The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) necessitates the rapid development of new therapies against coronavirus disease 2019 (COVID-19) infection. Here, we present the identification of 200 approved drugs, appropriate for repurposing against COVID-19. We constructed a SARS-CoV-2–induced protein network, based on disease signatures defined by COVID-19 multiomics datasets, and cross-examined these pathways against approved drugs. This analysis identified 200 drugs predicted to target SARS-CoV-2–induced pathways, 40 of which are already in COVID-19 clinical trials, testifying to the validity of the approach. Using artificial neural network analysis, we classified these 200 drugs into nine distinct pathways, within two overarching mechanisms of action (MoAs): viral replication (126) and immune response (74). Two drugs (proguanil and sulfasalazine) implicated in viral replication were shown to inhibit replication in cell assays. This unbiased and validated analysis opens new avenues for the rapid repurposing of approved drugs into clinical trials.

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SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico approaches which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored . As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

The vascular endothelium is a favorite early target of cardiovascular risk factors, including cigarette smoking. Here, we investigated the synergistic effects of Sanghuang–Danshen (SD) bioactives on vascular stiffness in a controlled clinical trial of healthy chronic smokers (n = 72). Relative to placebo, 4-week SD consumption at 900 mg/day improves pulse wave velocity (p = 0.0497), reduces systolic blood pressure (peripheral, p = 0.0008; brachial, p = 0.0046; and ankle, p = 0.0066), and increases endothelial nitric oxide synthase activation (p < 0.0001). We then mapped all differential markers obtained from the clinical data, Affymetrix microarray, and 1H NMR metabolomics, together with 12 SD bioactives, onto the network platform termed the context-oriented directed associations. The resulting vascular subnetwork demonstrates that ellagic acid, caffeic acid, protocatechuic acid, cryptotanshinone, tanshinone I, and tanshinone IIA are linked to NOS3, ARG2, and EDN1 for vascular dilation, implicated with arginine/proline metabolism. They are also linked to SUCLG1, CYP1A1, and succinate related to the mitochondrial metabolism and detoxification, implicated with various metabolic pathways. These results could explain the synergistic action mechanisms of SD bioactives in the regulation of vascular endothelial dilation and metabolism, confirming the potential of SD in improving vascular stiffness and blood pressure in healthy smokers.

In this paper, we study the secure communication of cognitive energy harvesting relay networks when there exist multiple eavesdroppers who can overhear the message of the second hop, and multiple primary users are present. The data transmission from the secondary source to the secondary destination is assisted by the best decode-and-forward relay, which is selected by means of three relay selection schemes. We study the system security performance by deriving the exact analytical secrecy outage probability. These analytical expressions are then verified by comparison to the results of Monte Carlo simulations. Herein we evaluate and discuss the outage performance of the three schemes under variations in important system parameters: the number and locations of relay nodes, primary user nodes, and eavesdroppers; the transmit power threshold; the energy harvesting efficiency coefficient; the power splitting ratio; and the target secure rate.

A clinical trial protocol defines the procedural methods that should be performed during a clinical trial. Every clinical trial starts with the design of its protocol. In designing the protocol, it is common for researchers to refer to electronic databases and extract protocol elements from clinical trial information by using a keyword search. However, state-of-the-art retrieval systems only offer text-based searches based on user-entered keywords. In this paper, we present an interactive retrieval system with a context-dependent protocol-element-selection system for the successful designing of a clinical trial protocol. In addition, we introduce a database for a protocol retrieval system constructed from a combined element analysis. The database is built using individual protocol data extracted from 184,634 clinical trials and provides 13,210 integrated structural data points. Furthermore, the database contains various semantic information that enables the protocols to be filtered during the search operation.Moreover, we developed a web application called the clinical trial protocol database system (CLIPS; available at https://corus.kaist.edu/clips), which enables an interactive search based on protocol elements. CLIPS provides options to select the next element according to the previous element in the form of a connected tree, thus enabling an interactive search for combinations of protocol elements. The results of technical validation show that our method achieves better performance than existing databases in predicting phenotypic features.

BackgroundBiological systems are robust and complex to maintain stable phenotypes under various conditions. In these systems, drugs reported the limited efficacy and unexpected side-effects. To remedy this situation, many pharmaceutical laboratories have begun to research combination drugs and some of them have shown successful clinical results. Complementary action of multiple compounds could increase efficacy as well as reduce side-effects through pharmacological interactions. However, experimental approach requires vast cost of preclinical experiments and tests as the number of possible combinations of compound dosages increases exponentially. Computer model-based experiments have been emerging as one of the most promising solutions to cope with such complexity. Though there have been many efforts to model specific molecular pathways using qualitative and quantitative formalisms, they suffer from unexpected results caused by distant interactions beyond their localized models.ResultsIn this work, we propose a rule-based multi-scale modelling platform. We have tested this platform with Type 2 diabetes (T2D) model, which involves the malfunction of numerous organs such as pancreas, circulation system, liver, and adipocyte. We have extracted T2D-related 190 rules by manual curation from literature, pathway databases and converting from different types of existing models. We have simulated twenty-two T2D drugs. The results of our simulation show drug effect pathways of T2D drugs and whether combination drugs have efficacy or not and how combination drugs work on the multi-scale model.ConclusionsWe believe that our simulation would help to understand drug mechanism for the drug development and provide a new way to effectively apply existing drugs for new target. It also would give insight for identifying effective combination drugs.

Background Although non-teaching community hospitals form the majority of healthcare providers in South Korea, there is limited data on antibiotic usage in them. To evaluate the pattern of antibiotic usage and its appropriateness in hospitals with < 400 beds in South Korea. Methods A multicentre retrospective study was conducted in 10 hospitals (six long-term care hospitals, three acute care hospitals, and one orthopaedic hospital), with < 400 beds in South Korea. We analysed patterns of antibiotic prescription in 2019, and their appropriateness in the participating hospitals. For the evaluation of the appropriateness of the prescription, 25 patients under antibiotic therapy were randomly selected at each hospital, over two separate periods. Due to the heterogeneity of their characteristics, the orthopaedics hospital was excluded from the analysis. Results The most commonly prescribed antibiotics in long-term care hospitals was fluoroquinolone, followed by beta-lactam/beta-lactamase inhibitor (anti-pseudomonal). In acute care hospitals, these were third generation cephalosporin, followed by first generation cephalosporin, and second generation cephalosporin. The major antibiotics that were prescribed in the orthopedics hospital was first generation cephalosporin Only 2.3% of the antibiotics were administered inappropriately. In comparison, 15.3% of patients were prescribed an inappropriate dose. The proportion of inappropriate antibiotic prescriptions was 30.6% of the total antibiotic prescriptions. Conclusions The antibiotic usage patterns vary between non-teaching community hospitals in South Korea. The proportion of inappropriate prescriptions exceeded 30% of the total antibiotic prescriptions.