Fluoxetine for COVID-19

AbstractTo circumvent time-consuming clinical trials, testing whether existing drugs are effective inhibitors of SARS-CoV-2, has led to the discovery of Remdesivir. We decided to follow this path and screened approved medications "off-label" against SARS-CoV-2. Fluoxetine inhibited SARS-CoV-2 at a concentration of 0.8 µg/ml significantly in these screenings, and the EC50 was determined with 387 ng/ml. Furthermore, Fluoxetine reduced viral infectivity in precision-cut human lung slices showing its activity in relevant human tissue targeted in severe infections. Fluoxetine treatment resulted in a decrease in viral protein expression. Fluoxetine is a racemate consisting of both stereoisomers, while the S-form is the dominant serotonin reuptake inhibitor. We found that both isomers show similar activity on the virus, indicating that the R-form might specifically be used for SARS-CoV-2 treatment. Fluoxetine inhibited neither Rabies virus, human respiratory syncytial virus replication nor the Human Herpesvirus 8 or Herpes simplex virus type 1 gene expression, indicating that it acts virus-specific. Moreover, since it is known that Fluoxetine inhibits cytokine release, we see the role of Fluoxetine in the treatment of SARS-CoV-2 infected patients of risk groups.

The coronavirus disease 2019 (COVID-19) pandemic continues to cause significant morbidity and mortality worldwide. Since a large portion of the world’s population is currently unvaccinated or incompletely vaccinated and has limited access to approved treatments against COVID-19, there is an urgent need to continue research on treatment options, especially those at low cost and which are immediately available to patients, particularly in low- and middle-income countries. Prior in vitro and observational studies have shown that fluoxetine, possibly through its inhibitory effect on the acid sphingomyelinase/ceramide system, could be a promising antiviral and anti-inflammatory treatment against COVID-19. In this report, we evaluated the potential antiviral and anti-inflammatory activities of fluoxetine in a K18-hACE2 mouse model of SARS-CoV-2 infection, and against variants of concern in vitro, i.e., SARS-CoV-2 ancestral strain, Alpha B.1.1.7, Gamma P1, Delta B1.617 and Omicron BA.5. Fluoxetine, administrated after SARS-CoV-2 infection, significantly reduced lung tissue viral titres and expression of several inflammatory markers (i.e., IL-6, TNFα, CCL2 and CXCL10). It also inhibited the replication of all variants of concern in vitro. A modulation of the ceramide system in the lung tissues, as reflected by the increase in the ratio HexCer 16:0/Cer 16:0 in fluoxetine-treated mice, may contribute to explain these effects. Our findings demonstrate the antiviral and anti-inflammatory properties of fluoxetine in a K18-hACE2 mouse model of SARS-CoV-2 infection, and its in vitro antiviral activity against variants of concern, establishing fluoxetine as a very promising candidate for the prevention and treatment of SARS-CoV-2 infection and disease pathogenesis.

AbstractRecently, COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, caused > 6 million deaths. Symptoms included respiratory strain and complications, leading to severe pneumonia. SARS-CoV-2 attaches to the ACE-2 receptor of the host cell membrane to enter. Targeting the SARS-CoV-2 entry may effectively inhibit infection. Acid sphingomyelinase (ASMase) is a lysosomal protein that catalyzes the conversion of sphingolipid (sphingomyelin) to ceramide. Ceramide molecules aggregate/assemble on the plasma membrane to form “platforms” that facilitate the viral intake into the cell. Impairing the ASMase activity will eventually disrupt viral entry into the cell. In this review, we identified the metabolism of sphingolipids, sphingolipids' role in cell signal transduction cascades, and viral infection mechanisms. Also, we outlined ASMase structure and underlying mechanisms inhibiting viral entry 40 with the aid of inhibitors of acid sphingomyelinase (FIASMAs). In silico molecular docking analyses of FIASMAs with inhibitors revealed that dilazep (S = − 12.58 kcal/mol), emetine (S = − 11.65 kcal/mol), pimozide (S = − 11.29 kcal/mol), carvedilol (S = − 11.28 kcal/mol), mebeverine (S = − 11.14 kcal/mol), cepharanthine (S = − 11.06 kcal/mol), hydroxyzin (S = − 10.96 kcal/mol), astemizole (S = − 10.81 kcal/mol), sertindole (S = − 10.55 kcal/mol), and bepridil (S = − 10.47 kcal/mol) have higher inhibition activity than the candidate drug amiodarone (S = − 10.43 kcal/mol), making them better options for inhibition.

Abstract Background Vaccination programs have been launched worldwide to halt the spread of COVID-19. However, the identification of existing, safe compounds with combined treatment and prophylactic properties would be beneficial to individuals who are waiting to be vaccinated, particularly in less economically developed countries, where vaccine availability may be initially limited. Methods We used a data-driven approach, combining results from the screening of a large transcriptomic database (L1000) and molecular docking analyses, with in vitro tests using a lung organoid model of SARS-CoV-2 entry, to identify drugs with putative multimodal properties against COVID-19. Results Out of thousands of FDA-approved drugs considered, we observed that atorvastatin was the most promising candidate, as its effects negatively correlated with the transcriptional changes associated with infection. Atorvastatin was further predicted to bind to SARS-CoV-2’s main protease and RNA-dependent RNA polymerase, and was shown to inhibit viral entry in our lung organoid model. Conclusions Small clinical studies reported that general statin use, and specifically, atorvastatin use, are associated with protective effects against COVID-19. Our study corroborrates these findings and supports the investigation of atorvastatin in larger clinical studies. Ultimately, our framework demonstrates one promising way to fast-track the identification of compounds for COVID-19, which could similarly be applied when tackling future pandemics.

AbstractDrug repurposing is a rapid approach to identifying therapeutics for the treatment of emerging infectious diseases such as COVID-19. To address the urgent need for treatment options, we carried out a quantitative high-throughput screen using a SARS-CoV-2 cytopathic assay with a compound collection of 8,810 approved and investigational drugs, mechanism-based bioactive compounds, and natural products. Three hundred and nineteen compounds with anti-SARS-CoV-2 activities were identified and confirmed, including 91 approved drug and 49 investigational drugs. Among these confirmed compounds, the anti-SARS-CoV-2 activities of 230 compounds, including 38 approved drugs, have not been previously reported. Chlorprothixene, methotrimeprazine, and piperacetazine were the three most potent FDA approved drugs with anti-SARS-CoV-2 activities. These three compounds have not been previously reported to have anti-SARS-CoV-2 activities, although their antiviral activities against SARS-CoV and Ebola virus have been reported. These results demonstrate that this comprehensive data set of drug repurposing screen for SARS-CoV-2 is useful for drug repurposing efforts including design of new drug combinations for clinical trials.

ABSTRACTCOVID-19 vaccines based on the Spike protein of SARS-CoV-2 have been developed that appear to be largely successful in stopping infection. However, vaccine escape variants might arise leading to a re-emergence of COVID. In anticipation of such a scenario, the identification of repurposed drugs that stop SARS-CoV-2 replication could have enormous utility in stemming the disease. Here, using a nano-luciferase tagged version of the virus (SARS-CoV-2- DOrf7a-NLuc) to quantitate viral load, we evaluated a range of human cell types for their ability to be infected and support replication of the virus, and performed a screen of 1971 FDA-approved drugs. Hepatocytes, kidney glomerulus, and proximal tubule cells were particularly effective in supporting SARS-CoV-2 replication, which is in- line with reported proteinuria and liver damage in patients with COVID-19. We identified 35 drugs that reduced viral replication in Vero and human hepatocytes when treated prior to SARS-CoV-2 infection and found amodiaquine, atovaquone, bedaquiline, ebastine, LY2835219, manidipine, panobinostat, and vitamin D3 to be effective in slowing SARS-CoV-2 replication in human cells when used to treat infected cells. In conclusion, our study has identified strong candidates for drug repurposing, which could prove powerful additions to the treatment of COVID.

Lipids perform a series of cellular functions, establishing cell and organelles’ boundaries, organizing signaling platforms, and creating compartments where specific reactions occur. Moreover, lipids store energy and act as secondary messengers whose distribution is tightly regulated. Disruption of lipid metabolism is associated with many diseases, including those caused by viruses. In this scenario, lipids can favor virus replication and are not solely used as pathogens’ energy source. In contrast, cells can counteract viruses using lipids as weapons. In this review, we discuss the available data on how coronaviruses profit from cellular lipid compartments and why targeting lipid metabolism may be a powerful strategy to fight these cellular parasites. We also provide a formidable collection of data on the pharmacological approaches targeting lipid metabolism to impair and treat coronavirus infection.

Drug repurposing is a rapid approach to identify therapeutics for the treatment of emerging infectious diseases such as COVID-19. To address the urgent need for treatment options, we carried out a quantitative high-throughput screen using a SARS-CoV-2 cytopathic assay with a compound collection of 8,810 approved and investigational drugs, mechanism-based bioactive compounds, and natural products. Three hundred and nineteen compounds with anti-SARS-CoV-2 activities were identified and confirmed, including 91 approved drugs and 49 investigational drugs. The anti-SARS-CoV-2 activities of 230 of these confirmed compounds, of which 38 are approved drugs, have not been previously reported. Chlorprothixene, methotrimeprazine, and piperacetazine were the three most potent FDA-approved drugs with anti-SARS-CoV-2 activities. These three compounds have not been previously reported to have anti-SARS-CoV-2 activities, although their antiviral activities against SARS-CoV and Ebola virus have been reported. These results demonstrate that this comprehensive data set is a useful resource for drug repurposing efforts, including design of new drug combinations for clinical trials for SARS-CoV-2.

COVID-19 vaccines based on the Spike protein of SARS-CoV-2 have been developed that appear to be largely successful in stopping infection. However, therapeutics that can help manage the disease are still required until immunity has been achieved globally. The identification of repurposed drugs that stop SARS-CoV-2 replication could have enormous utility in stemming the disease. Here, using a nano-luciferase tagged version of the virus (SARS-CoV-2-ΔOrf7a-NLuc) to quantitate viral load, we evaluated a range of human cell types for their ability to be infected and support replication of the virus, and performed a screen of 1971 FDA-approved drugs. Hepatocytes, kidney glomerulus, and proximal tubule cells were particularly effective in supporting SARS-CoV-2 replication, which is in-line with reported proteinuria and liver damage in patients with COVID-19. Using the nano-luciferase as a measure of virus replication we identified 35 drugs that reduced replication in Vero cells and human hepatocytes when treated prior to SARS-CoV-2 infection and found amodiaquine, atovaquone, bedaquiline, ebastine, LY2835219, manidipine, panobinostat, and vitamin D3 to be effective in slowing SARS-CoV-2 replication in human cells when used to treat infected cells. In conclusion, our study has identified strong candidates for drug repurposing, which could prove powerful additions to the treatment of COVID.

IntroductionWith the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease. Computational models have the ability to quickly predict potential reusable drug candidates to treat diseases.MethodsIn this study, two matrix decomposition-based methods, i.e., Matrix Decomposition with Heterogeneous Graph Inference (MDHGI) and Bounded Nuclear Norm Regularization (BNNR), were integrated to predict anti-viral drugs. Moreover, global leave-one-out cross-validation (LOOCV), local LOOCV, and 5-fold cross-validation were implemented to evaluate the performance of the proposed model based on datasets of DrugVirus that consist of 933 known associations between 175 drugs and 95 viruses.ResultsThe results showed that the area under the receiver operating characteristics curve (AUC) of global LOOCV and local LOOCV are 0.9035 and 0.8786, respectively. The average AUC and the standard deviation of the 5-fold cross-validation for DrugVirus datasets are 0.8856 ± 0.0032. We further implemented cross-validation based on MDAD and aBiofilm, respectively, to evaluate the performance of the model. In particle, MDAD (aBiofilm) dataset contains 2,470 (2,884) known associations between 1,373 (1,470) drugs and 173 (140) microbes. In addition, two types of case studies were carried out further to verify the effectiveness of the model based on the DrugVirus and MDAD datasets. The results of the case studies supported the effectiveness of MHBVDA in identifying potential virus-drug associations as well as predicting potential drugs for new microbes.

AbstractThe coronavirus disease 2019 (COVID-19) caused by coronavirus SARS-CoV-2 infection has become a global pandemic due to the high viral transmissibility and pathogenesis, bringing enormous burden to our society. Most patients infected by SARS-CoV-2 are asymptomatic or have mild symptoms. Although only a small proportion of patients progressed to severe COVID-19 with symptoms including acute respiratory distress syndrome (ARDS), disseminated coagulopathy, and cardiovascular disorders, severe COVID-19 is accompanied by high mortality rates with near 7 million deaths. Nowadays, effective therapeutic patterns for severe COVID-19 are still lacking. It has been extensively reported that host metabolism plays essential roles in various physiological processes during virus infection. Many viruses manipulate host metabolism to avoid immunity, facilitate their own replication, or to initiate pathological response. Targeting the interaction between SARS-CoV-2 and host metabolism holds promise for developing therapeutic strategies. In this review, we summarize and discuss recent studies dedicated to uncovering the role of host metabolism during the life cycle of SARS-CoV-2 in aspects of entry, replication, assembly, and pathogenesis with an emphasis on glucose metabolism and lipid metabolism. Microbiota and long COVID-19 are also discussed. Ultimately, we recapitulate metabolism-modulating drugs repurposed for COVID-19 including statins, ASM inhibitors, NSAIDs, Montelukast, omega-3 fatty acids, 2-DG, and metformin.

According to the World Health Organization (WHO), in the second half of 2022, there are about 606 million confirmed cases of COVID-19 and almost 6,500,000 deaths around the world. A pandemic was declared by the WHO in March 2020 when the new coronavirus spread around the world. The short time between the first cases in Wuhan and the declaration of a pandemic initiated the search for ways to stop the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or to attempt to cure the disease COVID-19. More than ever, research groups are developing vaccines, drugs, and immunobiological compounds, and they are even trying to repurpose drugs in an increasing number of clinical trials. There are great expectations regarding the vaccine’s effectiveness for the prevention of COVID-19. However, producing sufficient doses of vaccines for the entire population and SARS-CoV-2 variants are challenges for pharmaceutical industries. On the contrary, efforts have been made to create different vaccines with different approaches so that they can be used by the entire population. Here, we summarize about 8162 clinical trials, showing a greater number of drug clinical trials in Europe and the United States and less clinical trials in low-income countries. Promising results about the use of new drugs and drug repositioning, monoclonal antibodies, convalescent plasma, and mesenchymal stem cells to control viral infection/replication or the hyper-inflammatory response to the new coronavirus bring hope to treat the disease.

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

Abstract Background For both the current and future pandemics, there is a need for high-throughput drug screening methods to identify existing drugs with potential preventative and/or therapeutic activity. Epidemiologic studies could complement lab-focused efforts to identify possible therapeutic agents. Methods We performed a pharmacopeia-wide association study (PWAS) to identify commonly prescribed medications and medication classes that are associated with the detection of SARS-CoV-2 in older individuals (>65 years) in long-term care homes (LTCH) and the community, between January 15 th, 2020 and December 31 st, 2020, across the province of Ontario, Canada. Results 26,121 cases and 2,369,020 controls from LTCH and the community were included in this analysis. Many of the drugs and drug classes evaluated did not yield significant associations with SARS-CoV-2 detection. However, some drugs and drug classes appeared significantly associated with reduced SARS-CoV-2 detection, including cardioprotective drug classes such as statins (weighted OR 0.91, standard p-value <0.01, adjusted p-value <0.01) and beta-blockers (weighted OR 0.87, standard p-value <0.01, adjusted p-value 0.01), along with individual agents ranging from levetiracetam (weighted OR 0.70, standard p-value <0.01, adjusted p-value <0.01) to fluoxetine (weighted OR 0.86, standard p-value 0.013, adjusted p-value 0.198) to digoxin (weighted OR 0.89, standard p-value <0.01, adjusted p-value 0.02). Conclusions Using this epidemiologic approach which can be applied to current and future pandemics we have identified a variety of target drugs and drug classes that could offer therapeutic benefit in COVID-19 and may warrant further validation. Some of these agents (e.g. fluoxetine) have already been identified for their therapeutic potential.

The active global SARS-CoV-2 pandemic caused more than 426 million cases and 5.8 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process. Despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on \emph{knowledge graphs}, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi {\sl et al.} recently developed the \drcov \ model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of 8,070 candidate drugs, 32 of which are currently being tested in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the \drcov \ model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware --- we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking.