Lessons from the COVID-19 pandemic for advancing computational drug repurposing strategies

Repurposing FDA-approved drugs as NLRP3 inhibitors against inflammatory diseases: machine learning and molecular simulation approaches

Activation of NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) has been associated with multiple chronic pathologies, including diabetes, atherosclerosis, and rheumatoid arthritis. Moreover, histone deacetylases (HDACs), specifically HDAC6 is required for the NLRP3 inflammasome to assemble and activate. Thus, NLRP3 serves as an attractive target for the development of novel therapeutic approaches. Several companies are now attempting to develop specific modulators of the NLRP3 inflammasome, but only a handful of small molecules of NLRP3 inflammasome inhibitors, such as MCC950 and Tranilast, are currently available for clinical use. However, their use is limited due to severe side effects and short half-lives. Thus, the repurposing of FDA-approved drugs with NLRP3 inhibitory activity is needed. The present study was aimed at repurposing preexisting drugs that might act as safe and effective NLRP3 inhibitors. A library of 2,697 FDA-approved drugs was screened for binding with NLRP3 (PDB: 7ALV) using Glide (Schrödinger). The top seven FDA-approved drugs with potential binding affinities were selected based on docking scores and subjected to ADMET profiling using pkCSM and SwissADME. The binding of the ADMET-favorable FDA-approved drugs to NLRP3 was validated using MMGBSA (Prime) and Molecular Dynamics (Desmond) in the Schrödinger suite. ADMET profiling revealed that of the seven best docking drugs, empagliflozin and citicoline had good drug-likeness properties. Moreover, MMGBSA analysis and molecular dynamics demonstrated that empagliflozin and citicoline exhibited stable ligand-NLRP3 interactions in the presence of solvents. This study sheds light on the ability of various FDA-approved drugs to act as NLRP3 inhibitors.

Applications of Artificial Intelligence in Drug Repurposing

Drug repurposing identifies new therapeutic uses for the existing drugs originally developed for different indications, aiming at capitalizing on the established safety and efficacy profiles of known drugs. Thus, it is beneficial to bypass of early stages of drug development, and to reduction of the time and cost associated with bringing new therapies to market. Traditional experimental methods are often time-consuming and expensive, making artificial intelligence (AI) a promising alternative due to its lower cost, computational advantages, and ability to uncover hidden patterns. This review focuses on the availability of AI algorithms in drug development, and their positive and specific roles in revealing repurposing of the existing drugs, especially being integrated with virtual screening. It is shown that the existing AI algorithms excel at analyzing large-scale datasets, identifying the complicated patterns of drug responses from these datasets, and making predictions for potential drug repurposing. Building on these insights, challenges remain in developing efficient AI algorithms and future research, including integrating drug-related data across databases for better repurposing, enhancing AI computational efficiency, and advancing personalized medicine.

A new paradigm for drug discovery in the treatment of complex diseases: drug discovery and optimization

In the past, the drug research and development has predominantly followed a "single target, single disease" model. However, clinical data show that single-target drugs are difficult to interfere with the complete disease network, are prone to develop drug resistance and low safety in clinical use. The proposal of multi-target drug therapy (also known as "cocktail therapy") provides a new approach for drug discovery, which can affect the disease and reduce adverse reactions by regulating multiple targets. Natural products are an important source for multi-target innovative drug development, and more than half of approved small molecule drugs are related to natural products. However, there are many challenges in the development process of natural products, such as active drug screening, target identification and preclinical dosage optimization. Therefore, how to develop multi-target drugs with good drug resistance from natural products has always been a challenge. This article summarizes the applications and shortcomings of related technologies such as natural product bioactivity screening, clarify the mode of action of the drug (direct/indirect target), and preclinical dose optimization. Moreover, in response to the challenges faced by natural products in the development process and the trend of interdisciplinary and multi-technology integration, and a multi-target drug development strategy of "active substances - drug action mode - drug optimization" is proposed to solve the key challenges in the development of natural products from multiple dimensions and levels.

Computational drug repurposing: approaches, evaluation of in silico resources and case studies

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

A genetically based computational drug repurposing framework for rapid identification of candidate compounds: application to COVID-19

Background

The development and approval of novel drugs are typically time-intensive and expensive. Leveraging a computational drug repurposing framework that integrates disease-relevant genetically regulated gene expression (GReX) and large longitudinal electronic medical record (EMR) databases can expedite the repositioning of existing medications. However, validating computational predictions of the drug repurposing framework remains a challenge.

Methods

To benchmark the drug repurposing framework, we first performed a 5-method-rank-based computational drug prioritization pipeline by integrating multi-tissue GReX associated with COVID-19-related hospitalization, with drug transcriptional signature libraries from the Library of Integrated Network-Based Cellular Signatures. We prioritized FDA-approved medications from the 10 top-ranked compounds, and assessed their association with COVID-19 incidence within the Veterans Health Administration (VHA) cohort (~9 million individuals). In parallel, we evaluated in vitro SARS-CoV-2 replication inhibition in human lung epithelial cells for the selected candidates.

Results

Our in silico pipeline identified seven FDA-approved drugs among the top ten candidates. Six (imiquimod, nelfinavir and saquinavir, everolimus, azathioprine, and retinol) had sufficient prescribing rates or feasibility for further testing. In the VHA cohort, azathioprine (odds ratio [OR]=0.69, 95% CI 0.62-0.77) and retinol (OR=0.81, 95% CI 0.72-0.92) were significantly associated with reduced COVID-19 incidence. Conversely, nelfinavir and saquinavir demonstrated potent SARS-CoV-2 inhibition in vitro (~95% and ~65% viral load reduction, respectively). No single compound showed robust protection in both in vivo and in vitro settings.

Conclusions

These findings underscore the power of GReX-based drug repurposing in rapidly identifying existing therapies with potential clinical relevance; four out of six compounds showed a protective effect in one of the two validation approaches. Crucially, our results highlight how a complementary evaluation-combining epidemiological data and in vitro assays-helps refine the most promising candidates for subsequent mechanistic studies and clinical trials. This integrated validation approach may prove vital for accelerating therapeutic development against current and future health challenges.

Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms

Benchmarking is an important step in the improvement, assessment, and comparison of the performance of drug discovery platforms and technologies. We revised the existing benchmarking protocols in our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery platform to improve utility and performance. We optimized multiple parameters used in drug candidate prediction and assessment with these updated benchmarking protocols. CANDO ranked 7.4% of known drugs in the top 10 compounds for their respective diseases/indications based on drug-indication associations/mappings obtained from the Comparative Toxicogenomics Database (CTD) using these optimized parameters. This increased to 12.1% when drug-indication mappings were obtained from the Therapeutic Targets Database. Performance on an indication was weakly correlated (Spearman correlation coefficient >0.3) with indication size (number of drugs associated with an indication) and moderately correlated (correlation coefficient >0.5) with compound chemical similarity. There was also moderate correlation between our new and original benchmarking protocols when assessing performance per indication using each protocol. Benchmarking results were also dependent on the source of the drug-indication mapping used: a higher proportion of indication-associated drugs were recalled in the top 100 compounds when using the Therapeutic Targets Database (TTD), which only includes FDA-approved drug-indication associations (in contrast to the CTD, which includes associations drawn from the literature). We also created compbench, a publicly available head-to-head benchmarking protocol that allows consistent assessment and comparison of different drug discovery platforms. Using this protocol, we compared two pipelines for drug repurposing within CANDO; our primary pipeline outperformed another similarity-based pipeline still in development that clusters signatures based on their associated Gene Ontology terms. Our study sets a precedent for the complete, comprehensive, and comparable benchmarking of drug discovery platforms, resulting in more accurate drug candidate predictions.

Comprehensive applications of the artificial intelligence technology in new drug research and development

Purpose

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.

Methods

Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")].

Results

In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.

Conclusion

Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale

Biomedical network learning offers fresh prospects for expediting drug repositioning. However, traditional network architectures struggle to quantify the relationship between micro-scale drug spatial structures and corresponding macro-scale biomedical networks, limiting their ability to capture key pharmacological properties and complex biomedical information crucial for drug screening and therapeutic discovery. Moreover, challenges such as difficulty in capturing long-range dependencies hinder current network-based approaches. To address these limitations, we introduce the Spatial Hierarchical Network, modeling molecular 3D structures and biological associations into a unified network. We propose an end-to-end framework, SpHN-VDA, integrating spatial hierarchical information through triple attention mechanisms to enhance machine understanding of molecular functionality and improve the accuracy of virus-drug association identification. SpHN-VDA outperforms leading models across three datasets, particularly excelling in out-of-distribution and cold-start scenarios. It also exhibits enhanced robustness against data perturbation, ranging from 20% to 40%. It accurately identifies critical motifs for binding sites, even without protein residue annotations. Leveraging reliability of SpHN-VDA, we have identified 25 potential candidate drugs through gene expression analysis and CMap. Molecular docking experiments with the SARS-CoV-2 spike protein further corroborate the predictions. This research highlights the broad potential of SpHN-VDA to enhance drug repositioning and identify effective treatments for various diseases.

Identification and Ranking of Binding Sites from Structural Ensembles: Application to SARS-CoV-2

Target identification and evaluation is a critical step in the drug discovery process. Although time-intensive and complex, the challenge becomes even more acute in the realm of infectious disease, where the rapid emergence of new viruses, the swift mutation of existing targets, and partial effectiveness of approved antivirals can lead to outbreaks of significant public health concern. The COVID-19 pandemic, caused by the SARS-CoV-2 virus, serves as a prime example of this, where despite the allocation of substantial resources, Paxlovid is currently the only effective treatment. In that case, significant effort pre-pandemic had been expended to evaluate the biological target for the closely related SARS-CoV. In this work, we utilize the computational hot spot mapping method, FTMove, to rapidly identify and rank binding sites for a set of nine SARS-CoV-2 drug/potential drug targets. FTMove takes into account protein flexibility by mapping binding site hot spots across an ensemble of structures for a given target. To assess the applicability of the FTMove approach to a wide range of drug targets for viral pathogens, we also carry out a comprehensive review of the known SARS-CoV-2 ligandable sites. The approach is able to identify the vast majority of all known sites and a few additional sites, which may in fact be yet to be discovered as ligandable. Furthermore, a UMAP analysis of the FTMove features for each identified binding site is largely able to separate predicted sites with experimentally known binders from those without known binders. These results demonstrate the utility of FTMove to rapidly identify actionable sites across a range of targets for a given indication. As such, the approach is expected to be particularly useful for assessing target binding sites for any emerging pathogen, as well as for indications in other disease areas, and providing actionable starting points for structure-based drug design efforts.

Innovative Strategies in Drug Repurposing to Tackle Intracellular Bacterial Pathogens

Intracellular bacterial pathogens pose significant public health challenges due to their ability to evade immune defenses and conventional antibiotics. Drug repurposing has recently been explored as a strategy to discover new therapeutic uses for established drugs to combat these infections. Utilizing high-throughput screening, bioinformatics, and systems biology, several existing drugs have been identified with potential efficacy against intracellular bacteria. For instance, neuroleptic agents like thioridazine and antipsychotic drugs such as chlorpromazine have shown effectiveness against Staphylococcus aureus and Listeria monocytogenes. Furthermore, anticancer drugs including tamoxifen and imatinib have been repurposed to induce autophagy and inhibit bacterial growth within host cells. Statins and anti-inflammatory drugs have also demonstrated the ability to enhance host immune responses against Mycobacterium tuberculosis. The review highlights the complex mechanisms these pathogens use to resist conventional treatments, showcases successful examples of drug repurposing, and discusses the methodologies used to identify and validate these drugs. Overall, drug repurposing offers a promising approach for developing new treatments for bacterial infections, addressing the urgent need for effective antimicrobial therapies.

Effectiveness of dolutegravir in moderate severity COVID-19 patients: A single-center, randomized, double-blind, placebo-controlled trial

INTRODUCTION

Drug repurposing as a low-cost, time-saving, and often less risky strategy has been attractive for the treatment of coronavirus disease 2019 (COVID-19) during the pandemic. This trial aimed to evaluate the effectiveness of dolutegravir, an HIV-1 integrase inhibitor, in admitted patients with moderate COVID-19.

METHODS

This study was a randomized, double-blind, placebo-controlled clinical trial assessing the efficacy of dolutegravir in adults admitted to a hospital in Ghaemshahr, Mazandaran Province, Iran. Patients aged 18-80 years with early symptoms of moderate COVID-19, which was confirmed based on reverse transcription polymerase chain reaction (RT-PCR) and/or chest computed tomography (CT) scan, were considered to be included in this study. Patients were randomly assigned in a 1:1 ratio to receive 50 mg dolutegravir plus the standard treatment regimen or the same value of placebo plus the standard treatment regimen, daily for 7 days. The standard treatment regimen was remdesivir 200 mg on day 1 followed by 100 mg for five days or until discharge. The primary endpoint was recovery 10 days after the beginning of the study.

RESULTS

Between August 22 and October 23, 2021, of 120 patients who were enrolled, 93 patients were randomly assigned to receive 50 mg dolutegravir (n=46) or the placebo regimen (n=47). No significant difference was observed between the two intervention groups based on the obtained results including frequency of respiratory modes during the first five days of admission, respiratory rate, and O2 saturation during six time periods.

CONCLUSION

The results showed that in adult patients admitted to the hospital with moderate COVID-19, treatment with dolutegravir was not associated with improvement in clinical recovery. Larger randomized trials are required to provide more robust evidence about the effectiveness of dolutegravir.

Network-based approach for drug repurposing against mpox

The current outbreak of mpox presents a significant threat to the global community. However, the lack of mpox-specific drugs necessitates the identification of additional candidates for clinical trials. In this study, a network medicine framework was used to investigate poxviruses-human interactions to identify potential drugs effective against the mpox virus (MPXV). The results indicated that poxviruses preferentially target hubs on the human interactome, and that these virally-targeted proteins (VTPs) tend to aggregate together within specific modules. Comorbidity analysis revealed that mpox is closely related to immune system diseases. Based on predicted drug-target interactions, 268 drugs were identified using the network proximity approach, among which 23 drugs displaying the least side-effects and significant proximity to MPXV were selected as the final candidates. Lastly, specific drugs were explored based on VTPs, differentially expressed proteins, and intermediate nodes, corresponding to different categories. These findings provide novel insights that can contribute to a deeper understanding of the pathogenesis of MPXV and development of ready-to-use treatment strategies based on drug repurposing.

Identification of Mulberrofuran as a potent inhibitor of hepatitis A virus 3Cpro and RdRP enzymes through structure-based virtual screening, dynamics simulation, and DFT studies

Hepatitis is a medical condition characterized by inflammation of the liver. It is commonly caused by the hepatitis viruses A, B, C, D, and E. Hepatitis A virus (HAV) is highly contagious and can spread from infected individuals, through contaminated food, blood, or can also be water-borne. As per the statistics of World Health Organization (WHO), HAV infects about 1.4 million individuals each year globally. In this research work, we have focused on identifying natural product-based potential inhibitors for the two major enzymes of HAV namely 3C proteinase (3Cpro) and RNA-directed RNA polymerase (RdRP). The enzyme 3Cpro plays an important role in proteolytic activity that promotes viral maturation and infectivity. RNA-directed RNA polymerase facilitate viral replication and transcription. Structure-based virtual screening was carried out using NPACT database that contains a collection of 1574 curated plant-derived natural compounds that are validated by experiments. The screening procedure identified the phytochemical Mulberrofuran W, which could bind to both the targets 3Cpro and RdRP. The phytochemical Mulberrofuran W also had better binding affinity compared to the control compounds atropine and pyridinyl ester, which are previously identified inhibitors of HAV 3Cpro and RdRP, respectively. The Mulberrofuran W bound 3Cpro and RdRP complexes were subjected to 200 ns molecular dynamics simulations and were found to be stable and interacting with the active site of the enzymes throughout the course of complex MD simulations. In addition to DFT, MMGBSA studies were also performed to validate the identified potential inhibitor further. The identified phytochemical Mulberrofuran W can be considered as a new potential drug candidate and could be taken up for experimental evaluation against HAV infection.

Drug repurposing: databases and pipelines

The concept of drug repurposing is focused on the repositioning of drug molecules that have already undergone safety trials. There are different strategies for drug repurposing. Network-based strategy focuses on the evaluation of drug combinations in a molecular environment with multi-target hits and analysis of drug interactions. Implementation of any in silico strategy requires several databases and pipelines for executing the process of shortlisting appropriate drugs.

Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.

Computational Drug Design Strategies for Fighting the COVID-19 Pandemic

The advent of COVID-19 has brought the use of computer tools to the fore in health research. In recent years, computational methods have proven to be highly effective in a variety of areas, including genomic surveillance, host range prediction, drug target identification, and vaccine development. They were also instrumental in identifying new antiviral compounds and repurposing existing therapeutics to treat COVID-19. Using computational approaches, researchers have made significant advances in understanding the molecular mechanisms of COVID-19 and have developed several promising drug candidates and vaccines. This chapter highlights the critical importance of computational drug design strategies in elucidating various aspects of COVID-19 and their contribution to advancing global drug design efforts during the pandemic. Ultimately, the use of computing tools will continue to play an essential role in health research, enabling researchers to develop innovative solutions to combat new and emerging diseases.

Inhibitor Trapping in N-Myristoyltransferases as a Mechanism for Drug Potency

Predicting inhibitor potency is critical in drug design and development, yet it has remained one of computational biology's biggest unresolved challenges. Here, we show that in the case of the N-myristoyltransferase (NMT), this problem could be traced to the mechanisms by which the NMT enzyme is inhibited. NMT adopts open or closed conformations necessary for orchestrating the different steps of the catalytic process. The results indicate that the potency of the NMT inhibitors is determined by their ability to stabilize the enzyme conformation in the closed state, and that in this state, the small molecules themselves are trapped and locked inside the structure of the enzyme, creating a significant barrier for their dissociation. By using molecular dynamics simulations, we demonstrate that the conformational stabilization of the protein molecule in its closed form is highly correlated with the ligands activity and can be used to predict their potency. Hence, predicting inhibitor potency in silico might depend on modeling the conformational changes of the protein molecule upon binding of the ligand rather than estimating the changes in free binding energy that arise from their interaction.

DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19

The Coronavirus Disease 2019 (COVID-19) pandemic is still wreaking havoc worldwide. Therefore, the urgent need for efficient treatments pushes researchers and clinicians into screening effective drugs. Drug repurposing may be a promising and time-saving strategy to identify potential drugs against this disease. Here, we developed a novel computational approach, named Drug Target Set Enrichment Analysis (DTSEA), to identify potent drugs against COVID-19. DTSEA first mapped the disease-related genes into a gene functional interaction network, and then it used a network propagation algorithm to rank all genes in the network by calculating the network proximity of genes to disease-related genes. Finally, an enrichment analysis was performed on drug target sets to prioritize disease-candidate drugs. It was shown that the top three drugs predicted by DTSEA, including Ataluren, Carfilzomib, and Aripiprazole, were significantly enriched in the immune response pathways indicating the potential for use as promising COVID-19 inhibitors. In addition to these drugs, DTSEA also identified several drugs (such as Remdesivir and Olumiant), which have obtained emergency use authorization (EUA) for COVID-19. These results indicated that DTSEA could effectively identify the candidate drugs for COVID-19, which will help to accelerate the development of drugs for COVID-19. We then performed several validations to ensure the reliability and validity of DTSEA, including topological analysis, robustness analysis, and prediction consistency. Collectively, DTSEA successfully predicted candidate drugs against COVID-19 with high accuracy and reliability, thus making it a formidable tool to identify potential drugs for a specific disease and facilitate further investigation.

Ethical challenges of clinical trials with a repurposed drug in outbreaks

Drug repurposing is a strategy of identifying new potential uses for already existing drugs. Many researchers adopted this method to identify treatment or prevention during the COVID-19 pandemic. However, despite the considerable number of repurposed drugs that were evaluated, only some of them were labeled for new indications. In this article, we present the case of amantadine, a drug commonly used in neurology that attracted new attention during the COVID-19 outbreak. This example illustrates some of the ethical challenges associated with the launch of clinical trials to evaluate already approved drugs. In our discussion, we follow the ethics framework for prioritization of COVID-19 clinical trials proposed by Michelle N Meyer and colleagues (2021). We focus on four criteria: social value, scientific validity, feasibility, and consolidation/collaboration. We claim that launching amantadine trials was ethically justified. Although the scientific value was anticipated to be low, unusually, the social value was expected to be high. This was because of significant social interest in the drug. In our view, this strongly supports the need for evidence to justify why the drug should not be prescribed or privately accessed by interested parties. Otherwise, a lack of evidence-based argument could enhance its uncontrolled use. With this paper, we join the discussion on the lessons learned from the pandemic. Our findings will help to improve future efforts to decide on the launch of clinical trials on approved drugs when dealing with the widespread off-label use of the drug.

Medication-Related Problems Identified and Addressed by Pharmacists Dispensing COVID-19 Antivirals at a Community Pharmacy

Safe dispensing of coronavirus disease 2019 (COVID-19) oral antivirals requires comprehensive patient assessment to identify and address significant medication-related problems (MRPs). Given the fast-paced environment of community pharmacies and limited access to outside patient records, there are challenges with pharmacists ensuring the safe and appropriate dispensing of these medications. An independent community pharmacy in Pennsylvania developed and implemented a COVID-19 oral antiviral assessment protocol to systematically review all prescriptions dispensed for nirmatrelvir/ritonavir (Paxlovid™) and molnupiravir (Lagevrio™) to identify and address MRPs. A retrospective review was conducted to assess documented MRPs, including significant drug-drug interactions and inappropriate dosing requiring intervention, for prescriptions dispensed from 9 February 2022 to 29 April 2022. Pharmacists identified one or more significant MRPs requiring intervention on 42 of the 54 nirmatrelvir/ritonavir prescriptions (78%) and 0 of the 7 molnupiravir prescriptions. Most pharmacist interventions involved drug-drug interactions between nirmatrelvir/ritonavir and HMG-CoA reductase inhibitors and calcium channel blockers, along with four renal dose adjustments for nirmatrelvir/ritonavir. This study highlights the ability of community pharmacists to identify and address MRPs and promotes the use of a protocol to encourage safe dispensing practices for medications prone to MRPs.

Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations

COVID-19 is an infectious disease caused by SARS-CoV-2 leading to the ongoing global pandemic. Infected patients developed a range of respiratory symptoms, including respiratory failure, as well as other extrapulmonary complications. Multiple comorbidities, including hypertension, diabetes, cardiovascular diseases, and chronic kidney diseases, are associated with the severity and increased mortality of COVID-19. SARS-CoV-2 infection also causes a range of cardiovascular complications, including myocarditis, myocardial injury, heart failure, arrhythmias, acute coronary syndrome, and venous thromboembolism. Although a variety of methods have been developed and many clinical trials have been launched for drug repositioning for COVID-19, treatments that consider cardiovascular manifestations and cardiovascular disease comorbidities specifically are limited. In this review, we summarize recent advances in drug repositioning for COVID-19, including experimental drug repositioning, high-throughput drug screening, omics data-based, and network medicine-based computational drug repositioning, with particular attention on those drug treatments that consider cardiovascular manifestations of COVID-19. We discuss prospective opportunities and potential methods for repurposing drugs to treat cardiovascular complications of COVID-19.

Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network

After several waves of COVID-19 led to a massive loss of human life worldwide due to the changes in its variants and the vast explosion. Several researchers proposed neural network-based drug discovery techniques to fight against the pandemic; utilizing neural networks has limitations (Exponential time complexity, Non-Convergence, Mode Collapse, and Diminished Gradient). To overcome those difficulties, this paper proposed a hybrid architecture that will help to repurpose the most appropriate medicines for the treatment of COVID-19. A brief investigation of the sequences has been made to discover the gene density and noncoding proportion through the next gene sequencing. The paper tracks the exceptional locales in the virus DNA sequence as a Drug Target Region (DTR). Then the variable DNA neighborhood search is applied to this DTR to obtain the DNA interaction network to show how the genes are correlated. A drug database has been obtained based on the ontological property of the genomes with advanced D3Similarity so that all the chemical components of the drug database have been identified. Other methods obtained hydroxychloroquine as an effective drug which was rejected by WHO. However, The experimental results show that Remdesivir and Dexamethasone are the most effective drugs, with 97.41 and 97.93%, respectively.

DRONet: effectiveness-driven drug repositioning framework using network embedding and ranking learning

As one of the most vital methods in drug development, drug repositioning emphasizes further analysis and research of approved drugs based on the existing large amount of clinical and experimental data to identify new indications of drugs. However, the existing drug repositioning methods didn't achieve enough prediction performance, and these methods do not consider the effectiveness information of drugs, which make it difficult to obtain reliable and valuable results. In this study, we proposed a drug repositioning framework termed DRONet, which make full use of effectiveness comparative relationships (ECR) among drugs as prior information by combining network embedding and ranking learning. We utilized network embedding methods to learn the deep features of drugs from a heterogeneous drug-disease network, and constructed a high-quality drug-indication data set including effectiveness-based drug contrast relationships. The embedding features and ECR of drugs are combined effectively through a designed ranking learning model to prioritize candidate drugs. Comprehensive experiments show that DRONet has higher prediction accuracy (improving 87.4% on Hit@1 and 37.9% on mean reciprocal rank) than state of the art. The case analysis also demonstrates high reliability of predicted results, which has potential to guide clinical drug development.

In silico Screening of Potential SARS-CoV-2 Main Protease Inhibitors from Thymus schimperi

Background

COVID-19 is still instigating significant social and economic chaos worldwide; however, there is no approved antiviral drug yet. Here, we used in silico analysis to screen potential SARS-CoV-2 main protease (Mpro) inhibitors extracted from the essential oil of Thymus schimperi which could contribute to the discovery of potent anti-SARS-CoV-2 phytochemicals.

Methods

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of compounds were determined through SwissADME and ProToxII servers. AutoDock tools were used for molecular docking analysis studies, while Chimera, DS studio, and LigPlot were used for post-docking studies. Molecular dynamic simulations were performed for 200 ns under constant pressure.

Results

All compounds exhibited a bioavailability score of ≥0.55 entailing that at least 55% of the drugs can be absorbed unchanged. Only five (9%), nine (16%) and two (3.6%) of the compounds showed active hepatotoxicity, carcinogenicity, and immunotoxicity, respectively. Except for flourazophore P, which showed a little mutagenicity, all other compounds did not show mutagenic properties. On the other hand, only pinene beta was found to have a little cytotoxicity. Five compounds demonstrated effective binding to the catalytic dyad of the SARS-CoV-2 Mpro substrate binding pocket, while two of them (geranylisobutanoate and 3-octane) are found to be the best hits that formed hydrogen bonds with Glu166 and Ser144 of SARS-CoV-2 Mpro.

Conclusion

Based on our in silico analysis, top hits from Thymus schimperi may serve as potential anti-SARS-CoV-2 compounds. Further in vitro and in vivo studies are recommended to characterize these compounds for clinical applications.

Integrated Data Analysis Uncovers New COVID-19 Related Genes and Potential Drug Re-Purposing Candidates

The COVID-19 pandemic is an acute and rapidly evolving global health crisis. To better understand this disease's molecular basis and design therapeutic strategies, we built upon the recently proposed concept of an integrated cell, iCell, fusing three omics, tissue-specific human molecular interaction networks. We applied this methodology to construct infected and control iCells using gene expression data from patient samples and three cell lines. We found large differences between patient-based and cell line-based iCells (both infected and control), suggesting that cell lines are ill-suited to studying this disease. We compared patient-based infected and control iCells and uncovered genes whose functioning (wiring patterns in iCells) is altered by the disease. We validated in the literature that 18 out of the top 20 of the most rewired genes are indeed COVID-19-related. Since only three of these genes are targets of approved drugs, we applied another data fusion step to predict drugs for re-purposing. We confirmed with molecular docking that the predicted drugs can bind to their predicted targets. Our most interesting prediction is artenimol, an antimalarial agent targeting ZFP62, one of our newly identified COVID-19-related genes. This drug is a derivative of artemisinin drugs that are already under clinical investigation for their potential role in the treatment of COVID-19. Our results demonstrate further applicability of the iCell framework for integrative comparative studies of human diseases.

Anti-HIV and anti-HCV small molecule protease inhibitors in-silico repurposing against SARS-CoV-2 Mpro for the treatment of COVID-19

The COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is a global health emergency warranting development and implementation of targeted treatment. The enzyme main protease (M^pro; also known as 3C-like protease) is emerging as an attractive drug target. This enzyme plays an indispensable role in processing the translated polyproteins of viral RNA. Inhibiting the activity of M^pro would wedge viral replication. To facilitate the discovery of targeted therapy for COVID-19, we carried out the structure-assisted repurposing of existing protease inhibiting small molecules to target SARS-CoV-2 M^pro. Based on the structure of SARS-CoV-2 M^pro, here we report the small drug molecule namely saquinavir as its potent inhibitor. Findings support the premise that this promising antiviral protease inhibiting small drug molecule can be validated and implemented for the treatment and clinical management of COVID-19 pandemic disease.Communicated by Ramaswamy H. Sarma.

Effect of an Inhibitor on the ACE2-Receptor-Binding Domain of SARS-CoV-2

The recent outbreak of COVID-19 infection started in Wuhan, China, and spread across China and beyond. Since the WHO declared COVID-19 a pandemic (March 11, 2020), three vaccines and only one antiviral drug (remdesivir) have been approved (Oct 22, 2020) by the FDA. The coronavirus enters human epithelial cells by the binding of the densely glycosylated fusion spike protein (S protein) to a receptor (angiotensin-converting enzyme 2, ACE2) on the host cell surface. Therefore, inhibiting the viral entry is a promising treatment pathway for preventing or ameliorating the effects of COVID-19 infection. In the current work, we have used all-atom molecular dynamics (MD) simulations to investigate the influence of the MLN-4760 inhibitor on the conformational properties of ACE2 and its interaction with the receptor-binding domain (RBD) of SARS-CoV-2. We have found that the presence of an inhibitor tends to completely/partially open the ACE2 receptor where the two subdomains (I and II) move away from each other, while the absence results in partial or complete closure. The current study increases our understanding of ACE inhibition by MLN-4760 and how it modulates the conformational properties of ACE2.

Deep generative molecular design reshapes drug discovery

Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and accessible guide to the broad computational drug discovery and development community. We introduce deep generative models from different standpoints and describe the theoretical frameworks for representing chemical and biological structures and their applications. We discuss the data and technical challenges and highlight future directions of multimodal deep generative models for accelerating drug discovery.

Preparing for the next pandemic: Simulation-based deep reinforcement learning to discover and test multimodal control of systemic inflammation using repurposed immunomodulatory agents

Background

Preparation to address the critical gap in a future pandemic between non-pharmacological measures and the deployment of new drugs/vaccines requires addressing two factors: 1) finding virus/pathogen-agnostic pathophysiological targets to mitigate disease severity and 2) finding a more rational approach to repurposing existing drugs. It is increasingly recognized that acute viral disease severity is heavily driven by the immune response to the infection ("cytokine storm" or "cytokine release syndrome"). There exist numerous clinically available biologics that suppress various pro-inflammatory cytokines/mediators, but it is extremely difficult to identify clinically effective treatment regimens with these agents. We propose that this is a complex control problem that resists standard methods of developing treatment regimens and accomplishing this goal requires the application of simulation-based, model-free deep reinforcement learning (DRL) in a fashion akin to training successful game-playing artificial intelligences (AIs). This proof-of-concept study determines if simulated sepsis (e.g. infection-driven cytokine storm) can be controlled in the absence of effective antimicrobial agents by targeting cytokines for which FDA-approved biologics currently exist.

Methods

We use a previously validated agent-based model, the Innate Immune Response Agent-based Model (IIRABM), for control discovery using DRL. DRL training used a Deep Deterministic Policy Gradient (DDPG) approach with a clinically plausible control interval of 6 hours with manipulation of six cytokines for which there are existing drugs: Tumor Necrosis Factor (TNF), Interleukin-1 (IL-1), Interleukin-4 (IL-4), Interleukin-8 (IL-8), Interleukin-12 (IL-12) and Interferon-γ(IFNg).

Results

DRL trained an AI policy that could improve outcomes from a baseline Recovered Rate of 61% to one with a Recovered Rate of 90% over ~21 days simulated time. This DRL policy was then tested on four different parameterizations not seen in training representing a range of host and microbe characteristics, demonstrating a range of improvement in Recovered Rate by +33% to +56.

Discussion

The current proof-of-concept study demonstrates that significant disease severity mitigation can potentially be accomplished with existing anti-mediator drugs, but only through a multi-modal, adaptive treatment policy requiring implementation with an AI. While the actual clinical implementation of this approach is a projection for the future, the current goal of this work is to inspire the development of a research ecosystem that marries what is needed to improve the simulation models with the development of the sensing/assay technologies to collect the data needed to iteratively refine those models.

Celastrol: A lead compound that inhibits SARS-CoV-2 replication, the activity of viral and human cysteine proteases, and virus-induced IL-6 secretion

The global emergence of coronavirus disease 2019 (COVID-19) has caused substantial human casualties. Clinical manifestations of this disease vary from asymptomatic to lethal, and the symptomatic form can be associated with cytokine storm and hyperinflammation. In face of the urgent demand for effective drugs to treat COVID-19, we have searched for candidate compounds using in silico approach followed by experimental validation. Here we identified celastrol, a pentacyclic triterpene isolated from Tripterygium wilfordii Hook F, as one of the best compounds out of 39 drug candidates. Celastrol reverted the gene expression signature from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected cells and irreversibly inhibited the recombinant forms of the viral and human cysteine proteases involved in virus invasion, such as Mpro (main protease), PLpro (papain-like protease), and recombinant human cathepsin L. Celastrol suppressed SARS-CoV-2 replication in human and monkey cell lines and decreased interleukin-6 (IL-6) secretion in the SARS-CoV-2-infected human cell line. Celastrol acted in a concentration-dependent manner, with undetectable signs of cytotoxicity, and inhibited in vitro replication of the parental and SARS-CoV-2 variant. Therefore, celastrol is a promising lead compound to develop new drug candidates to face COVID-19 due to its ability to suppress SARS-CoV-2 replication and IL-6 production in infected cells.

A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery

Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.

Distinct role of mitochondrial function and protein kinase C in intimal and medial calcification in vitro

Introduction

Vascular calcification (VC) is a major risk factor for cardiovascular morbidity and mortality. Depending on the location of mineral deposition within the arterial wall, VC is classified as intimal and medial calcification. Using in vitro mineralization assays, we developed protocols triggering both types of calcification in vascular smooth muscle cells (SMCs) following diverging molecular pathways.

Materials and methods and results

Human coronary artery SMCs were cultured in osteogenic medium (OM) or high calcium phosphate medium (CaP) to induce a mineralized extracellular matrix. OM induces osteoblast-like differentiation of SMCs-a key process in intimal calcification during atherosclerotic plaque remodeling. CaP mimics hyperphosphatemia, associated with chronic kidney disease-a risk factor for medial calcification. Transcriptomic analysis revealed distinct gene expression profiles of OM and CaP-calcifying SMCs. OM and CaP-treated SMCs shared 107 differentially regulated genes related to SMC contraction and metabolism. Real-time extracellular efflux analysis demonstrated decreased mitochondrial respiration and glycolysis in CaP-treated SMCs compared to increased mitochondrial respiration without altered glycolysis in OM-treated SMCs. Subsequent kinome and in silico drug repurposing analysis (Connectivity Map) suggested a distinct role of protein kinase C (PKC). In vitro validation experiments demonstrated that the PKC activators prostratin and ingenol reduced calcification triggered by OM and promoted calcification triggered by CaP.

Conclusion

Our direct comparison results of two in vitro calcification models strengthen previous observations of distinct intracellular mechanisms that trigger OM and CaP-induced SMC calcification in vitro. We found a differential role of PKC in OM and CaP-calcified SMCs providing new potential cellular and molecular targets for pharmacological intervention in VC. Our data suggest that the field should limit the generalization of results found in in vitro studies using different calcification protocols.

Molecular dynamics studies reveal structural and functional features of the SARS-CoV-2 spike protein

The SARS-CoV-2 virus is responsible for the COVID-19 pandemic the world experience since 2019. The protein responsible for the first steps of cell invasion, the spike protein, has probably received the most attention in light of its central role during infection. Computational approaches are among the tools employed by the scientific community in the enormous effort to study this new affliction. One of these methods, namely molecular dynamics (MD), has been used to characterize the function of the spike protein at the atomic level and unveil its structural features from a dynamic perspective. In this review, we focus on these main findings, including spike protein flexibility, rare S protein conformational changes, cryptic epitopes, the role of glycans, drug repurposing, and the effect of spike protein variants.

RP-HPLC Method Development, Validation, and Drug Repurposing of Sofosbuvir Pharmaceutical Dosage Form: A Multidimensional Study

A smooth, exceptionally sensitive, correct, and extra reproducible RP-HPLC technique was developed and demonstrated to estimate Sofosbuvir (SOF) in pharmaceutical dosage formulations. This process was carried out by Agilent High-Pressure Liquid Chromatograph 1260 with GI311C Quat. Pump, Phenomenex Luna C-18 (150 mm × 4.6 mm × 5 μm) (USA), and Photodiode Array Detector (PDA) G1315D. The cell section, including acetonitrile and methanol with 80:20 v/v and solution (B) 0.1% phosphoric acid (40:60), was used for the study. However, 10 μL of the sample was injected with a drift flow of 1 mL/min. The separation occurred at a column temperature of 30 °C, and the eluents used PDA set at 260 nm. The retention time of SOF was 5 min. The calibration curve was modified linearly within the range of 0.05-0.15 mg/mL with a correlation coefficient of 0.99 and genuine linear dating among top vicinity and consciousness in the calibration curve. The detection and quantification restrictions were 0.001 and 0.003 mg/mL, respectively. SOF recovery from pharmaceutical components ranged from 98% to 99%. The percentage assay of SOF was 99%. Analytical validation parameters, such as specificity, linearity, precision, accuracy, and selectivity, were studied, and the percentage relative standard deviation (%RSD) was less than 2%. All other key parameters were observed within the desired thresholds. Hence, the proposed RP-HPLC technique was proven effective for developing SOF in bulk and pharmaceutical pill dosage forms. SOF was found to interact with SARS-COV-2 nsp12, and molecular docking results revealed its high affinity and firm binding within the active site groove of nsp12. The key interacting residues include; LYS-72, GLN-75, MET-80 ALA-99, ASN-99, TRP-100, TYR-101 with ASN-99 and TRP-100 forming hydrogen bonds. Molecular Dynamics simulation of SOF and nsp12 complex elucidated that the system was stable throughout 20ns. Therefore, this drug repurposing strategy for SOF can be used for treating COVID-19 infections by performing animal experiments and accurate clinical trials in the future.

Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2

The unprecedented outbreak of the severe acute respiratory syndrome (SARS) Coronavirus-2, across the globe, triggered a worldwide uproar in the search for immediate treatment strategies. With no specific drug and not much data available, alternative approaches such as drug repurposing came to the limelight. To date, extensive research on the repositioning of drugs has led to the identification of numerous drugs against various important protein targets of the coronavirus strains, with hopes of the drugs working against the major variants of concerns (alpha, beta, gamma, delta, omicron) of the virus. Advancements in computational sciences have led to improved scope of repurposing via techniques such as structure-based approaches including molecular docking, molecular dynamic simulations and quantitative structure activity relationships, network-based approaches, and artificial intelligence-based approaches with other core machine and deep learning algorithms. This review highlights the various approaches to repurposing drugs from a computational biological perspective, with various mechanisms of action of the drugs against some of the major protein targets of SARS-CoV-2. Additionally, clinical trials data on potential COVID-19 repurposed drugs are also highlighted with stress on the major SARS-CoV-2 targets and the structural effect of variants on these targets. The interaction modelling of some important repurposed drugs has also been elucidated. Furthermore, the merits and demerits of drug repurposing are also discussed, with a focus on the scope and applications of the latest advancements in repurposing.

A Network-Guided Approach to Discover Phytochemical-Based Anticancer Therapy: Targeting MARK4 for Hepatocellular Carcinoma

MAP/microtubule affinity-regulating kinase 4 (MARK4) is associated with various biological functions, including neuronal migration, cell polarity, microtubule dynamics, apoptosis, and cell cycle regulation, specifically in the G1/S checkpoint, cell signaling, and differentiation. It plays a critical role in different types of cancers. Hepatocellular carcinoma (HCC) is the one of the most common forms of liver cancer caused due to mutations, epigenetic aberrations, and altered gene expression patterns. Here, we have applied an integrated network biology approach to see the potential links of MARK4 in HCC, and subsequently identified potential herbal drugs. This work focuses on the naturally-derived compounds from medicinal plants and their properties, making them targets for potential anti-hepatocellular treatments. We further analyzed the HCC mutated genes from the TCGA database by using cBioPortal and mapped out the MARK4 targets among the mutated list. MARK4 and Mimosin, Quercetin, and Resveratrol could potentially interact with critical cancer-associated proteins. A set of the hepatocellular carcinoma altered genes is directly the part of infection, inflammation, immune systems, and cancer pathways. Finally, we conclude that among all these drugs, Gingerol and Fisetin appear to be the highly promising drugs against MARK4-based targets, followed by Quercetin, Resveratrol, and Apigenin.

Multi-Omics and Artificial Intelligence-Guided Drug Repositioning: Prospects, Challenges, and Lessons Learned from COVID-19

Drug repurposing is of interest for therapeutics innovation in many human diseases including coronavirus disease 2019 (COVID-19). Methodological innovations in drug repurposing are currently being empowered by convergence of omics systems science and digital transformation of life sciences. This expert review article offers a systematic summary of the application of artificial intelligence (AI), particularly machine learning (ML), to drug repurposing and classifies and introduces the common clustering, dimensionality reduction, and other methods. We highlight, as a present-day high-profile example, the involvement of AI/ML-based drug discovery in the COVID-19 pandemic and discuss the collection and sharing of diverse data types, and the possible futures awaiting drug repurposing in an era of AI/ML and digital technologies. The article provides new insights on convergence of multi-omics and AI-based drug repurposing. We conclude with reflections on the various pathways to expedite innovation in drug development through drug repurposing for prompt responses to the current COVID-19 pandemic and future ecological crises in the 21st century.

Drug repurposing against SARS-CoV-2 using computational approaches

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has generated a critical need for treatments to reduce morbidity and mortality associated with this disease. However, traditional drug development takes many years, which is not practical solution given the current pandemic. Therefore, a viable option is to repurpose existing drugs. The structural data of several proteins vital for the virus became available shortly after the start of the pandemic. In this review, we discuss the importance of these targets and their available potential inhibitors predicted by the computational approaches. Among the hits identified by computational approaches, 35 candidates were suggested for further evaluation, among which ten drugs are in clinical trials (Phase III and IV) for treating Coronavirus 2019 (COVID-19).

The IDentif.AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens

IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow. A starting pool of 12 candidate drugs developed in collaboration with a community of infectious disease clinicians was first narrowed down to a six-drug pool and then interrogated in 50 combination regimens at three dosing levels per drug, representing 729 possible combinations. IDentif.AI-x revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived, and pinpointed a number of clinically actionable drug interactions, which were further reconfirmed in SARS-CoV-2 variants B.1.351 (Beta) and B.1.617.2 (Delta). IDentif.AI-x prioritized promising drug combinations for clinical translation and can be immediately adjusted and re-executed with a new pool of promising therapies in an actionable path towards rapidly optimizing combination therapy following pandemic emergence.

Approach to the management of COVID-19 patients: When home care can represent the best practice

BACKGROUND

The pandemic that began around February 2020, caused by the viral pathogen SARS-CoV-2 (COVID-19), has still not completed its course at present in June 2022.

OBJECTIVE

The open research to date highlights just how varied and complex the outcome of the contagion can be.

METHOD

The clinical pictures observed following the contagion present variabilities that cannot be explained completely by the patient's age (which, with the new variants, is rapidly changing, increasingly affecting younger patients) nor by symptoms and concomitant pathologies (which are no longer proving to be decisive in recent cases) in relation to medium-to-long term sequelae. In particular, the functions of the vascular endothelium and vascular lesions at the pre-capillary level represent the source of tissue hypoxia and other damage, resulting in the clinical evolution of COVID-19.

RESULTS

Keeping the patient at home with targeted therapeutic support, aimed at not worsening vascular endothelium damage with early and appropriate stimulation of endothelial cells, ameliorates the glycocalyx function and improves the prognosis and, in some circumstances, could be the best practice suitable for certain patients.

CONCLUSION

Clinical information thus far collected may be of immense value in developing a better understanding of the present pandemic and future occurrences regarding patient safety, pharmaceutical care and therapy liability.

A review on drug repurposing in COVID-19: from antiviral drugs to herbal alternatives

BACKGROUND

COVID-19 is an illness caused by severe acute respiratory syndrome coronavirus 2. Due to its rapid spread, in March 2020 the World Health Organization (WHO) declared pandemic. Since the outbreak of pandemic many governments, scientists, and institutions started to work on new vaccines and finding of new and repurposing drugs. Drug repurposing is an excellent option for discovery of already used drugs, effective against COVID-19, lowering the cost of production, and shortening the period of delivery, especially when preclinical safety studies have already been performed. There are many approved drugs that showed significant results against COVID-19, like ivermectin and hydrochloroquine, including alternative treatment options against COVID-19, utilizing herbal medicine.

SHORT CONCLUSION

This article summarized 11 repurposing drugs, their positive and negative health implications, along with traditional herbal alternatives, that harvest strong potential in efficient treatments options against COVID-19, with small or no significant side effects. Out of 11 repurposing drugs, four drugs are in status of emergency approval, most of them being in phase IV clinical trials. The first repurposing drug approved for clinical usage is remdesivir, whereas chloroquine and hydrochloroquine approval for emergency use was revoked by FDA for COVID-19 treatment in June 2020.

Potential of Microneedle Systems for COVID-19 Vaccination: Current Trends and Challenges

To prevent the coronavirus disease 2019 (COVID-19) pandemic and aid restoration to prepandemic normality, global mass vaccination is urgently needed. Inducing herd immunity through mass vaccination has proven to be a highly effective strategy for preventing the spread of many infectious diseases, which protects the most vulnerable population groups that are unable to develop immunity, such as people with immunodeficiencies or weakened immune systems due to underlying medical or debilitating conditions. In achieving global outreach, the maintenance of the vaccine potency, transportation, and needle waste generation become major issues. Moreover, needle phobia and vaccine hesitancy act as hurdles to successful mass vaccination. The use of dissolvable microneedles for COVID-19 vaccination could act as a major paradigm shift in attaining the desired goal to vaccinate billions in the shortest time possible. In addressing these points, we discuss the potential of the use of dissolvable microneedles for COVID-19 vaccination based on the current literature.

Probing the Immune System Dynamics of the COVID-19 Disease for Vaccine Designing and Drug Repurposing Using Bioinformatics Tools

The pathogenesis of COVID-19 is complicated by immune dysfunction. The impact of immune-based therapy in COVID-19 patients has been well documented, with some notable studies on the use of anti-cytokine medicines. However, the complexity of disease phenotypes, patient heterogeneity and the varying quality of evidence from immunotherapy studies provide problems in clinical decision-making. This review seeks to aid therapeutic decision-making by giving an overview of the immunological responses against COVID-19 disease that may contribute to the severity of the disease. We have extensively discussed theranostic methods for COVID-19 detection. With advancements in technology, bioinformatics has taken studies to a higher level. The paper also discusses the application of bioinformatics and machine learning tools for the diagnosis, vaccine design and drug repurposing against SARS-CoV-2.

Computational prediction of potential inhibitors for SARS-COV-2 main protease based on machine learning, docking, MM-PBSA calculations, and metadynamics

The development of new drugs is a very complex and time-consuming process, and for this reason, researchers have been resorting heavily to drug repurposing techniques as an alternative for the treatment of various diseases. This approach is especially interesting when it comes to emerging diseases with high rates of infection, because the lack of a quickly cure brings many human losses until the mitigation of the epidemic, as is the case of COVID-19. In this work, we combine an in-house developed machine learning strategy with docking, MM-PBSA calculations, and metadynamics to detect potential inhibitors for SARS-COV-2 main protease among FDA approved compounds. To assess the ability of our machine learning strategy to retrieve potential compounds we calculated the Enrichment Factor of compound datasets for three well known protein targets: HIV-1 reverse transcriptase (PDB 4B3P), 5-HT2A serotonin receptor (PDB 6A94), and H1 histamine receptor (PDB 3RZE). The Enrichment Factor for each target was, respectively, 102.5, 12.4, 10.6, which are considered significant values. Regarding the identification of molecules that can potentially inhibit the main protease of SARS-COV-2, compounds output by the machine learning step went through a docking experiment against SARS-COV-2 Mpro. The best scored poses were the input for MM-PBSA calculations and metadynamics using CHARMM and AMBER force fields to predict the binding energy for each complex. Our work points out six molecules, highlighting the strong interaction obtained for Mpro-mirabegron complex. Among these six, to the best of our knowledge, ambenonium has not yet been described in the literature as a candidate inhibitor for the SARS-COV-2 main protease in its active pocket.

A Cell-Free Assay for Rapid Screening of Inhibitors of hACE2-Receptor-SARS-CoV-2-Spike Binding

We present a cell-free assay for rapid screening of candidate inhibitors of protein binding, focusing on inhibition of the interaction between the SARS-CoV-2 Spike receptor binding domain (RBD) and human angiotensin-converting enzyme 2 (hACE2). The assay has two components: fluorescent polystyrene particles covalently coated with RBD, termed virion-particles (v-particles), and fluorescently labeled hACE2 (hACE2F) that binds the v-particles. When incubated with an inhibitor, v-particle-hACE2F binding is diminished, resulting in a reduction in the fluorescent signal of bound hACE2F relative to the noninhibitor control, which can be measured via flow cytometry or fluorescence microscopy. We determine the amount of RBD needed for v-particle preparation, v-particle incubation time with hACE2F, hACE2F detection limit, and specificity of v-particle binding to hACE2F. We measure the dose response of the v-particles to known inhibitors. Finally, utilizing an RNA-binding protein tdPP7 incorporated into hACE2F, we demonstrate that RNA-hACE2F granules trap v-particles effectively, providing a basis for potential RNA-hACE2F therapeutics.

In silico analysis of SARS-CoV-2 proteins as targets for clinically available drugs

The ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires treatments with rapid clinical translatability. Here we develop a multi-target and multi-ligand virtual screening method to identify FDA-approved drugs with potential activity against SARS-CoV-2 at traditional and understudied viral targets. 1,268 FDA-approved small molecule drugs were docked to 47 putative binding sites across 23 SARS-CoV-2 proteins. We compared drugs between binding sites and filtered out compounds that had no reported activity in an in vitro screen against SARS-CoV-2 infection of human liver (Huh-7) cells. This identified 17 "high-confidence", and 97 "medium-confidence" drug-site pairs. The "high-confidence" group was subjected to molecular dynamics simulations to yield six compounds with stable binding poses at their optimal target proteins. Three drugs-amprenavir, levomefolic acid, and calcipotriol-were predicted to bind to 3 different sites on the spike protein, domperidone to the Mac1 domain of the non-structural protein (Nsp) 3, avanafil to Nsp15, and nintedanib to the nucleocapsid protein involved in packaging the viral RNA. Our "two-way" virtual docking screen also provides a framework to prioritize drugs for testing in future emergencies requiring rapidly available clinical drugs and/or treating diseases where a moderate number of targets are known.

Identification of potential biological targets of oxindole scaffolds via in silico repositioning strategies

Background: Drug repurposing is an alternative strategy to traditional drug discovery that aims at predicting new uses for already existing drugs or clinical candidates. Drug repurposing has many advantages over traditional drug development, such as reduced attrition rates, time and costs. This is especially the case considering that most drugs investigated for repurposing have already been assessed for their safety in clinical trials. Repurposing campaigns can also be designed for libraries of already synthesized molecules at different levels of biological experimentation, from null to in vitro and in vivo. Such an extension of the "repurposing" concept is expected to provide significant advantages for the identification of novel drugs, as the synthetic accessibility of the desired compounds is often one of the limiting factors in the traditional drug discovery pipeline. Methods: In this work, we performed a computational repurposing campaign on a library of previously synthesized oxindole-based compounds, in order to identify potential new targets for this versatile scaffold. To this aim, ligand-based approaches were firstly applied to evaluate the similarity degree of the investigated compound library, with respect to ligands extracted from the DrugBank, Protein Data Bank (PDB) and ChEMBL databases. In particular, the 2D fingerprint-based and 3D shape-based similarity profiles were evaluated and compared for the oxindole derivates. Results: The analyses predicted a set of potential candidate targets for repurposing, some of them emerging by consensus of different computational analyses. One of the identified targets, i.e., the vascular endothelial growth factor receptor 2 (VEGFR-2) kinase, was further investigated by means of docking calculations, followed by biological testing of one candidate. Conclusions: While the compound did not show potent inhibitory activity towards VEGFR-2, the study highlighted several other possibilities of therapeutically relevant targets that may be worth of consideration for drug repurposing.