Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model

Repurposing of the Syk inhibitor fostamatinib using a machine learning algorithm

TAM (TYRO3, AXL, MERTK) receptor tyrosine kinases (RTKs) have intrinsic roles in tumor cell proliferation, migration, chemoresistance, and suppression of antitumor immunity. The overexpression of TAM RTKs is associated with poor prognosis in various types of cancer. Single-target agents of TAM RTKs have limited efficacy because of an adaptive feedback mechanism resulting from the cooperation of TAM family members. This suggests that multiple targeting of members has the potential for a more potent anticancer effect. The present study used a deep-learning based drug-target interaction (DTI) prediction model called molecule transformer-DTI (MT-DTI) to identify commercially available drugs that may inhibit the three members of TAM RTKs. The results showed that fostamatinib, a spleen tyrosine kinase (Syk) inhibitor, could inhibit the three receptor kinases of the TAM family with an IC50 <1 µM. Notably, no other Syk inhibitors were predicted by the MT-DTI model. To verify this result, this study performed in vitro studies with various types of cancer cell lines. Consistent with the DTI results, this study observed that fostamatinib suppressed cell proliferation by inhibiting TAM RTKs, while other Syk inhibitors showed no inhibitory activity. These results suggest that fostamatinib could exhibit anticancer activity as a pan-TAM inhibitor. Taken together, these findings demonstrated that this artificial intelligence model could be effectively used for drug repurposing and repositioning. Furthermore, by identifying its novel mechanism of action, this study confirmed the potential for fostamatinib to expand its indications as a TAM inhibitor.

Exploring TMPRSS2 Drug Target to Combat Influenza and Coronavirus Infection

Respiratory viral infections, including influenza and coronaviruses, present significant health risks worldwide. The recent COVID-19 pandemic highlights the urgent need for novel and effective antiviral agents. The host cell protease, transmembrane serine protease 2 (TMPRSS2), facilitates viral pathogenesis by playing a critical role in viral invasion and disease progression. This protease is coexpressed with the viral receptors of angiotensin-converting enzyme 2 (ACE2) for SARS-CoV-2 in the human respiratory tract and plays a significant role in activating viral proteins and spreading. TMPRSS2 activates the coronavirus spike (S) protein and permits membrane fusion and viral entry by cleaving the virus surface glycoproteins. It also activates the hemagglutinin (HA) protein, an enzyme necessary for the spread of influenza virus. TMPRSS2 inhibitors can reduce viral propagation and morbidity by blocking viral entry into respiratory cells and reducing viral spread, inflammation, and disease severity. This review examines the role of TMPRSS2 in viral replication and pathogenicity. It also offers potential avenues to develop targeted antivirals to inhibit TMPRSS2 function, suggesting a possible focus on targeted antiviral development. Ultimately, the review seeks to contribute to improving public health outcomes related to these viral infections.

Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives

Mental disorders are a representative type of brain disorder, including anxiety, major depressive depression (MDD), and autism spectrum disorder (ASD), that are caused by multiple etiologies, including genetic heterogeneity, epigenetic dysregulation, and aberrant morphological and biochemical conditions. Psychedelic drugs such as psilocybin and lysergic acid diethylamide (LSD) have been renewed as fascinating treatment options and have gradually demonstrated potential therapeutic effects in mental disorders. However, the multifaceted conditions of psychiatric disorders resulting from individuality, complex genetic interplay, and intricate neural circuits impact the systemic pharmacology of psychedelics, which disturbs the integration of mechanisms that may result in dissimilar medicinal efficiency. The precise prescription of psychedelic drugs remains unclear, and advanced approaches are needed to optimize drug development. Here, recent studies demonstrating the diverse pharmacological effects of psychedelics in mental disorders are reviewed, and emerging perspectives on structural function, the microbiota-gut-brain axis, and the transcriptome are discussed. Moreover, the applicability of deep learning is highlighted for the development of drugs on the basis of big data. These approaches may provide insight into pharmacological mechanisms and interindividual factors to enhance drug discovery and development for advanced precision medicine.

Bile acids and bile acid activated receptors in the treatment of Covid-19

Since its first outbreak in 2020, the pandemic caused by the Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) has caused the death of almost 7 million people worldwide. Vaccines have been fundamental in disease prevention and to reduce disease severity especially in patients with comorbidities. Nevertheless, treatment of COVID-19 has been proven difficult and several approaches have failed to prevent disease onset or disease progression, particularly in patients with comorbidities. Interrogation of drug data bases has been widely used since the beginning of pandemic to repurpose existing drugs/natural substances for the prevention/treatment of COVID-19. Steroids, including bile acids such as ursodeoxycholic acid (UDCA) and chenodeoxycholic acid (CDCA) have shown to be promising for their potential in modulating SARS-CoV-2/host interaction. Bile acids have proven to be effective in preventing binding of spike protein with the Angiotensin Converting Enzyme II (ACE2), thus preventing virus uptake by the host cells and inhibiting its replication, as well as in indirectly modulating immune response. Additionally, the two main bile acid activated receptors, GPBAR1 and FXR, have proven effective in modulating the expression of ACE2, suggesting an indirect role for these receptors in regulating SARS-CoV-2 infectiveness and immune response. In this review we have examined how the potential of bile acids and their receptors as anti-COVID-19 therapies and how these biochemical mechanisms translate into clinical efficacy.

Repurposed drugs in combinations exert additive anti-chikungunya virus activity: an in-vitro study

Chikungunya virus (CHIKV) infection causes chikungunya, a viral disease that currently has no specific antiviral treatment. Several repurposed drug candidates have been investigated for the treatment of the disease. In order to improve the efficacy of the known drugs, combining drugs for treatment is a promising approach. The current study was undertaken to explore the antiviral activity of a combination of repurposed drugs that were reported to have anti-CHIKV activity. We explored the effect of different combinations of six effective drugs (2-fluoroadenine, emetine, lomibuvir, enalaprilat, metyrapone and resveratrol) at their non-toxic concentrations against CHIKV under post infection treatment conditions in Vero cells. Focus-forming unit assay, real time RT-PCR, immunofluorescence assay, and western blot were used to determine the virus titre. The results revealed that the combination of 2-fluoroadenine with either metyrapone or emetine or enalaprilat exerted inhibitory activity against CHIKV under post-infection treatment conditions. The effect of these drug combinations was additive in nature compared to the effect of the individual drugs. The results suggest an additive anti-viral effect of these drug combinations against CHIKV. The findings could serve as an outline for the development of an innovative therapeutic approach in the future to treat CHIKV-infected patients.

Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

Polypharmacology guided drug repositioning approach for SARS-CoV2

Drug repurposing has emerged as an important strategy and it has a great potential in identifying therapeutic applications for COVID-19. An extensive virtual screening of 4193 FDA approved drugs has been carried out against 24 proteins of SARS-CoV2 (NSP1-10 and NSP12-16, envelope, membrane, nucleoprotein, spike, ORF3a, ORF6, ORF7a, ORF8, and ORF9b). The drugs were classified into top 10 and bottom 10 drugs based on the docking scores followed by the distribution of their therapeutic indications. As a result, the top 10 drugs were found to have therapeutic indications for cancer, pain, neurological disorders, and viral and bacterial diseases. As drug resistance is one of the major challenges in antiviral drug discovery, polypharmacology and network pharmacology approaches were employed in the study to identify drugs interacting with multiple targets and drugs such as dihydroergotamine, ergotamine, bisdequalinium chloride, midostaurin, temoporfin, tirilazad, and venetoclax were identified among the multi-targeting drugs. Further, a pathway analysis of the genes related to the multi-targeting drugs was carried which provides insight into the mechanism of drugs and identifying targetable genes and biological pathways involved in SARS-CoV2.

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.

A perspective study of the possible impact of obeticholic acid against SARS-CoV-2 infection

The causative agent of CoV disease 2019 is a new coronavirus CoV type 2, affecting the respiratory tract with severe manifestations (SARS-CoV-2). Covid-19 is mainly symptomless, with slight indications in about 85% of the affected cases. Many efforts were done to face this pandemic by testing different drugs and agents to make treatment protocols in different countries. However, the use of these proposed drugs is associated with the development of adverse events. Remarkably, the successive development of SARS-CoV-2 variants which could affect persons even they were vaccinated, prerequisite wide search to find efficient and safe agents to face SARS-CoV-2 infection. Obeticholic acid (OCA), which has anti-inflammatory effects, may efficiently treat Covid-19. Thus, the goal of this perspective study is to focus on the possible medicinal effectiveness in managing Covid-19. OCA is a powerful farnesoid X receptor (FXR) agonist possessing marked antiviral and anti-inflammatory effects. FXR is dysregulated in Covid-19 resulting in hyper-inflammation with concurrent occurrence of hypercytokinemia. Interestingly, OCA inhibits the reaction between this virus and angiotensin-converting enzyme type 2 (ACE2) receptors. FXR agonists control the expression of ACE2 and the inflammatory signaling pathways in this respiratory syndrome, which weakens the effects of Covid-19 disease and accompanied complications. Taken together, FXR agonists like OCA may reveal both direct and indirect impacts in the modulation of immune reaction in SARS-CoV-2 conditions. It is highly recommended to perform many investigations regarding different phases of the discovery of new drugs.

AXL inhibitors selected by molecular docking: Option for reducing SARS-CoV-2 entry into cells

The COVID-19 pandemic is ongoing and the benefit from vaccines is still insufficient since COVID-19 continues to be dia g-nosed in vaccinated individuals. It is, therefore, necessary to propose specific pharmacological treatments against COVID-19. A new therapeutic target on the human cellular membrane is AXL (anexelekto), proposed as an independent pathway by which interaction with the S protein of SARS-CoV-2 allows the virus to enter the cell, without the participation of ACE2. AXL serves as another gate through which SARS-CoV-2 can enter cells. Therefore, any stage of COVID-19 could be ameliorated by hindering the interaction between AXL and SARS-CoV-2. This study proposes ten compounds (1-10), selected by mole-cu lar docking and using a library of nearly 500,000 compounds, to develop a new drug that will decrease the interaction of AXL with the S protein of SARS-CoV-2. These compounds have a specific potential site of interaction with AXL, between Glu59, His61, Glu70 and Ser74 amino acids. This site is necessary for the interaction of AXL with the S protein. With this, we propose to develop a new adjuvant treatment against COVID-19.

Development of Nafamostat Mesylate Immediate-Release Tablet by Drug Repositioning Using Quality-by-Design Approach

We aimed to develop nafamostat mesylate immediate-release tablets for the treatment of COVID-19 through drug repositioning studies of nafamostat mesylate injection. Nafamostat mesylate is a serine protease inhibitor known to inhibit the activity of the transmembrane protease, serine 2 enzyme that affects the penetration of the COVID-19 virus, thereby preventing the binding of the angiotensin-converting enzyme 2 receptor in vivo and the spike protein of the COVID-19 virus. The formulation was selected through a stability study after manufacturing by a wet granulation process and a direct tableting process to develop a stable nafamostat mesylate immediate-release tablet. Formulation issues for the selected processes were addressed using the design of experiments and quality-by-design approaches. The dissolution rate of the developed tablet was confirmed to be >90% within 30 min in the four major dissolutions, except in the pH 6.8 dissolution medium. Additionally, an in vivo pharmacokinetic study was performed in monkeys, and the pharmacokinetic profiles of nafamostat injections, oral solutions, and tablets were compared. The half-life during oral administration was confirmed to be significantly longer than the reported literature value of 8 min, and the bioavailability of the tablet was approximately 25% higher than that of the oral solution.

A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Signaling repurposable drug combinations against COVID-19 by developing the heterogeneous deep herb-graph method

BACKGROUND

Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication.

MOTIVATION

Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage.

METHOD

We initially employed the virtual screening method to construct the 'Herb-Compound' and 'Compound-Protein' docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the 'Herb-Compound-Protein' heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies.

RESULTS

There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.

Poloxamer-188 Adjuvant Efficiently Maintains Adaptive Immunity of SARS-CoV-2 RBD Subunit Vaccination through Repressing p38MAPK Signaling

Poloxamer-188 (P188) is a nonionic triblock linear copolymer that can be used as a pharmaceutical excipient because of its amphiphilic nature. This study investigated whether P188 can act as an adjuvant to improve the immunogenicity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) receptor binding domain (RBD) subunit vaccine. BALB/c mice were vaccinated twice with the RBD antigen alone or in combination with P188 or MF59 (a commercial adjuvant for comparison purposes). The resulting humoral and cellular immunity were assessed. Results showed that P188 helped elicit higher neutralizing activity than MF59 after vaccination. P188 induced significant humoral immune response, along with type 1 T helper (Th1) and type 2 T helper (Th2) cellular immune response when compared with MF59 due to repressing p38MAPK phosphorylation. Furthermore, P188 did not result in adverse effects such as fibrosis of liver or kidney after vaccination. In conclusion, P188 is a novel adjuvant that may be used for safe and effective immune enhancement of the SARS-CoV-2 RBD antigen.

A fuzzy logic-based computational method for the repurposing of drugs against COVID-19

Introduction: COVID-19 has spread out all around the world and seriously interrupted human activities. Being a newfound disease, not only many aspects of the disease are unknown, but also there is not an effective medication to cure the disease. Besides, designing a drug is a time-consuming process and needs large investment. Hence, drug repurposing techniques, employed to discover the hidden benefits of the existing drugs, maybe a useful option for treating COVID-19. Methods: The present study exploits the drug repositioning concepts and introduces some candidate drugs which may be effective in controlling COVID-19. The suggested method consists of three main steps. First, the required data such as the amino acid sequences of targets and drug-target interactions are extracted from the public databases. Second, the similarity score between the targets (protein/enzymes) and genome of SARS-COV-2 is computed using the proposed fuzzy logic-based method. Since the classical approaches yield outcomes which may not be useful for the real-world applications, the fuzzy technique can address the issue. Third, after ranking targets based on the obtained scores, the usefulness of drugs affecting them is examined for managing COVID-19. Results: The results indicate that antiviral medicines, designed for curing hepatitis C, may also cure COVID-19. According to the findings, ribavirin, simeprevir, danoprevir, and XTL-6865 may be helpful in controlling the disease. Conclusion: It can be concluded that the similarity-based drug repurposing techniques may be the most suitable option for managing emerging diseases such as COVID-19 and can be applied to a wide range of data. Also, fuzzy logic-based scoring methods can produce outcomes which are more consistent with the real-world biological applications than others.

In silico analysis and characterization of medicinal mushroom cystathionine beta-synthase as an angiotensin converting enzyme (ACE) inhibitory protein

Angiotensin-converting enzyme (ACE) regulates blood pressure and has been implicated in several conditions including lung injury, fibrosis and Alzheimer's disease. Medicinal mushroom Ganordema lucidum (Reishi) cystathionine beta-synthase (GlCBS) was previously reported to possess ACE inhibitory activities. However, the inhibitory mechanism of CBS protein remains unreported. Therefore, this study integrates in silico sequencing, structural and functional based-analysis, protein modelling, molecular docking and binding affinity calculation to elucidate the inhibitory mechanism of GlCBS and Lignosus rhinocerus (Tiger milk mushroom) CBS protein (LrCBS) towards ACE. In silico analysis indicates that CBSs from both mushrooms share high similarities in terms of physical properties, structural properties and domain distribution. Protein-protein docking analysis revealed that both GlCBS and LrCBS potentially modulate the C-terminal domain of ACE (C-ACE) activity via regulation of chloride activation and/or prevention of substrate entry. GICBS and LrCBS were also shown to interact with ACE at the same region that presumably inhibits the function of ACE.

Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

Computational and in vitro experimental analyses of the anti-COVID-19 potential of Mortaparib and MortaparibPlus

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has become a global health emergency. Although new vaccines have been generated and being implicated, discovery and application of novel preventive and control measures are warranted. We aimed to identify compounds that may possess the potential to either block the entry of virus to host cells or attenuate its replication upon infection. Using host cell surface receptor expression (angiotensin-converting enzyme 2 (ACE2) and Transmembrane protease serine 2 (TMPRSS2)) analysis as an assay, we earlier screened several synthetic and natural compounds and identified candidates that showed ability to down-regulate their expression. Here, we report experimental and computational analyses of two small molecules, Mortaparib and MortaparibPlus that were initially identified as dual novel inhibitors of mortalin and PARP-1, for their activity against SARS-CoV-2. In silico analyses showed that MortaparibPlus, but not Mortaparib, stably binds into the catalytic pocket of TMPRSS2. In vitro analysis of control and treated cells revealed that MortaparibPlus caused down-regulation of ACE2 and TMPRSS2; Mortaparib did not show any effect. Furthermore, computational analysis on SARS-CoV-2 main protease (Mpro) that also predicted the inhibitory activity of MortaparibPlus. However, cell-based antiviral drug screening assay showed 30-60% viral inhibition in cells treated with non-toxic doses of either MortaparibPlus or Mortaparib. The data suggest that these two closely related compounds possess multimodal anti-COVID-19 activities. Whereas MortaparibPlus works through direct interactions/effects on the host cell surface receptors (ACE2 and TMPRSS2) and the virus protein (Mpro), Mortaparib involves independent mechanisms, elucidation of which warrants further studies.

Deep Learning Approach for Discovery of In Silico Drugs for Combating COVID-19

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than −18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19.

A State-of-the-Art Survey on Artificial Intelligence to Fight COVID-19

Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI's role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges.

In silico identification of available drugs targeting cell surface BiP to disrupt SARS-CoV-2 binding and replication: Drug repurposing approach

Aims

Cell surface binding immunoglobin protein (csBiP) is predicted to be susceptible to SARS-CoV-2 binding. With a substrate-binding domain (SBD) that binds to polypeptides and a nucleotide-binding domain (NBD) that can initiate extrinsic caspase-dependent apoptosis, csBiP may be a promising therapeutic target for COVID-19. This study aims to identify FDA-approved drugs that can neutralize viral binding and prevent viral replication by targeting the functional domains of csBiP.

Methods

In silico screening of 1999 FDA-approved drugs against the functional domains of BiP were performed using three molecular docking programs to avoid bias from individual docking programs. Top ligands were selected by averaging the ligand rankings from three programs. Interactions between top ligands and functional domains of BiP were analyzed.

Key findings

The top 10 SBD-binding candidates are velpatasvir, irinotecan, netupitant, lapatinib, doramectin, conivaptan, fenoverine, duvelisib, irbesartan, and pazopanib. The top 10 NBD-binding candidates are nilotinib, eltrombopag, grapiprant, topotecan, acetohexamide, vemurafenib, paritaprevir, pixantrone, azosemide, and piperaquine-phosphate. Among them, Velpatasvir and paritaprevir are antiviral agents that target the protease of hepatitis C virus. Netupitant is an anti-inflammatory drug that inhibits neurokinin-1 receptor, which contributes to acute inflammation. Grapiprant is an anti-inflammatory drug that inhibits the prostaglandin E₂ receptor protein subtype 4, which is expressed on immune cells and triggers inflammation. These predicted SBD-binding drugs could disrupt SARS-CoV-2 binding to csBiP, and NBD-binding drugs may falter viral attachment and replication by locking the SBD in closed conformation and triggering apoptosis in infected cells.

Significance

csBiP appears to be a novel therapeutic target against COVID-19 by preventing viral attachment and replication. These identified drugs could be repurposed to treat COVID-19 patients.