Darunavir for COVID-19

The new strain of Coronaviruses (SARS-CoV-2), and the resulting Covid-19 disease has spread swiftly across the globe after its initial detection in late December 2019 in Wuhan, China, resulting in a pandemic status declaration by WHO within 3 months. Given the heavy toll of this pandemic, researchers are actively testing various strategies including new and repurposed drugs as well as vaccines. In the current brief report, we adopted a repositioning approach using insilico molecular modeling screening using FDA approved drugs with established safety profiles for potential inhibitory effects on Covid-19 virus. We started with structure based drug design by screening more than 2000 FDA approved drugsagainst Covid-19 virus main protease enzyme (Mpro) substrate-binding pocket to identify potential hits based on their binding energies, binding modes, interacting amino acids, and therapeutic indications. In addition, we elucidate preliminary pharmacophore features for candidates bound to Covid-19 virus Mpro substratebinding pocket. The top hits include anti-viral drugs such as Darunavir, Nelfinavirand Saquinavir, some of which are already being tested in Covid-19 patients. Interestingly, one of the most promising hits in our screen is the hypercholesterolemia drug Rosuvastatin. These results certainly do not confirm or indicate antiviral activity, but can rather be used as a starting point for further in vitro and in vivo testing, either individually or in combination.

Drug repurposing is a fast way to rapidly discover a drug for clinical use. In such circumstances of the spreading of the highly contagious COVID-19, searching for already known drugs is a worldwide demand. In this study, many drugs were evaluated by molecular docking. Among the test compounds, aliskiren (the best), dipyridamole, mopidamol and rosuvastatin showed higher energies of binding than that of the co-crystallized ligand N3 with COVID-19 main protease Mpro. Rolitetracycline showed the best binding with the catalytic center of the protease enzyme through binding with CYS 145 and HIS 41. Metamizole showed about 86 % of the binding energy of the ligand N3 while the protease inhibitor darunavir showed little bit lower binding energy than N3. These results are promising for using these drugs in the treatment and management of the spreading of COVID-19 virus. Also, it could stimulate clinical trials for the use of these drugs by systemic or inhalation route.The results stimulate the evaluation of these drugs as anti COVID-19 especially aliskiren which showed the highest score of binding with the binding site of N3. This will be added to its renin inhibition and advantage of renin inhibition and possibility of the reduced expression of ACE2[12]. Dipyridamole and mopidamol showed a potential to be more Mpro inhibitor than ligand N3 and darunavir. Also, dipyridamole has the property of antiviral activity beside its use to decrease the hypercoagulabilty that happens due to COVID infection in addition to the property of promoting type I interferon (IFN) responses and protect mice from viral pneumonia [30]. Rolitetracycling is an amazing in its binding mode in the active site of the protease pocket it seemed as it is tailored to be buried in that pocket. Mopidamol and rosuvastatin are slightly better than the co-crystallized ligand N3 and darunavir in binding mode which nominate the as COVID-19 protease inhibitors. Hopefully this study will help in the repurposing a drug for the treatment of COVID-19.

ABSTRACTBackgroundThe outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2.Methods and findingsWe perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naïve Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we suggest that the antiretroviral drug Atazanavir (DrugBank ID – DB01072) would probably be one of the most effective drugs based on the selected criterions.ConclusionsOur study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.Author summaryWhy was this study done?The recent outbreak of novel coronavirus disease (COVID-19) is now considered to be a pandemic threat to the global population. The new coronavirus, 2019-nCoV has now affected more than 200 countries with over 17,83,941 cases confirmed and 1,09,312 deaths reported all over the world [as on 12 April 2020].There is an urgent need for the development of drugs or vaccine which can save people worldwide. However, the vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease. Recently, Artificial Intelligence (AI) technology have been utilized to revolutionize the drug development process. Can we use AI based repurposing of existing drugs for accelerated clinical trial in the treatment of COVID-19?What did the researchers do and find?Here, we report the Machine Learning (ML) model based on the Naïve Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the..

The present coronavirus disease 2019 (COVID-19) pandemic scenario has posed a difficulty for cancer treatment. Even under ideal conditions, malignancies like small cell lung cancer (SCLC) are challenging to treat because of their fast development and early metastases. The treatment of these patients must not be jeopardized, and they must be protected as much as possible from the continuous spread of the COVID-19 infection. Initially identified in December 2019 in Wuhan, China, the contagious coronavirus illness 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Finding inhibitors against the druggable targets of SARS-CoV-2 has been a significant focus of research efforts across the globe. The primary motivation for using molecular modeling tools against SARS-CoV-2 was to identify candidates for use as therapeutic targets from a pharmacological database. In the published study, scientists used a combination of medication repurposing and virtual drug screening methodologies to target many structures of SARS-CoV-2. This virus plays an essential part in the maturation and replication of other viruses. In addition, the total binding free energy and molecular dynamics (MD) modeling findings showed that the dynamics of various medications and substances were stable; some of them have been tested experimentally against SARS-CoV-2. Different virtual screening (VS) methods have been discussed as potential means by which the evaluated medications that show strong binding to the active site might be repurposed for use against SARS-CoV-2.

The COVID-19 pandemic has spurred intense research efforts to identify effective treatments for SARS-CoV-2. In silico studies have emerged as a powerful tool in the drug discovery process, particularly in the search for drug candidates that interact with various SARS-CoV-2 receptors. These studies involve the use of computer simulations and computational algorithms to predict the potential interaction of drug candidates with target receptors. The primary receptors targeted by drug candidates include the RNA polymerase, main protease, spike protein, ACE2 receptor, and transmembrane protease serine 2 (TMPRSS2). In silico studies have identified several promising drug candidates, including Remdesivir, Favipiravir, Ribavirin, Ivermectin, Lopinavir/Ritonavir, and Camostat Mesylate, among others. The use of in silico studies offers several advantages, including the ability to screen a large number of drug candidates in a relatively short amount of time, thereby reducing the time and cost involved in traditional drug discovery methods. Additionally, in silico studies allow for the prediction of the binding affinity of the drug candidates to target receptors, providing insight into their potential efficacy. This study is aimed at assessing the useful contributions of the application of computational instruments in the discovery of receptors targeted in SARS-CoV-2. It further highlights some identified advantages and limitations of these studies, thereby revealing some complementary experimental validation to ensure the efficacy and safety of identified drug candidates.

Background The global pandemics of severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 have caused unprecedented crises for public health. Coronaviruses are constantly evolving, and it is unknown which new coronavirus will emerge and when the next coronavirus will sweep across the world. Knowledge graphs are expected to help discover the pathogenicity and transmission mechanism of viruses. Objective The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph–based approach. Methods We propose a computational and evidence-based knowledge discovery approach to identify potential targets and candidate drugs for coronaviruses from biomedical literature and well-known knowledge bases. To organize the semantic triples extracted automatically from biomedical literature, a semantic conversion model was designed. The literature knowledge was associated and integrated with existing drug and gene knowledge through semantic mapping, and the coronavirus knowledge graph (CovKG) was constructed. We adopted both the knowledge graph embedding model and the semantic reasoning mechanism to discover unrecorded mechanisms of drug action as well as potential targets and drug candidates. Furthermore, we have provided evidence-based support with a scoring and backtracking mechanism. Results The constructed CovKG contains 17,369,620 triples, of which 641,195 were extracted from biomedical literature, covering 13,065 concept unique identifiers, 209 semantic types, and 97 semantic relations of the Unified Medical Language System. Through multi-source knowledge integration, 475 drugs and 262 targets were mapped to existing knowledge, and 41 new drug mechanisms of action were found by semantic reasoning, which were not recorded in the existing knowledge base. Among the knowledge graph embedding models, TransR outperformed others (mean reciprocal rank=0.2510, Hits@10=0.3505). A total of 33 potential targets and 18 drug candidates were identified for coronaviruses. Among them, 7 novel drugs (ie, quinine, nelfinavir, ivermectin, asunaprevir, tylophorine, Artemisia annua extract, and resveratrol) and 3 highly ranked targets (ie, angiotensin converting enzyme 2, transmembrane serine protease 2, and M protein) were further discussed. Conclusions We showed the effectiveness of a knowledge graph–based approach in potential target discovery and drug repurposing for coronaviruses. Our approach can be extended to other viruses or diseases for biomedical knowledge discovery and relevant applications.

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.

COVID-19 pandemic has spurred intense research efforts to identify effective treatments for SARS-CoV-2. In silico studies have emerged as a powerful tool in the drug discovery process, particularly in the search for drug candidates that interact with various SARS-CoV-2 receptors. These studies involve the use of computer simulations and computational algorithms to predict the potential interaction of drug candidates with target receptors. The primary receptors targeted by drug candidates include the RNA polymerase, main protease, spike protein, ACE2 receptor, TMPRSS2, and AP2-associated protein kinase 1. In silico studies have identified several promising drug candidates, including Remdesivir, Favipiravir, Ribavirin, Ivermectin, Lopinavir/Ritonavir, and Camostat mesylate, among others. The use of in silico studies offers several advantages, including the ability to screen a large number of drug candidates in a relatively short amount of time, thereby reducing the time and cost involved in traditional drug discovery methods. Additionally, in silico studies allow for the prediction of the binding affinity of drug candidates to target receptors, providing insight into their potential efficacy. However, it is crucial to consider both the advantages and limitations of these studies and to complement them with experimental validation to ensure the efficacy and safety of identified drug candidates.

The rapid spread of the new coronavirus disease (COVID-19) caused by SARS-CoV-2 has become a global pandemic. Although specific vaccines are available and natural drugs are being researched, supportive care and specific treatments to alleviate symptoms and improve patient quality of life remain critical. Chinese medicine (CM) has been employed in China due to the similarities between the epidemiology, genomics, and pathogenesis of SARS-CoV-2 and SARS-CoV. Moreover, the integration of other traditional oriental medical systems into the broader framework of integrative medicine can offer a powerful approach to managing the disease. Additionally, it has been reported that integrated medicine has better effects and does not increase adverse drug reactions in the context of COVID-19. This article examines preventive measures, potential infection mechanisms, and immune responses in Western medicine (WM), as well as the pathophysiology based on principles of complementary medicine (CM). The convergence between WM and CM approaches, such as the importance of maintaining a strong immune system and promoting preventive care measures, is also addressed. Current treatment options, traditional therapies, and classical prescriptions based on empirical knowledge are also explored, with individual patient circumstances taken into account. An analysis of the potential benefits and challenges associated with the integration of complementary and Western medicine (WM) in the treatment of COVID-19 can provide valuable guidance, enrichment, and empowerment for future research endeavors.

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.

This work is a bibliographic review. The search for the necessary information was carried out in the months of November 2022 and January 2023. The databases used were as follows: Pubmed, Academic Google, Scielo, Scopus, and Cochrane library. Results: In total, 101 articles were selected after a review of 486 articles from databases and after applying the inclusion and exclusion criteria. The update on the molecular mechanism of human coronavirus (HCoV) infection was reviewed, describing possible therapeutic targets in the viral response phase. There are different strategies to prevent or hinder the introduction of the viral particle, as well as the replicative mechanism ((protease inhibitors and RNA-dependent RNA polymerase (RdRp)). The second phase of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) involves the activation of hyperinflammatory cascades of the host’s immune system. It is concluded that there are potential therapeutic targets and drugs under study in different proinflammatory pathways such as hydroxychloroquine, JAK inhibitors, interleukin 1 and 6 inhibitors, and interferons.

Abstract: COVID-19, an extremely transmissible and pathogenic viral disease, triggered a global pandemic that claimed lives worldwide. To date, there is no clear and fully effective treatment for COVID-19 disease. Nevertheless, the urgency to discover treatments that can turn the tide has led to the development of a variety of preclinical drugs that are potential candidates for probative results. Although most of these supplementary drugs are constantly being tested in clinical trials against COVID-19, recognized organizations have aimed to outline the prospects in which their use could be considered. A narrative assessment of current articles on COVID-19 disease and its therapeutic regulation was performed. This review outlines the use of various potential treatments against SARS CoV-2, categorized as fusion inhibitors, protease inhibitors, and RNA-dependent RNA polymerase inhibitors, which include antiviral drugs such as Umifenovir, Baricitinib, Camostatmesylate, Nafamostatmesylate, Kaletra, Paxlovide, Darunavir, Atazanavir, Remdesivir, Molnupiravir, Favipiravir, and Ribavirin. To understand the virology of SARS-CoV-2, potential therapeutic approaches for the treatment of COVID-19 disease, synthetic methods of potent drug candidates, and their mechanisms of action have been addressed in this review. It intends to help readers approach the accessible statistics on the helpful treatment strategies for COVID-19 disease and to serve as a valuable resource for future research in this area.

Abstract Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.

Abstract The COVID-19 pandemic poses a fundamental challenge to global health. Since the outbreak of SARS-CoV-2, great efforts have been made to identify antiviral strategies and develop therapeutic drugs to combat the disease. There are different strategies for developing small molecular anti-SARS-CoV-2 drugs, including targeting coronavirus structural proteins (e.g. spike protein), non-structural proteins (nsp) (e.g. RdRp, Mpro, PLpro, helicase, nsp14, and nsp16), host proteases (e.g. TMPRSS2, cathepsin, and furin) and the pivotal proteins mediating endocytosis (e.g. PIKfyve), as well as developing endosome acidification agents and immune response modulators. Favipiravir and chloroquine are the anti-SARS-CoV-2 agents that were identified earlier in this epidemic and repurposed for COVID-19 clinical therapy based on these strategies. However, their efficacies are controversial. Currently, three small molecular anti-SARS-CoV-2 agents, remdesivir, molnupiravir, and Paxlovid (PF-07321332 plus ritonavir), have been granted emergency use authorization or approved for COVID-19 therapy in many countries due to their significant curative effects in phase III trials. Meanwhile, a large number of promising anti-SARS-CoV-2 drug candidates have entered clinical evaluation. The development of these drugs brings hope for us to finally conquer COVID-19. In this account, we conducted a comprehensive review of the recent advances in small molecule anti-SARS-CoV-2 agents according to the target classification. Here we present all the approved drugs and most of the important drug candidates for each target, and discuss the challenges and perspectives for the future research and development of anti-SARS-CoV-2 drugs.

Aims: To predict potential drugs for COVID-19 by using molecular docking for virtual screening of drugs approved for other clinical applications. Background: SARS-CoV-2 is the betacoronavirus responsible for the COVID-19 pandemic. It was listed as a potential global health threat by the WHO due to high mortality, high basic reproduction number, and lack of clinically approved drugs and vaccines. The genome of the virus responsible for COVID-19 has been sequenced. In addition, the three-dimensional structure of the main protease has been determined experimentally. Objective: To identify potential drugs that can be repurposed for treatment of COVID-19 by using molecular docking based virtual screening of all approved drugs. Methods: A list of drugs approved for clinical use was obtained from the SuperDRUG2 database. The structure of the target in the apo form, as well as structures of several target-ligand complexes, were obtained from RCSB PDB. The structure of SARS-CoV-2 Mpro determined from X-ray diffraction data was used as the target. Data regarding drugs in clinical trials for COVID-19 was obtained from clinicaltrials.org. Input for molecular docking based virtual screening was prepared by using Obabel and customized python, bash, and awk scripts. Molecular docking calculations were carried out with Vina and SMINA, and the docked conformations were analyzed and visualized with PLIP, Pymol, and Rasmol. Results: Among the drugs that are being tested in clinical trials for COVID-19, Danoprevir and Darunavir were predicted to have the highest binding affinity for the Main protease (Mpro) target of SARS-CoV-2. Saquinavir and Beclabuvir were identified as the best novel candidates for COVID-19 therapy by using Virtual Screening of drugs approved for other clinical indications. Conclusion: Protease inhibitors approved for treatment of other viral diseases have the potential to be repurposed for treatment of COVID-19.