Yu Hyejin, Choi Kwanyong, Kim Ji Yeon, Yoo Sunyong
Department of Intelligent Electronics and Computer Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea.
Department of Food Science and Biotechnology, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea.
Brief Bioinform. 2025 Jul 2;26(4). doi: 10.1093/bib/bbaf328.
Many cultures worldwide have widely used traditional medicine (TM) to prevent or treat diseases. Herbal materials and their compounds used in TM offer many advantages for drug discovery, including cost-effectiveness, fewer side effects, and improved metabolism. However, the multi-compound and multi-target characteristics of TM prescriptions complicate drug discovery; meanwhile, previous studies have been limited by a lack of high-quality data, complex interpretation, and/or narrow analytical ranges. Thus, this study proposed a framework to identify potential therapeutic combinations of herbal materials and their compounds currently used in TM by integrating association rule mining (ARM) and network pharmacology analysis across multiple TM and biological levels. Subsequently, we collected prescriptions, herbal materials, compounds, genes, phenotypes, and all ensuing interactions to identify effective combinations of herbal materials and compounds using ARM for various symptoms and diseases. This proposed analytical approach was also applied to screen effective herbal material combinations and compounds for five phenotypes: asthma, diabetes, arthritis, stroke, and inflammation. The potential pharmacological effects of the inferred candidates were identified at the molecular level using structural network analysis and a literature review. In addition, compounds from Morus alba, Ephedra sinica, Perilla frutescens, and Pinellia ternata, which were strongly associated with asthma, were validated in vitro. Collectively, our study provides ethnopharmacological and biological evidence for the polypharmacological effects of herbal materials and their compounds, thus enhancing the understanding of the mechanisms involved in TM and suggesting potential candidates for prescriptions, dietary supplements, and drug combinations. The source code and results are available at https://github.com/bmil-jnu/InPETM.
世界上许多文化都广泛使用传统医学来预防或治疗疾病。传统医学中使用的草药材料及其化合物在药物发现方面具有许多优势，包括成本效益、副作用少和新陈代谢改善。然而，传统医学处方的多化合物和多靶点特性使药物发现变得复杂；与此同时，以往的研究受到缺乏高质量数据、解释复杂和/或分析范围狭窄的限制。因此，本研究提出了一个框架，通过整合跨多个传统医学和生物学水平的关联规则挖掘（ARM）和网络药理学分析，来识别目前传统医学中使用的草药材料及其化合物的潜在治疗组合。随后，我们收集了处方、草药材料、化合物、基因、表型以及所有随之而来的相互作用，以使用ARM识别针对各种症状和疾病的草药材料和化合物的有效组合。这种提出的分析方法还应用于筛选针对哮喘、糖尿病、关节炎、中风和炎症这五种表型的有效草药材料组合和化合物。使用结构网络分析和文献综述在分子水平上确定了推断候选物的潜在药理作用。此外，对与哮喘密切相关的桑白皮、麻黄、紫苏和半夏中的化合物进行了体外验证。总的来说，我们的研究为草药材料及其化合物的多药理作用提供了民族药理学和生物学证据，从而加深了对传统医学所涉及机制的理解，并为处方、膳食补充剂和药物组合提出了潜在候选物。源代码和结果可在https://github.com/bmil-jnu/InPETM上获取。
