Abstract: BACKGROUND: Gout is a common disease with high incidence due to unhealthy diet and living habits. Simiao Powder, as a classic formula consisted of four common herbs, has been widely used in clinical practice since ancient times to prevent and treat gout. However, the pharmacological mechanism of Simiao Powder is still unclear. METHODS: Based on network pharmacology, Simiao Powder active compounds were identified in TCMSP, ETCM and BATMAN database, used to establish a network of interaction between potential targets of Simiao Powder and known therapeutic targets of gout. Subsequently, the key potential targets are being used for protein-protein interaction, GO enrichment analysis and KEGG pathway enrichment analysis through several authoritative open databases. Molecular docking through AutoDockTools software can verify interaction between molecules. Finally, to validate the predicted results, in vivo experiments based on hyperuricemic-gout mice model were designed and treated with Simiao powder and allopurinol. Serum levels of uric acid (UA), creatinine (Cr), blood urea nitrogen (BUN) and xanthine oxidase (XOD) were determined using a customized assay kit while the expression of PPAR-gamma, PTGS1, IL-6 and Bcl2 mRNA were analyzed through qRT-PCR. RESULTS: Disease-target-compound network was visualized basing on the 20 bioactive compounds and the 19 potential targets using Cytoscape software. The results of PPI analysis, GO enrichment and KEGG pathway enrichment analysis indicate that the potential mechanism of Simiao Powder in treating gout may be achieved by regulating immune and inflammatory reactions, improving metabolism and endocrine. The results of molecular docking show that most of the targets and components have good binding activity. In vivo experiments revealed that Simiao powder can decreased serum UA and XOD levels in hyperuricemic-gout mice, and improved renal function. Furthermore, Simiao powder certainly regulates the expression of PPAR-gamma, PTGS1, IL-6 and Bcl2 mRNA in ankle tissue in hyperuricemic-gout mice. CONCLUSION: Collectively, this research predicted a multiple compounds, targets, and pathways model mechanism of Simiao Powder in the prevention and treatment of gout, providing new ideas and methods for in-depth research, via vivo experiments.

Abstract: BACKGROUND: Components of the antioxidant defense system in Trypanosoma cruzi are potential targets for new drug development. Superoxide dismutases (SODs) constitute key components of antioxidant defense systems, removing excess superoxide anions by converting them into oxygen and hydrogen peroxide. The main goal of the present study was to investigate the genes coding for iron superoxide dismutase (FeSOD) in T. cruzi strains from an evolutionary perspective. METHODS: In this study, molecular biology methods and phylogenetic studies were combined with drug assays. The FeSOD-A and FeSOD-B genes of 35 T. cruzi strains, belonging to six discrete typing units (Tcl-TcVI), from different hosts and geographical regions were amplified by PCR and sequenced using the Sanger method. Evolutionary trees were reconstructed based on Bayesian inference and maximum likelihood methods. Drugs that potentially interacted with T. cruzi FeSODs were identified and tested against the parasites. RESULTS: Our results suggest that T. cruzi FeSOD types are members of distinct families. Gene copies of FeSOD-A (n = 2), FeSOD-B (n = 4) and FeSOD-C (n = 4) were identified in the genome of the T. cruzi reference clone CL Brener. Phylogenetic inference supported the presence of two functional variants of each FeSOD type across the T. cruzi strains. Phylogenetic trees revealed a monophyletic group of FeSOD genes of T. cruzi TcIV strains in both distinct genes. Altogether, our results support the hypothesis that gene duplication followed by divergence shaped the evolution of T. cruzi FeSODs. Two drugs, mangafodipir and polaprezinc, that potentially interact with T. cruzi FeSODs were identified and tested in vitro against amastigotes and trypomastigotes: mangafodipir had a low trypanocidal effect and polaprezinc was inactive. CONCLUSIONS: Our study contributes to a better understanding of the molecular biodiversity of T. cruzi FeSODs. Herein we provide a successful approach to the study of gene/protein families as potential drug targets.

Abstract: This review is dedicated to the cross-talk between the (endo)cannabinoid and renin angiotensin systems (RAS). Activation of AT1 receptors (AT1Rs) by angiotensin II (Ang II) can release endocannabinoids that, by acting at cannabinoid CB1 receptors (CB1Rs), modify the response to AT1R stimulation. CB1R blockade may enhance AT1R-mediated responses (mainly vasoconstrictor effects) or reduce them (mainly central nervous system-mediated effects). The final effects depend on whether stimulation of CB1Rs and AT1Rs induces opposite or the same effects. Second, CB1R blockade may diminish AT1R levels. Third, phytocannabinoids modulate angiotensin-converting enzyme-2. Additional studies are required to clarify (1) the existence of a cross-talk between the protective axis of the RAS (Ang II-AT2 receptor system or angiotensin 1-7-Mas receptor system) with components of the endocannabinoid system, (2) the influence of Ang II on constituents of the endocannabinoid system and (3) the (patho)physiological significance of AT1R-CB1R heteromerization. As a therapeutic consequence, CB1R antagonists may influence effects elicited by the activation or blockade of the RAS; phytocannabinoids may be useful as adjuvant therapy against COVID-19; single drugs acting on the (endo)cannabinoid system (cannabidiol) and the RAS (telmisartan) may show pharmacokinetic interactions since they are substrates of the same metabolizing enzyme of the transport mechanism.

Abstract: Multiple sclerosis is an autoimmune disease with a strong neuroinflammatory component that contributes to severe demyelination, neurodegeneration and lesions formation in white and grey matter of the spinal cord and brain. Increasing attention is being paid to the signaling of the biogenic amine histamine in the context of several pathological conditions. In multiple sclerosis, histamine regulates the differentiation of oligodendrocyte precursors, reduces demyelination, and improves the remyelination process. However, the concomitant activation of histamine H1-H4 receptors can sustain either damaging or favorable effects, depending on the specifically activated receptor subtype/s, the timing of receptor engagement, and the central versus peripheral target district. Conventional drug development has failed so far to identify curative drugs for multiple sclerosis, thus causing a severe delay in therapeutic options available to patients. In this perspective, drug repurposing offers an exciting and complementary alternative for rapidly approving some medicines already approved for other indications. In the present work, we have adopted a new network-medicine-based algorithm for drug repurposing called SAveRUNNER, for quantifying the interplay between multiple sclerosis-associated genes and drug targets in the human interactome. We have identified new histamine drug-disease associations and predicted off-label novel use of the histaminergic drugs amodiaquine, rupatadine, and diphenhydramine among others, for multiple sclerosis. Our work suggests that selected histamine-related molecules might get to the root causes of multiple sclerosis and emerge as new potential therapeutic strategies for the disease.

Abstract: Natural products (NPs) are a valuable source for anti-inflammatory drug discovery. However, they are limited by the unpredictability of the structures and functions. Therefore, computational and data-driven pre-evaluation could enable more efficient NP-inspired drug development. Since NPs possess structural features that differ from synthetic compounds, models trained with synthetic compounds may not perform well with NPs. There is also an urgent demand for well-curated databases and user-friendly predictive tools. We presented a comprehensive online web platform (InflamNat, http://www.inflamnat.com/ or http://39.104.56.4/ ) for anti-inflammatory natural product research. InflamNat is a database containing the physicochemical properties, cellular anti-inflammatory bioactivities, and molecular targets of 1351 NPs that tested on their anti-inflammatory activities. InflamNat provides two machine learning-based predictive tools specifically designed for NPs that (a) predict the anti-inflammatory activity of NPs, and (b) predict the compound-target relationship for compounds and targets collected in the database but lacking existing relationship data. A novel multi-tokenization transformer model (MTT) was proposed as the sequential encoder for both predictive tools to obtain a high-quality representation of sequential data. The experimental results showed that the proposed predictive tools achieved an AUC value of 0.842 and 0.872 in the prediction of anti-inflammatory activity and compound-target interactions, respectively.

Abstract: Despite the availability of highly efficacious vaccines, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lacks effective drug treatment, which results in a high rate of mortality. To address this therapeutic shortcoming, we applied a systems biology approach to the study of patients hospitalized with severe COVID. We show that, at the time of hospital admission, patients who were equivalent on the clinical ordinal scale displayed significant differential monocyte epigenetic and transcriptomic attributes between those who would survive and those who would succumb to COVID-19. We identified messenger RNA metabolism, RNA splicing, and interferon signaling pathways as key host responses overactivated by patients who would not survive. Those pathways are prime drug targets to reduce mortality of critically ill patients with COVID-19, leading us to identify tacrolimus, zotatifin, and nintedanib as three strong candidates for treatment of severely ill patients at the time of hospital admission.

Abstract: OBJECTIVES: The COVID-19 pandemic has stimulated growing research on treatment options. We aim to provide an overview of the characteristics of studies evaluating COVID-19 treatment. DESIGN: Rapid scoping review DATA SOURCES: Medline, Embase and biorxiv/medrxiv from inception to 15 May 2021. SETTING: Hospital and community care. PARTICIPANTS: COVID-19 patients of all ages. INTERVENTIONS: COVID-19 treatment. RESULTS: The literature search identified 616 relevant primary studies of which 188 were randomised controlled trials and 299 relevant evidence syntheses. The studies and evidence syntheses were conducted in 51 and 39 countries, respectively.Most studies enrolled patients admitted to acute care hospitals (84%), included on average 169 participants, with an average age of 60 years, study duration of 28 days, number of effect outcomes of four and number of harm outcomes of one. The most common primary outcome was death (32%).The included studies evaluated 214 treatment options. The most common treatments were tocilizumab (11%), hydroxychloroquine (9%) and convalescent plasma (7%). The most common therapeutic categories were non-steroidal immunosuppressants (18%), steroids (15%) and antivirals (14%). The most common therapeutic categories involving multiple drugs were antimalarials/antibiotics (16%), steroids/non-steroidal immunosuppressants (9%) and antimalarials/antivirals/antivirals (7%). The most common treatments evaluated in systematic reviews were hydroxychloroquine (11%), remdesivir (8%), tocilizumab (7%) and steroids (7%).The evaluated treatment was in favour 50% and 36% of the evaluations, according to the conclusion of the authors of primary studies and evidence syntheses, respectively. CONCLUSIONS: This rapid scoping review characterised a growing body of comparative-effectiveness primary studies and evidence syntheses. The results suggest future studies should focus on children, elderly >/=65 years of age, patients with mild symptoms, outpatient treatment, multimechanism therapies, harms and active comparators. The results also suggest that future living evidence synthesis and network meta-analysis would provide additional information for decision-makers on managing COVID-19.

Abstract: Experimental evidence demonstrated that fluoro-edenite (FE) can develop chronic respiratory diseases and elicit carcinogenic effects. Environmental exposure to FE fibers is correlated with malignant pleural mesothelioma (MPM). An early diagnosis of MPM, and a comprehensive health monitoring of the patients exposed to FE fibers are two clinical issues that may be solved by the identification of specific biomarkers. We reported the microRNA (miRNA) and transfer RNA-derived non coding RNA (tRNA-derived ncRNA) transcriptome in human normal mesothelial and malignant mesothelioma cell lines exposed or not exposed to several concentration FE fibers. Furthermore, an interactive mesothelioma-based network was derived by using NetME tool. In untreated condition, the expression of miRNAs and tRNA-derived ncRNAs in tumor cells was significantly different with respect to non-tumor samples. Moreover, interesting and significant changes were found after the exposure of both cells lines to FE fibers. The network-based pathway analysis showed several signaling and metabolic pathways potentially involved in the pathogenesis of MPM. From papers analyzed by NetME, it is clear that many miRNAs can positively or negatively influence various pathways involved in MPM. For the first time, the analysis of tRNA-derived ncRNAs molecules in the context of mesothelioma has been made by using in vitro systems. Further studies will be designed to test and validate their diagnostic potential in high-risk individuals' liquid biopsies.

Abstract: In recent years, due to the complementary action of drug combinations over mono-therapy, the multiple-drugs for multiple-targets paradigm has received increased attention to treat bacterial infections and complex diseases. Although new drug combinations screening has benefited from experimental tests like automated high throughput screening, it is limited due to the large number of possible drug combinations. The task of drug combination screening can be streamlined through computational methods and models. Such models require up-to-date databases; however, existing databases are static and consist of the data collected at the time of their creation. This paper introduces the Continuous Drug Combination Database (CDCDB), a continuously updated drug combination database. The CDCDB includes over 40,795 drug combinations, of which 17,107 are unique combinations consisting of more than 4,129 individual drugs, curated from ClinicalTrials.gov, the FDA Orange Book((R)), and patents. To create CDCDB, we use various methods, including natural language processing techniques, to improve the process of drug combination discovery, ensuring that our database can be used for drug synergy prediction. Website: https://icc.ise.bgu.ac.il/medical_ai/CDCDB/ .

Abstract: Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network framework constrained by prior knowledge of the signaling network with ligand-concentrations as input and transcription factor-activity as output. Applied to synthetic data, it predicts unseen test-data (Pearson correlation r = 0.98) and the effects of gene knockouts (r = 0.8). We stimulate macrophages with 59 different ligands, with and without the addition of lipopolysaccharide, and collect transcriptomics data. The framework predicts this data under cross-validation (r = 0.8) and knockout simulations suggest a role for RIPK1 in modulating the lipopolysaccharide response. This work demonstrates the feasibility of genome-scale simulations of intracellular signaling.