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Molecular Mechanisms ◽  
Synergistic Effects ◽  
Molecular Networks ◽  
Agile Development ◽  
Therapeutic Effects ◽  
Systematic Analysis

Traditional Chinese medicine (TCM) formula has been playing a very important role in health protection and disease control for thousands of years. Guided by TCM syndrome theories, formula are designed to contain a combination of various kinds of crude drugs that, when combined, will achieve synergistic efficacy. However, the precise mechanism of synergistic action remains poorly understood. One example is a famous TCM formula Yinchenhao Tang (YCHT), whose efficacy in treating hepatic injury (HI) and Jaundice syndrome, has recently been well established as a case study. We also conducted a systematic analysis of synergistic effects of the principal compound using biochemistry, pharmacokinetics and systems biology, to explore the key molecular mechanisms. We had found that the three component (6,7-dimethylesculetin (D), geniposide (G), and rhein (R)) combination exerts a more robust synergistic effect than any one or two of the three individual compounds by hitting multiple targets. They can regulate molecular networks through activating both intrinsic and extrinsic pathways to synergistically cause intensified therapeutic effects. This paper provides an overview of the recent and potential developments of chemical fingerprinting coupled with systems biology advancing drug discovery towards more agile development of targeted combination therapies for the YCHT.

NEW DRUG R&D OF TRADITIONAL CHINESE MEDICINE: ROLE OF DATA MINING APPROACHES

2009 ◽  
Vol 17 (03) ◽  
pp. 329-347 ◽  
Author(s):  
HONGJUN YANG ◽  
JIANXIN CHEN ◽  
SHIHUAN TANG ◽  
ZHENKUN LI ◽  
YISONG ZHEN ◽  
...  
Keyword(s):  
Data Mining ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Unsupervised Clustering ◽  
Training Set ◽  
Regression Methods ◽  
New Drug ◽  
Clustering Approach

Traditional Chinese Medicine (TCM) documented about 100,000 formulae during past 2500 years. To use and customize them by modern pharmaceutical industry, we make an interdisciplinary effort to study the activity of new drug research and development (R&D) in TCM by introducing data mining approaches to it. We used the migraine formulae as a training set to investigate the possibility of developing new prescription by means of data mining. The activity of new drug R&D of TCM consists of two steps. The first step is to discover new prescriptions (candidates for drugs) from migraine formulae. We present an unsupervised clustering approach based on data mining theory to address the problem in the first step and automatically discover ten new prescriptions from the formulae data. The second step is to develop and optimize the prescriptions discovered by current biomedical approaches. Since Ligusticum chuanxiong Hort (LCH), a kind of herb, is often used to treat migraine and appears in the new prescriptions, we use it as an example and apply supervised regression method based on data mining theory to study the drug R&D activity of TCM. We revised two linear regression methods in order to establish the nonlinear association between three chemical ingredients of LCH and corresponding pharmacological activity and used it to predict the activities. The association is validated by in vitro experiments and we found that the experimental results are consistent with the prediction. Unsupervised clustering and supervised regression cover most part of data mining theory, which means that data mining approaches play a crucial role in new drug R&D in TCM and present a better solution to establish the platform of drug R&D in TCM.

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