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10
Jul

Exploring the combination and modular characteristics of herbs for alopecia treatment in traditional Chinese medicine

Abstract

BACKGROUND:
Although alopecia affects the quality of life, its pathogenesis is unknown, because cellular interactions in the hair follicle are complex. Several authors have suggested using herbal medicine to treat alopecia, and bioinformatics and network pharmacology may constitute a new research strategy in this regard because herbal medicines contain various chemical components. This study used association rule mining (ARM) and network analysis to analyze the combinations of medicinal herbs used to treat alopecia.

METHODS:
We searched Chinese, Korean, and English databases for literature about alopecia treatment, extracting the names of each herbal prescription and herb. The meridian tropism and classification category of each herb were also investigated. Using ARM, we identified frequently combined two-herb and three-herb sets. Using network analysis, we divided the herbs into several modules according to prescription pattern.

RESULTS:
Fifty-six articles and 489 herbal medicines were included-312 internal and 177 external medicines. Among the 312 medicinal herbs used in internal medicine group, the most frequently combined two-herb set was Polygonum multiflorum Thunb. () and Angelica sinensis (Oliv.) Dlels (). The most frequently used three-herb combination was Polygonum multiflorum Thunb., Angelica sinensis (Oliv.) Dlels, and Ligusticum chuanxiong Hort. (). In network analysis, three modules were identified. The herbs of Module 1 were related to the liver and kidney meridians, and those of Module 3 were related to the Stomach meridian.

CONCLUSIONS:
We identified the frequency, characteristics, and functional modules of herb combinations frequently used in alopecia treatment. We confirmed the value of classical medicinal herb theory. This finding will prompt further bioinformatics and network pharmacology research on alopecia.

Autoren: Leem J1,2, Jung W3,4, Kim Y5, Kim B6,7, Kim K8,9.

Publiziert in: BMC Complement Altern Med. 2018 Jul 4;18(1):204. doi: 10.1186/s12906-018-2269-7.

Quelle: https://www.ncbi.nlm.nih.gov/pubmed/29973199

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