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http://dx.doi.org/10.5483/BMBRep.2022.55.9.102

Systems pharmacology approaches in herbal medicine research: a brief review  

Lee, Myunggyo (Department of Pharmacy, College of Pharmacy, Pusan National University)
Shin, Hyejin (Korean Medicine (KM) Convergence Research Division, Korea Institute of Oriental Medicine)
Park, Musun (Korean Medicine (KM) Data Division, Korea Institute of Oriental Medicine)
Kim, Aeyung (Korean Medicine (KM) Application Center, Korea Institute of Oriental Medicine)
Cha, Seongwon (Korean Medicine (KM) Data Division, Korea Institute of Oriental Medicine)
Lee, Haeseung (Department of Pharmacy, College of Pharmacy, Pusan National University)
Publication Information
BMB Reports / v.55, no.9, 2022 , pp. 417-428 More about this Journal
Abstract
Herbal medicine, a multi-component treatment, has been extensively practiced for treating various symptoms and diseases. However, its molecular mechanism of action on the human body is unknown, which impedes the development and application of herbal medicine. To address this, recent studies are increasingly adopting systems pharmacology, which interprets pharmacological effects of drugs from consequences of the interaction networks that drugs might have. Most conventional network-based approaches collect associations of herb-compound, compound-target, and target-disease from individual databases, respectively, and construct an integrated network of herb-compound-target-disease to study the complex mechanisms underlying herbal treatment. More recently, rapid advances in high-throughput omics technology have led numerous studies to exploring gene expression profiles induced by herbal treatments to elicit information on direct associations between herbs and genes at the genome-wide scale. In this review, we summarize key databases and computational methods utilized in systems pharmacology for studying herbal medicine. We also highlight recent studies that identify modes of action or novel indications of herbal medicine by harnessing drug-induced transcriptome data.
Keywords
Drug-induced transcriptome; Herbal medicine; Network pharmacology; Systems pharmacology; Traditional medicine;
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