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Review
. 2020 Dec:89:107384.
doi: 10.1016/j.compbiolchem.2020.107384. Epub 2020 Sep 29.

Uncovering the pharmacological mechanism of motherwort (Leonurus japonicus Houtt.) for treating menstrual disorders: A systems pharmacology approach

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Review

Uncovering the pharmacological mechanism of motherwort (Leonurus japonicus Houtt.) for treating menstrual disorders: A systems pharmacology approach

Chenglong Wang et al. Comput Biol Chem. 2020 Dec.

Abstract

Leonurus japonicus (motherwort) is a traditional Chinese medicine that is widely used to treat menstrual disorders (MDs). However, the pharmacological mechanisms that underlie its clinical application remain unclear. In this study, a network pharmacology-based approach was used that integrated drug-likeness evaluation, oral bioavailability prediction, target exploration, network construction, bioinformatic annotation and molecular docking to investigate the mechanisms that underlie motherwort treatment for MDs. In total, 29 bioactive compounds were collected from 51 compounds in motherwort, which shared 17 common MDs-related targets. Network analysis indicated that motherwort played a therapeutic role in MDs treatment through multiple components that acted on multiple targets. Pathway enrichment analysis showed that the putative targets of motherwort were primarily involved in various pathways associated with the endocrine system, cancers, vascular system, and anti-inflammation process. Notably, five targets (i.e., AKT1, PTGS2, ESR1, AR and PPARG) were screened as hub genes based on a degree algorithm. Moreover, most of the bioactive components in motherwort had good binding ability with these genes, implying that motherwort could regulate their biological function. Collectively, this study elucidated the molecular mechanisms that underlay the efficiency of motherwort against MDs and demonstrated the potential of network pharmacology as an approach to uncover the action mechanism of herbal medicines.

Keywords: Leonurus japonicus; Menstrual disorders; Molecular docking; Systems pharmacology; Target prediction.

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