Drug repurposing for COVID-19 based on an integrative meta-analysis of SARS-CoV-2 induced gene signature in human airway epithelium

De novo generation of dual-target ligands for the treatment of SARS-CoV-2 using deep learning, virtual screening, and molecular dynamic simulations

De novo generation of molecules with the necessary features offers a promising opportunity for artificial intelligence, such as deep generative approaches. However, creating novel compounds having biological activities toward two distinct targets continues to be a very challenging task. In this study, we develop a unique computational framework for the de novo synthesis of bioactive compounds directed at two predetermined therapeutic targets. This framework is referred to as the dual-target ligand generative network. Our approach uses a stochastic policy to explore chemical spaces called a sequence-based simple molecular input line entry system (SMILES) generator. The steps in the high-level workflow would be to gather and prepare the training data for both targets' molecules, build a neural network model and train it to make molecules, create new molecules using generative AI, and then virtually screen the newly validated molecules against the SARS-CoV-2 PLpro and 3CLpro drug targets. Results shows that novel molecules generated have higher binding affinity with both targets than the conventional drug i.e. Remdesivir being used for the treatment of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Development and validation of a novel biomarker panel for Crohn's disease and rheumatoid arthritis diagnosis and treatment

BACKGROUND

Crohn's disease (CD) and rheumatoid arthritis (RA) are immune-mediated inflammatory diseases. However, the molecular mechanisms linking these two diseases remain unclear.

METHODS

To identify shared core genes between CD and RA, we employed differential gene analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Functional annotation of these core biomarkers was performed using consensus clustering and gene set enrichment analysis. We also constructed a protein-protein network and a miRNA-mRNA network using multiple databases, and potential therapeutic agents targeting the core biomarkers were predicted. Finally, we confirmed the expression of the genes in the biomarker panel in both CD and RA using quantitative PCR.

RESULTS

A total of five shared core genes, namely C-X-C motif chemokine ligand 10 (CXCL10), C-X-C motif chemokine ligand 9 (CXCL9), aquaporin 9 (AQP9), secreted phosphoprotein 1 (SPP1), and metallothionein 1M (MT1M), were identified as core biomarkers. These biomarkers activate classical pro-inflammatory and immune signaling pathways, influencing immune cell aggregation. Additionally, testosterone was identified as a potential therapeutic agent targeting the biomarkers identified in this study. The expression of genes in the biomarker panel in CD and RA was confirmed through quantitative PCR.

CONCLUSION

Our study revealed some core genes shared between CD and RA and established a novel biomarker panel with potential implications for the diagnosis and treatment of these diseases.

Bioinformatics approach to identify the hub gene associated with COVID-19 and idiopathic pulmonary fibrosis

The coronavirus disease 2019 (COVID-19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS-CoV-2 infection, deserves attention. As COVID-19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID-19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID-19. A risk prediction model was developed to assess the prognosis of patients infected with SARS-CoV-2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS-CoV-2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID-19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID-19. With the increasing availability of COVID-19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines.

Repurposing and computational design of PARP inhibitors as SARS-CoV-2 inhibitors

Coronavirus disease 2019 (COVID-19) is a recent pandemic that caused serious global emergency. To identify new and effective therapeutics, we employed a drug repurposing approach. The poly (ADP ribose) polymerase inhibitors were used for this purpose and were repurposed against the main protease (Mpro) target of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The results from these studies were used to design compounds using the 'Grow Scaffold' modules available on Discovery Studio v2018. The three designed compounds, olaparib 1826 and olaparib 1885, and rucaparib 184 demonstrated better CDOCKER docking scores for Mpro than their parent compounds. Moreover, the compounds adhered to Lipinski's rule of five and demonstrated a synthetic accessibility score of 3.55, 3.63, and 4.30 for olaparib 1826, olaparib 1885, and rucaparib 184, respectively. The short-range Coulombic and Lennard-Jones potentials also support the potential binding of the modified compounds to Mpro. Therefore, we propose these three compounds as novel SARS-CoV-2 inhibitors.

4’-fluorouridine as a potential COVID-19 oral drug?: a review

The available antiviral drugs against coronavirus disease 2019 (COVID-19) are limited. Oral drugs that can be prescribed to non-hospitalized patients are required. The 4′-fluoruridine, a nucleoside analog similar to remdesivir, is one of the promising candidates for COVID-19 oral therapy due to its ability to stall viral RdRp. Available data suggested that 4'-fluorouridine has antiviral activity against the respiratory syncytial virus, hepatitis C virus, lymphocytic choriomeningitis virus, and other RNA viruses, including SARS-CoV-2. In vivo study revealed that SARS-CoV-2 is highly susceptible to 4'-fluorouridine and was effective with a single daily dose versus molnupiravir administered twice daily. Although  4'-fluorouridine is considered as strong candidates, further studies are required to determine its efficacy in the patients and  it’s genetic effects on humans. In this review, we the antiviral activity of 4′-fluorouridine is reviewed and compared it to other drugs currently in development. The current literature on 4′-fluorouridine's antiviral activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is compiled and discussed.

Mapping CircRNA-miRNA-mRNA regulatory axis identifies hsa_circ_0080942 and hsa_circ_0080135 as a potential theranostic agents for SARS-CoV-2 infection

Non-coding RNAs (ncRNAs) can control the flux of genetic information; affect RNA stability and play crucial roles in mediating epigenetic modifications. A number of studies have highlighted the potential roles of both virus-encoded and host-encoded ncRNAs in viral infections, transmission and therapeutics. However, the role of an emerging type of non-coding transcript, circular RNA (circRNA) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has not been fully elucidated so far. Moreover, the potential pathogenic role of circRNA-miRNA-mRNA regulatory axis has not been fully explored as yet. The current study aimed to holistically map the regulatory networks driven by SARS-CoV-2 related circRNAs, miRNAs and mRNAs to uncover plausible interactions and interplay amongst them in order to explore possible therapeutic options in SARS-CoV-2 infection. Patient datasets were analyzed systematically in a unified approach to explore circRNA, miRNA, and mRNA expression profiles. CircRNA-miRNA-mRNA network was constructed based on cytokine storm related circRNAs forming a total of 165 circRNA-miRNA-mRNA pairs. This study implies the potential regulatory role of the obtained circRNA-miRNA-mRNA network and proposes that two differentially expressed circRNAs hsa_circ_0080942 and hsa_circ_0080135 might serve as a potential theranostic agents for SARS-CoV-2 infection. Collectively, the results shed light on the functional role of circRNAs as ceRNAs to sponge miRNA and regulate mRNA expression during SARS-CoV-2 infection.

The Delta and Omicron Variants of SARS-CoV-2: What We Know So Far

The world has not yet completely overcome the fear of the havoc brought by SARS-CoV-2. The virus has undergone several mutations since its initial appearance in China in December 2019. Several variations (i.e., B.1.616.1 (Kappa variant), B.1.617.2 (Delta variant), B.1.617.3, and BA.2.75 (Omicron variant)) have emerged throughout the pandemic, altering the virus's capacity to spread, risk profile, and even symptoms. Humanity faces a serious threat as long as the virus keeps adapting and changing its fundamental function to evade the immune system. The Delta variant has two escape alterations, E484Q and L452R, as well as other mutations; the most notable of these is P681R, which is expected to boost infectivity, whereas the Omicron has about 60 mutations with certain deletions and insertions. The Delta variant is 40-60% more contagious in comparison to the Alpha variant. Additionally, the AY.1 lineage, also known as the "Delta plus" variant, surfaced as a result of a mutation in the Delta variant, which was one of the causes of the life-threatening second wave of coronavirus disease 2019 (COVID-19). Nevertheless, the recent Omicron variants represent a reminder that the COVID-19 epidemic is far from ending. The wave has sparked a fervor of investigation on why the variant initially appeared to propagate so much more rapidly than the other three variants of concerns (VOCs), whether it is more threatening in those other ways, and how its type of mutations, which induce minor changes in its proteins, can wreck trouble. This review sheds light on the pathogenicity, mutations, treatments, and impact on the vaccine efficacy of the Delta and Omicron variants of SARS-CoV-2.

Drug repositioning for idiopathic epilepsy using gene expression signature data

Epilepsy is one of the most common neurological disorders, affecting millions of patients with a substantial economic and human burden. About 30-40% of epileptic patients remain un-treated after the therapeutic option. Genetic or idiopathic epilepsy count about 40% of total epilepsy patients, showing a maximum percentage for drug-resistant epilepsy. Since the last century basic approach to understanding disease progression and drug discovery has been through the prism, exploring all possible causes and treatment options. Here we report about the gene expression-based drug repositioning study for epilepsy. Epilepsy gene expression data was retrieved from the Gene Expression Omnibus database, while drugs-associated gene expression data was retrieved from the Connectivity map (CMAP). The study predicted309 drug compounds which can alter genetic epilepsy-mediated gene signature using an in-house developed R-script. These compounds were docked against identified epilepsy targets- Voltage-gated sodium channel subunit α2 (Nav1.2); GABA receptor α1-β1; and Voltage-gated calcium channel α1G (Cav3.1)using Carbamazepine, Clonazepam, and Pregabalin as standard drugs, respectively. Twenty-one predicted drug compounds showed better binding affinity than respective standards against the selected epileptic receptors. Among these drug compounds, Ergocalciferol, Oxaprozin, Flunarizine, Triprolidine and Cyproheptadine have been previously reported for anti-epileptic activities and can be potential hits to target idiopathic epilepsy.

Novel covalent and non-covalent complex-based pharmacophore models of SARS-CoV-2 main protease (Mpro) elucidated by microsecond MD simulations

As the world enters its second year of the pandemic caused by SARS-CoV-2, intense efforts have been directed to develop an effective diagnosis, prevention, and treatment strategies. One promising drug target to design COVID-19 treatments is the SARS-CoV-2 M^pro. To date, a comparative understanding of M^pro dynamic stereoelectronic interactions with either covalent or non-covalent inhibitors (depending on their interaction with a pocket called S1' or oxyanion hole) has not been still achieved. In this study, we seek to fill this knowledge gap using a cascade in silico protocol of docking, molecular dynamics simulations, and MM/PBSA in order to elucidate pharmacophore models for both types of inhibitors. After docking and MD analysis, a set of complex-based pharmacophore models was elucidated for covalent and non-covalent categories making use of the residue bonding point feature. The highest ranked models exhibited ROC-AUC values of 0.93 and 0.73, respectively for each category. Interestingly, we observed that the active site region of M^pro protein-ligand complex undergoes large conformational changes, especially within the S2 and S4 subsites. The results reported in this article may be helpful in virtual screening (VS) campaigns to guide the design and discovery of novel small-molecule therapeutic agents against SARS-CoV-2 M^pro protein.

Fenofibrate for COVID-19 and related complications as an approach to improve treatment outcomes: the missed key for Holy Grail

Introduction

Fenofibrate is an agonist of peroxisome proliferator activated receptor alpha (PPAR-α), that possesses anti-inflammatory, antioxidant, and anti-thrombotic properties. Fenofibrate is effective against a variety of viral infections and different inflammatory disorders. Therefore, the aim of critical review was to overview the potential role of fenofibrate in the pathogenesis of SARS-CoV-2 and related complications.

Results

By destabilizing SARS-CoV-2 spike protein and preventing it from binding angiotensin-converting enzyme 2 (ACE2), a receptor for SARS-CoV-2 entry, fenofibrate can reduce SARS-CoV-2 entry in human cells Fenofibrate also suppresses inflammatory signaling pathways, which decreases SARS-CoV-2 infection-related inflammatory alterations. In conclusion, fenofibrate anti-inflammatory, antioxidant, and antithrombotic capabilities may help to minimize the inflammatory and thrombotic consequences associated with SARSCoV-2 infection. Through attenuating the interaction between SARS-CoV-2 and ACE2, fenofibrate can directly reduce the risk of SARS-CoV-2 infection.

Conclusions

As a result, fenofibrate could be a potential treatment approach for COVID-19 control.

Drug repurposing against main protease and RNA-dependent RNA polymerase of SARS-CoV-2 using molecular docking, MM-GBSA calculations and molecular dynamics

A virus called severe acute respiratory distress syndrome coronavirus type 2 (SARS‐CoV‐2) is the causing organism of coronavirus disease 2019 (COVID-19), which has severely affected human life and threatened public health. The pandemic took millions of lives worldwide and caused serious negative effects on human society and the economy. SARS-CoV-2 main protease (Mpro) and RNA-dependent RNA polymerase (RdRp) are interesting targets due to their crucial role in viral replication and growth. Since there is only one approved therapy for COVID-19, drug repurposing is a promising approach to finding molecules with potential activity against COVID-19 in a short time and at minimal cost. In this study, virtual screening was performed on the ChEMBL library containing 9923 FDA-approved drugs, using various docking filters with different accuracy. The best drugs with the highest docking scores were further examined for molecular dynamics (MD) studies and MM-GBSA calculations. The results of this study suggest that nadide, cangrelor and denufosol are promising potential candidates against COVID-19. Further in vitro, preclinical and clinical studies of these candidates would help to discover safe and effective anti-COVID-19 drugs.

In Silico Identification of Potential Inhibitors of the SARS-CoV-2 Nucleocapsid Through Molecular Docking-Based Drug Repurposing

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic, and its effects on people worldwide continue to grow. Protein-targeted therapeutics are currently unavailable for this virus. As with other coronaviruses, the nucleocapsid (N) protein is the most conserved RNA-binding structural protein of SARS-CoV-2. The N protein is an appealing target because of its functional role in viral transcription and replication. Therefore, molecular docking method for structure-based drug design was used to investigate the binding energy and binding modes of various anti-N inhibitors in depth. The inhibitors selected were originally developed to target stress granules and other molecules involved in RNA biology, and were either FDA-approved or in the process of clinical trials for COVID-19. We aimed at targeting the N-terminal RNA binding domain (NTD) for molecular docking-based screening, on the basis of the first resolved crystal structure of SARS-CoV-2 N protein (PDB ID: 6M3M) and C-terminal domain (CTD) dimerization of the nucleocapsid phosphoprotein of SARS-COV-2 (PDB ID: 6WJI). Silmitasertib, nintedanib, ternatin, luteolin, and fedratinib were found to interact with RNA binding sites and to form a predicted protein interface with high binding energy. Similarly, silmitasertib, sirolimus-rapamycin, dovitinib, nintedanib, and fedratinib were found to interact with the SARS-CoV-2 N protein at its CTD dimerization sites, according to previous studies. In addition, we investigated an information gap regarding the relationships among the energetic landscape and stability and drug binding of the SARS-CoV-2 N NTD and CTD. Our in silico results clearly indicated that several tested drugs as potent putative inhibitors for COVID-19 therapeutics, thus indicating that they should be further validated as treatments to slow the spread of SARS-CoV-2.

Classification and severity progression measure of COVID-19 patients using pairs of multi-omic factors

Early detection and effective treatment of severe COVID-19 patients remain two major challenges during the current pandemic. Analysis of molecular changes in blood samples of severe patients is one of the promising approaches to this problem. From thousands of proteomic, metabolomic, lipidomic, and transcriptomic biomarkers selected in other research, we identify several pairs of biomarkers that after additional nonlinear spline transformation are highly effective in classifying and predicting severe COVID-19 cases. The performance of these pairs is evaluated in-sample, in a cross-validation exercise, and in an out-of-sample analysis on two independent datasets. We further improve our classifier by identifying complementary pairs using hierarchical clustering. In a result, we achieve 96-98% AUC on the validation data. Our findings can help medical experts to identify small groups of biomarkers that after nonlinear transformation can be used to construct a cost-effective test for patient screening and prediction of severity progression.

4’-fluorouridine and its derivatives as potential COVID-19 oral drugs: a review

Background: Although vaccination is underway, antiviral drugs against coronavirus disease 2019 (COVID-19) are lacking. Remdesivir, a nucleoside analog that works by inhibiting the viral RNA-dependent RNA polymerase (RdRp), is the only fully approved antiviral for the treatment of COVID-19. However, it is limited to intravenous use and is usually recommended only for hospitalized patients with severe COVID-19; therefore, oral drugs that can be prescribed even to non-hospitalized patients are required. According to a recent study, 4′-fluoruridine, a nucleoside analog similar to remdesivir, is a promising candidate for COVID-19 oral therapy due to its ability to stall viral RdRp. Methods: We examined the antiviral activity of 4′-fluorouridine and compared it to other drugs currently in development. The current literature on 4′-fluorouridine's antiviral activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been compiled and discussed in this review. Results: The 4'-fluorouridine has antiviral activity against the respiratory syncytial virus, hepatitis C virus, lymphocytic choriomeningitis virus, and other RNA viruses, including SARS-CoV-2. In vitro studies have shown that SARS-CoV-2 is susceptible to 4'-fluorouridine, with the half-maximal effective concentration (EC50) of 0.2 to 0.6 M, and that the 4′-fluorouridine derivative, 4′-fluorouridine-5′-triphosphate, inhibited RdRp via a mechanism distinct from that of the already approved COVID-19 oral drug, molnupiravir. In addition, an in vivo study revealed that SARS-CoV-2 is highly susceptible to 4'-fluorouridine and was effective with a single daily dose versus molnupiravir administered twice daily. Conclusions: Concerns about the genetic effects of molnupiravir may be resolved by the use of 4′-fluorouridine and its derivative, which, unlike molnupiravir, do not alter genetics, but inhibit RdRp instead. Although they are currently considered as strong candidates, further studies are required to determine the antiviral activity of 4′-fluorouridine and its derivative against SARS-CoV-2 and their genetic effects on humans.

A global picture: therapeutic perspectives for COVID-19

The COVID-19 pandemic is a lethal virus outbreak by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which has severely affected human lives and the global economy. The most vital part of the research and development of therapeutic agents is to design drug products to manage COVID-19 efficiently. Numerous attempts have been in place to determine the optimal drug dose and combination of drugs to treat the disease on a global scale. This article documents the information available on SARS-CoV-2 and its life cycle, which will aid in the development of the potential treatment options. A consolidated summary of several natural and repurposed drugs to manage COVID-19 is depicted with summary of current vaccine development. People with high age, comorbity and concomitant illnesses such as overweight, metabolic disorders, pulmonary disease, coronary heart disease, renal failure, fatty liver and neoplastic disorders are more prone to create serious COVID-19 and its consequences. This article also presents an overview of post-COVID-19 complications in patients.

A global picture: therapeutic perspectives for COVID-19

The COVID-19 pandemic is a lethal virus outbreak by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which has severely affected human lives and the global economy. The most vital part of the research and development of therapeutic agents is to design drug products to manage COVID-19 efficiently. Numerous attempts have been in place to determine the optimal drug dose and combination of drugs to treat the disease on a global scale. This article documents the information available on SARS-CoV-2 and its life cycle, which will aid in the development of the potential treatment options. A consolidated summary of several natural and repurposed drugs to manage COVID-19 is depicted with summary of current vaccine development. People with high age, comorbity and concomitant illnesses such as overweight, metabolic disorders, pulmonary disease, coronary heart disease, renal failure, fatty liver and neoplastic disorders are more prone to create serious COVID-19 and its consequences. This article also presents an overview of post-COVID-19 complications in patients.

Identification of novel TMPRSS2 inhibitors against SARS-CoV-2 infection: a structure-based virtual screening and molecular dynamics study

The scientific insights gained from the severe acute respiratory syndrome (SARS) and the middle east respiratory syndrome (MERS) outbreaks are helping scientists to fast-track the antiviral drug discovery process against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Coronaviruses, as well as influenza viruses, depend on host type 2 transmembrane serine protease, TMPRSS2, for entry and propagation in the human cell. Recent studies show that SARS-CoV-2 also uses TMPRSS2 for its cell entry. In the present study, a structure-based virtual screening of 52,337, protease ligands downloaded from the Zinc database was carried out against the homology model of TMPRSS2 protein followed by the molecular dynamics-based simulation to identify potential TMPRSS2 hits. The virtual screening has identified 13 hits with a docking score range of -10.447 to -9.863 and glide energy range of -60.737 to -40.479 kcal/mol. The binding mode analysis shows that the hit molecules form H-bond (Asp180, Gly184 & Gly209), Pi-Pi stacking (His41), and salt bridge (Asp180) type of contacts with the active site residues of TMPRSS2. In the MD simulation of ZINC000013444414, ZINC000137976768, and ZINC000143375720 hits show that these molecules form a stable complex with TMPRSS2. The complex equilibrates well with a minimal RMSD and RMSF fluctuation. All three structures, as predicted in Glide XP docking, show a prominent interaction with the Asp180, Gly184, Gly209, and His41. Further, MD simulation also identifies a notable H-bond interaction with Ser181 for all three hits. Among these hits, ZINC000143375720 shows the most stable binding interaction with TMPRSS2. The present study is successful in identifying TMPRSS2 ligands from zinc data base for a possible application in the treatment of COVID-19.

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design

The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and beyond. Drug development is a costly and time-consuming business, and only a minority of approved drugs generate returns exceeding the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper reviews the most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research.

Computational prediction of the effect of mutations in the receptor-binding domain on the interaction between SARS-CoV-2 and human ACE2

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 continues to mutate. Numerous studies have indicated that this viral mutation, particularly in the receptor-binding domain area, may increase the viral affinity for human angiotensin-converting enzyme 2 (hACE2), the receptor for viral entry into host cells, thereby increasing viral virulence and transmission. In this study, we investigated the binding affinity of SARS-CoV-2 variants (Delta plus, Iota, Kappa, Mu, Lambda, and C.1.2) on hACE2 using computational modeling with a protein-protein docking approach. The simulation results indicated that there were differences in the interactions between the RBD and hACE2, including hydrogen bonding, salt bridge interactions, non-bonded interactions, and binding free energy differences among these variants. Molecular dynamics simulations revealed that mutations in the RBD increase the stability of the hACE2-spike protein complex relative to the wild type, following the global stability trend and increasing the binding affinity. The value of binding-free energy calculated using molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) indicated that all mutations in the spike protein increased the contagiousness of SARS-CoV-2 variants. The findings of this study provide a foundation for developing effective interventions against these variants. Computational modeling elucidates that the spike protein of SARS-CoV-2 variants binds considerably stronger than the wild-type to hACE2.