Indinavir for COVID-19

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.

Deep learning-integrated lab-on-a-chip in designing oral [3.1.0] bi and [4.2.0] tricyclic interceptors inhibiting multiple SARS-CoV-2 protomers.

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.

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 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.

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.

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.

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..

AbstractA consensus virtual screening protocol has been applied to ca. 2000 approved drugs to seek inhibitors of the main protease (Mpro) of SARS-CoV-2, the virus responsible for COVID-19. 42 drugs emerged as top candidates, and after visual analyses of the predicted structures of their complexes with Mpro, 17 were chosen for evaluation in a kinetic assay for Mpro inhibition. Remarkably 14 of the compounds at 100-μM concentration were found to reduce the enzymatic activity and 5 provided IC50 values below 40 μM: manidipine (4.8 μM), boceprevir (5.4 μM), lercanidipine (16.2 μM), bedaquiline (18.7 μM), and efonidipine (38.5 μM). Structural analyses reveal a common cloverleaf pattern for the binding of the active compounds to the P1, P1’, and P2 pockets of Mpro. Further study of the most active compounds in the context of COVID-19 therapy is warranted, while all of the active compounds may provide a foundation for lead optimization to deliver valuable chemotherapeutics to combat the pandemic.

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 novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) responsible for the 2019 coronavirus disease (COVID-19) has caused a global health challenge. The SARS-COV-2 main protease, 3CLpro/Mpro plays a critical role in the viral gene expression and replication and has been a major target for inhibiting viral maturation and enhancing host innate immune responses against COVID-19. In this study, we screened a library of 38 phytochemicals from Nigella sativa (blackseed), Trigonella foenum-graecum (Fenugreek) and Anona muricata (Soursop) potent medicinal plants with reported antiviral properties - in a molecular docking protocol on 3CLpro using Autodock4.0 tool implanted in PyRx followed by docking validation and insilico absorption, distribution, metabolism, excretion, and toxicology (ADMET) evaluations. The docking results were visualized using Accelrys Discovery Studio and Pymol software. Among the 38 ligands screened, 19 showed significant interaction through non-covalent hydrogen bonding, hydrophobic, and electrostatic interactions with binding affinities from -5.3kcal/mol to -8.1kcal/mol indicating significant binding interactions at the active site binding pocket. Another important interaction observed in the study which mostly involve the transfer of charges was pi-interactions such as Pi-Pi interaction, Pi-Alkyl interaction, Pi-Sulfur interaction, Pi- Sigma, and Pi-Pi stacking. The docking results revealed that phytochemicals from T. foenum-graecum showed more 3CLpro inhibitory potential compared to those from N. sativa and A. muricata. Insilico ADMET evaluations for drug-like and lead-like characteristics however demonstrated that only 8 ligands - apigenin, kaempferol, luteolin, dithymoquinone, naringenine, nornuciferine, quercetin and nigellidine were actually drug-like; showed best activities against 3CLpro, and lack hepatotoxicity effects while none was lead-like. Insilico results of this study further suggested that drug repurposing candidates, remdesivir, indinavir,hydroxychloroquine, chloroquine and ritonavir,exhibited various interactions with 3CLpro. Hence, further in vitro and in vivo studies are proposed.

Background: The emergence of Coronavirus disease (COVID-19) has been declared a pandemic and made a medical emergency worldwide. Various attempts have been made, including optimizing effective treatments against the disease or developing a vaccine. Since the SARS-CoV-2 protease crystal structure has been discovered, searching for its inhibitors by in silico technique becomes possible. Objective: This study aims to virtually screen the potential of phytoconstituents from the Begonia genus as 3Cl pro-SARS-CoV- 2 inhibitors, based on its crucial role in viral replication, hence making these proteases “promising” for the anti-SARS-CoV-2 target. Methods: In silico screening was carried out by molecular docking on the web-based program DockThor and validated by a retrospective method. Predictive binding affinity (Dock Score) was used for scoring the compounds. Further molecular dynamics on Desmond was performed to assess the complex stability. Results: Virtual screening protocol was valid with the area under curve value 0.913. Molecular docking revealed only β-sitosterol-3-O-β-D-glucopyranoside with a lower docking score of - 9.712 kcal/mol than positive control of indinavir. The molecular dynamic study showed that the compound was stable for the first 30 ns simulations time with Root Mean Square Deviation <3 Å, despite minor fluctuations observed at the end of simulation times. Root Mean Square Fluctuation of catalytic sites HIS41 and CYS145 was 0.756 Å and 0.773 Å, respectively. Conclusions: This result suggests that β-sitosterol-3-O-β-D- glucopyranoside might be a prospective metabolite compound that can be developed as anti-SARS-CoV-2.

Drug repurposing (or repositioning) is the process of finding new therapeutic uses for drugs already approved by drug regulatory authorities (e.g., the Food and Drug Administration (FDA) and Therapeutic Goods Administration (TGA)) for other diseases. This involves analyzing the interactions between different biological entities, such as drug targets (genes/proteins and biological pathways) and drug properties, to discover novel drug-target or drug-disease relations. Artificial intelligence methods such as machine learning and deep learning have successfully analyzed complex heterogeneous data in the biomedical domain and have also been used for drug repurposing. This study presents a novel unsupervised machine learning framework that utilizes a graph-based autoencoder for multi-feature type clustering on heterogeneous drug data. The dataset consists of 438 drugs, of which 224 are under clinical trials for COVID-19 (category A). The rest are systematically filtered to ensure the safety and efficacy of the treatment (category B). The framework solely relies on reported drug data, including its pharmacological properties, chemical/physical properties, interaction with the host, and efficacy in different publicly available COVID-19 assays. Our machine-learning framework reveals three clusters of interest and provides recommendations featuring the top 15 drugs for COVID-19 drug repurposing, which were shortlisted based on the predicted clusters that were dominated by category A drugs. The anti-COVID efficacy of the drugs should be verified by experimental studies. Our framework can be extended to support other datasets and drug repurposing studies, given open-source code and data availability.

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.

AbstractThe search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns “ensemble embeddings” of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing.