Study on network pharmacology of Ginkgo biloba extract against ischaemic stroke mechanism and establishment of UPLC-MS/MS methods for simultaneous determination of 19 main active components.

INTRODUCTION: Ginkgo biloba extract (GBE) is an effective substance from traditional Chinese medicine (TCM) G. biloba for treating ischaemic stroke (IS). However, its active ingredients and mechanism of action remain unclear. OBJECTIVES: This study aimed to reveal the potential active component group and possible anti-IS mechanism of GBE. MATERIALS AND METHODS: The network pharmacology method was used to reveal the possible anti-IS mechanism of these active ingredients in GBE. An ultra-high-performance liquid chromatography triple quadrupole electrospray tandem mass spectrometry (UPLC-MS/MS) method was established for the simultaneous detection of the active ingredients of GBE. RESULTS: The active components of GBE anti-IS were screened by literature integration. Network pharmacology results showed that the anti-IS effect of GBE is achieved through key active components such as protocatechuic acid, bilobalide, ginkgolide A, and so on. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the possible anti-IS mechanism of GBE is regulating the PI3K-Akt signalling pathway and other signal pathways closely related to inflammatory response and apoptosis regulation combined with AKT1, MAPK, TNF, ALB, CASP3, and other protein targets. Nineteen main constituents in seven batches of GBE were successfully analysed using the established UPLC-MS/MS method, and the results showed that the content of protocatechuic acid, gallic acid, ginkgolide A, and so forth was relatively high, which was consistent with network pharmacology results, indicating that these ingredients may be the key active anti-IS ingredients of GBE. CONCLUSION: This study revealed the key active components and the anti-IS mechanism of GBE. It also provided a simple and sensitive method for the quality control of related preparations.

Screening lifespan-extending drugs in Caenorhabditis elegans via label propagation on drug-protein networks.

BACKGROUND: One of the most challenging tasks in the exploration of anti-aging is to discover drugs that can promote longevity and delay the incidence of age-associated diseases of human. Up to date, a number of drugs, including some antioxidants, metabolites and synthetic compounds, have been found to effectively delay the aging of nematodes and insects. RESULTS: We proposed a label propagation algorithm on drug-protein network to infer drugs that can extend the lifespan of C. elegans. We collected a set of drugs of which functions on lifespan extension of C. elegans have been reliably determined, and then built a large-scale drug-protein network by collecting a set of high-confidence drugprotein interactions. A label propagation algorithm was run on the drug-protein bipartite network to predict new drugs with lifespan-extending effect on C. elegans. We calibrated the performance of the proposed method by conducting performance comparison with two classical models, kNN and SVM. We also showed that the screened drugs significantly mediate in the aging-related pathways, and have higher chemical similarities to the effective drugs than ineffective drugs in promoting longevity of C. elegans. Moreover, we carried out wet-lab experiments to verify a screened drugs, 2- Bromo-4'-nitroacetophenone, and found that it can effectively extend the lifespan of C. elegans. These results showed that our method is effective in screening lifespanextending drugs in C. elegans. CONCLUSIONS: In this paper, we proposed a semi-supervised algorithm to predict drugs with lifespan-extending effects on C. elegans. In silico empirical evaluations and in vivo experiments in C. elegans have demonstrated that our method can effectively narrow down the scope of candidate drugs needed to be verified by wet lab experiments.