Literature DB >> 31904505

Omics strategies decipher therapeutic discoveries of traditional Chinese medicine against different diseases at multiple layers molecular-level.

Rui Guo1, Xialin Luo1, Jingjing Liu1, Lian Liu2, Xijun Wang3, Haitao Lu4.   

Abstract

Traditional Chinese medicine (TCM) has been broadly used for the personalized treatment of many diseases in China for thousands of years. In the past century, TCM was also introduced to other Asian countries and even the Western world. Increasing evidence has shown that TCM has the capacity to treat numerous complex diseases in the clinic, such as cardiovascular diseases (CVDs), infectious diseases, metabolic diseases, and neurodegenerative diseases. However, the earlier lack of analytical strategies to annotate the chemical complexity has severely impeded the modern study and translational application of TCM. This critical review aims to explore and exploit applications of systems biology-driven omics methods in TCM against a diversity of diseases, toward the specific use of TCM to treat patients with different diseases. Such effort shall enhance the applicability of systems biology-driven omics strategies in deciphering the mechanisms by which TCM treats different diseases and may lead to the discovery of new therapeutic directions. In addition, we proposed the possible strategies to innovate the applicable pattern of omics technologies in TCM niches, such as precision-modification metabolomics and chinmedomics methods, allowing to unveil the complexity of TCM, which must enable TCM to serve better for the population-health. Taken together, this review eventually shall highlight the core value of omics technologies in innovating TCM to combat the diseases in a new horizon.
Copyright © 2020. Published by Elsevier Ltd.

Entities:  

Keywords:  Chinmedomics; Omics strategies; Precision-modification metabolomics; Systems biology; Therapeutic discovery; Traditional Chinese medicine

Year:  2020        PMID: 31904505     DOI: 10.1016/j.phrs.2020.104627

Source DB:  PubMed          Journal:  Pharmacol Res        ISSN: 1043-6618            Impact factor:   7.658


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