Drug target identification using network analysis: Taking active components in Sini decoction as an example

Abstract

Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

Integrating Network Pharmacology and Component Analysis Study on Anti‐Atherosclerotic Mechanisms of Total Flavonoids of Engelhardia roxburghiana Leaves in Mice

“…Recently, network pharmacology offers a new approach for traditional Chinese medicine research, aiming at clarifying the underlying therapeutic mechanisms . It leads huge changes towards drug discovery pattern from ‘one target, one drug’ to ‘network target, multi‐component therapeutic strategy’ …”

“…[13] It leads huge changes towards drug discovery pattern from 'one target, one drug' to 'network target, multi-component therapeutic strategy'. [14] The present study aimed to investigate the active ingredients of TFER and their actions on molecular networks in atherosclerosis, following by HPLC/MS/MS methods and network pharmacology analysis. Experimental verification in vivo was also carried out to confirm the mechanism of TFER on atherosclerosis.…”

Harnessing emerging paradigms in chemical engineering to accelerate the development of pharmaceutical products

“…For instance, we can now identify, predict, and test putative gene candidates for various disease states, target interactions, and drug responses with unprecedented speed and accuracy owing to availability of vast catalogues of metabolomics, proteomics, transcriptomics, and protein-protein interaction data, as well as better network and systems biology tools to analyze and interpret the data. [31][32][33][34] The repository of metabolic reactions that can be prospected computationally for use as biosynthons too has grown by leaps and bounds. For instance, at the time of writing, NCBI GenBank houses the sequences and complete annotations of approximately 3976 eukaryotic, 89 843 prokaryotic, and 7088 viral genomes, [35] and this vast volume of information can be effectively mined using the latest tools in network pharmacology, including molecular docking.…”

“…For instance, at the time of writing, NCBI GenBank houses the sequences and complete annotations of approximately 3976 eukaryotic, 89 843 prokaryotic, and 7088 viral genomes, [35] and this vast volume of information can be effectively mined using the latest tools in network pharmacology, including molecular docking. [33] 6 | RAPID PROTOTYPING OF PHARMACEUTICAL PRODUCTS…”

From systems biology to P4 medicine: applications in respiratory medicine

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