A critical assessment of Traditional Chinese Medicine databases as a source for drug discovery

Front Pharmacol. 2024 Apr 26:15:1303693. doi: 10.3389/fphar.2024.1303693. eCollection 2024.

Abstract

Traditional Chinese Medicine (TCM) has been used for thousands of years to treat human diseases. Recently, many databases have been devoted to studying TCM pharmacology. Most of these databases include information about the active ingredients of TCM herbs and their disease indications. These databases enable researchers to interrogate the mechanisms of action of TCM systematically. However, there is a need for comparative studies of these databases, as they are derived from various resources with different data processing methods. In this review, we provide a comprehensive analysis of the existing TCM databases. We found that the information complements each other by comparing herbs, ingredients, and herb-ingredient pairs in these databases. Therefore, data harmonization is vital to use all the available information fully. Moreover, different TCM databases may contain various annotation types for herbs or ingredients, notably for the chemical structure of ingredients, making it challenging to integrate data from them. We also highlight the latest TCM databases on symptoms or gene expressions, suggesting that using multi-omics data and advanced bioinformatics approaches may provide new insights for drug discovery in TCM. In summary, such a comparative study would help improve the understanding of data complexity that may ultimately motivate more efficient and more standardized strategies towards the digitalization of TCM.

Keywords: TCM databases; Traditional Chinese Medicine; drug discovery; mechanisms of action; network pharmacology.

Publication types

  • Systematic Review

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the European Research Council Starting Grant agreement (grant number 716063), the Academy of Finland Research Fellow funding (grant number 317680), the Helsinki Institute of Life Science Research Fellow funding, and the Program for Innovative Research Team of Jiangsu Province, Jiangsu Province Science Foundation for Youths (grant number BK202310). YW was supported by the China Scholarship Council (grant number 201706740080).