Medical Significance of Uterine Corpus Endometrial Carcinoma Patients Infected With SARS-CoV-2 and Pharmacological Characteristics of Plumbagin.

Background: Clinically, evidence shows that uterine corpus endometrial carcinoma (UCEC) patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may have a higher death-rate. However, current anti-UCEC/coronavirus disease 2019 (COVID-19) treatment is lacking. Plumbagin (PLB), a pharmacologically active alkaloid, is an emerging anti-cancer inhibitor. Accordingly, the current report was designed to identify and characterize the anti-UCEC function and mechanism of PLB in the treatment of patients infected with SARS-CoV-2 via integrated in silico analysis. Methods: The clinical analyses of UCEC and COVID-19 in patients were conducted using online-accessible tools. Meanwhile, in silico methods including network pharmacology and biological molecular docking aimed to screen and characterize the anti-UCEC/COVID-19 functions, bio targets, and mechanisms of the action of PLB. Results: The bioinformatics data uncovered the clinical characteristics of UCEC patients infected with SARS-CoV-2, including specific genes, health risk, survival rate, and prognostic index. Network pharmacology findings disclosed that PLB-exerted anti-UCEC/COVID-19 effects were achieved through anti-proliferation, inducing cytotoxicity and apoptosis, anti-inflammation, immunomodulation, and modulation of some of the key molecular pathways associated with anti-inflammatory and immunomodulating actions. Following molecular docking analysis, in silico investigation helped identify the anti-UCEC/COVID-19 pharmacological bio targets of PLB, including mitogen-activated protein kinase 3 (MAPK3), tumor necrosis factor (TNF), and urokinase-type plasminogen activator (PLAU). Conclusions: Based on the present bioinformatic and in silico findings, the clinical characterization of UCEC/COVID-19 patients was revealed. The candidate, core bio targets, and molecular pathways of PLB action in the potential treatment of UCEC/COVID-19 were identified accordingly.

Comparative Effectiveness of Pharmacological Interventions for Covid-19: A Systematic Review and Network Meta-Analysis.

Background: Several pharmacological interventions are now under investigation for the treatment of Covid-19, and the evidence is evolving rapidly. Our aim is to assess the comparative efficacy and safety of these drugs. Methods and Findings: We performed a systematic review and network meta-analysis searching Medline, Pubmed, Embase, Cochrane Covid-19 register, international trial registers, medRxiv, bioRxiv, and arXiv up to December 10, 2020. We included all randomised controlled trials (RCTs) comparing any pharmacological intervention for Covid-19 against any drugs, placebo or standard care (SC). Data extracted from published reports were assessed for risk of bias in accordance with the Cochrane tool, and using the GRADE framework. Primary outcomes were all-cause mortality, adverse events (AEs) and serious adverse events (SAEs). We estimated summary risk ratio (RR) using pairwise and network meta-analysis with random effects (Prospero, number CRD42020176914). We performed a systematic review and network meta-analysis searching Medline, Pubmed, Embase, Cochrane Covid-19 register, international trial registers, medRxiv, bioRxiv, and arXiv up to December 10, 2020. We included all randomised controlled trials (RCTs) comparing any pharmacological intervention for Covid-19 against any drugs, placebo or standard care (SC). Data extracted from published reports were assessed for risk of bias in accordance with the Cochrane tool, and using the GRADE framework. Primary outcomes were all-cause mortality, adverse events (AEs) and serious adverse events (SAEs). We estimated summary risk ratio (RR) using pairwise and network meta-analysis with random effects (Prospero, number CRD42020176914). We included 96 RCTs, comprising of 34,501 patients. The network meta-analysis showed in terms of all-cause mortality, when compared to SC or placebo, only corticosteroids significantly reduced the mortality rate (RR 0.90, 95%CI 0.83, 0.97; moderate certainty of evidence). Corticosteroids significantly reduced the mortality rate also when compared to hydroxychloroquine (RR 0.83, 95%CI 0.74, 0.94; moderate certainty of evidence). Remdesivir proved to be better in terms of SAEs when compared to SC or placebo (RR 0.75, 95%CI 0.63, 0.89; high certainty of evidence) and plasma (RR 0.57, 95%CI 0.34, 0.94; high certainty of evidence). The combination of lopinavir and ritonavir proved to reduce SAEs when compared to plasma (RR 0.49, 95%CI 0.25, 0.95; high certainty of evidence). Most of the RCTs were at unclear risk of bias (42 of 96), one third were at high risk of bias (34 of 96) and 20 were at low risk of bias. Certainty of evidence ranged from high to very low. Conclusion: At present, corticosteroids reduced all-cause mortality in patients with Covid-19, with a moderate certainty of evidence. Remdesivir appeared to be a safer option than SC or placebo, while plasma was associated with safety concerns. These preliminary evidence-based observations should guide clinical practice until more data are made public.

Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19.

The ongoing COVID-19 pandemic still requires fast and effective efforts from all fronts, including epidemiology, clinical practice, molecular medicine, and pharmacology. A comprehensive molecular framework of the disease is needed to better understand its pathological mechanisms, and to design successful treatments able to slow down and stop the impressive pace of the outbreak and harsh clinical symptomatology, possibly via the use of readily available, off-the-shelf drugs. This work engages in providing a wider picture of the human molecular landscape of the SARS-CoV-2 infection via a network medicine approach as the ground for a drug repurposing strategy. Grounding on prior knowledge such as experimentally validated host proteins known to be viral interactors, tissue-specific gene expression data, and using network analysis techniques such as network propagation and connectivity significance, the host molecular reaction network to the viral invasion is explored and exploited to infer and prioritize candidate target genes, and finally to propose drugs to be repurposed for the treatment of COVID-19. Ranks of potential target genes have been obtained for coherent groups of tissues/organs, potential and distinct sites of interaction between the virus and the organism. The normalization and the aggregation of the different scores allowed to define a preliminary, restricted list of genes candidates as pharmacological targets for drug repurposing, with the aim of contrasting different phases of the virus infection and viral replication cycle.