Lopinavir for COVID-19

Abstract The global efforts in the past year have led to the discovery of nearly 200 drug repurposing candidates for COVID-19. Gaining more insights into their mechanisms of action could facilitate a better understanding of infection and the development of therapeutics. Leveraging large-scale drug-induced gene expression profiles, we found 36% of the active compounds regulate genes related to cholesterol homeostasis and microtubule cytoskeleton organization. Following bioinformatics analyses revealed that the expression of these genes is associated with COVID-19 patient severity and has predictive power on anti-SARS-CoV-2 efficacy in vitro. Monensin, a top new compound that regulates these genes, was further confirmed as an inhibitor of SARS-CoV-2 replication in Vero-E6 cells. Interestingly, drugs co-targeting cholesterol homeostasis and microtubule cytoskeleton organization processes more likely present a synergistic effect with antivirals. Therefore, potential therapeutics could be centered around combinations of targeting these processes and viral proteins.

Amidst the ongoing global challenge of the SARS-CoV-2 pandemic, the quest for effective antiviral medications remains paramount. This comprehensive review delves into the dynamic landscape of FDA-approved medications repurposed for COVID-19, categorized as antiviral and non-antiviral agents. Our focus extends beyond conventional narratives, encompassing vaccination targets, repurposing efficacy, clinical studies, innovative treatment modalities, and future outlooks. Unveiling the genomic intricacies of SARS-CoV-2 variants, including the WHO-designated Omicron variant, we explore diverse antiviral categories such as Fusion inhibitors, Protease inhibitors, Transcription inhibitors, Neuraminidase inhibitors, Nucleoside reverse transcriptase, and non-antiviral interventions like Importin α/β1-mediated nuclear import inhibitors, Neutralizing antibodies and convalescent plasma. Notably, Molnupiravir emerges as a pivotal player, now licensed in the UK. This review offers a fresh perspective on the historical evolution of COVID-19 therapeutics, from repurposing endeavors to the latest developments in oral anti-SARS-CoV-2 treatments, ushering in a new era of hope in the battle against the pandemic.

Respiratory viral infections (VRTIs) rank among the leading causes of global morbidity and mortality, affecting millions of individuals each year across all age groups. These infections are caused by various pathogens, including rhinoviruses (RVs), adenoviruses (AdVs), and coronaviruses (CoVs), which are particularly prevalent during colder seasons. Although many VRTIs are self-limiting, their frequent recurrence and potential for severe health complications highlight the critical need for effective therapeutic strategies. Viral proteases are crucial for the maturation and replication of viruses, making them promising therapeutic targets. This review explores the pivotal role of viral proteases in the lifecycle of respiratory viruses and the development of protease inhibitors as a strategic response to these infections. Recent advances in antiviral therapy have highlighted the effectiveness of protease inhibitors in curtailing the spread and severity of viral diseases, especially during the ongoing COVID-19 pandemic. It also assesses the current efforts aimed at identifying and developing inhibitors targeting key proteases from major respiratory viruses, including human RVs, AdVs, and (severe acute respiratory syndrome coronavirus-2) SARS-CoV-2. Despite the recent identification of SARS-CoV-2, within the last five years, the scientific community has devoted considerable time and resources to investigate existing drugs and develop new inhibitors targeting the virus’s main protease. However, research efforts in identifying inhibitors of the proteases of RVs and AdVs are limited. Therefore, herein, it is proposed to utilize this knowledge to develop new inhibitors for the proteases of other viruses affecting the respiratory tract or to develop dual inhibitors. Finally, by detailing the mechanisms of action and therapeutic potentials of these inhibitors, this review aims to demonstrate their significant role in transforming the management of respiratory viral diseases and to offer insights into future research directions.

The COVID-19 pandemic has posed a significant threat to society in recent times, endangering human health, life, and economic well-being. The disease spreads quickly due to the highly infectious SARS-CoV-2 virus, which has undergone numerous mutations. Despite intense research efforts by the scientific community since its emergence in 2019, no effective therapeutics have been discovered yet. While some repurposed drugs have been used to control the global outbreak and save lives, none have proven universally effective, particularly for severely infected patients. Although the spread of the disease is generally under control, anti-SARS-CoV-2 agents are still needed to combat current and future infections. This study reviews some of the most promising repurposed drugs containing indolyl heterocycle, which is an essential scaffold of many alkaloids with diverse bio-properties in various biological fields. The study also discusses natural and synthetic indole-containing compounds with anti-SARS-CoV-2 properties, as well as computer-aided drug design (in-silico studies) for optimizing anti-SARS-CoV-2 hits/leads.

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.

The emergence of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected many countries throughout the world. As urgency is a necessity, most efforts have focused on identifying small molecule drugs that can be repurposed for use as anti-SARS-CoV-2 agents. Although several drug candidates have been identified using in silico method and in vitro studies, most of these drugs require the support of in vivo data before they can be considered for clinical trials. Several drugs are considered promising therapeutic agents for COVID-19. In addition to the direct-acting antiviral drugs, supportive therapies including traditional Chinese medicine, immunotherapies, immunomodulators, and nutritional therapy could contribute a major role in treating COVID-19 patients. Some of these drugs have already been included in the treatment guidelines, recommendations, and standard operating procedures. In this article, we comprehensively review the approved and potential therapeutic drugs, immune cells-based therapies, immunomodulatory agents/drugs, herbs and plant metabolites, nutritional and dietary for COVID-19.

AbstractCOVID-19 is an emerging infectious disease and was recently declared as a pandemic by WHO. Currently, there is no vaccine or therapeutic available for this disease. Drug repositioning represents the only feasible option to address this global challenge and a panel of 48 FDA-approved drugs that have been pre-selected by an assay of SARS-CoV was screened to identify potential antiviral drug candidates against SARS-CoV-2 infection. We found a total of 24 drugs which exhibited antiviral efficacy (0.1 μM < IC50 < 10 μM) against SARS-CoV-2. In particular, two FDA-approved drugs - niclosamide and ciclesonide – were notable in some respects. These drugs will be tested in an appropriate animal model for their antiviral activities. In near future, these already FDA-approved drugs could be further developed following clinical trials in order to provide additional therapeutic options for patients with COVID-19.

Abstract To identify possible candidates for progression towards clinical studies against SARS-CoV-2, we screened a well-defined collection of 5632 compounds including 3488 compounds which have undergone clinical investigations (marketed drugs, phases 1 -3, and withdrawn) across 600 indications. Compounds were screened for their inhibition of viral induced cytotoxicity using the human epithelial colorectal adenocarcinoma cell line Caco-2 and a SARS-CoV-2 isolate. The primary screen of 5632 compounds gave 271 hits. A total of 64 compounds with IC50 <20 µM were identified, including 19 compounds with IC50 < 1 µM. Of this confirmed hit population, 90% have not yet been previously reported as active against SARS-CoV-2 in-vitro cell assays. Some 37 of the actives are launched drugs, 19 are in phases 1-3 and 10 pre-clinical. Several inhibitors were associated with modulation of host pathways including kinase signaling P53 activation, ubiquitin pathways and PDE activity modulation, with long chain acyl transferases were effective viral inhibitors.

The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.

AbstractIn December 2019, an infectious disease caused by the coronavirus SARS‐CoV‐2 appeared in Wuhan, China. This disease (COVID‐19) spread rapidly worldwide, and on March 2020 was declared a pandemic by the World Health Organization (WHO). Today, over 21 million people have been infected, with more than 750.000 casualties. Today, no vaccine or antiviral drug is available. While the development of a vaccine might take at least a year, and for a novel drug, even longer; finding a new use to an old drug (drug repurposing) could be the most effective strategy. We present a docking‐based screening using a quantum mechanical scoring of a library built from approved drugs and compounds undergoing clinical trials, against three SARS‐CoV‐2 target proteins: the spike or S‐protein, and two proteases, the main protease and the papain‐like protease. The S‐protein binds directly to the Angiotensin Converting Enzyme 2 receptor of the human host cell surface, while the two proteases process viral polyproteins. Following the analysis of our structure‐based compound screening, we propose several structurally diverse compounds (either FDA‐approved or in clinical trials) that could display antiviral activity against SARS‐CoV‐2. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against the disease. We hope that these findings may contribute to the rational drug design against 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.

AbstractSARS-CoV-2 is a novel coronavirus responsible for the COVID-19 pandemic, in which acute respiratory infections are associated with high socio-economic burden. We applied high-content screening to a well-defined collection of 5632 compounds including 3488 that have undergone previous clinical investigations across 600 indications. The compounds were screened by microscopy for their ability to inhibit SARS-CoV-2 cytopathicity in the human epithelial colorectal adenocarcinoma cell line, Caco-2. The primary screen identified 258 hits that inhibited cytopathicity by more than 75%, most of which were not previously known to be active against SARS-CoV-2 in vitro. These compounds were tested in an eight-point dose response screen using the same image-based cytopathicity readout. For the 67 most active molecules, cytotoxicity data were generated to confirm activity against SARS-CoV-2. We verified the ability of known inhibitors camostat, nafamostat, lopinavir, mefloquine, papaverine and cetylpyridinium to reduce the cytopathic effects of SARS-CoV-2, providing confidence in the validity of the assay. The high-content screening data are suitable for reanalysis across numerous drug classes and indications and may yield additional insights into SARS-CoV-2 mechanisms and potential therapeutic strategies.

Drug repositioning is the only feasible option to immediately address the COVID-19 global challenge. We screened a panel of 48 FDA-approved drugs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which were preselected by an assay of SARS-CoV. We identified 24 potential antiviral drug candidates against SARS-CoV-2 infection. Some drug candidates showed very low 50% inhibitory concentrations (IC 50 s), and in particular, two FDA-approved drugs—niclosamide and ciclesonide—were notable in some respects.

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.

Coronavirus has left severe health impacts on the human population, globally. Still a significant number of cases are reported daily as no specific medications are available for its effective treatment. The presence of the CD147 receptor (human basigin) on the host cell facilitates the severe acute respiratory disease coronavirus 2 (SARS-CoV-2) infection. Therefore, the drugs that efficiently alter the formation of CD147 and spike protein complex could be the right drug candidate to inhibit the replication of SARS-CoV-2. Hence, an e-Pharmacophore model was developed based on the receptor-ligand cavity of CD147 protein which was further mapped against pre-existing drugs of coronavirus disease treatment. A total of seven drugs were found to be suited as pharmacophores out of 11 drugs screened which was further docked with CD147 protein using CDOCKER of Biovia discovery studio. The active site sphere of the prepared protein was 101.44, 87.84, and 97.17 along with the radius being 15.33 and the root-mean-square deviation value obtained was 0.73 Å. The protein minimization energy was calculated to be –30,328.81547 kcal/mol. The docking results showed ritonavir as the best fit as it demonstrated a higher CDOCKER energy (–57.30) with correspond to CDOCKER interaction energy (–53.38). However, authors further suggest in vitro studies to understand the potential activity of the ritonavir.

The coronavirus disease 2019 (COVID-19) is a severe worldwide pandemic. Due to the emergence of various SARS-CoV-2 variants and the presence of only one Food and Drug Administration (FDA) approved anti-COVID-19 drug (remdesivir), the disease remains a mindboggling global public health problem. Developing anti-COVID-19 drug candidates that are effective against SARS-CoV-2 and its various variants is a pressing need that should be satisfied. This systematic review assesses the existing literature that used in silico models during the discovery procedure of anti-COVID-19 drugs. Cochrane Library, Science Direct, Google Scholar, and PubMed were used to conduct a literature search to find the relevant articles utilizing the search terms “In silico model,” “COVID-19,” “Anti-COVID-19 drug,” “Drug discovery,” “Computational drug designing,” and “Computer-aided drug design.” Studies published in English between 2019 and December 2022 were included in the systematic review. From the 1120 articles retrieved from the databases and reference lists, only 33 were included in the review after the removal of duplicates, screening, and eligibility assessment. Most of the articles are studies that use SARS-CoV-2 proteins as drug targets. Both ligand-based and structure-based methods were utilized to obtain lead anti-COVID-19 drug candidates. Sixteen articles also assessed absorption, distribution, metabolism, excretion, toxicity (ADMET), and drug-likeness properties. Confirmation of the inhibitory ability of the candidate leads by in vivo or in vitro assays was reported in only five articles. Virtual screening, molecular docking (MD), and molecular dynamics simulation (MDS) emerged as the most commonly utilized in silico models for anti-COVID-19 drug discovery.

Abstract Motivation The coronavirus disease 2019 (COVID-19) remains a global public health emergency. Although people, especially those with underlying health conditions, could benefit from several approved COVID-19 therapeutics, the development of effective antiviral COVID-19 drugs is still a very urgent problem. Accurate and robust drug response prediction to a new chemical compound is critical for discovering safe and effective COVID-19 therapeutics. Results In this study, we propose DeepCoVDR, a novel COVID-19 drug response prediction method based on deep transfer learning with graph transformer and cross-attention. First, we adopt a graph transformer and feed-forward neural network to mine the drug and cell line information. Then, we use a cross-attention module that calculates the interaction between the drug and cell line. After that, DeepCoVDR combines drug and cell line representation and their interaction features to predict drug response. To solve the problem of SARS-CoV-2 data scarcity, we apply transfer learning and use the SARS-CoV-2 dataset to fine-tune the model pretrained on the cancer dataset. The experiments of regression and classification show that DeepCoVDR outperforms baseline methods. We also evaluate DeepCoVDR on the cancer dataset, and the results indicate that our approach has high performance compared with other state-of-the-art methods. Moreover, we use DeepCoVDR to predict COVID-19 drugs from FDA-approved drugs and demonstrate the effectiveness of DeepCoVDR in identifying novel COVID-19 drugs. Availability and implementation https://github.com/Hhhzj-7/DeepCoVDR.

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.

Acute respiratory distress syndrome (ARDS) is intricately linked with SARS-CoV-2-associated disease severity and mortality, especially in patients with co-morbidities. Lung tissue injury caused as a consequence of ARDS leads to fluid build-up in the alveolar sacs, which in turn affects oxygen supply from the capillaries. ARDS is a result of a hyperinflammatory, non-specific local immune response (cytokine storm), which is aggravated as the virus evades and meddles with protective anti-viral innate immune responses. Treatment and management of ARDS remain a major challenge, first, because the condition develops as the virus keeps replicating and, therefore, immunomodulatory drugs are required to be used with caution. Second, the hyperinflammatory responses observed during ARDS are quite heterogeneous and dependent on the stage of the disease and the clinical history of the patients. In this review, we present different anti-rheumatic drugs, natural compounds, monoclonal antibodies, and RNA therapeutics and discuss their application in the management of ARDS. We also discuss on the suitability of each of these drug classes at different stages of the disease. In the last section, we discuss the potential applications of advanced computational approaches in identifying reliable drug targets and in screening out credible lead compounds against ARDS.

Aims: This paper aimed to investigate the antiviral drugs against Sars-Cov-2 main protease (MPro) using in silico methods.
 Material and Method: A search was made for antiviral drugs in the PubChem database and antiviral drugs such as Bictegravir, Emtricitabine, Entecavir, Lamivudine, Tenofovir, Favipiravir, Hydroxychloroquine, Lopinavir, Oseltamavir, Remdevisir, Ribavirin, Ritonavir were included in our study. The protein structure of Sars-Cov-2 Mpro (PDB ID: 6LU7) was taken from the Protein Data Bank (www.rcsb. Org) system and included in our study. Molecular docking was performed using AutoDock/Vina, a computational docking program. Protein-ligand interactions were performed with the AutoDock Vina program. 3D visualizations were made with the Discovery Studio 2020 program. N3 inhibitor method was used for our validation.
 Results: In the present study, bictegravir, remdevisir and lopinavir compounds in the Sars-Cov-2 Mpro structure showed higher binding affinity compared to the antiviral compounds N3 inhibitor, according to our molecular insertion results. However, the favipiravir, emtricitabine and lamuvidune compounds were detected very low binding affinity. Other antiviral compounds were found close binding affinity with the N3 inhibitor.
 Conclusion: Bictegravir, remdevisir and lopinavir drugs showed very good results compared to the N3 inhibitor. Therefore, they could be inhibitory in the Sars Cov-2 Mpro target.
 Keywords: Sars-CoV-2 Main Protease; Antiviral Drugs; Molecular Docking

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.

COVID-19, a new strain of coronavirus (CoV), was identified in Wuhan, China, in 2019. No specific therapies are available and investigations regarding COVID-19 treatment are lacking. Liu et al. (2020) successfully crystallised the COVID-19 main protease (Mpro), which is a potential drug target. The present study aimed to assess bioactive compounds found in medicinal plants as potential COVID-19 Mpro inhibitors, using a molecular docking study. Molecular docking was performed using Autodock 4.2, with the Lamarckian Genetic Algorithm, to analyse the probability of docking. COVID-19 Mpro was docked with several compounds, and docking was analysed by Autodock 4.2, Pymol version 1.7.4.5 Edu, and Biovia Discovery Studio 4.5. Nelfinavir and lopinavir were used as standards for comparison. The binding energies obtained from the docking of 6LU7 with native ligand, nelfinavir, lopinavir, kaempferol, quercetin, luteolin-7-glucoside, demethoxycurcumin, naringenin, apigenin-7-glucoside, oleuropein, curcumin, catechin, epicatechin-gallate, zingerol, gingerol, and allicin were -8.37, -10.72, -9.41, -8.58, -8.47, -8.17, -7.99, -7.89, -7.83, -7.31, -7.05, -7.24, -6.67, -5.40, -5.38, and -4.03 kcal/mol, respectively. Therefore, nelfinavir and lopinavir may represent potential treatment options, and kaempferol, quercetin, luteolin-7-glucoside, demethoxycurcumin, naringenin, apigenin-7-glucoside, oleuropein, curcumin, catechin, and epicatechin-gallate appeared to have the best potential to act as COVID-19 Mpro inhibitors. However, further research is necessary to investigate their potential medicinal use.

Novel Coronavirus (2019-nCOV) causes inflammatory response with worsening symptoms. Classification of potential anti-viral and anti-inflammatory drugs in managing the symptoms of the COVID-19 and reducing morbidity is important. The objective of this study is to identify a group of drugs, best suited for COVID-19 treatment based on recent developments in clinical trials, FDA drug evaluation, directions and developments and from drug therapies globally. Online literature search was done on Medline, PubMed and google scholar databases for studies on various treatments and drug therapies for COVID-19 and relevant studies were identified and the identified drugs are described in detail as per their Pharmacological, pharmaceutical properties of the drugs, mechanism of action, current COVID-19 drug therapy, contraindications and drug-drug interactions Certain drugs can inhibit action against viral infection and protect lungs from severe inflammatory response. This article summarizes several drugs like Hydroxychloroquine, Chloroquine, Remdesivir, Favipiravir, Lopinavir, Ritonavir, Dexamethasone, Ivermectin, Baricitinib, Casirivimab / imdevimab, Bamlanivimab along with auxiliary treatment like convalescent plasma transfusion. Remdesivir is first drug approved by FDA. Hydroxychloroquine, dexamethasone and remdesivir are showing results against COVID-19 but it is important to test the efficacy and safety of such drugs though some drugs have shown remarkable results.

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.

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.

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.

<ns4:p><ns4:bold>Background:</ns4:bold> COVID-19 has become a global threat. Since its first outbreak from Wuhan, China in December 2019, the SARS-CoV-2 virus has gone through structural changes arising due to mutations in its surface glycoprotein. These mutations have led to the emergence of different genetic variants threatening public health due to increased transmission and virulence. As new drug development is a long process, repurposing existing antiviral drugs with potential activity against SARS-CoV-2 might be a possible solution to mitigate the current situation.</ns4:p><ns4:p> <ns4:bold>Methods:</ns4:bold> This study focused on utilizing molecular docking to determine the effect of potential drugs on several variants of concern (VOCs). The effect of various drugs such as baricitinib, favipiravir, lopinavir, remdesivir and dexamethasone, which might have the potential to treat SARS-CoV-2 infections as evident from previous studies, was investigated for different VOCs.</ns4:p><ns4:p> <ns4:bold>Results:</ns4:bold> Remdesivir showed promising results for B.1.351 variant (binding energy: -7.3 kcal/mol) with residues Gln319 and Val503 facilitating strong binding. Favipiravir showed favorable results against B.1.1.7 (binding energy: -5.6 kcal/mol), B.1.351 (binding energy: -5.1 kcal/mol) and B.1.617.2 (binding energy: -5 kcal/mol). Molecular dynamics simulation for favipiravir/B.1.1.7 was conducted and showed significant results in agreement with our findings.</ns4:p><ns4:p> <ns4:bold>Conclusions:</ns4:bold> From structural modeling and molecular docking experiments, it is evident that mutations outside the receptor binding domain of surface glycoprotein do not have a sharp impact on drug binding affinity. Thus, the potential use of these drugs should be explored further for their antiviral effect against SARS-CoV-2 VOCs.</ns4:p>

Despite the extensive vaccination campaigns in many countries, COVID-19 is still a major worldwide health problem because of its associated morbidity and mortality. Therefore, finding efficient treatments as fast as possible is a pressing need. Drug repurposing constitutes a convenient alternative when the need for new drugs in an unexpected medical scenario is urgent, as is the case with COVID-19. Using data from a central registry of electronic health records (the Andalusian Population Health Database, BPS), the effect of prior consumption of drugs for other indications previous to the hospitalization with respect to patient survival was studied on a retrospective cohort of 15,968 individuals, comprising all COVID-19 patients hospitalized in Andalusia between January and November 2020. Covariate-adjusted hazard ratios and analysis of lymphocyte progression curves support a significant association between consumption of 21 different drugs and better patient survival. Contrarily, one drug, furosemide, displayed a significant increase in patient mortality.