BETA: a comprehensive benchmark for computational drug-target prediction.

Internal validation is the most popular evaluation strategy used for drug-target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug-drug and protein-protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.

Signaling repurposable drug combinations against COVID-19 by developing the heterogeneous deep herb-graph method.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. MOTIVATION: Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage. METHOD: We initially employed the virtual screening method to construct the 'Herb-Compound' and 'Compound-Protein' docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the 'Herb-Compound-Protein' heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies. RESULTS: There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.

Repurposing Ayush-64 for COVID-19: A Computational Study Based on Network Pharmacology and Molecular Docking.

BACKGROUND: As COVID-19 pandemic continues to affect people's lives, the government of India gave emergency use approval to the ayurvedic antimalarial drug Ayush-64 in April 2021 to treat asymptomatic COVID-19 positive and mild COVID-19 positive patients. OBJECTIVE: This study aims to explore the therapeutic potential of Ayush-64 to treat COVID-19 and provide a new approach for repurposing Ayurvedic drugs. METHODS: The bioactives present in Ayush-64 were found along with their targets, and a plantbioactive- target network was created. A protein-protein interaction network of the common targets of Ayush-64 and COVID-19 was constructed and analyzed to find the key targets of Ayush-64 associated with the disease. Gene ontology and pathway enrichment analysis were performed to find COVID-19 related biological processes and pathways involved by the key targets. The key bioactives were docked with SARS-CoV-2 main protease 3CL, native Human Angiotensin-converting Enzyme ACE2, Spike protein S1, and RNA-dependent RNA polymerase RdRp. RESULTS: From the 336 targets for Ayush-64, we found 38 key targets. Functional enrichment analysis of the key targets resulted in 121 gene ontology terms and 38 pathways. When molecular docking was performed with four receptors, thirteen bioactives showed good binding affinity comparable to that of the eight drugs presently used to treat COVID-19. CONCLUSION: Network pharmacological analysis and molecular docking study of Ayush-64 revealed that it can be recommended to treat COVID-19. Further in vitro and in vivo studies are needed to confirm the results. The study demonstrated a new approach for repurposing Ayurvedic drugs.

Machine Learning Applications in Drug Repurposing.

The coronavirus disease (COVID-19) has led to an rush to repurpose existing drugs, although the underlying evidence base is of variable quality. Drug repurposing is a technique by taking advantage of existing known drugs or drug combinations to be explored in an unexpected medical scenario. Drug repurposing, hence, plays a vital role in accelerating the pre-clinical process of designing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repurposing depends on massive observed data from existing drugs and diseases, the tremendous growth of publicly available large-scale machine learning methods supplies the state-of-the-art application of data science to signaling disease, medicine, therapeutics, and identifying targets with the least error. In this article, we introduce guidelines on strategies and options of utilizing machine learning approaches for accelerating drug repurposing. We discuss how to employ machine learning methods in studying precision medicine, and as an instance, how machine learning approaches can accelerate COVID-19 drug repurposing by developing Chinese traditional medicine therapy. This article provides a strong reasonableness for employing machine learning methods for drug repurposing, including during fighting for COVID-19 pandemic.

The inhibitory effect of trimetazidine on detrusor contractility - a potential repositioning of trimetazidine for the treatment of overactive bladder.

OBJECTIVES: This study aimed to identify the effect of trimetazidine (TMZ), an antianginal drug, on detrusor smooth muscle (DSM) contractility and its possible mechanisms of action. METHODS: We performed in-vitro contractility studies on isolated mouse DSM strips and investigated the effect of TMZ on Ca2+ levels in fura-2-loaded A7r5 cells. KEY FINDINGS: TMZ (300 or 1000 µM) inhibited carbachol (CCh)- and KCl-induced contractions and produced a concentration-dependent (10-1000 µM) relaxation in KCl-precontracted DSM strips. TMZ-induced relaxation was markedly decreased by BaCl2, an inward-rectifying K+ channel blocker, but was not altered by preincubation with tetraethylammonium, glibenclamide, 4-aminopyridine, propranolol, L-NAME or methylene blue. TMZ (300 or 1000 µM) reduced both the CaCl2-induced contraction of depolarized DSM strips under Ca2+-free conditions and the CCh-induced contraction of DSM strips preincubated with nifedipine in Ca2+-containing Krebs solution. Furthermore, TMZ (1000 µM) significantly decreased the Ca2+ levels in fura-2-loaded A7r5 cells. CONCLUSIONS: TMZ decreased DSM contractility and caused a concentration-dependent relaxation of the tissue possibly through its actions on Ca2+ transients and K+ channels. Our results provide preclinical evidence that TMZ would be a potential candidate to treat disorders related to the overactivity of the bladder.

Molecular Docking and Dynamics Investigations for Identifying Potential Inhibitors of the 3-Chymotrypsin-like Protease of SARS-CoV-2: Repurposing of Approved Pyrimidonic Pharmaceuticals for COVID-19 Treatment.

This study demonstrates the inhibitory effect of 42 pyrimidonic pharmaceuticals (PPs) on the 3-chymotrypsin-like protease of SARS-CoV-2 (3CLpro) through molecular docking, molecular dynamics simulations, and free binding energies by means of molecular mechanics-Poisson Boltzmann surface area (MM-PBSA) and molecular mechanics-generalized Born surface area (MM-GBSA). Of these tested PPs, 11 drugs approved by the US Food and Drug Administration showed an excellent binding affinity to the catalytic residues of 3CLpro of His41 and Cys145: uracil mustard, cytarabine, floxuridine, trifluridine, stavudine, lamivudine, zalcitabine, telbivudine, tipiracil, citicoline, and uridine triacetate. Their percentage of residues involved in binding at the active sites ranged from 56 to 100, and their binding affinities were in the range from -4.6 ± 0.14 to -7.0 ± 0.19 kcal/mol. The molecular dynamics as determined by a 200 ns simulation run of solvated docked complexes confirmed the stability of PP conformations that bound to the catalytic dyad and the active sites of 3CLpro. The free energy of binding also demonstrates the stability of the PP-3CLpro complexes. Citicoline and uridine triacetate showed free binding energies of -25.53 and -7.07 kcal/mol, respectively. Therefore, I recommend that they be repurposed for the fight against COVID-19, following proper experimental and clinical validation.

Entrectinib-A SARS-CoV-2 Inhibitor in Human Lung Tissue (HLT) Cells.

Since the start of the COVID-19 outbreak, pharmaceutical companies and research groups have focused on the development of vaccines and antiviral drugs against SARS-CoV-2. Here, we apply a drug repurposing strategy to identify drug candidates that are able to block the entrance of the virus into human cells. By combining virtual screening with in vitro pseudovirus assays and antiviral assays in Human Lung Tissue (HLT) cells, we identify entrectinib as a potential antiviral drug.

Drug Repositioning For Allosteric Modulation of VIP and PACAP Receptors.

Vasoactive intestinal peptide (VIP) and pituitary adenylate cyclase-activating polypeptide (PACAP) are two neuropeptides that contribute to the regulation of intestinal motility and secretion, exocrine and endocrine secretions, and homeostasis of the immune system. Their biological effects are mediated by three receptors named VPAC1, VPAC2 and PAC1 that belong to class B GPCRs. VIP and PACAP receptors have been identified as potential therapeutic targets for the treatment of chronic inflammation, neurodegenerative diseases and cancer. However, pharmacological use of endogenous ligands for these receptors is limited by their lack of specificity (PACAP binds with high affinity to VPAC1, VPAC2 and PAC1 receptors while VIP recognizes both VPAC1 and VPAC2 receptors), their poor oral bioavailability (VIP and PACAP are 27- to 38-amino acid peptides) and their short half-life. Therefore, the development of non-peptidic small molecules or specific stabilized peptidic ligands is of high interest. Structural similarities between VIP and PACAP receptors are major causes of difficulties in the design of efficient and selective compounds that could be used as therapeutics. In this study we performed structure-based virtual screening against the subset of the ZINC15 drug library. This drug repositioning screen provided new applications for a known drug: ticagrelor, a P2Y12 purinergic receptor antagonist. Ticagrelor inhibits both VPAC1 and VPAC2 receptors which was confirmed in VIP-binding and calcium mobilization assays. A following analysis of detailed ticagrelor binding modes to all three VIP and PACAP receptors with molecular dynamics revealed its allosteric mechanism of action. Using a validated homology model of inactive VPAC1 and a recently released cryo-EM structure of active VPAC1 we described how ticagrelor could block conformational changes in the region of 'tyrosine toggle switch' required for the receptor activation. We also discuss possible modifications of ticagrelor comparing other P2Y12 antagonist - cangrelor, closely related to ticagrelor but not active for VPAC1/VPAC2. This comparison with inactive cangrelor could lead to further improvement of the ticagrelor activity and selectivity for VIP and PACAP receptor sub-types.

Tumor microenvironment-based screening repurposes drugs targeting cancer stem cells and cancer-associated fibroblasts.

The tumorous niche may drive the plasticity of heterogeneity and cancer stemness, leading to drug resistance and metastasis, which is the main reason of treatment failure in most cancer patients. The aim of this study was to establish a tumor microenvironment (TME)-based screening to identify drugs that can specifically target cancer stem cells (CSCs) and cancer-associated fibroblasts (CAFs) in the TME. Methods: Lung cancer patient-derived cancer cell and CAFs were utilized to mimic the TME and reproduce the stemness properties of CSCs in vitro and develop a high-throughput drug screening platform with phenotypical parameters. Limiting dilution assay, sphere-forming and ALDH activity assay were utilized to measure the cancer stemness characteristics. In vivo patient-derived xenograft (PDX) models and single-cell RNA sequencing were used to evaluate the mechanisms of the compounds in CSCs and CAFs. Results: The TME-based drug screening platform could comprehensively evaluate the response of cancer cells, CSCs and CAFs to different treatments. Among the 1,524 compounds tested, several drugs were identified to have anti-CAFs, anticancer and anti-CSCs activities. Aloe-emodin and digoxin both show anticancer and anti-CSCs activity in vitro and in vivo, which was further confirmed in the lung cancer PDX model. The combination of digoxin and chemotherapy improved therapeutic efficacy. The single-cell transcriptomics analysis revealed that digoxin could suppress the CSCs subpopulation in CAFs-cocultured cancer cells and cytokine production in CAFs. Conclusions: The TME-based drug screening platform provides a tool to identify and repurpose compounds targeting cancer cells, CSCs and CAFs, which may accelerate drug development and therapeutic application for lung cancer patients.

Network neighbors of viral targets and differentially expressed genes in COVID-19 are drug target candidates.

The COVID-19 pandemic is raging. It revealed the importance of rapid scientific advancement towards understanding and treating new diseases. To address this challenge, we adapt an explainable artificial intelligence algorithm for data fusion and utilize it on new omics data on viral-host interactions, human protein interactions, and drugs to better understand SARS-CoV-2 infection mechanisms and predict new drug-target interactions for COVID-19. We discover that in the human interactome, the human proteins targeted by SARS-CoV-2 proteins and the genes that are differentially expressed after the infection have common neighbors central in the interactome that may be key to the disease mechanisms. We uncover 185 new drug-target interactions targeting 49 of these key genes and suggest re-purposing of 149 FDA-approved drugs, including drugs targeting VEGF and nitric oxide signaling, whose pathways coincide with the observed COVID-19 symptoms. Our integrative methodology is universal and can enable insight into this and other serious diseases.

Drug repurposing for COVID-19 based on an integrative meta-analysis of SARS-CoV-2 induced gene signature in human airway epithelium.

Drug repurposing has the potential to bring existing de-risked drugs for effective intervention in an ongoing pandemic-COVID-19 that has infected over 131 million, with 2.8 million people succumbing to the illness globally (as of April 04, 2021). We have used a novel `gene signature'-based drug repositioning strategy by applying widely accepted gene ranking algorithms to prioritize the FDA approved or under trial drugs. We mined publically available RNA sequencing (RNA-Seq) data using CLC Genomics Workbench 20 (QIAGEN) and identified 283 differentially expressed genes (FDR<0.05, log2FC>1) after a meta-analysis of three independent studies which were based on severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection in primary human airway epithelial cells. Ingenuity Pathway Analysis (IPA) revealed that SARS-CoV-2 activated key canonical pathways and gene networks that intricately regulate general anti-viral as well as specific inflammatory pathways. Drug database, extracted from the Metacore and IPA, identified 15 drug targets (with information on COVID-19 pathogenesis) with 46 existing drugs as potential-novel candidates for repurposing for COVID-19 treatment. We found 35 novel drugs that inhibit targets (ALPL, CXCL8, and IL6) already in clinical trials for COVID-19. Also, we found 6 existing drugs against 4 potential anti-COVID-19 targets (CCL20, CSF3, CXCL1, CXCL10) that might have novel anti-COVID-19 indications. Finally, these drug targets were computationally prioritized based on gene ranking algorithms, which revealed CXCL10 as the common and strongest candidate with 2 existing drugs. Furthermore, the list of 283 SARS-CoV-2-associated proteins could be valuable not only as anti-COVID-19 targets but also useful for COVID-19 biomarker development.

Multilevel Approach for the Treatment of Giardiasis by Targeting Arginine Deiminase.

Giardiasis represents a latent problem in public health due to the exceptionally pathogenic strategies of the parasite Giardia lamblia for evading the human immune system. Strains resistant to first-line drugs are also a challenge. Therefore, new antigiardial therapies are urgently needed. Here, we tested giardial arginine deiminase (GlADI) as a target against giardiasis. GlADI belongs to an essential pathway in Giardia for the synthesis of ATP, which is absent in humans. In silico docking with six thiol-reactive compounds was performed; four of which are approved drugs for humans. Recombinant GlADI was used in enzyme inhibition assays, and computational in silico predictions and spectroscopic studies were applied to follow the enzyme's structural disturbance and identify possible effective drugs. Inhibition by modification of cysteines was corroborated using Ellman's method. The efficacy of these drugs on parasite viability was assayed on Giardia trophozoites, along with the inhibition of the endogenous GlADI. The most potent drug against GlADI was assayed on Giardia encystment. The tested drugs inhibited the recombinant GlADI by modifying its cysteines and, potentially, by altering its 3D structure. Only rabeprazole and omeprazole decreased trophozoite survival by inhibiting endogenous GlADI, while rabeprazole also decreased the Giardia encystment rate. These findings demonstrate the potential of GlADI as a target against giardiasis.

COVID-19 challenges and its therapeutics.

COVID-19, an infectious disease, has emerged as one of the leading causes of death worldwide, making it one of the severe public health issues in recent decades. nCoV, the novel SARS coronavirus that causes COVID-19, has brought together scientists in the quest for possible therapeutic and preventive measures. The development of new drugs to manage COVID-19 effectively is a challenging and time-consuming process, thus encouraging extensive investigation of drug repurposing and repositioning candidates. Several medications, including remdesivir, hydroxychloroquine, chloroquine, lopinavir, favipiravir, ribavirin, ritonavir, interferons, azithromycin, capivasertib and bevacizumab, are currently under clinical trials for COVID-19. In addition, several medicinal plants with considerable antiviral activities are potential therapeutic candidates for COVID-19. Statistical data show that the pandemic is yet to slow down, and authorities are placing their hopes on vaccines. Within a short period, four types of vaccines, namely, whole virus, viral vector, protein subunit, and nucleic acid (RNA/DNA), which can confer protection against COVID-19 in different ways, were already in a clinical trial. SARS-CoV-2 variants spread is associated with antibody escape from the virus Spike epitopes, which has grave concerns for viral re-infection and even compromises the effectiveness of the vaccines. Despite these efforts, COVID-19 treatment is still solely based on clinical management through supportive care. We aim to highlight the recent trends in COVID-19, relevant statistics, and clinical findings, as well as potential therapeutics, including in-line treatment methods, preventive measures, and vaccines to combat the prevalence of COVID-19.

A secondary approach with conventional medicines and supplements to recuperate current COVID-19 status.

Novel coronavirus 2019 (COVID-19) is a zoonosis that revised the global economic and societal progress since early 2020. The SARS-CoV-2 has been recognized as the responsible pathogen for COVID-19 with high infection and mortality rate potential. It has spread in 192 countries and infected about 1.5% of the world population, and still, a proper therapeutic approach is not unveiled. COVID-19 indication starts with fever to shortness of breathing, leading to ICU admission with the ventilation support in severe conditions. Besides the symptomatic mainstay clinical therapeutic approach, only Remdesivir has been approved by the FDA. Several pharmaceutical companies claimed different vaccines with exceptionally high efficacy (90-95%) against COVID-19; how long these vaccines can protect and long-term safety with the new variants are unpredictable. After the worldwide spread of the COVID-19 pandemic, numerous clinical trials with different phases are being performed to find the most appropriate solution to this condition. Some of these trials with old FDA-approved drugs showed promising results. In this review, we have precisely compiled the efforts to curb the disease and discussed the clinical findings of Ivermectin, Doxycycline, Vitamin-D, Vitamin-C, Zinc, and cannabidiol and their combinations. Additionally, the correlation of these molecules on the prophylactic and diseased ministration against COVID-19 has been explored.

DrugWAS: Drug-wide Association Studies for COVID-19 Drug Repurposing.

This study aimed to systematically investigate if any of the available drugs in the electronic health record (EHR) can be repurposed as potential treatment for coronavirus disease 2019 (COVID-19). Based on a retrospective cohort analysis of EHR data, drug-wide association studies (DrugWAS) were performed on 9,748 patients with COVID-19 at Vanderbilt University Medical Center (VUMC). For each drug study, multivariable logistic regression with overlap weighting using propensity score was applied to estimate the effect of drug exposure on COVID-19 disease outcomes. Patient exposure to a drug between 3-months prior to the pandemic and the COVID-19 diagnosis was chosen as the exposure of interest. All-cause of death was selected as the primary outcome. Hospitalization, admission to the intensive care unit, and need for mechanical ventilation were identified as secondary outcomes. Overall, 17 drugs were significantly associated with decreased COVID-19 severity. Previous exposure to two types of 13-valent pneumococcal conjugate vaccines, PCV13 (odds ratio (OR), 0.31, 95% confidence interval (CI), 0.12-0.81 and OR, 0.33, 95% CI, 0.15-0.73), diphtheria toxoid and tetanus toxoid vaccine (OR, 0.38, 95% CI, 0.15-0.93) were significantly associated with a decreased risk of death (primary outcome). Secondary analyses identified several other significant associations showing lower risk for COVID-19 outcomes: acellular pertussis vaccine, 23-valent pneumococcal polysaccharide vaccine (PPSV23), flaxseed extract, ethinyl estradiol, estradiol, turmeric extract, ubidecarenone, azelastine, pseudoephedrine, dextromethorphan, omega-3 fatty acids, fluticasone, and ibuprofen. In conclusion, this cohort study leveraged EHR data to identify a list of drugs that could be repurposed to improve COVID-19 outcomes. Further randomized clinical trials are needed to investigate the efficacy of the proposed drugs.

Drug repurposing: Iron in the fire for older drugs.

Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.

Drug repurposing screens identify chemical entities for the development of COVID-19 interventions.

The ongoing pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), necessitates strategies to identify prophylactic and therapeutic drug candidates for rapid clinical deployment. Here, we describe a screening pipeline for the discovery of efficacious SARS-CoV-2 inhibitors. We screen a best-in-class drug repurposing library, ReFRAME, against two high-throughput, high-content imaging infection assays: one using HeLa cells expressing SARS-CoV-2 receptor ACE2 and the other using lung epithelial Calu-3 cells. From nearly 12,000 compounds, we identify 49 (in HeLa-ACE2) and 41 (in Calu-3) compounds capable of selectively inhibiting SARS-CoV-2 replication. Notably, most screen hits are cell-line specific, likely due to different virus entry mechanisms or host cell-specific sensitivities to modulators. Among these promising hits, the antivirals nelfinavir and the parent of prodrug MK-4482 possess desirable in vitro activity, pharmacokinetic and human safety profiles, and both reduce SARS-CoV-2 replication in an orthogonal human differentiated primary cell model. Furthermore, MK-4482 effectively blocks SARS-CoV-2 infection in a hamster model. Overall, we identify direct-acting antivirals as the most promising compounds for drug repurposing, additional compounds that may have value in combination therapies, and tool compounds for identification of viral host cell targets.

LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network.

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.

Magic Shotgun Nature with Scattergun Approach of Curcumin Repurposing in Obsessive-compulsive Disorder: A Novel Metaphysician of Drug Discovery.

BACKGROUND: Obsessive-Compulsive Disorder (OCD) is an intricate, debilitating neuropsychiatric disorder. Exclusively, Selective Serotonin Reuptake Inhibitors (SSRIs) are effective agents used for the treatment of OCD. However, SSRIs are not a magic pill-they do not respond adequately to everybody. In this consideration, a single drug target (magic bullet) is only a slightly superior option for all patients with a lot of pathognomonic signs. OBJECTIVE: The principal aim of the current study was to check the potential contribution of repurposing of magic shotgun nature of curcumin (rhizomes of Curcuma longa) with scattergun approach- proceeding a pioneer 'fine-tune' for obsessive-compulsive disorder. METHOD: Swiss albino mice (male 20 to 25 gram) were grouped into different groups (n = 6) used for the MBB (marble-burying behaviour) and MA (motor activity) test as a model for evaluation of anti-compulsive activity (Anti-OCD). Ethanolic extract of Curcuma longa (EECL-10, 15, 25, 40 mg/kg), or SSRI (fluoxetine 5, 10, 15 mg/kg) followed by pre-treated with either sub effective dose of fluoxetine attenuated MBB without effected the MA, or neurotoxin p-chlorophenyl alanine induced compulsive behavior and specific 5-HT receptors agonists/ antagonist, intraperitoneally revealed neuromodulation. RESULTS: EECL (40 mg/kg) significantly attenuated the MBB. Although, during treatments, none of the above had any critical impact on MA. p < 0.05 was considered significant in every case. CONCLUSION: Multiple drug-target interactions with multifarious biogenic receptors, supervene unexpected side effects followed by the repurposing of wanted effects (scattergun effect) were evoked by curcumin treatment. Finally, the study shows that EECL (curcumin) has anti-compulsive activity, which is mediated by neuromodulation with 5-HT receptors.

Use of repurposed and adjuvant drugs in hospital patients with covid-19: multinational network cohort study.

OBJECTIVE: To investigate the use of repurposed and adjuvant drugs in patients admitted to hospital with covid-19 across three continents. DESIGN: Multinational network cohort study. SETTING: Hospital electronic health records from the United States, Spain, and China, and nationwide claims data from South Korea. PARTICIPANTS: 303 264 patients admitted to hospital with covid-19 from January 2020 to December 2020. MAIN OUTCOME MEASURES: Prescriptions or dispensations of any drug on or 30 days after the date of hospital admission for covid-19. RESULTS: Of the 303 264 patients included, 290 131 were from the US, 7599 from South Korea, 5230 from Spain, and 304 from China. 3455 drugs were identified. Common repurposed drugs were hydroxychloroquine (used in from <5 (<2%) patients in China to 2165 (85.1%) in Spain), azithromycin (from 15 (4.9%) in China to 1473 (57.9%) in Spain), combined lopinavir and ritonavir (from 156 (<2%) in the VA-OMOP US to 2,652 (34.9%) in South Korea and 1285 (50.5%) in Spain), and umifenovir (0% in the US, South Korea, and Spain and 238 (78.3%) in China). Use of adjunctive drugs varied greatly, with the five most used treatments being enoxaparin, fluoroquinolones, ceftriaxone, vitamin D, and corticosteroids. Hydroxychloroquine use increased rapidly from March to April 2020 but declined steeply in May to June and remained low for the rest of the year. The use of dexamethasone and corticosteroids increased steadily during 2020. CONCLUSIONS: Multiple drugs were used in the first few months of the covid-19 pandemic, with substantial geographical and temporal variation. Hydroxychloroquine, azithromycin, lopinavir-ritonavir, and umifenovir (in China only) were the most prescribed repurposed drugs. Antithrombotics, antibiotics, H2 receptor antagonists, and corticosteroids were often used as adjunctive treatments. Research is needed on the comparative risk and benefit of these treatments in the management of covid-19.