Cells. 2022 Nov 3;11(21):3482. doi: 10.3390/cells11213482.

ABSTRACT

Pancreatic cancer is a leading cause of cancer mortality and is projected to become the second-most common cause of cancer mortality in the next decade. While gene-wide association studies and next generation sequencing analyses have identified molecular patterns and transcriptome profiles with prognostic relevance, therapeutic opportunities remain limited. Among the genes that are upregulated in pancreatic ductal adenocarcinomas (PDAC), the leukaemia inhibitory factor (LIF), a cytokine belonging to IL-6 family, has emerged as potential therapeutic candidate. LIF is aberrantly secreted by tumour cells and promotes tumour progression in pancreatic and other solid tumours through aberrant activation of the LIF receptor (LIFR) and downstream signalling that involves the JAK1/STAT3 pathway. Since there are no LIFR antagonists available for clinical use, we developed an in silico strategy to identify potential LIFR antagonists and drug repositioning with regard to LIFR antagonists. The results of these studies allowed the identification of mifepristone, a progesterone/glucocorticoid antagonist, clinically used in medical abortion, as a potent LIFR antagonist. Computational studies revealed that mifepristone binding partially overlapped the LIFR binding site. LIF and LIFR are expressed by human PDAC tissues and PDAC cell lines, including MIA-PaCa-2 and PANC-1 cells. Exposure of these cell lines to mifepristone reverses cell proliferation, migration and epithelial mesenchymal transition induced by LIF in a concentration-dependent manner. Mifepristone inhibits LIFR signalling and reverses STAT3 phosphorylation induced by LIF. Together, these data support the repositioning of mifepristone as a potential therapeutic agent in the treatment of PDAC.

PMID:36359879 | DOI:10.3390/cells11213482

Biomolecules. 2022 Nov 10;12(11):1666. doi: 10.3390/biom12111666.

ABSTRACT

Drug repositioning, an important method of drug development, is utilized to discover investigational drugs beyond the originally approved indications, expand the application scope of drugs, and reduce the cost of drug development. With the emergence of increasingly drug-disease-related biological networks, the challenge still remains to effectively fuse biological entity data and accurately achieve drug-disease repositioning. This paper proposes a new drug repositioning method named EMPHCN based on enhanced message passing and hypergraph convolutional networks (HGCN). It firstly constructs the homogeneous multi-view information with multiple drug similarity features and then extracts the intra-domain embedding of drugs through the combination of HGCN and channel attention mechanism. Secondly, inter-domain information of known drug-disease associations is extracted by graph convolutional networks combining node and edge embedding (NEEGCN), and a heterogeneous network composed of drugs, proteins and diseases is built as an important auxiliary to enhance the inter-domain message passing of drugs and diseases. Besides, the intra-domain embedding of diseases is also extracted through HGCN. Ultimately, intra-domain and inter-domain embeddings of drugs and diseases are integrated as the final embedding for calculating the drug-disease correlation matrix. Through 10-fold cross-validation on some benchmark datasets, we find that the AUPR of EMPHCN reaches 0.593 (T1) and 0.526 (T2), respectively, and the AUC achieves 0.887 (T1) and 0.961 (T2) respectively, which shows that EMPHCN has an advantage over other state-of-the-art prediction methods. Concerning the new disease association prediction, the AUC of EMPHCN through the five-fold cross-validation reaches 0.806 (T1) and 0.845 (T2), which are 4.3% (T1) and 4.0% (T2) higher than the second best existing methods, respectively. In the case study, EMPHCN also achieves satisfactory results in real drug repositioning for breast carcinoma and Parkinson's disease.

PMID:36359016 | DOI:10.3390/biom12111666

Biomolecules. 2022 Nov 5;12(11):1641. doi: 10.3390/biom12111641.

ABSTRACT

Corticosteroids, oral or transtympanic, remain the mainstay for inner ear diseases characterized by hearing fluctuation or sudden changes in hearing, including sudden sensorineural hearing loss (SSNHL), Meniere's disease (MD), and autoimmune inner ear disease (AIED). Despite their use across these diseases, the rate of complete recovery remains low, and results across the literature demonstrates significant heterogeneity with respect to the effect of corticosteroids, suggesting a need to identify more efficacious treatment options. Previously, our group has cross-referenced steroid-responsive genes in the cochlea with published single-cell and single-nucleus transcriptome datasets to demonstrate that steroid-responsive differentially regulated genes are expressed in spiral ganglion neurons (SGN) and stria vascularis (SV) cell types. These differentially regulated genes represent potential druggable gene targets. We utilized multiple gene target databases (DrugBank, Pharos, and LINCS) to identify orally administered, FDA approved medications that potentially target these genes. We identified 42 candidate drugs that have been shown to interact with these genes, with an emphasis on safety profile, and tolerability. This study utilizes multiple databases to identify drugs that can target a number of druggable genes in otologic disorders that are commonly treated with steroids, providing a basis for establishing novel repurposing treatment trials.

PMID:36358991 | DOI:10.3390/biom12111641

Biomolecules. 2022 Oct 26;12(11):1559. doi: 10.3390/biom12111559.

ABSTRACT

We present the Pharmacorank search tool as an objective means to obtain prioritized protein drug targets and their associated medications according to user-selected diseases. This tool could be used to obtain prioritized protein targets for the creation of novel medications or to predict novel indications for medications that already exist. To prioritize the proteins associated with each disease, a gene similarity profiling method based on protein functions is implemented. The priority scores of the proteins are found to correlate well with the likelihoods that the associated medications are clinically relevant in the disease's treatment. When the protein priority scores are plotted against the percentage of protein targets that are known to bind medications currently indicated to treat the disease, which we termed the pertinency score, a strong correlation was observed. The correlation coefficient was found to be 0.9978 when using a weighted second-order polynomial fit. As the highly predictive fit was made using a broad range of diseases, we were able to identify a general threshold for the pertinency score as a starting point for considering drug repositioning candidates. Several repositioning candidates are described for proteins that have high predicated pertinency scores, and these provide illustrative examples of the applications of the tool. We also describe focused reviews of repositioning candidates for Alzheimer's disease. Via the tool's URL, https://protein.som.geisinger.edu/Pharmacorank/, an open online interface is provided for interactive use; and there is a site for programmatic access.

PMID:36358909 | DOI:10.3390/biom12111559

Mol Biotechnol. 2022 Nov 10. doi: 10.1007/s12033-022-00595-w. Online ahead of print.

ABSTRACT

Monkeypox Virus (MPXV), the causative agent of Monkeypox (MPX) disease, is an emerging zoonotic pathogen spreading in different endemic and non-endemic nations and creating outbreaks. MPX treatment mainly includes Cidofovir and Tecovirimat but they have several side effects and solely depending on these drugs may promote the emergence of drug-resistant variants. Hence, new drugs are required to control the spread of the disease. In this study, we explored the MPXV proteome to suggest repurposable drugs. DrugBank screening revealed drugs such as Brinzolamide, Dorzolamide, Methazolamide, Zidovudine, Gemcitabine, Hydroxyurea, Fludarabine, and Tecovirimat as controls. Structural analogs of these compounds were extracted from ChEMBL Database. After Molecular docking and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET)-based screening, we identified Zidovudine (binding affinity-5.9 kcal/mol) and a Harmala alkaloid (2S,4R)-4-(9H-Pyrido[3,4-b]indol-1-yl)-1,2,4-butanetriol (binding affinity - 6.6 kcal/mol) against L2R receptor (Thymidine Kinase). Moreover, Fludarabine (binding affinity - 6.4 kcal/mol) and 5'-Dehydroadenosine (binding affinity - 6.4 kcal/mol) can strongly interact with the I4L receptor (Ribonucleotide reductase large subunit R1). Molecular Dynamics (MD) simulations suggest all of these compounds can change the C-alpha backbone, residue mobility, compactness, and solvent accessible surface area of L2R and I4L. Our results strongly suggest that these drug repurposing small molecules are worth exploring in vivo and in vitro for clinical applications.

PMID:36357534 | DOI:10.1007/s12033-022-00595-w

Pharmaceuticals (Basel). 2022 Oct 28;15(11):1333. doi: 10.3390/ph15111333.

ABSTRACT

SARS-CoV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdowns, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure COVID-19. In the first step, a total of 133 drug-likeness bioactive molecules are retrieved from the ChEMBL database against SARS coronavirus 3CL Protease. Based on the standard IC50, the dataset is divided into three classes: active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR)-based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting-, XGBoost-, Support Vector-, Decision Tree-, and Random Forest-based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with the ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134, and 426898. These molecules are highly suitable drug candidates for SARS-CoV-2 3CL Protease. In the next step, the efficacy of the bioactive molecules is computed in terms of binding affinity using molecular docking, and then six bioactive molecules are shortlisted, with the ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-CoV-2. It is anticipated that the pharmacologist and/or drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-CoV-2. They can adopt these promising compounds for their downstream drug development stages.

PMID:36355505 | DOI:10.3390/ph15111333

Curr Issues Mol Biol. 2022 Nov 1;44(11):5379-5389. doi: 10.3390/cimb44110364.

ABSTRACT

Candida albicans is the chief etiological agent of candidiasis, a mycosis prevalent in individuals with acquired immunodeficiency syndrome (AIDS). In recent years, the introduction of human immunodeficiency virus (HIV) protease inhibitors (HIV-PI) has reduced the prevalence of candidiasis in these patients. Seeking new therapeutic strategies based on the perspective of drug repositioning, we evaluated the effects of two second-generation HIV-PIs, atazanavir (ATV) and darunavir (DRV), on virulence factors of C. albicans and experimental candidiasis. For this, clinical strains of C. albicans were subjected to in vitro and in vivo treatments with ATV or DRV. As a result, ATV and DRV exhibited antifungal activity against fungal cells at 512 μg/mL, reduced the viability and biomass of biofilms, and inhibited filamentation of C. albicans. In addition, these HIV-PIs downregulated the expression of SAP2 and BRC1 genes of C. albicans. In an in vivo study, prophylactic use of ATV and DRV prolonged the survival rate of Galleria mellonella larvae infected with C. albicans. Therefore, ATV and DRV showed activity against C. albicans by reducing cell growth, biofilm formation, filamentation, and expression of virulence genes. Furthermore, ATV and DRV decreased experimental candidiasis, suggesting the repurposing of HIV-PIs as antifungal treatments for C. albicans infections.

PMID:36354676 | DOI:10.3390/cimb44110364

Future Microbiol. 2022 Nov 10. doi: 10.2217/fmb-2022-0085. Online ahead of print.

ABSTRACT

Aim: The antimicrobial and antibiofilm activities of the antihistamine desloratadine against multidrug-resistant (MDR) Acinetobacter baumannii were evaluated. Results: Desloratadine inhibited 90% bacterial growth at a concentration of 64 μg/ml. The combination of desloratadine with meropenem reduced the MIC by twofold in the planktonic state and increased the antibiofilm activity by eightfold. Survival curves showed that combinations of these drugs were successful in eradicating all bacterial cells within 16 h. Scanning electron microscopy also confirmed a synergistic effect in imparting a harmful effect on the cellular structure of MDR A. baumannii. An in vivo model showed significant protection of up to 83% of Caenorhabditis elegans infected with MDR A. baumannii. Conclusion: Our results indicate that repositioning of desloratadine may be a safe and low-cost alternative as an antimicrobial and antibiofilm agent for the treatment of MDR A. baumannii infections.

PMID:36353984 | DOI:10.2217/fmb-2022-0085

Cell Rep. 2022 Nov 8;41(6):111601. doi: 10.1016/j.celrep.2022.111601.

ABSTRACT

Melanoma is a deadly form of cancer characterized by remarkable therapy resistance. Analyzing the transcriptome of MAPK inhibitor sensitive- and resistant-melanoma, we discovered that APAF-1 is negatively regulated by MITF in resistant tumors. This study identifies the MITF/APAF-1 axis as a molecular driver of MAPK inhibitor resistance. A drug-repositioning screen identified quinacrine and methylbenzethonium as potent activators of apoptosis in a context that mimics drug resistance mediated by APAF-1 inactivation. The compounds showed anti-tumor activity in in vitro and in vivo models, linked to suppression of MITF function. Both drugs profoundly sensitize melanoma cells to MAPK inhibitors, regulating key signaling networks in melanoma, including the MITF/APAF-1 axis. Significant activity of the two compounds in inhibiting specific epigenetic modulators of MITF/APAF-1 expression, such as histone deacetylases, was observed. In summary, we demonstrate that targeting the MITF/APAF-1 axis may overcome resistance and could be exploited as a potential therapeutic approach to treat resistant melanoma.

PMID:36351409 | DOI:10.1016/j.celrep.2022.111601

Head Neck. 2022 Nov 8. doi: 10.1002/hed.27238. Online ahead of print.

ABSTRACT

BACKGROUND: Trifluoperazine (TFP) is a typical antipsychotic primarily used to treat schizophrenia. In this study, we aimed to evaluate whether TFP can be used as a therapeutic agent against nasopharyngeal carcinoma (NPC) and identify its underlying molecular mechanisms.

METHODS: We used NPC-TW01, TW03, TW04, and BM to assess the anticancer effects of TFP by using cytotoxicity, wound healing, colony formation, and cell invasion assays. An in vivo animal study was conducted. RNA sequencing combined with Ingenuity Pathways Analysis was performed to identify the mechanism by which TFP influences NPC cells.

RESULTS: Our data revealed that TFP decreased NPC cell viability in a dose-dependent manner. The invasion and migration of NPC tumor cells were inhibited by TFP. An in vivo study also demonstrated the anticancer effects of TFP. RNA sequencing revealed several anticancer molecular mechanisms following TFP administration.

CONCLUSIONS: The antipsychotic drug TFP could be a potential therapeutic regimen for NPC treatment.

PMID:36349408 | DOI:10.1002/hed.27238

Brief Bioinform. 2022 Nov 8:bbac469. doi: 10.1093/bib/bbac469. Online ahead of print.

ABSTRACT

The discovery and repurposing of drugs require a deep understanding of the mechanism of drug action (MODA). Existing computational methods mainly model MODA with the protein-protein interaction (PPI) network. However, the molecular interactions of drugs in the human body are far beyond PPIs. Additionally, the lack of interpretability of these models hinders their practicability. We propose an interpretable deep learning-based path-reasoning framework (iDPath) for drug discovery and repurposing by capturing MODA on by far the most comprehensive multilayer biological network consisting of the complex high-dimensional molecular interactions between genes, proteins and chemicals. Experiments show that iDPath outperforms state-of-the-art machine learning methods on a general drug repurposing task. Further investigations demonstrate that iDPath can identify explicit critical paths that are consistent with clinical evidence. To demonstrate the practical value of iDPath, we apply it to the identification of potential drugs for treating prostate cancer and hypertension. Results show that iDPath can discover new FDA-approved drugs. This research provides a novel interpretable artificial intelligence perspective on drug discovery.

PMID:36347526 | DOI:10.1093/bib/bbac469

Genomics. 2022 Nov 5:110519. doi: 10.1016/j.ygeno.2022.110519. Online ahead of print.

ABSTRACT

High altitude cerebral edema (HACE) is a serious subtype of acute mountain sickness (AMS). Studies have suggested that increased expression of corticotropin releasing hormone receptor 1 (CRFR1) in pituitary is related to the development of HACE, but no study has revealed the molecular landscape of pituitary function changes in this process. Rat model of HACE was established by simulating the high-altitude hypobaric hypoxia environment. Then RNA-sequencing was performed of rat pituitary gland (PG) in HACE and non-HACE groups. The function annotations, enrichment analysis, protein-protein interaction (PPI) network, chromosome location and drug repositioning of differentially expressed genes (DEGs) were explored based on the transcriptomic data. And we found pituitary secretion function was disordered in HACE, which was partly due to activated inflammation and oxidative stress. In addition, we identified potential biomarkers for early recognition of pituitary dysfunction and potential protective drugs for pituitary function in HACE.

PMID:36347325 | DOI:10.1016/j.ygeno.2022.110519

Brain. 2022 Nov 8:awac420. doi: 10.1093/brain/awac420. Online ahead of print.

ABSTRACT

Understanding how variations in the plasma and brain proteome contribute to multiple sclerosis susceptibility can provide important insights to guide drug repurposing and therapeutic development for multiple sclerosis. However, the role of genetically predicted protein abundance in multiple sclerosis remains largely unknown. Integrating plasma proteomics (n = 3,301) and brain proteomics (n = 376 discovery; n = 152 replication) into multiple sclerosis genome-wide association studies (n = 14,802 cases and 26,703 controls), we employed summary-based methods to identify candidate proteins involved in multiple sclerosis susceptibility. Next, we evaluated associations of the corresponding genes with multiple sclerosis at tissue-level using large gene expression quantitative trait data from whole-blood (n = 31,684) and brain (n = 1,194) tissue. Further, to assess transcriptional profiles for candidate proteins at cell-level, we examined gene expression patterns in immune cell types (dataset 1: n = 73 cases and 97 controls; dataset 2: n = 31 cases and 31 controls) for identified plasma proteins, and in brain cell types (dataset 1: n = 4 cases and 5 controls; dataset 2: n = 5 cases and 3 controls) for identified brain proteins. In a longitudinal multiple sclerosis cohort (n = 203 cases followed up to 15 years), we also assessed the corresponding gene-level associations with the outcome of disability worsening. We identified 39 novel proteins associated with multiple sclerosis risk. Based on five identified plasma proteins, four available corresponding gene candidates showed consistent associations with multiple sclerosis risk in whole-blood, and we found TAPBPL upregulation in multiple sclerosis B cells, CD8+ T cells and natural killer cells compared to controls. Among the 34 candidate brain proteins, 18 were replicated in a smaller cohort and 14 of 21 available corresponding gene candidates also showed consistent associations with multiple sclerosis risk in brain tissue. In cell-specific analysis, six identified brain candidates showed consistent differential gene expression in neuron and oligodendrocyte cell clusters. Based on the 39 protein-coding genes, we found 23 genes that were associated with disability worsening in multiple sclerosis cases. The findings present a set of candidate protein biomarkers for multiple sclerosis, reinforced by high concordance in downstream transcriptomics findings at tissue-level. This study also highlights the heterogeneity of cell-specific transcriptional profiles for the identified proteins, and that numerous candidates were also implicated in disease progression. Together, these findings can serve as an important anchor for future studies of disease mechanisms and therapeutic development.

PMID:36346149 | DOI:10.1093/brain/awac420

Curr Pharm Des. 2022 Nov 7. doi: 10.2174/1381612829666221107123841. Online ahead of print.

ABSTRACT

Study background & Objective: After the influenza pandemic (1918), COVID-19 was declared a Vth pandemic by the WHO in 2020. SARS-CoV-2 is an RNA-enveloped single-stranded virus. Based on the structure and life cycle, Protease (3CLpro), rdrp, ACE2, IL-6, and TMPRSS2 are the major targets for drug development against COVID-19. Pre-existing several drugs (FDA-approved) are used to inhibit the above targets in different diseases. In coronavirus treatment, these drugs are also in different clinical trial stages. Remdesivir (rdrp inhibitor) is the only FDA-approved medicine for coronavirus treatment. In the present study, by using the drug repurposing strategy, 70 preexisting clinical or under clinical trial molecules were used in scrutiny for rdrp inhibitor potent molecules in coronavirus treatment being surveyed via docking studies. Molecular simulation studies further confirmed the binding mechanism and stability of the most potent compounds.

MATERIAL AND METHODS: Docking studies were performed using the Maestro 12.9 module of Schrodinger software over 70 molecules with rdrp as the target and remdesivir as the standard drug and further confirmed by simulation studies.

RESULTS: The docking studies showed that many HIV protease inhibitors demonstrated remarkable binding interactions with the target rdrp. Protease inhibitors such as lopinavir and ritonavir are effective. Along with these, AT-527, ledipasvir, bicalutamide, and cobicistat showed improved docking scores. RMSD and RMSF were further analyzed for potent ledipasvir and ritonavir by simulation studies and were identified as potential candidates for corona disease.

CONCLUSION: The drug repurposing approach provides a new avenue in COVID-19 treatment.

PMID:36345244 | DOI:10.2174/1381612829666221107123841

Drug Discov Today. 2022 Oct 28:103422. doi: 10.1016/j.drudis.2022.103422. Online ahead of print.

ABSTRACT

The use of real-world data in drug repurposing has emerged due to well-established advantages of drug repurposing in supplementing de novo drug discovery and incentives in incorporating real-world evidence in regulatory approvals. We conducted a scoping review to characterize repurposing studies using real-world data and discuss their potential challenges and solutions. A total of 250 studies met the inclusion criteria, of which 36 were original studies on hypothesis generation, 101 on hypothesis validation, and seven on safety assessment. Key challenges that should be addressed for future progress in using real-world data for repurposing include isolated data sources with poor clinical granularity, false-positive signals from data mining, the sensitivity of hypothesis validation to bias and confounding, and the lack of clear regulatory guidance.

PMID:36341896 | DOI:10.1016/j.drudis.2022.103422

J Chem Inf Model. 2022 Nov 7. doi: 10.1021/acs.jcim.2c00832. Online ahead of print.

ABSTRACT

The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein-ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method's promise for lead hopping within or outside a protein target, directly based on binding site information.

PMID:36341715 | DOI:10.1021/acs.jcim.2c00832

Front Immunol. 2022 Oct 20;13:1020721. doi: 10.3389/fimmu.2022.1020721. eCollection 2022.

ABSTRACT

OBJECTIVE: Finding target genes and target pathways of existing drugs for drug repositioning in multiple sclerosis (MS) based on transcriptomic changes in MS immune cells.

MATERIALS AND METHODS: Based on transcriptome data from Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) in MS patients without treatment were identified by bioinformatics analysis according to the type of immune cells, as well as DEGs in MS patients before and after drug administration. Hub target genes of the drug for MS were analyzed by constructing the protein-protein interaction network, and candidate drugs targeting 2 or more hub target genes were obtained through the connectivity map (CMap) database and Drugbank database. Then, the enriched pathways of MS patients without treatment and the enriched pathways of MS patients before and after drug administration were intersected to obtain the target pathways of the drug for MS, and the candidate drugs targeting 2 or more target pathways were obtained through Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

RESULTS: We obtained 50 hub target genes for CD4+ T cells in Fingolimod for MS, 15 hub target genes for Plasmacytoid dendritic cells (pDCs) and 7 hub target genes for Peripheral blood mononuclear cells (PBMC) in interferon-β (IFN-β) for MS. 6 candidate drugs targeting two or more hub targets (Fostamatinib, Copper, Artenimol, Phenethyl isothiocyanate, Aspirin and Zinc) were obtained. In addition, we obtained 4 target pathways for CD19+ B cells and 15 target pathways for CD4+ T cells in Fingolimod for MS, 7 target pathways for pDCs and 6 target pathways for PBMC in IFN-β for MS, most of which belong to the immune system and viral infectious disease pathways. We obtained 69 candidate drugs targeting two target pathways.

CONCLUSION: We found that applying candidate drugs that target both the "PI3K-Akt signaling pathway" and "Chemokine signaling pathway" (e.g., Nemiralisib and Umbralisib) or applying tyrosine kinase inhibitors (e.g., Fostamatinib) may be potential therapies for the treatment of MS.

PMID:36341423 | PMC:PMC9630342 | DOI:10.3389/fimmu.2022.1020721

ACS Omega. 2022 Oct 18;7(43):38448-38458. doi: 10.1021/acsomega.2c03671. eCollection 2022 Nov 1.

ABSTRACT

Staphylococcus aureus is considered as one of the most widespread bacterial pathogens and continues to be a prevalent cause of mortality and morbidity across the globe. FmtA is a key factor linked with methicillin resistance in S. aureus. Consequently, new antibacterial compounds are crucial to combat S. aureus resistance. Here, we present the virtual screening of a set of compounds against the available crystal structure of FmtA. The findings indicate that gemifloxacin, paromomycin, streptomycin, and tobramycin were the top-ranked potential drug molecules based on the binding affinity. Furthermore, these drug molecules were analyzed with molecular dynamics simulations, which showed that the identified molecules formed highly stable FmtA-inhibitor(s) complexes. Molecular mechanics Poisson-Boltzmann surface area and quantum mechanics/molecular mechanics calculations suggested that the active site residues (Ser127, Lys130, Tyr211, and Asp213) of FmtA are crucial for the interaction with the inhibitor(s) to form stable protein-inhibitor(s) complexes. Moreover, fluorescence- and isothermal calorimetry-based binding studies showed that all the molecules possess dissociation constant values in the micromolar scale, revealing a strong binding affinity with FmtAΔ80, leading to stable protein-drug(s) complexes. The findings of this study present potential beginning points for the rational development of advanced, safe, and efficacious antibacterial agents targeting FmtA.

PMID:36340146 | PMC:PMC9631409 | DOI:10.1021/acsomega.2c03671

Front Pharmacol. 2022 Oct 21;13:1062640. doi: 10.3389/fphar.2022.1062640. eCollection 2022.

NO ABSTRACT

PMID:36339602 | PMC:PMC9634533 | DOI:10.3389/fphar.2022.1062640

Front Pharmacol. 2022 Oct 21;13:1046406. doi: 10.3389/fphar.2022.1046406. eCollection 2022.

ABSTRACT

Drug repurposing is an attractive, pragmatic approach to drug discovery that has yielded success across medical fields over the years. The use of existing medicines for novel indications enables dramatically reduced development costs and timescales compared with de novo drug discovery and is therefore a promising strategy in cardiovascular disease, where new drug approvals lag significantly behind that of other fields. Extensive evidence from pre-clinical and clinical studies show that chronic inflammation is a driver of pathology in cardiovascular disease, and many efforts have been made to target cardiovascular inflammation therapeutically. This approach has been met with significant challenges however, namely off-target effects associated with broad-spectrum immunosuppression, particularly in long-term conditions such as cardiovascular disease. Nevertheless, multiple anti-inflammatory medicines have been assessed for efficacy in cardiovascular clinical trials, with most of these being repurposed from their original indications in autoimmune conditions like rheumatoid arthritis. In this review, we discuss the mixed successes of clinical trials investigating anti-inflammatory drugs in cardiovascular disease, with examples such as anti-cytokine monoclonal antibodies, colchicine, and methotrexate. Looking to the future, we highlight potential new directions for drug repurposing in cardiovascular inflammation, including the emerging concepts of drug re-engineering and chrono-pharmacology.

PMID:36339576 | PMC:PMC9634418 | DOI:10.3389/fphar.2022.1046406