Abstract: OBJECTIVE: Previous research reported cognitive and psychomotor impairments in long-term users of benzodiazepine receptor agonists (BZRAs). This article explores the role of acute intoxication and clinical complaints. METHODS: Neurocognitive and on-road driving performance of 19 long-term (>/=6 months) regular (>/=twice weekly) BZRA users with estimated plasma concentrations, based on self-reported use, exceeding the therapeutic threshold (CBZRA +), and 31 long-term regular BZRA users below (CBZRA -), was compared to that of 76 controls. RESULTS: BZRA users performed worse on tasks of response speed, processing speed, and sustained attention. Age, but not CBZRA or self-reported clinical complaints, was a significant covariate. Road-tracking performance was explained by CBZRA only. The CBZRA + group exhibited increased mean standard deviation of lateral position comparable to that at blood-alcohol concentrations of 0.5 g/L. CONCLUSIONS: Functional impairments in long-term BZRA users are not attributable to self-reported clinical complaints or estimated BZRA concentrations, except for road-tracking, which was impaired in CBZRA + users. Limitations to address are the lack of assessment of objective clinical complaints, acute task related stress, and actual BZRA plasma concentrations. In conclusion, the results confirm previous findings that demonstrate inferior performance across several psychomotor and neurocognitive domains in long-term BZRA users.

Abstract: The minimum dominating set (MDSet) comprises the smallest number of graph nodes, where other graph nodes are connected with at least one MDSet node. The MDSet has been successfully applied to extract proteins that control protein-protein interaction (PPI) networks and to reveal the correlation between structural analysis and biological functions. Although the PPI network contains many MDSets, the identification of multiple MDSets is an NP-complete problem, and it is difficult to determine the best MDSets, enriched with biological functions. Therefore, the MDSet model needs to be further expanded and validated to find constrained solutions that differ from those generated by the traditional models. Moreover, by identifying the critical set of the network, the set of nodes common to all MDSets can be time-consuming. Herein, the authors adopted the minimisation of metabolic adjustment (MOMA) algorithm to develop a new framework, called maximisation of interaction adjustment (MOIA). In MOIA, they provide three models; the first one generates two MDSets with a minimum number of shared proteins, the second model generates constrained multiple MDSets ( k -MDSets), and the third model generates user-defined MDSets, containing the maximum number of essential genes and/or other important genes of the PPI network. In practice, these models significantly reduce the cost of finding the critical set and classifying the graph nodes. Herein, the authors termed the critical set as the k -critical set, where k is the number of MDSets generated by the proposed model. Then, they defined a new set of proteins called the ( k - 1 ) -critical set, where each node belongs to ( k - 1 ) MDSets. This set has been shown to be as important as the k -critical set and contains many essential genes, transcription factors, and protein kinases as the k -critical set. The ( k - 1 ) -critical set can be used to extend the search for drug target proteins. Based on the performance of the MOIA models, the authors believe the proposed methods contribute to answering key questions about the MDSets of PPI networks, and their results and analysis can be extended to other network types.

Abstract: Treatment and control of schistosomiasis depends on a single drug, praziquantel, but this is not ideal for several reasons including lack of potency against the juvenile stage of the parasite, dose size, and risk of resistance. We have optimised the properties of a series of compounds we discovered through high throughput screening and have designed candidates for clinical development. The best compounds demonstrate clearance of both juvenile and adult S. mansoni worms in a mouse model of infection from a single oral dose of < 10 mg/kg. Several compounds in the series are predicted to treat schistosomiasis in humans across a range of species with a single oral dose of less than 5 mg/kg.

Abstract: COVID-19 is a highly contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The case-fatality rate is significantly higher in older patients and those with diabetes, cancer or cardiovascular disorders. The human proteins, angiotensin-converting enzyme 2 (ACE2), transmembrane protease serine 2 (TMPRSS2) and basigin (BSG), are involved in high-confidence host-pathogen interactions with SARS-CoV-2 proteins. We considered these three proteins as seed nodes and applied the random walk with restart method on the human interactome to construct a protein-protein interaction sub-network, which captures the effects of viral invasion. We found that 'Insulin resistance', 'AGE-RAGE signaling in diabetic complications' and 'adipocytokine signaling' were the common pathways associated with diabetes, cancer and cardiovascular disorders. The association of these critical pathways with aging and its related diseases explains the molecular basis of COVID-19 fatality. We further identified drugs that have effects on these proteins/pathways based on gene expression studies. We particularly focused on drugs that significantly downregulate ACE2 along with other critical proteins identified by the network-based approach. Among them, COL-3 had earlier shown activity against acute lung injury and acute respiratory distress, while entinostat and mocetinostat have been investigated for non-small-cell lung cancer. We propose that these drugs can be repurposed for COVID-19.

Abstract: An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID-19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development. LUNAR can well integrate various topological structure information in heterogeneous networks, and skillfully combine attention mechanisms to reflect the importance of neighborhood information of different types of nodes, improving the interpretability of the model. The area under the curve(AUC) of the model is 0.949 and the accurate recall curve (AUPR) is 0.866 using 10-fold cross-validation. These two performance indexes show that the model has superior predictive performance. Besides, some of the drugs screened out by our model have appeared in some clinical studies to further illustrate the effectiveness of the model.

Abstract: Pain contributes substantially to reduced quality of life in individuals living with hidradenitis suppurativa (HS). Although improved understanding of HS pathogenesis and treatment has resulted in improved evidence-based HS management guidelines, comprehensive pain management guidelines have yet to be developed. Few HS-specific data exist to guide pharmacologic analgesia; however, recognizing HS pain as either acute or chronic and predominantly nociceptive (aching and gnawing pain due to tissue damage) versus neuropathic (burning-type pain due to somatosensory nervous system dysfunction) provides a conceptual framework for applying outside pain management practices to HS management. This article incorporates the best available evidence from the HS and pain literature to propose an HS pain algorithm that integrates psychological, pharmacologic, and complementary and alternative treatment modalities.

Abstract: Respiratory transmission is the primary route of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Angiotensin I converting enzyme 2 (ACE2) is the known receptor of SARS-CoV-2 surface spike glycoprotein for entry into human cells. A recent study reported absent to low expression of ACE2 in a variety of human lung epithelial cell samples. Three bioprojects (PRJEB4337, PRJNA270632 and PRJNA280600) invariably found abundant expression of ACE1 (a homolog of ACE2 and also known as ACE) in human lungs compared to very low expression of ACE2. In fact, ACE1 has a wider and more abundant tissue distribution compared to ACE2. Although it is not obvious from the primary sequence alignment of ACE1 and ACE2, comparison of X-ray crystallographic structures show striking similarities in the regions of the peptidase domains (PD) of these proteins, which is known (for ACE2) to interact with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. Critical amino acids in ACE2 that mediate interaction with the viral spike protein are present and organized in the same order in the PD of ACE1. In silico analysis predicts comparable interaction of SARS-CoV-2 spike protein with ACE1 and ACE2. In addition, this study predicts from a list of 1263 already approved drugs that may interact with ACE2 and/or ACE1 and potentially interfere with the entry of SARS-CoV-2 inside the host cells.

Abstract: Objective: To investigate the mechanisms of andrographolide against non-alcoholic steatohepatitis (NASH) based on network pharmacology, so as to provide a reference for further study of andrographolide in the treatment of NASH and other metabolic diseases. Methods: The methionine- and choline-deficient (MCD) diet-induced NASH mice were treated by administration of andrographolide, and serum transaminase and pathological changes were analyzed. The network pharmacology-based bioinformatic strategy was then used to search the potential targets, construct protein-protein interaction (PPI) network, analyze gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment, and conduct molecular docking to explore the molecular mechanisms. Results: The predicted core targets TNF, MAPK8, IL6, IL1B and AKT1 were enriched in non-alcoholic fatty liver disease (NAFLD) signaling pathway and against NASH by regulation of de novo fatty acids synthesis, anti-inflammation and anti-oxidation. Conclusion: This work provides a scientific basis for further demonstration of the anti-NASH mechanisms of andrographolide.

Abstract: Rapid repurposing of existing drugs as new therapeutics for COVID-19 has been an important strategy in the management of disease severity during the ongoing SARS-CoV-2 pandemic. Here, we used high-throughput docking to screen 6000 compounds within the DrugBank library for their potential to bind and inhibit the SARS-CoV-2 3 CL main protease, a chymotrypsin-like enzyme that is essential for viral replication. For 19 candidate hits, parallel in vitro fluorescence-based protease-inhibition assays and Vero-CCL81 cell-based SARS-CoV-2 replication-inhibition assays were performed. One hit, diclazuril (an investigational anti-protozoal compound), was validated as a SARS-CoV-2 3 CL main protease inhibitor in vitro (IC50 value of 29 microM) and modestly inhibited SARS-CoV-2 replication in Vero-CCL81 cells. Another hit, lenvatinib (approved for use in humans as an anti-cancer treatment), could not be validated as a SARS-CoV-2 3 CL main protease inhibitor in vitro, but serendipitously exhibited a striking functional synergy with the approved nucleoside analogue remdesivir to inhibit SARS-CoV-2 replication, albeit this was specific to Vero-CCL81 cells. Lenvatinib is a broadly-acting host receptor tyrosine kinase (RTK) inhibitor, but the synergistic effect with remdesivir was not observed with other approved RTK inhibitors (such as pazopanib or sunitinib), suggesting that the mechanism-of-action is independent of host RTKs. Furthermore, time-of-addition studies revealed that lenvatinib/remdesivir synergy probably targets SARS-CoV-2 replication subsequent to host-cell entry. Our work shows that combining computational and cellular screening is a means to identify existing drugs with repurposing potential as antiviral compounds. Future studies could be aimed at understanding and optimizing the lenvatinib/remdesivir synergistic mechanism as a therapeutic option.

Abstract: Background: Ovarian cancer is one of the rarest lethal oncologic diseases that have hardly any specific biomarkers. The availability of high-throughput genomic data and advancement in bioinformatics tools allow us to predict gene biomarkers and apply systems biology approaches to get better diagnosis, and prognosis of the disease with a tentative drug that may be repurposed. Objective: To perform genome-wide association studies using microarray gene expression of ovarian cancer and identify gene biomarkers, construction and analyze networks, perform survival analysis, and drug interaction studies for better diagnosis, prognosis, and treatment of ovarian cancer. Method: The gene expression profiles of both healthy and serous ovarian cancer epithelial samples were considered. We applied a series of bioinformatics methods and tools, including fold-change statistics for differential expression analysis, DisGeNET and NCBI-Gene databases for gene-disease association mapping, DAVID 6.8 for GO enrichment analysis, GeneMANIA for network construction, Cytoscape 3.8 with its plugins for network visualization, analysis, and module detection, the UALCAN for patient survival analysis, and PubChem, DrugBank and DGIdb for gene-drug interaction. Results: We identified 8 seed genes that were subjected for drug-gene interaction studies. Because of over-expression in all the four stages of ovarian cancer, we discern that genes HMGA1 and PSAT1 are potential therapeutic biomarkers for its diagnosis at an early stage (stage I). Our analysis suggests that there are 11 drugs common in the seed genes. However, hypermethylated seed genes HMGA1 and PSAT1 showcased a good interaction affinity with drugs cisplatin, cyclosporin, bisphenol A, progesterone, and sunitinib, and are crucial in the proliferation of ovarian cancer. Conclusion: Our study reveals that HMGA1 and PSAT1 can be deployed for initial screening of ovarian cancer and drugs cisplatin, bisphenol A, cyclosporin, progesterone, and sunitinib are effective in curbing the epigenetic alteration.