Network-Based Identification and Experimental Validation of Drug Candidates Toward SARS-CoV-2 via Targeting Virus-Host Interactome

The efficacy and mechanism of salmeterol against influenza A virus in vitro and in vivo

Influenza A virus (IAV) is the most harmful pathogen to human beings among the various subtypes of influenza virus, which can lead to immune response, cause serious inflammation and damage to the lung. Salmeterol is a candidate compound with anti-IAV activity screened by virtual network proximity predication. In this paper, we further evaluated the pharmacodynamics of salmeterol against IAV in vivo and in vitro. The results showed that salmeterol could inhibit the activity of three IAV strains (H1N1, H3N2 and H1N1 strain resistant to oseltamivir and amantadine) in the MDCK cells. In vivo, salmeterol could improve the survival state of infected mice, and further mechanism studies shown that salmeterol could improve the pathological characteristics of the lungs, reduce the loads of virus and the expression of M2 and IFITM3 proteins in the lungs of mice. In addition, salmeterol could inhibit the formation of NLRP3 inflammasome, thus reducing the production of the TNF-α, IL-6 and MCP-1 and alleviating inflammatory symptoms. Further results showed that salmeterol can protect A549 cells from cytopathic effect caused by IAV and reduce the production of inflammasome by decreasing the expression of RIG-1 in A549 cells. Finally, salmeterol could improve the spleen morphology and significantly increase the ratio of lymphocyte CD4⁺/CD8⁺ to improve immune function of infected mice. In our study, it is confirmed that salmeterol has certain anti-IAV activity through pharmacodynamic study in vivo and in vitro, which lays an important research foundation for the new indication of salmeterol and discovery of new drug against IAV.

Network-based drug repurposing for potential stroke therapy

Stroke is the leading cause of death and disability worldwide, with a growing number of incidences in developing countries. However, there are currently few medical therapies for this disease. Emerged as an effective drug discovery strategy, drug repurposing which owns lower cost and shorter time, is able to identify new indications from existing drugs. In this study, we aimed at identifying potential drug candidates for stroke via computationally repurposing approved drugs from Drugbank database. We first developed a drug-target network of approved drugs, employed network-based approach to repurpose these drugs, and altogether identified 185 drug candidates for stroke. To validate the prediction accuracy of our network-based approach, we next systematically searched for previous literature, and found 68 out of 185 drug candidates (36.8 %) exerted therapeutic effects on stroke. We further selected several potential drug candidates with confirmed neuroprotective effects for testing their anti-stroke activity. Six drugs, including cinnarizine, orphenadrine, phenelzine, ketotifen, diclofenac and omeprazole, have exhibited good activity on oxygen-glucose deprivation/reoxygenation (OGD/R) induced BV2 cells. Finally, we showcased the anti-stroke mechanism of actions of cinnarizine and phenelzine via western blot and Olink inflammation panel. Experimental results revealed that they both played anti-stroke effects in the OGD/R induced BV2 cells via inhibiting the expressions of IL-6 and COX-2. In summary, this study provides efficient network-based methodologies for in silico identification of drug candidates toward stroke.

Identification of phytochemicals in Qingfei Paidu decoction for the treatment of coronavirus disease 2019 by targeting the virus-host interactome

Qingfei Paidu decoction (QFPDD) has been clinically proven to be effective in the treatment of coronavirus disease 2019 (COVID-19). However, the bioactive components and therapeutic mechanisms remain unclear. This study aimed to explore the effective components and underlying mechanisms of QFPDD in the treatment of COVID-19 by targeting the virus-host interactome and verifying the antiviral activities of its active components in vitro. Key active components and targets were identified by analysing the topological features of a compound-target-pathway-disease regulatory network of QFPDD for the treatment of COVID-19. The antiviral activity of the active components was determined by a live virus infection assay, and possible mechanisms were analysed by pseudotyped virus infection and molecular docking assays. The inhibitory effects of the components tested on the virus-induced release of IL-6, IL-1β and CXCL-10 were detected by ELISA. Three components of QFPDD, oroxylin A, hesperetin and scutellarin, exhibited potent antiviral activities against live SARS-CoV-2 virus and HCoV-OC43 virus with IC₅₀ values ranging from 18.68 to 63.27 μM. Oroxylin A inhibited the entry of SARS-CoV-2 pseudovirus into target cells and inhibited SARS-CoV-2 S protein-mediated cell-cell fusion by binding with the ACE2 receptor. The active components of QFPDD obviously inhibited the IL-6, IL-1β and CXCL-10 release induced by the SARS-CoV-2 S protein. This study supports the clinical application of QFPDD and provides an effective analysis method for the in-depth study of the mechanisms of traditional Chinese medicine (TCM) in the prevention and treatment of COVID-19.

Methodology of network pharmacology for research on Chinese herbal medicine against COVID-19: A review

Traditional Chinese medicine, as a complementary and alternative medicine, has been practiced for thousands of years in China and possesses remarkable clinical efficacy. Thus, systematic analysis and examination of the mechanistic links between Chinese herbal medicine (CHM) and the complex human body can benefit contemporary understandings by carrying out qualitative and quantitative analysis. With increasing attention, the approach of network pharmacology has begun to unveil the mystery of CHM by constructing the heterogeneous network relationship of "herb-compound-target-pathway," which corresponds to the holistic mechanisms of CHM. By integrating computational techniques into network pharmacology, the efficiency and accuracy of active compound screening and target fishing have been improved at an unprecedented pace. This review dissects the core innovations to the network pharmacology approach that were developed in the years since 2015 and highlights how this tool has been applied to understanding the coronavirus disease 2019 and refining the clinical use of CHM to combat it.

Uric Acid Predicts Recovery of Left Ventricular Function and Adverse Events in Heart Failure With Reduced Ejection Fraction: Potential Mechanistic Insight From Network Analyses

Background and Aims

Heart failure with reduced ejection fraction (HFrEF) still carries a high risk for a sustained decrease in left ventricular ejection fraction (LVEF) even with the optimal medical therapy. Currently, there is no effective tool to stratify these patients according to their recovery potential. We tested the hypothesis that uric acid (UA) could predict recovery of LVEF and prognosis of HFrEF patients and attempted to explore mechanistic relationship between hyperuricemia and HFrEF.

Methods

HFrEF patients with hyperuricemia were selected from the National Inpatient Sample (NIS) 2016–2018 database and our Xianyang prospective cohort study. Demographics, cardiac risk factors, and cardiovascular events were identified. Network-based analysis was utilized to examine the relationship between recovery of LVEF and hyperuricemia, and we further elucidated the underlying mechanisms for the impact of hyperuricemia on HFrEF.

Results

After adjusting confounding factors by propensity score matching, hyperuricemia was a determinant of HFrEF [OR 1.247 (1.172–1.328); P < 0.001] of NIS dataset. In Xianyang prospective cohort study, hyperuricemia is a significant and independent risk factor for all-cause death (adjusted HR 2.387, 95% CI 1.141–4.993; P = 0.021), heart failure readmission (adjusted HR 1.848, 95% CI 1.048–3.259; P = 0.034), and composite events (adjusted HR 1.706, 95% CI 1.001–2.906; P = 0.049) in HFrEF patients. UA value at baseline was negatively correlated to LVEF of follow-ups (r = −0.19; P = 0.046). Cutoff UA value of 312.5 μmmol/L at baseline can work as a predictor of LVEF recovery during follow-up, with the sensitivity of 66.7%, the specificity of 35.1%, and the accuracy of 0.668 (95% CI, 0.561–0.775; P = 0.006). Moreover, gene overlap analysis and network proximity analysis demonstrated a strong correlation between HFrEF and Hyperuricemia.

Conclusion

Lower baseline UA value predicted the LVEF recovery and less long-term adverse events in HFrEF patients. Our results provide new insights into underlying mechanistic relationship between hyperuricemia and HFrEF.

In Silico Identification and Mechanism Exploration of Active Ingredients against Stroke from An-Gong-Niu-Huang-Wan (AGNHW) Formula

An-Gong-Niu-Huang-Wan (AGNHW) is a well-known formula for treating cerebrovascular diseases, with roles including clearing away heat, detoxification, and wake-up consciousness. In recent years, AGNHW has been commonly used for the treatment of ischemic stroke, but the mechanism by which AGNHW relieves stroke has not been clearly elucidated. In the current study, we developed a multiple systems pharmacology-based framework to identify the potential antistroke ingredients in AGNHW and explore the underlying mechanisms of action (MOA) of AGNHW against stroke from a holistic perspective. Specifically, we performed a network-based method to identify the potential antistroke ingredients in AGNHW by integrating the drug-target network and stroke-associated genes. Furthermore, the oxygen-glucose deprivation/reoxygenation (OGD/R) model was used to validate the anti-inflammatory effects of the key ingredients by determining the levels of inflammatory cytokines, including interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α. The antiapoptotic effects of the key ingredients were also confirmed in vitro. Integrated pathway analysis of AGNHW revealed that it might regulate three biological signaling pathways, including IL-17, TNF, and PI3K-AKT, to play a protective role in stroke. Moreover, 30 key antistroke ingredients in AGNHW were identified via network-based in silico prediction and were confirmed to have known neuroprotective effects. After drug-like property evaluation and pharmacological validation in vitro, scutellarein (SCU) and caprylic acid (CA) were selected for further antistroke investigation. Finally, systems pharmacology-based analysis of CA and SCU indicated that they might exert antistroke effects via the apoptotic signaling pathway and inflammatory response, which was further validated in an in vitro stroke model. Overall, the current study proposes an integrative systems pharmacology approach to identify antistroke ingredients and demonstrate the underlying pharmacological MOA of AGNHW in stroke, which provides an alternative strategy to investigate novel traditional Chinese medicine formulas for complex diseases.