Pharmacol Ther. 2021 Oct 27:108027. doi: 10.1016/j.pharmthera.2021.108027. Online ahead of print.

ABSTRACT

Responding quickly to emerging respiratory viruses, such as SARS-CoV-2 the causative agent of coronavirus disease 2019 (COVID-19) pandemic, is essential to stop uncontrolled spread of these pathogens and mitigate their socio-economic impact globally. This can be achieved through drug repurposing, which tackles inherent time- and resource-consuming processes associated with conventional drug discovery and development. In this review, we examine key preclinical and clinical therapeutic and prophylactic approaches that have been applied for treatment of SARS-CoV-2 infection. We break these strategies down into virus- versus host-targeting and discuss their reported efficacy, advantages, and disadvantages. Importantly, we highlight emerging evidence on application of host serine protease-inhibiting anticoagulants, such as nafamostat mesylate, as a potentially powerful therapy to inhibit virus activation and offer cross-protection against multiple strains of coronavirus, lower inflammatory response independent of its antiviral effect, and modulate clotting problems seen in COVID-19 pneumonia.

PMID:34718070 | DOI:10.1016/j.pharmthera.2021.108027

BMC Bioinformatics. 2021 Oct 30;22(1):532. doi: 10.1186/s12859-021-04406-y.

ABSTRACT

BACKGROUND: Drug repositioning has caught the attention of scholars at home and abroad due to its effective reduction of the development cost and time of new drugs. However, existing drug repositioning methods that are based on computational analysis are limited by sparse data and classic fusion methods; thus, we use autoencoders and adaptive fusion methods to calculate drug repositioning.

RESULTS: In this study, a drug repositioning algorithm based on a deep autoencoder and adaptive fusion was proposed to mitigate the problems of decreased precision and low-efficiency multisource data fusion caused by data sparseness. Specifically, a drug is repositioned by fusing drug-disease associations, drug target proteins, drug chemical structures and drug side effects. First, drug feature data integrated by drug target proteins and chemical structures were processed with dimension reduction via a deep autoencoder to characterize feature representations more densely and abstractly. Then, disease similarity was computed using drug-disease association data, while drug similarity was calculated with drug feature and drug-side effect data. Predictions of drug-disease associations were also calculated using a top-k neighbor method that is commonly used in predictive drug repositioning studies. Finally, a predicted matrix for drug-disease associations was acquired after fusing a wide variety of data via adaptive fusion. Based on experimental results, the proposed algorithm achieves a higher precision and recall rate than the DRCFFS, SLAMS and BADR algorithms with the same dataset.

CONCLUSION: The proposed algorithm contributes to investigating the novel uses of drugs, as shown in a case study of Alzheimer's disease. Therefore, the proposed algorithm can provide an auxiliary effect for clinical trials of drug repositioning.

PMID:34717542 | DOI:10.1186/s12859-021-04406-y

PLoS One. 2021 Oct 29;16(10):e0259129. doi: 10.1371/journal.pone.0259129. eCollection 2021.

ABSTRACT

Influenza viruses cause respiratory tract infections and substantial health concerns. Infection may result in mild to severe respiratory disease associated with morbidity and some mortality. Several anti-influenza drugs are available, but these agents target viral components and are susceptible to drug resistance. There is a need for new antiviral drug strategies that include repurposing of clinically approved drugs. Drugs that target cellular machinery necessary for influenza virus replication can provide a means for inhibiting influenza virus replication. We used RNA interference screening to identify key host cell genes required for influenza replication, and then FDA-approved drugs that could be repurposed for targeting host genes. We examined the effects of Clopidogrel and Triamterene to inhibit A/WSN/33 (EC50 5.84 uM and 31.48 uM, respectively), A/CA/04/09 (EC50 6.432 uM and 3.32 uM, respectively), and B/Yamagata/16/1988 (EC50 0.28 uM and 0.11 uM, respectively) replication. Clopidogrel and Triamterene provide a druggable approach to influenza treatment across multiple strains and subtypes.

PMID:34714852 | DOI:10.1371/journal.pone.0259129

Front Pharmacol. 2021 Oct 12;12:714643. doi: 10.3389/fphar.2021.714643. eCollection 2021.

ABSTRACT

This research was designed to analyze the composition of immune cells in obesity and identify novel and potent drugs for obesity management by epigenetic and transcriptomic conjoint analysis. DNA methylation data set (GSE166611) and mRNA expression microarray (GSE18897) were obtained from the Gene Expression Omnibus database. A total of 72 objects (35 obese samples and 37 controls) were included in the study. Immune cell composition analysis, drug repositioning, and gene set enrichment analysis (GSEA) were performed using CIBERSORT, connectivity map (CMap), and GSEA tools. Besides, we performed a single-cell RNA-seq of the immune cells from whole blood samples obtained from one obese patient and one healthy control. mRNA levels of drug target genes were analyzed by qPCR assay in blood samples from six patients and six healthy controls. Immune cell composition analysis found that CD8 + T cells and NK cells were significantly lower in the obese group. 11 drugs/compounds are considered to possess obesity-control potential, such as atorvastatin. Moreover, the expression of drug targets (STAT3, MCL1, PMAIP1, SOD2, FOX O 3, FOS, FKBP5) in obese patients were higher than those in controls. In conclusion, immune cells are potential therapeutic targets for obesity. Our results also contribute to accelerate research on drug development of obesity.

PMID:34712134 | PMC:PMC8546369 | DOI:10.3389/fphar.2021.714643

Med Hypotheses. 2021 Oct 20;157:110713. doi: 10.1016/j.mehy.2021.110713. Online ahead of print.

ABSTRACT

Osteoarthritis is still a disease burden for pharmaceutical scientists and strategy makers. It is associated with the chronic inflammation of joints especially weight-bearing joints like knee, hip, backbone, and phalanges. NSAIDs that are used for the management of inflammation associated with osteoarthritis have high side effects related to gastric upset, gastric ulcer, and long term treatment associated with liver and kidney damage. Nanotechnology has gained a huge scope for the management of arthritis as it can reach out to the deep inside the cell and alter cellular physiology as desired. The present study hypothesizes the use of polyion complex nanoparticles of hyaluronic acid linked Pentosan polysulfate sodium, a disease-modifying agent for the treatment of osteoarthritis administered through transdermal route. The hypothesis involves the use of drug repurposing as the drug was initially approved for interstitial cystitis, a condition of the urinary bladder associated with pain and swelling. Being very low oral bioavailability and gastric irritation profile, the transdermal route would be beneficial. To overcome the problem associated with the oral route, there is a need for the targeted approach that will particularly reach at inflammatory sites. Thereby transdermal delivery of hyaluronic acid linked Pentosan polysulfate sodium through polyion complex nanoparticle therapy will be a novel therapeutic approach to combat osteoarthritis.

PMID:34710749 | DOI:10.1016/j.mehy.2021.110713

Mol Syst Biol. 2021 Nov;17(11):e10396. doi: 10.15252/msb.202110396.

ABSTRACT

Treatment options for COVID-19, caused by SARS-CoV-2, remain limited. Understanding viral pathogenesis at the molecular level is critical to develop effective therapy. Some recent studies have explored SARS-CoV-2-host interactomes and provided great resources for understanding viral replication. However, host proteins that functionally associate with SARS-CoV-2 are localized in the corresponding subnetwork within the comprehensive human interactome. Therefore, constructing a downstream network including all potential viral receptors, host cell proteases, and cofactors is necessary and should be used as an additional criterion for the validation of critical host machineries used for viral processing. This study applied both affinity purification mass spectrometry (AP-MS) and the complementary proximity-based labeling MS method (BioID-MS) on 29 viral ORFs and 18 host proteins with potential roles in viral replication to map the interactions relevant to viral processing. The analysis yields a list of 693 hub proteins sharing interactions with both viral baits and host baits and revealed their biological significance for SARS-CoV-2. Those hub proteins then served as a rational resource for drug repurposing via a virtual screening approach. The overall process resulted in the suggested repurposing of 59 compounds for 15 protein targets. Furthermore, antiviral effects of some candidate drugs were observed in vitro validation using image-based drug screen with infectious SARS-CoV-2. In addition, our results suggest that the antiviral activity of methotrexate could be associated with its inhibitory effect on specific protein-protein interactions.

PMID:34709727 | DOI:10.15252/msb.202110396

Mol Syst Biol. 2021 Nov;17(11):e10260. doi: 10.15252/msb.202110260.

ABSTRACT

Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy.

PMID:34709707 | DOI:10.15252/msb.202110260

NPJ Syst Biol Appl. 2021 Oct 27;7(1):40. doi: 10.1038/s41540-021-00199-1.

ABSTRACT

The utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and an intuitive scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for four different cancers, outperforming six comparable state-of-the-art methods. We also show how this approach can deconvolute a drugs' mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs' effect on a given patient.

PMID:34707117 | DOI:10.1038/s41540-021-00199-1

Appl Biochem Biotechnol. 2021 Oct 27. doi: 10.1007/s12010-021-03718-2. Online ahead of print.

ABSTRACT

Cervical cancer is the second most common cause of cancer deaths in women worldwide and remains the main reason of mortality among women of reproductive age in developing countries. Nitric oxide is involved in several physiological functions inclusive of inflammatory and immune responses. However, the function of NO in tumor biology is debatable. The inducible NOS (iNOS/NOS2) isoform is the one responsible to maintain the levels of NO, and it exhibits pleotropic effects in various cancers with concentration-dependent pro- and anti-tumor effects. iNOS triggers angiogenesis and endothelial cell migration in tumors by regulating the levels of vascular endothelial growth factor (VEGF). In drug discovery, drug repurposing involves investigations of approved drug candidates to treat various other diseases. In this study, we used anti-cancer drugs and small molecules to target iNOS and identify a potential selective iNOS inhibitor. The structures of ligands were geometrically optimized and energy minimized using Hyperchem software. Molecular docking was performed using Molegro virtual docker, and ligands were selected based on MolDock score, Rerank score, and H-bonding energy. In the study shown, venetoclax compound demonstrated excellent binding affinity to iNOS protein. This compound exhibited the lowest MolDock score and Rerank score with better H-bonding energy to iNOS. The binding efficacy of venetoclax was analyzed by performing molecular docking and molecular dynamic simulations. Multiple parameters were used to analyze the simulation trajectory, like root mean square deviation (RMSD), radius of gyration (Rg), and hydrogen bond interactions. Based on the results, venetoclax emerges to be a promising potential iNOS inhibitor to curtail cervical cancer progression.

PMID:34705247 | DOI:10.1007/s12010-021-03718-2

Aging Cell. 2021 Oct 27:e13497. doi: 10.1111/acel.13497. Online ahead of print.

ABSTRACT

The study of parental lifespan has emerged as an innovative tool to advance aging biology and our understanding of the genetic architecture of human longevity and aging-associated diseases. Here, we leveraged summary statistics of a genome-wide association study including over one million parental lifespans to identify genetically regulated genes from the Genotype-Tissue Expression project. Through a combination of multi-tissue transcriptome-wide association analyses and genetic colocalization, we identified novel genes that may be associated with parental lifespan. Mendelian randomization (MR) analyses also identified circulating proteins and metabolites causally associated with parental lifespan and chronic diseases offering new drug repositioning opportunities such as those targeting apolipoprotein-B-containing lipoproteins. Liver expression of HP, the gene encoding haptoglobin, and plasma haptoglobin levels were causally linked with parental lifespan. Phenome-wide MR analyses were used to map genetically regulated genes, proteins and metabolites with other human traits as well as the disease-related phenome in the FinnGen cohorts (n = 135,638). Altogether, this study identified new candidate genes, circulating proteins and metabolites that may influence human aging as well as potential therapeutic targets for chronic diseases that warrant further investigation.

PMID:34704651 | DOI:10.1111/acel.13497

Sci Rep. 2021 Oct 26;11(1):21038. doi: 10.1038/s41598-021-00499-w.

ABSTRACT

Circadian rhythm is an approximately 24 h endogenous biological rhythm. Chronic disruption of the circadian clock leads to an increased risk of diabetes, cardiovascular disease, and cancer. Hence, it is important to develop circadian clock modulators. Natural organisms are a good source of several medicines currently in use. Crude drugs used in Japanese traditional Kampo medicine or folk medicines are an excellent source for drug discovery. Furthermore, identifying new functions for existing drugs, known as the drug repositioning approach, is a popular and powerful tool. In this study, we screened 137 crude drug extracts to act as circadian clock modulators in human U2OS cells stably expressing the clock reporter Bmal1-dLuc, and approximately 12% of these modulated the circadian rhythm. We further examined the effects of several crude drugs in Rat-1 fibroblasts stably expressing Per2-luc, explant culture of lung from Per2::Luciferase knockin mice, and zebrafish larvae in vivo. Notably, more than half of the major ingredients of these crude drugs were reported to target AKT and its relevant signaling pathways. As expected, analysis of the major ingredients targeting AKT signaling confirmed the circadian clock-modulating effects. Furthermore, activator and inhibitor of AKT, and triple knockdown of AKT isoforms by siRNA also modulated the circadian rhythm. This study, by employing the drug repositioning approach, shows that Kampo medicines are a useful source for the identification of underlying mechanisms of circadian clock modulators and could potentially be used in the treatment of circadian clock disruption.

PMID:34702865 | DOI:10.1038/s41598-021-00499-w

Brief Bioinform. 2021 Oct 26:bbab425. doi: 10.1093/bib/bbab425. Online ahead of print.

ABSTRACT

Target identification of small molecules is an important and still changeling work in the area of drug discovery, especially for botanical drug development. Indistinct understanding of the relationships of ligand-protein interactions is one of the main obstacles for drug repurposing and identification of off-targets. In this study, we collected 9063 crystal structures of ligand-binding proteins released from January, 1995 to April, 2021 in PDB bank, and split the complexes into 5133 interaction pairs of ligand atoms and protein fragments (covalently linked three heavy atoms) with interatomic distance ≤5 Å. The interaction pairs were grouped into ligand atoms with the same SYBYL atom type surrounding each type of protein fragment, which were further clustered via Bayesian Gaussian Mixture Model (BGMM). Gaussian distributions with ligand atoms ≥20 were identified as significant interaction patterns. Reliability of the significant interaction patterns was validated by comparing the difference of number of significant interaction patterns between the docked poses with higher and lower similarity to the native crystal structures. Fifty-one candidate targets of brucine, strychnine and icajine involved in Semen Strychni (Mǎ Qián Zǐ) and eight candidate targets of astragaloside-IV, formononetin and calycosin-7-glucoside involved in Astragalus (Huáng Qí) were predicted by the significant interaction patterns, in combination with docking, which were consistent with the therapeutic effects of Semen Strychni and Astragalus for cancer and chronic pain. The new strategy in this study improves the accuracy of target identification for small molecules, which will facilitate discovery of botanical drugs.

PMID:34698349 | DOI:10.1093/bib/bbab425

Noncoding RNA. 2021 Sep 22;7(4):60. doi: 10.3390/ncrna7040060.

ABSTRACT

The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 (2019-nCoV) has devastated global healthcare and economies. Despite the stabilization of infectivity rates in some developed nations, several countries are still under the grip of the pathogenic viral mutants that are causing a significant increase in infections and hospitalization. Given this urgency, targeting of key host factors regulating SARS-CoV-2 life cycle is postulated as a novel strategy to counter the virus and its associated pathological outcomes. In this regard, Poly (ADP)-ribose polymerase-1 (PARP-1) is being increasingly recognized as a possible target. PARP-1 is well studied in human diseases such as cancer, central nervous system (CNS) disorders and pathology of RNA viruses. Emerging evidence indicates that regulation of PARP-1 by non-coding RNAs such as microRNAs is integral to cell survival, redox balance, DNA damage response, energy homeostasis, and several other cellular processes. In this short perspective, we summarize the recent findings on the microRNA/PARP-1 axis and its therapeutic potential for COVID-19 pathologies.

PMID:34698261 | DOI:10.3390/ncrna7040060

Med Res Rev. 2021 Oct 26. doi: 10.1002/med.21862. Online ahead of print.

ABSTRACT

This review makes a critical evaluation of 61 peer-reviewed manuscripts that use a docking step in a virtual screening (VS) protocol to predict SARS-CoV-2 M-pro (M-pro) inhibitors in approved or investigational drugs. Various manuscripts predict different compounds, even when they use a similar initial dataset and methodology, and most of them do not validate their methodology or results. In addition, a set of known 150 SARS-CoV-2 M-pro inhibitors extracted from the literature and a second set of 81 M-pro inhibitors and 113 inactive compounds obtained from the COVID Moonshot project were used to evaluate the reliability of using docking scores as feasible predictors of the potency of a SARS-CoV-2 M-pro inhibitor. Using two SARS-CoV-2 M-pro structures and five protein-ligand docking programs, we proved that the correlation between the pIC50 and docking scores is not good. Neither was any correlation found between the pIC50 and the ∆G calculated with an MM-GBSA method. When a group of experimentally known inactive compounds was added, neither the docking scores or the ∆G were able to distinguish between compounds with or without M-pro experimental inhibitory activity. Performances improved when covalent and noncovalent inhibitors were treated separately, but were not good enough to fully support using a docking score as a cutoff value for selecting new putative M-pro inhibitors or predicting the relative bioactivity of a compound by comparison with a reference compound. The two sets of known SARS-CoV-2 M-pro inhibitors presented here could be used for validating future VS protocols which aim to predict M-pro inhibitors.

PMID:34697818 | DOI:10.1002/med.21862

In Vivo. 2021 Nov-Dec;35(6):3039-3044. doi: 10.21873/invivo.12599.

ABSTRACT

Certain diseases and age groups are associated with a higher incidence of cancer. Cancer prevention can be achieved using repositioned drugs that have anticancer ability, thereby reducing the incidence of cancer in susceptible individuals. This implies that the selection of repositioned drugs can have dual benefits: controlling pre-existing diseases and facilitating cancer prevention. This report outlines the rationale underlying drug repositioning for medications with an anticancer effect and discusses its advantages. We discuss repositioned drugs with anticancer effects that may contribute to cancer prevention in susceptible individuals and the general population with temporary, treatable conditions. The discussion of drug repositioning in this review should facilitate the initiation of clinical trials and lead to therapeutic application of such drugs to reduce the incidence of cancer in susceptible individuals.

PMID:34697135 | DOI:10.21873/invivo.12599

Cancer Genomics Proteomics. 2021 Nov-Dec;18(6):757-769. doi: 10.21873/cgp.20295.

ABSTRACT

BACKGROUND/AIM: Colon cancer is one of the most common cancer types and the second leading cause of death due to cancer. Many efforts have been performed towards the investigation of molecular alterations during colon cancer progression. However, the identification of stage-specific molecular markers remains a challenge. The aim of this study was to develop a novel computational methodology for the analysis of alterations in differential gene expression and pathway deregulation across colon cancer stages in order to reveal stage-specific biomarkers and reinforce drug repurposing investigation.

MATERIALS AND METHODS: Transcriptomic datasets of colon cancer were used to identify (a) differentially expressed genes with monotonicity in their fold changes (MEGs) and (b) perturbed pathways with ascending monotonic enrichment (MEPs) related to the number of the participating differentially expressed genes (DEGs), across the four colon cancer stages. Through an in silico drug repurposing pipeline we identified drugs that regulate the expression of MEGs and also target the resulting MEPs.

RESULTS: Our methodology highlighted 15 MEGs and 32 candidate repurposed drugs that affect their expression. We also found 51 MEPs divided into two groups according to their rate of DEG content alteration across colon cancer stages. Focusing on the target MEPs of the highlighted repurposed drugs, we found that one of them, the neuroactive ligand-receptor interaction, was targeted by the majority of the candidate drugs. Moreover, we observed that two of the drugs (PIK-75 and troglitazone) target the majority of the resulting MEPs.

CONCLUSION: These findings highlight significant genes and pathways that can be used as stage-specific biomarkers and facilitate the discovery of new potential repurposed drugs for colon cancer. We expect that the computational methodology presented can be applied in a similar way to the analysis of any progressive disease.

PMID:34697067 | DOI:10.21873/cgp.20295

Viruses. 2021 Oct 18;13(10):2092. doi: 10.3390/v13102092.

ABSTRACT

The dengue virus (DENV) causes the most prevalent arthropod-borne viral disease worldwide. While its incidence is increasing in many countries, there is no approved antiviral therapy currently available. In infected cells, the DENV induces extensive morphological alterations of the endoplasmic reticulum (ER) to generate viral replication organelles (vRO), which include convoluted membranes (CM) and vesicle packets (VP) hosting viral RNA replication. The viral non-structural protein NS4B localizes to vROs and is absolutely required for viral replication through poorly defined mechanisms, which might involve cellular protein partners. Previous interactomic studies identified the ATPase valosin-containing protein (VCP) as a DENV NS4B-interacting host factor in infected cells. Using both pharmacological and dominant-negative inhibition approaches, we show, in this study, that VCP ATPase activity is required for efficient DENV replication. VCP associates with NS4B when expressed in the absence of other viral proteins while in infected cells, both proteins colocalize within large DENV-induced cytoplasmic structures previously demonstrated to be CMs. Consistently, VCP inhibition dramatically reduces the abundance of DENV CMs in infected cells. Most importantly, using a recently reported replication-independent plasmid-based vRO induction system, we show that de novo VP biogenesis is dependent on VCP ATPase activity. Overall, our data demonstrate that VCP ATPase activity is required for vRO morphogenesis and/or stability. Considering that VCP was shown to be required for the replication of other flaviviruses, our results argue that VCP is a pan-flaviviral host dependency factor. Given that new generation VCP-targeting drugs are currently evaluated in clinical trials for cancer treatment, VCP may constitute an attractive broad-spectrum antiviral target in drug repurposing approaches.

PMID:34696522 | DOI:10.3390/v13102092

Viruses. 2021 Oct 15;13(10):2084. doi: 10.3390/v13102084.

ABSTRACT

Nuclear transport and vesicle trafficking are key cellular functions involved in the pathogenesis of RNA viruses. Among other pleiotropic effects on virus-infected host cells, ivermectin (IVM) inhibits nuclear transport mechanisms mediated by importins and atorvastatin (ATV) affects actin cytoskeleton-dependent trafficking controlled by Rho GTPases signaling. In this work, we first analyzed the response to infection in nasopharyngeal swabs from SARS-CoV-2-positive and -negative patients by assessing the gene expression of the respective host cell drug targets importins and Rho GTPases. COVID-19 patients showed alterations in KPNA3, KPNA5, KPNA7, KPNB1, RHOA, and CDC42 expression compared with non-COVID-19 patients. An in vitro model of infection with Poly(I:C), a synthetic analog of viral double-stranded RNA, triggered NF-κB activation, an effect that was halted by IVM and ATV treatment. Importin and Rho GTPases gene expression was also impaired by these drugs. Furthermore, through confocal microscopy, we analyzed the effects of IVM and ATV on nuclear to cytoplasmic importin α distribution, alone or in combination. Results showed a significant inhibition of importin α nuclear accumulation under IVM and ATV treatments. These findings confirm transcriptional alterations in importins and Rho GTPases upon SARS-CoV-2 infection and point to IVM and ATV as valid drugs to impair nuclear localization of importin α when used at clinically-relevant concentrations.

PMID:34696514 | DOI:10.3390/v13102084

Viruses. 2021 Oct 15;13(10):2082. doi: 10.3390/v13102082.

ABSTRACT

We report the in vitro efficacy of ion-channel inhibitors amantadine, memantine and rimantadine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In VeroE6 cells, rimantadine was most potent followed by memantine and amantadine (50% effective concentrations: 36, 80 and 116 µM, respectively). Rimantadine also showed the highest selectivity index, followed by amantadine and memantine (17.3, 12.2 and 7.6, respectively). Similar results were observed in human hepatoma Huh7.5 and lung carcinoma A549-hACE2 cells. Inhibitors interacted in a similar antagonistic manner with remdesivir and had a similar barrier to viral escape. Rimantadine acted mainly at the viral post-entry level and partially at the viral entry level. Based on these results, rimantadine showed the most promise for treatment of SARS-CoV-2.

PMID:34696509 | DOI:10.3390/v13102082

Exp Dermatol. 2021 Oct 25. doi: 10.1111/exd.14481. Online ahead of print.

ABSTRACT

Chronic wounds present a major disease burden in people with recessive dystrophic epidermolysis bullosa (RDEB), an inherited blistering skin disorder caused by mutations in COL7A1 encoding type VII collagen, the major component of anchoring fibrils at the dermal-epidermal junction. Treatment of RDEB wounds is mostly symptomatic and there is considerable unmet need in trying to improve and accelerate wound healing. In this study, we defined transcriptomic profiles and gene pathways in RDEB wounds and compared these to intact skin in RDEB and healthy control subjects. We then used a reverse transcriptomics approach to discover drugs or compounds which might restore RDEB wound profiles towards intact skin. Differential expression analysis identified >2000 differences between RDEB wounds and intact skin, with RDEB wounds displaying aberrant cytokine-cytokine interactions, Toll-like receptor signalling, and JAK-STAT signalling pathways. In silico prediction for compounds that reverse gene expression signatures highlighted methotrexate as a leading candidate. Overall, this study provides insight into the molecular profiles of RDEB wounds and underscores the possible clinical value of reverse transcriptomics data analysis in RDEB, and the potential of this approach in discovering or repurposing drugs for other diseases.

PMID:34694680 | DOI:10.1111/exd.14481