The oral microbiome of treated and untreated chronic HCV infection: A preliminary study.

OBJECTIVES: Chronic hepatitis C virus (HCV) infection is a debilitating disease that is lately treated using direct-acting antivirals (DAAs). Changes in the oral microbiome were detected in other liver diseases; however, oral microbiome was never investigated in patients having chronic HCV infection, whether pre- or post-treatment. MATERIALS AND METHODS: This case-control preliminary study enrolled three equal groups: Group (I): untreated HCV patients; group (II): HCV patients who achieved viral clearance after DAA administration; and group (III): healthy controls. For each participant, a buccal swab was harvested and its 16S rRNA was sequenced. RESULTS: The oral microbiome of chronic HCV patients had a significantly distinct bacterial community compared to healthy controls, characterized by high diversity and abundance of certain pathogenic species. These changes resemble that of oral lichen planus patients. After treatment by DAAs, the oral microbiome shifted to a community with partial similarity to both the diseased and the healthy ones. CONCLUSIONS: Chronic HCV is associated with dysbiotic oral microbiome having abundant pathogenic bacteria. With HCV clearance by DAAs, the oral microbiome shifts to approach the healthy composition.

In Silico and In Vivo Evaluation of SARS-CoV-2 Predicted Epitopes-Based Candidate Vaccine.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2, the causative agent of coronavirus disease (COVID-19)) has caused relatively high mortality rates in humans throughout the world since its first detection in late December 2019, leading to the most devastating pandemic of the current century. Consequently, SARS-CoV-2 therapeutic interventions have received high priority from public health authorities. Despite increased COVID-19 infections, a vaccine or therapy to cover all the population is not yet available. Herein, immunoinformatics and custommune tools were used to identify B and T-cells epitopes from the available SARS-CoV-2 sequences spike (S) protein. In the in silico predictions, six B cell epitopes QTGKIADYNYK, TEIYQASTPCNGVEG, LQSYGFQPT, IRGDEVRQIAPGQTGKIADYNYKLPD, FSQILPDPSKPSKRS and PFAMQMAYRFNG were cross-reacted with MHC-I and MHC-II T-cells binding epitopes and selected for vaccination in experimental animals for evaluation as candidate vaccine(s) due to their high antigenic matching and conserved score. The selected six peptides were used individually or in combinations to immunize female Balb/c mice. The immunized mice raised reactive antibodies against SARS-CoV-2 in two different short peptides located in receptor binding domain and S2 region. In combination groups, an additive effect was demonstrated in-comparison with single peptide immunized mice. This study provides novel epitope-based peptide vaccine candidates against SARS-CoV-2.

Evaluation of computational techniques for predicting non-synonymous single nucleotide variants pathogenicity.

The human genetic diseases associated with many factors, one of these factors is the non-synonymous Single Nucleotide Variants (nsSNVs) cause single amino acid change with another resulting in protein function change leading to disease. Many computational techniques have been released to expect the impacts of amino acid alteration on protein function and classify mutations as pathogenic or neutral. Here in this article, we assessed the performance of eight techniques; FATHMM, SIFT, Provean, iFish, Mutation Assessor, PANTHER, SNAP2, and PON- P2 using a VaribenchSelectedPure dataset of 2144 pathogenic variants and 3777 neutral variants extracted from the free standard database "Varibench." The first five techniques achieve (45.60-83.75) % specificity, (52.64-94.13) % sensitivity, (51.00-88.90) % AUC, and (49.76-88.24) % ACC on whole dataset, while all eight techniques achieve (36.54-77.88) % specificity, (50.00-75.00) % sensitivity, (51.00-76.40) % AUC, and (25.00-77.78) % ACC on random sample dataset. We also created a Meta classifier (CSTJ48) that combines FATHMM, iFish, and Mutation Assessor. It registers 96.33% specificity, 86.07% sensitivity, 91.20% AUC, and 91.89 ACC. By comparing the results, it's clear that FATHMM gives the highest performance over the seven individual techniques, where it achieves 83.75% and 77.88% specificity, 94.13%, and 75.00% sensitivity, 88.90% and 76.40% AUC, and 88.24% and 77.78% ACC on whole and random sample dataset, respectively. Also, the launched Meta classifier (CSTJ48) is outperforming over all the eight individual tools that compared here.

DeNovo: virus-host sequence-based protein-protein interaction prediction.

MOTIVATION: Can we predict protein-protein interactions (PPIs) of a novel virus with its host? Three major problems arise: the lack of known PPIs for that virus to learn from, the cost of learning about its proteins and the sequence dissimilarity among viral families that makes most methods inapplicable or inefficient. We develop DeNovo, a sequence-based negative sampling and machine learning framework that learns from PPIs of different viruses to predict for a novel one, exploiting the shared host proteins. We tested DeNovo on PPIs from different domains to assess generalization. RESULTS: By solving the challenge of generating less noisy negative interactions, DeNovo achieved accuracy up to 81 and 86% when predicting PPIs of viral proteins that have no and distant sequence similarity to the ones used for training, receptively. This result is comparable to the best achieved in single virus-host and intra-species PPI prediction cases. Thus, we can now predict PPIs for virtually any virus infecting human. DeNovo generalizes well; it achieved near optimal accuracy when tested on bacteria-human interactions. AVAILABILITY AND IMPLEMENTATION: Code, data and additional supplementary materials needed to reproduce this study are available at: https://bioinformatics.cs.vt.edu/~alzahraa/denovo CONTACT: alzahraa@vt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

New benzimidazoles and their antitumor effects with Aurora A kinase and KSP inhibitory activities.

A newly synthesized series of anticancer compounds comprising thiazolo[3,2-a]pyrimidine derivatives 6a-q bearing a benzimidazole moiety was produced via a one-pot reaction of N-(4-(1H-benzo[d]imidazol-2-yl)phenyl)-2-cyanoacetamide 5 with 2-aminothiazole and an appropriate aromatic aldehyde. Compound 7 was obtained via the reaction of 4-(1H-benzo[d]imidazol-2yl)benzenamide 1 with carbon disulphide and methyl iodide in the presence of concentrated aqueous solution of NaOH, then treated with o-phenylenediamine to give N-(4-1H-benzo[d]imidazol-2-yl)phenyl)-1H-benzo[d]imidazol-2-amine 8. The structures of the newly synthesized compounds were confirmed by analytical and spectroscopic measurements (IR, MS, and (1) H NMR). The synthesized products were screened and studied for their in vitro antitumor activity against three human cancer cell lines (namely colorectal cancer cell line HCT116, human liver cancer cell line HepG2, and human ovarian cancer cell line A2780) and their Aurora A kinase and KSP inhibitory activities. All newly synthesized compounds revealed marked results comparable with the standard drug CK0106023. The compounds 6e and 6k of the thiazolopyrimidine derivatives were the most active compounds when tested against the three cell lines in comparison with the standard drug CK0106023, and showed potent dual KSP and Aurora A kinase inhibition.

Genetic variability of the core protein in hepatitis C virus genotype 4 in Saudi Arabian patients and its implication on pegylated interferon and ribavirin therapy.

BACKGROUND: Hepatitis C virus (HCV) shows a remarkable genetic diversity, contributing to its high persistence and varied susceptibilities to antiviral treatment. Previous studies have reported that the substitution of amino acids in the HCV subgenotype 1b core protein in infected patients is associated with a poor response to pegylated interferon and ribavirin (PEG-IFN/RBV) combined therapy. OBJECTIVES: Because the role of the core protein in HCV genotype 4 infections is unclear, we aimed in this study to compare the full-length core protein sequences of HCV genotype 4 between Saudi patients who responded (SVR) and did not respond (non-SVR) to PEG-IFN/RBV therapy. STUDY DESIGN: Direct sequencing of the full-length core protein and bioinformatics sequence analysis were utilized. RESULTS: Our data revealed that there is a significant association between core protein mutations, particularly at position 70 (Arg70Gln), and treatment outcome in HCV subgenotype 4d patients. However, HCV subgenotype 4a showed no significant association between core protein mutations and treatment outcome. In addition, amino acid residue at position 91 was well-conserved among studied patients where Cys91 is the dominant amino acid residue. CONCLUSIONS: These findings provide a new insight into HCV genotype 4 among affected Saudi population where the knowledge of HCV core gene polymorphisms is inadequate.

Preparation and Optimization of MiR-375 Nano-Vector Using Two Novel Chitosan-Coated Nano-Structured Lipid Carriers as Gene Therapy for Hepatocellular Carcinoma.

Currently, there is still a lack of effective carriers with minimal side effects to deliver therapeutic miRNA. Thus, it is crucial to optimize novel drug delivery systems. MiR-375 has proven superior therapeutic potency in Hepatocellular carcinoma (HCC). The purpose of this study was to fabricate 2 novel and smart nano-carriers for the transportation efficiency of miR-375 in HCC cells and enhance its anti-tumor effects. We established the miR-375 construct through the pEGP- miR expression vector. Two nano-carriers of solid/liquid lipids and chitosan (CS) were strategically selected, prepared by high-speed homogenization, and optimized by varying nano-formulation factors. Thus, the two best nano-formulations were designated as F1 (0.5% CS) and F2 (1.5% CS) and were evaluated for miR-375 conjugation efficiency by gel electrophoresis and nanodrop assessment. Then, physio-chemical characteristics and stability tests for the miR-375 nano-plexes were all studied. Next, its efficiencies as replacement therapy in HepG2 cells have been assessed by fluorescence microscopy, flow cytometry, and cytotoxicity assay. The obtained data showed that two cationic nanostructured solid/liquid lipid carriers (NSLCs); F1 and F2 typically had the best physio-chemical parameters and long-term stability. Moreover, both F1 and F2 could form nano-plexes with the anionic miR-375 construct at weight ratios 250/1 and 50/1 via electrostatic interactions. In addition, these nano-plexes exhibited physical stability after three months and protected miR-375 from degradation in the presence of 50% fetal bovine serum (FBS). Furthermore, both nano-plexes could simultaneously deliver miR-375 into HepG2 cells and they ensure miR re-expression even in the presence of 50% FBS compared to free miR-375 (p-value < 0.001). Moreover, both F1 and F2 alone significantly exhibited minimal cytotoxicity in treated cells. In contrast, the nano-plexes significantly inhibited cell growth compared to free miR-375 or doxorubicin (DOX), respectively. More importantly, F2/miR-375 nano-plex exhibited more anti-proliferative activity in treated cells although its IC50 value was 55 times lower than DOX (p-value < 0.001). Collectively, our findings clearly emphasized the multifunctionality of the two CS-coated NSLCs in terms of their enhanced biocompatibility, biostability, conjugation, and transfection efficiency of therapeutic miR-375. Therefore, the NSLCs/miR-375 nano-plexes could serve as a novel and promising therapeutic strategy for HCC.

SIRT1 as a potential key regulator for mediating apoptosis in oropharyngeal cancer using cyclophosphamide and all-trans retinoic acid.

Although cyclophosphamide (CTX) has been used for recurrent or metastatic head and neck cancers, resistance is usually expected. Thus, we conducted this study to examine the effect of adding all-trans retinoic acid (ATRA) to CTX, to increase efficacy of CTX and reduce the risk of resistance developed. In this study, we investigated the combined effect of ATRA and CTX on the expression of apoptotic and angiogenesis markers in oropharyngeal carcinoma cell line (NO3), and the possible involved mechanisms. ATRA and CTX in combination significantly inhibited the proliferation of NO3 cells. Lower dose of CTX in combination with ATRA exhibited significant cytotoxicity than that of CTX when used alone, implying lower expected toxicity. Results showed that ATRA and CTX modulated oxidative stress; increased NOx and MDA, reduced GSH, and mRNA expression of Cox-2, SIRT1 and AMPK. Apoptosis was induced through elevating mRNA expressions of Bax and PAR-4 and suppressing that of Bcl-xl and Bcl-2, parallel with increased caspases 3 and 9 and decreased VEGF, endothelin-1 and CTGF levels. The primal action of the combined regimen on inflammatory signaling highlights its impact on cell death in NO3 cell line which was mediated by oxidative stress associated with apoptosis and suppression of angiogenesis.

In Silico Analysis of MicroRNA Expression Data in Liver Cancer.

Abnormal miRNA expression has been evidenced to be directly linked to HCC initiation and progression. This study was designed to detect possible prognostic, diagnostic, and/or therapeutic miRNAs for HCC using computational analysis of miRNAs expression. Methods: miRNA expression datasets meta-analysis was performed using the YM500v2 server to compare miRNA expression in normal and cancerous liver tissues. The most significant differentially regulated miRNAs in our study undergone target gene analysis using the mirWalk tool to obtain their validated and predicted targets. The combinatorial target prediction tool; miRror Suite was used to obtain the commonly regulated target genes. Functional enrichment analysis was performed on the resulting targets using the DAVID tool. A network was constructed based on interactions among microRNAs, their targets, and transcription factors. Hub nodes and gatekeepers were identified using network topological analysis. Further, we performed patient data survival analysis based on low and high expression of identified hubs and gatekeeper nodes, patients were stratified into low and high survival probability groups. Results: Using the meta-analysis option in the YM500v2 server, 34 miRNAs were found to be significantly differentially regulated (P-value ⩽ .05); 5 miRNAs were down-regulated while 29 were up-regulated. The validated and predicted target genes for each miRNA, as well as the combinatorially predicted targets, were obtained. DAVID enrichment analysis resulted in several important cellular functions that are directly related to the main cancer hallmarks. Among these functions are focal adhesion, cell cycle, PI3K-Akt signaling, insulin signaling, Ras and MAPK signaling pathways. Several hub genes and gatekeepers were found that could serve as potential drug targets for hepatocellular carcinoma. POU2F1 and PPARA showed a significant difference between low and high survival probabilities (P-value ⩽ .05) in HCC patients. Our study sheds light on important biomarker miRNAs for hepatocellular carcinoma along with their target genes and their regulated functions.

A structural-based virtual screening and in vitro validation reveals novel effective inhibitors for SARS-CoV-2 helicase and endoribonuclease.

Researchers worldwide are looking for molecules that might disrupt the COVID-19 life cycle. Endoribonuclease, which is responsible for processing viral RNA to avoid detection by the host defense system, and helicase, which is responsible for unwinding the RNA helices for replication, are two key non-structural proteins. This study performs a hierarchical structure-based virtual screening approach for NSP15 and helicase to reach compounds with high binding probabilities. In this investigation, we incorporated a variety of filtering strategies for predicting compound interactions. First, we evaluated 756,275 chemicals from four databases using a deep learning method (NCI, Drug Bank, Maybridge, and COCONUT). Following that, two docking techniques (extra precision and induced fit) were utilized to evaluate the compounds' binding affinity, followed by molecular dynamic simulation supported by the MM-GBSA free binding energy calculation. Remarkably, two compounds (90616 and CNP0111740) exhibited high binding affinity values of -66.03 and -12.34 kcal/mol for helicase and NSP15, respectively. The VERO-E6 cell line was employed to test their in vitro therapeutic impact. The CC50 for CNP0111740 and 90616 were determined to be 102.767 µg/ml and 379.526 µg/ml, while the IC50 values were 140.176 µg/ml and 5.147 µg/ml, respectively. As a result, the selectivity index for CNP0111740 and 90616 is 0.73 and 73.73, respectively. Finally, these compounds were found to be novel, effective inhibitors for the virus; however, further in vivo validation is needed.Communicated by Ramaswamy H. Sarma.

A cellular screening platform, stably expressing DENV2 NS5, defines a novel anti-DENV mechanism of action of Apigenin based on STAT2 activation.

Type I interferon (IFN-I) evasion by Dengue virus (DENV) is key in DENV pathogenesis. The non-structural protein 5 (NS5) antagonizes IFN-I response through the degradation of the signal transducer and activator of transcription 2 (STAT2). We developed a K562 cell-based platform, for high throughput screening of compounds potentially counteracting the NS5-mediated antagonism of IFN-I signaling. Upon a screening with a library of 1220 approved drugs, 3 compounds previously linked to DENV inhibition (Apigenin, Chrysin, and Luteolin) were identified. Luteolin and Apigenin determined a significant inhibition of DENV2 replication in Huh7 cells and the restoration of STAT2 phosphorylation in both cell systems. Apigenin and Luteolin were able to stimulate STAT2 even in the absence of infection. Despite the "promiscuous" and "pan-assay-interfering" nature of Luteolin, Apigenin promotes STAT2 Tyr 689 phosphorylation and activation, highlighting the importance of screening for compounds able to interact with host factors, to counteract viral proteins capable of dampening innate immune responses.

Multiple virtual screening approaches for finding new hepatitis C virus RNA-dependent RNA polymerase inhibitors: structure-based screens and molecular dynamics for the pursue of new poly pharmacological inhibitors.

The RNA polymerase NS5B of Hepatitis C virus (HCV) is a well-characterised drug target with an active site and four allosteric binding sites. This work presents a workflow for virtual screening and its application to Drug Bank screening targeting the Hepatitis C Virus (HCV) RNA polymerase non-nucleoside binding sites. Potential polypharmacological drugs are sought with predicted active inhibition on viral replication, and with proven positive pharmaco-clinical profiles. The approach adopted was receptor-based. Docking screens, guided with contact pharmacophores and neural-network activity prediction models on all allosteric binding sites and MD simulations, constituted our analysis workflow for identification of potential hits. Steps included: 1) using a two-phase docking screen with Surflex and Glide Xp. 2) Ranking based on scores, and important H interactions. 3) a machine-learning target-trained artificial neural network PIC prediction model used for ranking. This provided a better correlation of IC50 values of the training sets for each site with different docking scores and sub-scores. 4) interaction pharmacophores-through retrospective analysis of protein-inhibitor complex X-ray structures for the interaction pharmacophore (common interaction modes) of inhibitors for the five non-nucleoside binding sites were constructed. These were used for filtering the hits according to the critical binding feature of formerly reported inhibitors. This filtration process resulted in identification of potential new inhibitors as well as formerly reported ones for the thumb II and Palm I sites (HCV-81) NS5B binding sites. Eventually molecular dynamics simulations were carried out, confirming the binding hypothesis and resulting in 4 hits.

The design of optimal therapeutic small interfering RNA molecules targeting diverse strains of influenza A virus.

MOTIVATION: There is an urgent need for new medications to combat influenza pandemics. METHODS: Using the genome analysis of the influenza A virus performed previously, we designed and performed a combinatorial exhaustive systematic methodology for optimal design of universal therapeutic small interfering RNA molecules (siRNAs) targeting all diverse influenza A viral strains. The rationale was to integrate the factors for highly efficient design in a pipeline of analysis performed on possible influenza-targeting siRNAs. This analysis selects specific siRNAs that has the ability to target highly conserved, accessible and biologically significant regions. This would require minimal dosage and side effects. RESULTS AND DISCUSSION: First, >6000 possible siRNAs were designed. Successive filtration followed where a novel method for siRNA scoring filtration layers was implemented. This method excluded siRNAs below the 90% experimental inhibition mapped scores using the intersection of 12 different scoring algorithms. Further filtration of siRNAs is done by eliminating those with off-targets in the human genome and those with undesirable properties and selecting siRNA targeting highly probable single-stranded regions. Finally, the optimal properties of the siRNA were ensured through selection of those targeting 100% conserved, biologically functional short motifs. Validation of a predicted active (sh114) and a predicted inactive (sh113) (that was filtered out in Stage 8) silencer of the NS1 gene showed significant inhibition of the NS1 gene for sh114, with negligible decrease for sh113 which failed target accessibility. This demonstrated the fertility of this methodology. CONTACT: mahef@aucegypt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MysiRNA: improving siRNA efficacy prediction using a machine-learning model combining multi-tools and whole stacking energy (ΔG).

The investigation of small interfering RNA (siRNA) and its posttranscriptional gene-regulation has become an extremely important research topic, both for fundamental reasons and for potential longer-term therapeutic benefits. Several factors affect the functionality of siRNA including positional preferences, target accessibility and other thermodynamic features. State of the art tools aim to optimize the selection of target siRNAs by identifying those that may have high experimental inhibition. Such tools implement artificial neural network models as Biopredsi and ThermoComposition21, and linear regression models as DSIR, i-Score and Scales, among others. However, all these models have limitations in performance. In this work, a neural-network trained new siRNA scoring/efficacy prediction model was developed based on combining two existing scoring algorithms (ThermoComposition21 and i-Score), together with the whole stacking energy (ΔG), in a multi-layer artificial neural network. These three parameters were chosen after a comparative combinatorial study between five well known tools. Our developed model, 'MysiRNA' was trained on 2431 siRNA records and tested using three further datasets. MysiRNA was compared with 11 alternative existing scoring tools in an evaluation study to assess the predicted and experimental siRNA efficiency where it achieved the highest performance both in terms of correlation coefficient (R(2)=0.600) and receiver operating characteristics analysis (AUC=0.808), improving the prediction accuracy by up to 18% with respect to sensitivity and specificity of the best available tools. MysiRNA is a novel, freely accessible model capable of predicting siRNA inhibition efficiency with improved specificity and sensitivity. This multiclassifier approach could help improve the performance of prediction in several bioinformatics areas. MysiRNA model, part of MysiRNA-Designer package [1], is expected to play a key role in siRNA selection and evaluation.

Identifying potential novel insights for COVID-19 pathogenesis and therapeutics using an integrated bioinformatics analysis of host transcriptome.

The molecular mechanisms underlying the pathogenesis of COVID-19 have not been fully discovered. This study aims to decipher potentially hidden parts of the pathogenesis of COVID-19, potential novel drug targets, and identify potential drug candidates. Two gene expression profiles were analyzed, and overlapping differentially expressed genes (DEGs) were selected for which top enriched transcription factors and kinases were identified, and pathway analysis was performed. Protein-protein interaction (PPI) of DEGs was constructed, hub genes were identified, and module analysis was also performed. DGIdb database was used to identify drugs for the potential targets (hub genes and the most enriched transcription factors and kinases for DEGs). A drug-potential target network was constructed, and drugs were ranked according to the degree. L1000FDW was used to identify drugs that can reverse transcriptional profiles of COVID-19. We identified drugs currently in clinical trials, others predicted by different methods, and novel potential drug candidates Entrectinib, Omeprazole, and Exemestane for combating COVID-19. Besides the well-known pathogenic pathways, it was found that axon guidance is a potential pathogenic pathway. Sema7A, which may exacerbate hypercytokinemia, is considered a potential novel drug target. Another potential novel pathway is related to TINF2 overexpression, which may induce potential telomere dysfunction and damage DNA that may exacerbate lung fibrosis. This study identified new potential insights regarding COVID-19 pathogenesis and treatment, which might help us improve our understanding of the mechanisms of COVID-19.

Derivation of "Egyptian varices prediction (EVP) index": A novel noninvasive index for diagnosing esophageal varices in HCV Patients.

Introduction: Esophageal Varices (EVs) is one of the major dangerous complications of liver fibrosis. Upper Gastrointestinal (UGI) Endoscopy is necessary for its diagnosis. Repeated examinations for EVs screening severely burden endoscopic units in terms of cost and other side implications; moreover, the lack of public health resources in rural areas and primary hospitals should be considered, particularly in developing countries. So, an accurate noninvasive marker for EV is highly needed for liver disease patients. Objectives: This study sought to evaluate the values of several indices to determine how adequate are they in predicting EV and build a novel accurate prediction index. Methods: Five thousand and thirteen patients were enrolled. The laboratory tests, abdominal ultrasonography, liver stiffness measurement using Fibro-scan, and UGI endoscopy were performed. Ten common indices: Fib-4 score, AST-to-platelet ratio index, Fibrosis index, AST/ALT ratio Varices Prediction Rule, Baveno VI, APRI-Fib4 Combo, King score, "Model for End-Stage Liver Disease", and Lok Score were calculated. The significant predictors for EVs were identified by using "P-value Correlation-based Filter Selection" method, where a novel Egyptian Varices Prediction (EVP) index was developed using binary logistic regression. The diagnostic performance was evaluated by some parameters and the Area Under Curve (AUC). Results: EVP Index was correlated to EVs at 0.5; it achieved higher performance (AUC 0.788, accuracy 73.3%, and sensitivity 78%) than the other indices at a cutoff point of 0.423. Conclusion: EVP Index was a good noninvasive predictor. It had an acceptable performance for diagnosing EVs and it was only required regular laboratory tests and imaging data. It can provide a tool for classifying or arranging the patients according to the degree pre-emptive for selective endoscopy and the degree of severity. Also, it will enable clinicians to concentrate on one marker instead of a wide set of parameters.

Drug repurposing through virtual screening and in vitro validation identifies tigecycline as a novel putative HCV polymerase inhibitor.

The repurposing of marketed drugs for new indications is an elegant strategy to quickly and cost-efficiently address unmet medical needs. The hepatitis C virus (HCV) RNA-dependent RNA polymerase (RdRp) has been shown to be a valid drug target. We performed structure-based virtual screening to assess the off-label utilization of existing drugs as novel HCV inhibitors. The virtual screen showed that tigecycline could potentially dock with high affinity to the palm site of the HCV RdRp. In vitro validation showed that tigecycline had therapeutic indexes (CC50/EC50) greater than 13 and 6.5 against infectious HCV and subgenomic HCV replicons, respectively. Furthermore, tigecycline displayed synergistic activity with sofosbuvir and daclatasvir against HCV. In silico screening identified tigecycline as a putative inhibitor of HCV RdRp, which was validated in vitro and demonstrated synergistic effects in combination with first-line anti-HCV therapies.

Progress, pitfalls, and path forward of drug repurposing for COVID-19 treatment.

On 30 January 2020, the World Health Organization (WHO) declared the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic a public health emergency of international concern. The viral outbreak led in turn to an exponential growth of coronavirus disease 2019 (COVID-19) cases, that is, a multiorgan disease that has led to more than 6.3 million deaths worldwide, as of June 2022. There are currently few effective drugs approved for treatment of SARS-CoV-2/COVID-19 patients. Many of the compounds tested so far have been selected through a drug repurposing approach, that is, by identifying novel indications for drugs already approved for other conditions. We here present an up-to-date review of the main Food and Drug Administration (FDA)-approved drugs repurposed against SARS-CoV-2 infection, discussing their mechanism of action and their most important preclinical and clinical results. Reviewed compounds were chosen to privilege those that have been approved for use in SARS-CoV-2 patients or that have completed phase III clinical trials. Moreover, we also summarize the evidence on some novel and promising repurposed drugs in the pipeline. Finally, we discuss the current stage and possible steps toward the development of broadly effective drug combinations to suppress the onset or progression of COVID-19.

Targeting SARS-CoV-2 endoribonuclease: a structure-based virtual screening supported by in vitro analysis.

Researchers are focused on discovering compounds that can interfere with the COVID-19 life cycle. One of the important non-structural proteins is endoribonuclease since it is responsible for processing viral RNA to evade detection of the host defense system. This work investigates a hierarchical structure-based virtual screening approach targeting NSP15. Different filtering approaches to predict the interactions of the compounds have been included in this study. Using a deep learning technique, we screened 823,821 compounds from five different databases (ZINC15, NCI, Drug Bank, Maybridge, and NCI Diversity set III). Subsequently, two docking protocols (extra precision and induced fit) were used to assess the binding affinity of the compounds, followed by molecular dynamic simulation supported by the MM-GBSA free binding energy. Interestingly, one compound (ZINC000104379474) from the ZINC15 database has been found to have a good binding affinity of - 7.68 kcal/Mol. The VERO-E6 cell line was used to investigate its therapeutic effect in vitro. Half-maximal cytotoxic concentration and Inhibitory concentration 50 were determined to be 0.9 mg/ml and 0.01 mg/ml, respectively; therefore, the selectivity index is 90. In conclusion, ZINC000104379474 was shown to be a good hit for targeting the virus that needs further investigations in vivo to be a drug candidate.

Generation, characterization, and docking studies of DNA-hydrolyzing recombinant F(ab) antibodies.

Previously we established a series of catalytic antibodies (catAbs) capable of hydrolyzing DNA prepared by hybridoma technology. A group of these catAbs exhibited high reactivity and substrate specificity. To determine the molecular basis for these catAbs, we cloned, sequenced, and expressed the variable regions of this group of antibodies as functional F(ab) fragments. The nucleotide and deduced amino acid sequences of the expressed light chain (Vκ) germline gene assignments confidently belonged to germline family Vκ1A, gene bb1.1 and GenBank accession number EF672207 while heavy chain variable region V(H) genes belonged to V(H) 1/V(H) J558, gene V130.3 and GenBank accession number EF672221. A well-established expression system based on the pARA7 vector was examined for its ability to produce catalytically active antibodies. Recombinant F(ab) (rF(ab) ) fragments were purified and their hydrolyzing activity was analyzed against supercoiled pUC19 plasmid DNA (scDNA). The study of rF(ab) provides important information about the potential catalytic activities of antibodies whose structure allows us to understand their basis of catalysis. Molecular surface analysis and docking studies were performed on the molecular interactions between the antibodies and poly(dA9), poly(dG9), poly(dT9), and poly(dC9) oligomers. Surface analysis identified the important sequence motifs at the binding sites, and different effects exerted by arginine and tyrosine residues at different positions in the light and heavy chains. This study demonstrates the potential usefulness of the protein DNA surrogate in the investigation of the origin of anti-DNA antibodies. These studies may define important features of DNA catAbs.