Information Loss in Network Pharmacology

Study on the Mechanism of Xiaotan Sanjie Recipe in the Treatment of Colon Cancer Based on Network Pharmacology

The aim of the study is to investigate the mechanism of action of Disulfiram against colon cancer through a network pharmacology approach. The targets were then imported into the Cytoscape 3.7.2 software to construct a network of active ingredient targets and were imported into the STRING database to construct a protein-protein interaction (PPI) network, and the Bisogenet plug-in in Cytoscape 3.7.2 was used for network topology analysis. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the potential targets of Yiqi and Baiyu Tang for colon cancer using the R-language Bioconductor platform, and the results were imported into Cytoscape 3.7.2 to obtain KEGG network relationship maps. Molecular docking software Autodock Vina was used to map the core targets to the active ingredients. A total of 119 chemical components and 694 disease targets were obtained, including 113 intersecting targets. The key targets included AKT1 and TP53, and GO functional analysis mainly related to ubiquitination and apoptosis, etc. KEGG analysis showed that the treatment of colon cancer with Ganchenzan mainly acted through cancer-related signaling pathways such as AGE-RAGE and P13K-Akt, and the molecular docking results showed the best binding performance with TP53.

Network pharmacological prediction and molecular docking analysis of the combination of Atractylodes macrocephala Koidz. and Paeonia lactiflora Pall. in the treatment of functional constipation and its verification

Background

We aimed to reveal the mechanism of functional constipation in the treatment of Atractylodes macrocephala Koidz. (AMK) and Paeonia lactiflora Pall. (PLP).

Methods

The main active ingredients of AMK and PLP were screened by the Traditional Chinese Medicine Systems Pharmacology (TCMSP) platform. A database of functional constipation targets was established by GeneCard and OMIM. An “ingredient‐target” network map was constructed with Cytoscape software (version 3.7.1), and molecular docking analysis was performed on the components and genes with the highest scores. The rats in the normal group were given saline, and those in the other groups were given 10 mg/kg diphenoxylate once a day for 14 days. The serum and intestinal tissue levels of adenosine monophosphate (cAMP), protein kinase A (PKA), and adenylyl cyclase (AC) of the rats and aquaporin (AQP)1, AQP3, and AQP8 were measured.

Results

AMK and PLP had a significant role in the regulation of targets in the treatment of functional constipation. After treatment with AMK, PLP, or mosapride, the serum and intestinal tissue levels of AC, cAMP, and PKA were significantly downregulated. Groups receiving AMK and PLP or mosapride exhibited a reduction in the level of AQP1, AQP3, and AQP8 to varying degrees.

Conclusion

Molecular docking analysis revealed that AMK and PLP had a significant role in the regulation of targets in the treatment of functional constipation. Studies have confirmed that AMK and PLP can also affect AC, cAMP, and PKA. AC, cAMP, and PKA in model rats were significantly downregulated. AQP expression is closely related to AC, cAMP, and PKA. AMK and PLP can reduce the expression of AQP1, AQP3, and AQP9 in the colon of constipated rats.

A systematic study of traditional Chinese medicine treating hepatitis B virus-related hepatocellular carcinoma based on target-driven reverse network pharmacology

Hepatocellular carcinoma (HCC) is a serious global health problem, and hepatitis B virus (HBV) infection remains the leading cause of HCC. It is standard care to administer antiviral treatment for HBV-related HCC patients with concurrent anti-cancer therapy. However, a drug with repressive effects on both HBV infection and HCC has not been discovered yet. In addition, drug resistance and side effects have made existing therapeutic regimens suboptimal. Traditional Chinese medicine (TCM) has multi-ingredient and multi-target advantages in dealing with multifactorial HBV infection and HCC. TCM has long been served as a valuable source and inspiration for discovering new drugs. In present study, a target-driven reverse network pharmacology was applied for the first time to systematically study the therapeutic potential of TCM in treating HBV-related HCC. Firstly, 47 shared targets between HBV and HCC were screened as HBV-related HCC targets. Next, starting from 47 targets, the relevant chemical components and herbs were matched. A network containing 47 targets, 913 chemical components and 469 herbs was established. Then, the validated results showed that almost 80% of the herbs listed in chronic hepatitis B guidelines and primary liver cancer guidelines were included in the 469 herbs. Furthermore, functional analysis was conducted to understand the biological processes and pathways regulated by these 47 targets. The docking results indicated that the top 50 chemical components bound well to targets. Finally, the frequency statistical analysis results showed the 469 herbs against HBV-related HCC were mainly warm in property, bitter in taste, and distributed to the liver meridians. Taken together, a small library of 913 chemical components and 469 herbs against HBV-related HCC were obtained with a target-driven approach, thus paving the way for the development of therapeutic modalities to treat HBV-related HCC.

Capturing a Comprehensive Picture of Biological Events From Adverse Outcome Pathways in the Drug Exposome

Background: The chemical part of the exposome, including drugs, may explain the increase of health effects with outcomes such as infertility, allergies, metabolic disorders, which cannot be only explained by the genetic changes. To better understand how drug exposure can impact human health, the concepts of adverse outcome pathways (AOPs) and AOP networks (AONs), which are representations of causally linked events at different biological levels leading to adverse health, could be used for drug safety assessment.

Methods: To explore the action of drugs across multiple scales of the biological organization, we investigated the use of a network-based approach in the known AOP space. Considering the drugs and their associations to biological events, such as molecular initiating event and key event, a bipartite network was developed. This bipartite network was projected into a monopartite network capturing the event–event linkages. Nevertheless, such transformation of a bipartite network to a monopartite network had a huge risk of information loss. A way to solve this problem is to quantify the network reduction. We calculated two scoring systems, one measuring the uncertainty and a second one describing the loss of coverage on the developed event–event network to better investigate events from AOPs linked to drugs.

Results: This AON analysis allowed us to identify biological events that are highly connected to drugs, such as events involving nuclear receptors (ER, AR, and PXR/SXR). Furthermore, we observed that the number of events involved in a linkage pattern with drugs is a key factor that influences information loss during monopartite network projection. Such scores have the potential to quantify the uncertainty of an event involved in an AON, and could be valuable for the weight of evidence assessment of AOPs. A case study related to infertility, more specifically to “decrease, male agenital distance” is presented.

Conclusion: This study highlights that computational approaches based on network science may help to understand the complexity of drug health effects, with the aim to support drug safety assessment.

Identification of COVID-19 and Dengue Host Factor Interaction Networks Based on Integrative Bioinformatics Analyses

Background

The outbreak of Coronavirus disease 2019 (COVID-19) has become an international public health crisis, and the number of cases with dengue co-infection has raised concerns. Unfortunately, treatment options are currently limited or even unavailable. Thus, the aim of our study was to explore the underlying mechanisms and identify potential therapeutic targets for co-infection.

Methods

To further understand the mechanisms underlying co-infection, we used a series of bioinformatics analyses to build host factor interaction networks and elucidate biological process and molecular function categories, pathway activity, tissue-specific enrichment, and potential therapeutic agents.

Results

We explored the pathologic mechanisms of COVID-19 and dengue co-infection, including predisposing genes, significant pathways, biological functions, and possible drugs for intervention. In total, 460 shared host factors were collected; among them, CCL4 and AhR targets were important. To further analyze biological functions, we created a protein-protein interaction (PPI) network and performed Molecular Complex Detection (MCODE) analysis. In addition, common signaling pathways were acquired, and the toll-like receptor and NOD-like receptor signaling pathways exerted a significant effect on the interaction. Upregulated genes were identified based on the activity score of dysregulated genes, such as IL-1, Hippo, and TNF-α. We also conducted tissue-specific enrichment analysis and found ICAM-1 and CCL2 to be highly expressed in the lung. Finally, candidate drugs were screened, including resveratrol, genistein, and dexamethasone.

Conclusions

This study probes host factor interaction networks for COVID-19 and dengue and provides potential drugs for clinical practice. Although the findings need to be verified, they contribute to the treatment of co-infection and the management of respiratory disease.

Drug-target-ADR Network and Possible Implications of Structural Variants in Adverse Events

Adverse drug reactions (ADRs) are of major concern in drug safety. However, due to the biological complexity of human systems, understanding the underlying mechanisms involved in development of ADRs remains a challenging task. Here, we applied network sciences to analyze a tripartite network between 1000 drugs, 1407 targets, and 6164 ADRs. It allowed us to suggest drug targets susceptible to be associated to ADRs and organs, based on the system organ class (SOC). Furthermore, a score was developed to determine the contribution of a set of proteins to ADRs. Finally, we identified proteins that might increase the susceptibility of genes to ADRs, on the basis of knowledge about genomic structural variation in genes encoding proteins targeted by drugs. Such analysis should pave the way to individualize drug therapy and precision medicine.