Doxycycline for COVID-19

Background After admission to hospital, COVID-19 progresses in a substantial proportion of patients to critical disease that requires intensive care unit (ICU) admission. Methods In a pragmatic, non-blinded trial, 387 patients aged 40–90 years were randomised to receive treatment with SoC plus doxycycline (n = 192) or SoC only (n = 195). The primary outcome was the need for ICU admission as judged by the attending physicians. Three types of analyses were carried out for the primary outcome: “Intention to treat” (ITT) based on randomisation; “Per protocol” (PP), excluding patients not treated according to randomisation; and “As treated” (AT), based on actual treatment received. The trial was undertaken in six hospitals in India with high-quality ICU facilities. An online application serving as the electronic case report form was developed to enable screening, randomisation and collection of outcomes data. Results Adherence to treatment per protocol was 95.1%. Among all 387 participants, 77 (19.9%) developed critical disease needing ICU admission. In all three primary outcome analyses, doxycycline was associated with a relative risk reduction (RRR) and absolute risk reduction (ARR): ITT 31.6% RRR, 7.4% ARR (P = 0.063); PP 40.7% RRR, 9.6% ARR (P = 0.017); AT 43.2% RRR, 10.8% ARR (P = 0.007), with numbers needed to treat (NTT) of 13.4 (ITT), 10.4 (PP), and 9.3 (AT), respectively. Doxycycline was well tolerated with not a single patient stopping treatment due to adverse events. Conclusions In hospitalized COVID-19 patients, doxycycline, a safe, inexpensive, and widely available antibiotic with anti-inflammatory properties, reduces the need for ICU admission when added to SoC.

Doxycycline has revealed potential effects in animal studies to prevent thrombosis and reduce mortality. However, less is known about its antithrombotic role in patients with COVID-19. Our study aimed to evaluate doxycycline's impact on clinical outcomes in critically ill patients with COVID-19. A multicenter retrospective cohort study was conducted between March 1, 2020, and July 31, 2021. Patients who received doxycycline in intensive care units (ICUs) were compared to patients who did not (control). The primary outcome was the composite thrombotic events. The secondary outcomes were 30-day and in-hospital mortality, length of stay, ventilator-free days, and complications during ICU stay. Propensity score (PS) matching was used based on the selected criteria. Logistic, negative binomial, and Cox proportional hazards regression analyses were used as appropriate. After PS (1:3) matching, 664 patients (doxycycline n = 166, control n = 498) were included. The number of thromboembolic events was lower in the doxycycline group (OR: 0.54; 95% CI: 0.26-1.08; P = .08); however, it failed to reach to a statistical significance. Moreover, D-dimer levels and 30-day mortality were lower in the doxycycline group (beta coefficient [95% CI]: −0.22 [−0.46, 0.03; P = .08]; HR: 0.73; 95% CI: 0.52-1.00; P = .05, respectively). In addition, patients who received doxycycline had significantly lower odds of bacterial/fungal pneumonia (OR: 0.65; 95% CI: 0.44-0.94; P = .02). The use of doxycycline as adjunctive therapy in critically ill patients with COVID-19 might may be a desirable therapeutic option for thrombosis reduction and survival benefits.

In December 2019, a new severe acute respiratory syndrome coronavirus (SARS-CoV-2), causing coronavirus disease 2019 (COVID-19), emerged in Wuhan, China. Despite containment measures, SARS-CoV-2 spread in Asia, Southern Europe, then in America and currently in Africa. Identifying effective antiviral drugs is urgently needed. An efficient approach to drug discovery is to evaluate whether existing approved drugs can be efficient against SARS-CoV-2. Doxycycline, which is a second-generation tetracycline with broad-spectrum antimicrobial, antimalarial and anti-inflammatory activities, showed in vitro activity on Vero E6 cells infected with a clinically isolated SARS-CoV-2 strain (IHUMI-3) with median effective concentration (EC50) of 4.5 ± 2.9 µM, compatible with oral uptake and intravenous administrations. Doxycycline interacted both on SARS-CoV-2 entry and in replication after virus entry. Besides its in vitro antiviral activity against SARS-CoV-2, doxycycline has anti-inflammatory effects by decreasing the expression of various pro-inflammatory cytokines and could prevent co-infections and superinfections due to broad-spectrum antimicrobial activity. Therefore, doxycycline could be a potential partner of COVID-19 therapies. However, these results must be taken with caution regarding the potential use in SARS-CoV-2-infected patients: it is difficult to translate in vitro study results to actual clinical treatment in patients. In vivo evaluation in animal experimental models is required to confirm the antiviral effects of doxycycline on SARS-CoV-2 and more trials of high-risk patients with moderate to severe COVID-19 infections must be initiated.

AbstractTo attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

The COVID-19 pandemic caused by SARS-CoV-2 has posed unparalleled challenges due to its rapid transmission, ability to mutate, high mortality and morbidity, and enduring health complications. Vaccines have exhibited effectiveness, but their efficacy diminishes over time while new variants continue to emerge. Antiviral medications offer a viable alternative, but their success has been inconsistent. Therefore, there remains an ongoing need to identify innovative antiviral drugs for treating COVID-19 and its post-infection complications. The ORF3a (open reading frame 3a) protein found in SARS-CoV-2, represents a promising target for antiviral treatment due to its multifaceted role in viral pathogenesis, cytokine storms, disease severity, and mortality. ORF3a contributes significantly to viral pathogenesis by facilitating viral assembly and release, essential processes in the viral life cycle, while also suppressing the body’s antiviral responses, thus aiding viral replication. ORF3a also has been implicated in triggering excessive inflammation, characterized by NF-κB-mediated cytokine production, ultimately leading to apoptotic cell death and tissue damage in the lungs, kidneys, and the central nervous system. Additionally, ORF3a triggers the activation of the NLRP3 inflammasome, inciting a cytokine storm, which is a major contributor to the severity of the disease and subsequent mortality. As with the spike protein, ORF3a also undergoes mutations, and certain mutant variants correlate with heightened disease severity in COVID-19. These mutations may influence viral replication and host cellular inflammatory responses. While establishing a direct link between ORF3a and mortality is difficult, its involvement in promoting inflammation and exacerbating disease severity likely contributes to higher mortality rates in severe COVID-19 cases. This review offers a comprehensive and detailed exploration of ORF3a’s potential as an innovative antiviral drug target. Additionally, we outline potential strategies for discovering and developing ORF3a inhibitor drugs to counteract its harmful effects, alleviate tissue damage, and reduce the severity of COVID-19 and its lingering complications.

To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an in silico method to identify candidate drugs for treating COVID-19.

Background: COVID-19 is a critical pandemic that has affected human communities worldwide. Although it is urgent to rapidly develop effective drugs, large number of candidate drug compounds may be useful for treating COVID-19, and evaluation of these drugs is time-consuming and costly. Thus, screening to identify potentially effective drugs prior to experimental validation is necessary. Method: In this study, we applied the recently proposed method tensor decomposition (TD)-based unsupervised feature extraction (FE) to gene expression profiles of multiple lung cancer cell lines infected with severe acute respiratory syndrome coronavirus 2. We identified drug candidate compounds that significantly altered the expression of the 163 genes selected by TD-based unsupervised FE. Results: Numerous drugs were successfully screened, including many known antiviral drug compounds. Conclusions: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19.

Abstract Using as a template the crystal structure of the SARS-CoV-2 main protease, we developed a pharmacophore model of functional centers of the protease inhibitor-binding pocket. With this model, we conducted data mining of the conformational database of FDA-approved drugs. This search brought 64 compounds that can be potential inhibitors of the SARS-CoV-2 protease. The conformations of these compounds undergone 3D fingerprint similarity clusterization. Then we conducted docking of possible conformers of these drugs to the binding pocket of the protease. We also conducted the same docking of random compounds. Free energies of the docking interaction for the selected compounds were clearly lower than random compounds. Three of the selected compounds were carfilzomib, cyclosporine A, and azithromycin—the drugs that already are tested for COVID-19 treatment. Among the selected compounds are two HIV protease inhibitors and two hepatitis C protease inhibitors. We recommend testing of the selected compounds for treatment of COVID-19.

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.

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.

(1) Background: Drug repositioning is an unconventional drug discovery approach to explore new therapeutic benefits of existing drugs. Currently, it emerges as a rapid avenue to alleviate the COVID-19 pandemic disease. (2) Methods: Herein, we tested the antiviral activity of anti-microbial and anti-inflammatory Food and Drug Administration (FDA)-approved drugs, commonly prescribed to relieve respiratory symptoms, against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the viral causative agent of the COVID-19 pandemic. (3) Results: Of these FDA-approved antimicrobial drugs, Azithromycin, Niclosamide, and Nitazoxanide showed a promising ability to hinder the replication of a SARS-CoV-2 isolate, with IC50 of 0.32, 0.16, and 1.29 µM, respectively. We provided evidence that several antihistamine and anti-inflammatory drugs could partially reduce SARS-CoV-2 replication in vitro. Furthermore, this study showed that Azithromycin can selectively impair SARS-CoV-2 replication, but not the Middle East Respiratory Syndrome Coronavirus (MERS-CoV). A virtual screening study illustrated that Azithromycin, Niclosamide, and Nitazoxanide bind to the main protease of SARS-CoV-2 (Protein data bank (PDB) ID: 6lu7) in binding mode similar to the reported co-crystalized ligand. Also, Niclosamide displayed hydrogen bond (HB) interaction with the key peptide moiety GLN: 493A of the spike glycoprotein active site. (4) Conclusions: The results suggest that Piroxicam should be prescribed in combination with Azithromycin for COVID-19 patients.

Background. Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in discovering potential therapeutic agents for this disease. In this regard, we conducted a systematic review through an overview of drug development (in silico, in vitro, and in vivo) for treating COVID-19. Methods. A systematic search was carried out in major databases including PubMed, Web of Science, Scopus, EMBASE, and Google Scholar from December 2019 to March 2021. A combination of the following terms was used: coronavirus, COVID-19, SARS-CoV-2, drug design, drug development, In silico, In vitro, and In vivo. A narrative synthesis was performed as a qualitative method for the data synthesis of each outcome measure. Results. A total of 2168 articles were identified through searching databases. Finally, 315 studies (266 in silico, 34 in vitro, and 15 in vivo) were included. In studies with in silico approach, 98 article study repurposed drug and 91 studies evaluated herbal medicine on COVID-19. Among 260 drugs repurposed by the computational method, the best results were observed with saquinavir (n = 9), ritonavir (n = 8), and lopinavir (n = 6). Main protease (n = 154) following spike glycoprotein (n = 62) and other nonstructural protein of virus (n = 45) was among the most studied targets. Doxycycline, chlorpromazine, azithromycin, heparin, bepridil, and glycyrrhizic acid showed both in silico and in vitro inhibitory effects against SARS-CoV-2. Conclusion. The preclinical studies of novel drug design for COVID-19 focused on main protease and spike glycoprotein as targets for antiviral development. From evaluated structures, saquinavir, ritonavir, eucalyptus, Tinospora cordifolia, aloe, green tea, curcumin, pyrazole, and triazole derivatives in in silico studies and doxycycline, chlorpromazine, and heparin from in vitro and human monoclonal antibodies from in vivo studies showed promised results regarding efficacy. It seems that due to the nature of COVID-19 disease, finding some drugs with multitarget antiviral actions and anti-inflammatory potential is valuable and some herbal medicines have this potential.

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the pathogenic cause of coronavirus disease 2019 (COVID-19). The RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is a potential target for the treatment of COVID-19. An RdRp complex:dsRNA structure suitable for docking simulations was prepared using a cryo-electron microscopy (cryo-EM) structure (PDB ID: 7AAP; resolution, 2.60 Å) that was reported recently. Structural refinement was performed using energy calculations. Structure-based virtual screening was performed using the ChEMBL database. Through 1,838,257 screenings, 249 drugs (37 approved, 93 clinical, and 119 preclinical drugs) were predicted to exhibit a high binding affinity for the RdRp complex:dsRNA. Nine nucleoside triphosphate analogs with anti-viral activity were included among these hit drugs, and among them, remdesivir-ribonucleoside triphosphate and favipiravir-ribonucleoside triphosphate adopted a similar docking mode as that observed in the cryo-EM structure. Additional docking simulations for the predicted compounds with high binding affinity for the RdRp complex:dsRNA suggested that 184 bioactive compounds could be anti-SARS-CoV-2 drug candidates. The hit bioactive compounds mainly consisted of a typical noncovalent major groove binder for dsRNA. Three-layer ONIOM (MP2/6-31G:AM1:AMBER) geometry optimization calculations and frequency analyses (MP2/6-31G:AMBER) were performed to estimate the binding free energy of a representative bioactive compound obtained from the docking simulation, and the fragment molecular orbital calculation at the MP2/6-31G level of theory was subsequently performed for analyzing the detailed interactions. The procedure used in this study represents a possible strategy for discovering anti-SARS-CoV-2 drugs from drug libraries that could significantly shorten the clinical development period for drug repositioning.

Background: COVID-19, first reported in China, from the new strain of severe acute respiratory syndrome coronaviruses (SARS-CoV-2), poses a great threat to the world by claiming uncountable lives. SARS-CoV-2 is a highly infectious virus that has been spreading rapidly throughout the world. In the absence of any specific medicine to cure COVID-19, there is an urgent need to develop novel therapeutics, including drug repositioning along with diagnostics and vaccines to combat the COVID-19. Many antivirals, antimalarials, antiparasitic, antibacterials, immunosuppressive anti-inflammatory, and immunoregulatory agents are being clinically investigated for the treatment of COVID-19. Objectives: The earlier developed one parameter regression model correlating the dock scores with in vitro anti-SARS-CoV-2 main protease activity well predicted the six drugs viz remdesivir, chloroquine, favipiravir, ribavirin, penciclovir, and nitazoxanide as potential anti-COVID agents. To further validate our earlier model, the biological activity of nine more recently published SARS-CoV-2 main protease inhibitors has been predicted using our previously reported model. Methods: In the present study, this regression model has been used to screen the existing antiviral, antiparasitic, antitubercular, and anti pneumonia chemotherapeutics utilizing dock score analyses to explore the potential including mechanism of action of these compounds in combating SARS-CoV-2 main protease. Results: The high correlation (R=0.91) explaining 82.3% variance between the experimental versus predicted activities for the nine compounds is observed. It proves the robustness of our developed model. Therefore, this robust model has been further improved, taking a total number of 15 compounds to formulate another model with an R-value of 0.887 and the explained variance of 78.6%. These models have been used for high throughput screening (HTS) of the 21 diverse compounds belonging to antiviral, antiparasitic, antitubercular, and anti pneumonia chemotherapeutics as potential repurpose agents to combat SARS-CoV-2 main protease. The models screened that the drugs bedaquiline and lefamulin have higher binding affinities (dock scores of -8.989 and -9.153 Kcal/mol respectively) than the reference compound N-[2-(5-fluoranyl-1~H-indol-3-yl)ethyl]ethanamide (dock score of -7.998 Kcal/Mol), as well as higher predicted activities with pEC50 of 0.783 and 0.937 μM and the 0.611 and 0.724 μM respectively. The clinically used repurposed drugs dexamethasone and cefixime have been predicted with pEC50 values of -0.463 and -0.622 μM and -0.311 and -0.428 μM respectively for optimal inhibition. The drugs such as doxycycline, cefpodoxime, ciprofloxacin, sparfloxacin, moxifloxacin, and TBAJ-876 showed moderate binding affinity corresponding to the moderate predicted activity (-1.540 to -1.109 μM). Conclusion: In the present study, validation of our previously developed dock score-based one..