Dissection of the potential anti‐diabetes mechanism of salvianolic acid B by metabolite profiling and network pharmacology
Rationale Salvianolic acid B (Sal B), the Q‐marker in Salvia miltiorrhiza, was proved to present an obvious anti‐diabetes effect when treated as a food intake. Until now, the metabolism feature, tissue distribution and anti‐diabetes mechanism of Sal B have not been fully elucidated. Methods The meta...
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Veröffentlicht in: | Rapid communications in mass spectrometry 2022, Vol.36 (1), p.e9205-n/a |
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Zusammenfassung: | Rationale
Salvianolic acid B (Sal B), the Q‐marker in Salvia miltiorrhiza, was proved to present an obvious anti‐diabetes effect when treated as a food intake. Until now, the metabolism feature, tissue distribution and anti‐diabetes mechanism of Sal B have not been fully elucidated.
Methods
The metabolites of Sal B in rats were profiled using ultrahigh‐performance liquid chromatography coupled with time‐of‐flight mass spectrometry. The potential anti‐diabetes mechanism of Sal B was predicted by network pharmacology.
Results
A total of 31 metabolites were characterized in rats after ingestion of Sal B at a dosage of 40 mg/kg, including 1 in plasma, 19 in urine, 31 in feces, 0 in heart, 0 in liver, 0 in spleen, 1 in lung, 1 in kidney and 0 in brain. Among them, 18 metabolites were reported for the first time. Phase I reactions of hydrolysis, hydrogenation, dehydroxylation, hydroxylation, decarboxylation and isomerization, and phase II reactions of methylation were found in Sal B. Notably, decarboxylation and dehydroxylation were revealed in Sal B for the first time. The pharmacology network results showed that Sal B and its metabolites could regulate ALB, PLG, ACE, CASP3, MMP9, MMP2, MTOR, etc. The above targets were involved in insulin signaling pathway, PI3K‐Akt signaling pathway, HIF‐1 signaling pathway, TNF signaling pathway, etc.
Conclusions
The metabolism feature of Sal B in vivo was systematically revealed, and its anti‐diabetes mechanism for further pharmacological validations was predicted based on metabolite profiling and network pharmacology for the first time. |
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ISSN: | 0951-4198 1097-0231 |
DOI: | 10.1002/rcm.9205 |