OMICS. 2025 Apr 10. doi: 10.1089/omi.2024.0205. Online ahead of print.

ABSTRACT

The COVID-19 pandemic phase caused by the SARS-CoV-2 has ended, but the emergence of new variants continues to threaten public health. The public health toolbox for COVID-19 is in need of not only vaccines but also drug discovery against the SARS-CoV-2 virus, the causative agent for the ongoing COVID-19 infections. We report here an in silico molecular docking and dynamics study that uncovered the interactions of 26 flexible heteroarotinoids (FHT18), which are a class of anti-cancer compounds, as potential inhibitors against all 24 SARS-CoV-2 proteins. Of the 624 docked complexes, 69 displayed binding energies between -9.0 and -11.6 kcal/mol, indicating good to strong binding affinities. At least five of these compounds displayed excellent binding affinities against the nonstructural protein 2, papain-like protease, nonstructural protein 4 (Nsp4), proof-reading exoribonuclease, membrane protein, and nucleocapsid protein. Structure-activity relationship (SAR) analyses of these results revealed that a urea linker in place of a thiourea linker, enhanced the hydrophobic side chains attached to the chromane unit, and a CF3 or OCF3 functional group attached to the benzene ring contributed to increased binding affinities. Further, the molecular dynamics simulation study of the best-docked complex FHT18-6c with Nsp4 remained stable for at least 200 ns, leading to decreased structural fluctuations and increased compactness of the binding site. In conclusion, FHT18-6c deserves further translational research to explore its potential for repurposing as a potent drug candidate to combat COVID-19. We also call for continued drug discovery efforts to enrich the public health toolbox for COVID-19.

PMID:40205995 | DOI:10.1089/omi.2024.0205

Mol Biol Rep. 2025 Apr 9;52(1):373. doi: 10.1007/s11033-025-10474-7.

ABSTRACT

BACKGROUND: Gastric cancer is a highly heterogeneous and aggressive disease with limited treatment options, necessitating innovative therapeutic strategies. Drug repurposing, a cost-effective approach, offers opportunities to identify new applications for existing medications. This study systematically investigated the apoptotic effects of serotonergic drugs on MKN-45 gastric cancer cells, providing a novel perspective on serotonin signaling in cancer therapy.

METHODS AND RESULTS: MKN-45 cells were treated with concentrations of Tropisetron, Imipramine, Ketanserin, Citalopram, and Cyproheptadine. The IC50 values were determined using an MTT assay, while acridine orange/ethidium bromide staining and Annexin V/PI flow cytometry assessed apoptotic activity. Gene expression related to serotonin receptors (HTR2A, HTR2B, HTR3A), Serotonin transporter (SLC6A4), apoptosis (Bcl-2, Bax), and proliferation (PCNA) was evaluated via real-time PCR. Tropisetron, Imipramine, Ketanserin, and Cyproheptadine demonstrated statistically significant apoptotic induction compared to untreated cells. These treatments significantly reduced anti-apoptotic Bcl-2 and PCNA, proliferation marker, expression, while pro-apoptotic Bax expression was markedly elevated (p < 0.05).

CONCLUSIONS: This study highlights the potential of Tropisetron, Imipramine, Ketanserin, and Cyproheptadine as repurposed drugs for gastric cancer therapy, with Tropisetron and Imipramine showing particularly promising apoptotic effects. These findings pave the way for further preclinical and clinical investigations, offering a foundation for personalized therapeutic strategies in gastric cancer management.

PMID:40202572 | DOI:10.1007/s11033-025-10474-7

J Pharm Anal. 2025 Mar;15(3):101045. doi: 10.1016/j.jpha.2024.101045. Epub 2024 Jul 18.

ABSTRACT

Targeted covalent inhibitors, primarily targeting cysteine residues, have attracted great attention as potential drug candidates due to good potency and prolonged duration of action. However, their discovery is challenging. In this research, a database-assisted liquid chromatography-tandem mass spectrometry (LC-MS/MS) strategy was developed to quickly discover potential cysteine-targeting compounds. First, compounds with potential reactive groups were selected and incubated with N-acetyl-cysteine in microsomes. And the precursor ions of possible cysteine-adducts were predicted based on covalent binding mechanisms to establish in-house database. Second, substrate-independent product ions produced from N-acetyl-cysteine moiety were selected. Third, multiple reaction monitoring scan was conducted to achieve sensitive screening for cysteine-targeting compounds. This strategy showed broad applicability, and covalent compounds with diverse structures were screened out, offering structural resources for covalent inhibitors development. Moreover, the screened compounds, norketamine and hydroxynorketamine, could modify synaptic transmission-related proteins in vivo, indicating their potential as covalent inhibitors. This experimental-based screening strategy provides a quick and reliable guidance for the design and discovery of covalent inhibitors.

PMID:40201900 | PMC:PMC11978337 | DOI:10.1016/j.jpha.2024.101045

Bioinform Adv. 2025 Feb 18;5(1):vbaf029. doi: 10.1093/bioadv/vbaf029. eCollection 2025.

ABSTRACT

MOTIVATION: Understanding which genes are significantly affected by drugs is crucial for drug repurposing, as drugs targeting specific pathways in one disease might be effective in another with similar genetic profiles. By analyzing gene expression changes in cells before and after drug treatment, we can identify the genes most impacted by drugs.

RESULTS: The Biologically-Aware Interpretable Differential Gene Expression Ranking (BADGER) model is an interpretable model designed to predict gene expression changes resulting from interactions between cancer cell lines and chemical compounds. The model enhances explainability through integration of prior knowledge about drug targets via pathway information, handles novel cancer cell lines through similarity-based embedding, and employs three attention blocks that mimic the cascading effects of chemical compounds. This model overcomes previous limitations of cell line range and explainability constraints in drug-cell response studies. The model demonstrates superior performance over baselines in both unseen cell and unseen pair split evaluations, showing robust prediction capabilities for untested drug-cell line combinations.

AVAILABILITY AND IMPLEMENTATION: This makes it particularly valuable for drug repurposing scenarios, especially in developing therapeutic plans for new or resistant diseases by leveraging similarities with other diseases. All code and data used in this study are available at https://github.com/dmis-lab/BADGER.git.

PMID:40201234 | PMC:PMC11978390 | DOI:10.1093/bioadv/vbaf029

J Mol Graph Model. 2025 Apr 5;138:109036. doi: 10.1016/j.jmgm.2025.109036. Online ahead of print.

ABSTRACT

The effective treatment of neurological diseases, particularly Alzheimer's disease (AD), is a significant source of frustration for drug discovery scientists. The lengthy process of drug discovery further makes this task exceedingly challenging. To enable a rapid stride in drug discovery, we focused on the drug repurposing strategy to identify new N-methyl-D-aspartate receptor (NMDAR) inhibitors from the pool of 1827 approved USFDA drugs. The high throughput virtual screening (HTVS) followed by molecular docking and molecular mechanics studies enabled us to identify two drugs, Ertugliflozin (Dock Score: -9.43 kcal/mol, MMGBSA: -104.50 kcal/mol) and Selpercatinib (Dock Score: 8.11 kcal/mol, MMGBSA: 83.62 kcal/mol), with a high affinity towards the NMDAR. The molecular dynamics analysis on these identified drugs led us to choose Ertugliflozin for its better stability as a lead for further studies. The corroboration of in silico findings led us to deduce that Ertugliflozin can inhibit NMDAR with an IC50 of 613.19 nM. These results were confirmed by the anti-NMDAR ELISA-based analysis, which was further deduced via western blotting. The work is further supported by strong literature evidence that concludes the impact of antidiabetic molecules on AD progression, along with the evidence that Ertugliflozin possesses efficacy against AD with unequivocal evidence on the biological target and the mechanism. Further work, however, is required to establish this association in the in vivo or suitable model that could mimic the AD microenvironment as a part of future research.

PMID:40199086 | DOI:10.1016/j.jmgm.2025.109036

Am J Respir Cell Mol Biol. 2025 Apr 8. doi: 10.1165/rcmb.2024-0303OC. Online ahead of print.

ABSTRACT

Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disease lacking effective treatment. Focusing on early COPD should help to discover disease modifying therapies. We examined the role of the CXCL12/CXCR4 axis in early COPD using human samples and murine models. Blood samples and lung tissues from both individuals with early COPD and controls were analyzed for CXCL12 and CXCR4 levels. To generate an early-like COPD model, 10-week-old male C57BL/6J mice were exposed to cigarette smoke (CS) for 10 weeks and intranasal instillations of polyinosinic-polycytidylic acid (poly(I:C)) for the last five weeks to mimic exacerbations. The number of cells expressing CXCR4 was increased in the blood of individuals with COPD, as well as in the blood of exposed mice. Lung CXCL12 expression was higher in both early COPD patients and exposed mice. Exposed mice presented mild airflow obstruction, peri-bronchial fibrosis, and right heart thickening. The density of fibrocyte-like cells expressing CXCR4 increased in the bronchial submucosa of these mice. Conditional inactivation of CXCR4 as well as pharmacological inhibition of CXCR4 with plerixafor injections improved lung function, reduced inflammation, and protected against CS and poly-(I:C)-induced airway and cardiac remodeling. CXCR4-/- and plerixafor-treated mice also had fewer CXCR4-expressing circulating cells and a lower density of peri-bronchial fibrocyte-like cells. We demonstrate that targeting CXCR4 has beneficial effects in an animal model mimicking early COPD. While these preclinical findings are encouraging, further research is needed to explore the potential for transferring these insights into clinical applications, including drug repurposing.

PMID:40198797 | DOI:10.1165/rcmb.2024-0303OC

Microbiol Spectr. 2025 Apr 8:e0233224. doi: 10.1128/spectrum.02332-24. Online ahead of print.

ABSTRACT

Tuberculosis remains the deadliest infectious disease of the 21st century. New antimicrobials are needed to improve treatment outcomes and enable therapy shortening. Drug repurposing is an alternative to the traditional drug discovery process. The avermectins are a family of macrocyclic lactones with anthelmintic activity active against Mycobacterium tuberculosis. However, their mode of action in mycobacteria remains unknown. In this study, we employed traditional mutant isolation approaches using Mycobacterium smegmatis, a non-pathogenic M. tuberculosis surrogate. We were only able to isolate mutants with decreased susceptibility to selamectin using the ∆nucS mutator M. smegmatis strain. This phenotype was caused by mutations in mps1 and mmpL11. Two of these mutants were used for a second experiment in which high-level selamectin-resistant mutants were isolated; however, specific mutations driving the phenotypic change to high-level resistance could not be identified. The susceptibility to selamectin in these mutants was restored to the basal level by subinhibitory concentrations of ethambutol. The selection of ethambutol resistance in a high-level selamectin-resistant mutant also resulted in multiple colonies becoming susceptible to selamectin again. These colonies carried mutations in embB, suggesting that the integrity of the cell envelope is a prerequisite for selamectin resistance. The absence of increased susceptibility to selamectin in an embB deletion strain demonstrated that the target of selamectin is not cytosolic. Our data show that the concurrence of specific multiple mutations and complete integrity of the mycobacterial envelope are necessary for selamectin resistance. Our studies provide first-time insights into the antimycobacterial mode of action of the antiparasitic avermectins.IMPORTANCETuberculosis is the deadliest infectious disease of the 21st century. New antibiotics are needed to improve treatment. However, developing new drugs is costly and lengthy. Drug repurposing is an alternative to the traditional drug discovery process. The avermectins are a family of drugs used to treat parasitic infections that are active against Mycobacterium tuberculosis, the bacterium that causes tuberculosis. However, their mode of action in mycobacteria remains unknown. Understanding how avermectins kill mycobacteria can facilitate its development as an anti-mycobacterial drug, including against M. tuberculosis.In this study, we used Mycobacterium smegmatis, a non-pathogenic M. tuberculosis surrogate model to understand the molecular mechanisms of how selamectin (a drug of the avermectin family selected for this study as a model) acts against mycobacteria. Our data show that the generation of resistance to selamectin is unlikely and that complete integrity of the mycobacterial envelope is necessary for selamectin resistance, providing first-time insights into the antimycobacterial mode of action of the avermectins.

PMID:40197087 | DOI:10.1128/spectrum.02332-24

bioRxiv [Preprint]. 2025 Mar 28:2025.03.24.644676. doi: 10.1101/2025.03.24.644676.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) is a complex neurodegenerative disorder with substantial molecular variability across different brain regions and individuals, hindering therapeutic development. This study introduces PRISM-ML, an interpretable machine learning (ML) framework integrating multiomics data to uncover patient-specific biomarkers, subtissue-level pathology, and drug repurposing opportunities.

METHODS: We harmonized transcriptomic and genomic data of three independent brain studies containing 2105 post-mortem brain samples (1363 AD, 742 controls) across nine tissues. A Random Forest classifier with SHapley Additive exPlanations (SHAP) identified patient-level biomarkers. Clustering further delineated each tissue into subtissues, and network analysis revealed critical "bottleneck" (hub) genes. Finally, a knowledge graph-based screening identified multi-target drug candidates, and a real-world pharmacoepidemiologic study evaluated their clinical relevance.

RESULTS: We uncovered 36 molecularly distinct subtissues, each defined by a set of associated unique biomarkers and genetic drivers. Through network analysis of gene-gene interactions networks, we highlighted 262 bottleneck genes enriched in synaptic, cytoskeletal, and membrane-associated processes. Knowledge graph queries identified six FDA-approved drugs predicted to target multiple bottleneck genes and AD-relevant pathways simultaneously. One candidate, promethazine, demonstrated an association with reduced AD incidence in a large healthcare dataset of over 364000 individuals (hazard ratios ≤ 0.43; p < 0.001). These findings underscore the potential for multi-target approaches, reveal connections between AD and cardiovascular pathways, and offer novel insights into the heterogeneous biology of AD.

CONCLUSIONS: PRISM-ML bridges interpretable ML with multi-omics and systems biology to decode AD heterogeneity, revealing region-specific mechanisms and repurposable therapeutics. The validation of promethazine in real-world data underscores the clinical relevance of multi-target strategies, paving the way for more personalized treatments in AD and other complex disorders.

PMID:40196631 | PMC:PMC11974764 | DOI:10.1101/2025.03.24.644676

Oncol Res. 2025 Mar 19;33(4):919-935. doi: 10.32604/or.2024.054537. eCollection 2025.

ABSTRACT

OBJECTIVE: Prostate cancer (PCA) is the second most widespread cancer among men globally, with a rising mortality rate. Enzyme-responsive lipid nanoparticles (ERLNs) are promising vectors for the selective delivery of anticancer agents to tumor cells. The goal of this study is to fabricate ERLNs for dual delivery of gefitinib (GF) and simvastatin (SV) to PCA cells.

METHODS: ERLNs loaded with GF and SV (ERLNGFSV) were assembled using bottom-up and top-down techniques. Subsequently, these ERLN cargoes were coated with triacylglycerol, and phospholipids and capped with chitosan (CS). The ERLNGFSV, and CS engineered ERLNGFSV (CERLNGFSV) formulations were characterized for particle size (PS), zeta potential (ZP), and polydispersity index (PDI). The biocompatibility, and cytotoxicity of the plain and GF plus SV-loaded ERLN cargoes were assessed using erythrocytes and PC-3 cell line. Additionally, molecular docking simulations (MDS) were conducted to examine the influence of GF and SV on succinate dehydrogenase (SDH), glutathione peroxidase-4 (GPX-4), and 5α-reductase (5α-RD).

RESULTS: These results showed that plain, ERLNGFSV, and CERLNGFSV cargoes have a nanoscale size and homogeneous appearance. Moreover, ERLNGFSV and CERLNGFSV were biocompatible, with no detrimental effects on erythrocytes. Treatment with GF, SV, GF plus SV, ERLNGFSV, and CERLNGFSV significantly reduced the viability of PC-3 cells compared to control cells. Particularly, the blend of GF and SV, as well as ERLNGFSV and CERLNGFSV augmented PC-3 cell death. Also, treating PC-3 cells with free drugs, their combination, ERLNGFSV, and CERLNGFSV formulations elevated the percentage of apoptotic cells. MDS studies demonstrated that GF and SV interact with the active sites of SDH, GPX-4, and 5α-reductase.

CONCLUSIONS: This study concludes that SVGF combination and ERLNs loading induce particular delivery, and synergism on PC-3 death through action on multiple pathways involved in cell proliferation, and apoptosis, besides the interaction with SDH, GPX-4, and 5α-RD. Therefore, GFSV-loaded ERLN cargoes are a promising strategy for PCA treatment. In vivo studies are necessary to confirm these findings for clinical applications.

PMID:40191728 | PMC:PMC11964872 | DOI:10.32604/or.2024.054537

ACS Omega. 2025 Mar 20;10(12):12366-12374. doi: 10.1021/acsomega.4c11382. eCollection 2025 Apr 1.

ABSTRACT

The increasing prevalence of infections byCandida hemeulonii sensu stricto, particularly due to its resistance to standard antifungal therapies, represents a significant healthcare challenge. Traditional treatments often fail, emphasizing the need to explore alternative therapeutic strategies. Drug repurposing, which reevaluates existing drugs for new applications, offers a promising path. This study examines the potential of repurposing alexidine dihydrochloride as an antifungal agent againstC. hemeulonii sensu stricto. Minimum Inhibitory Concentration (MIC) and Minimum Fungicidal Concentration (MFC) values were established using broth microdilution methods. To further assess antifungal activity, different assays were conducted, including growth inhibition, biofilm inhibition, biofilm eradication, and cell damage. Checkerboard assays were employed to study the compound's fungicidal potential and interactions with other antifungals. Additional tests, sorbitol protection assay, efflux pump inhibition, cell membrane permeability assays, and nucleotide leakage were performed. In vivo efficacy and safety were evaluated inTenebrio molitor larvae. Alexidine demonstrated fungicidal activity againstC. hemeulonii sensu stricto, with an MIC of 0.5 μg/mL. Biofilm formation was significantly inhibited, with a reduction of 78.69%. Mechanistic studies revealed nucleotide leakage, indicating membrane impact, but no significant protein leakage was detected. In vivo, alexidine displayed a favorable safety profile, with no evidence of hemolysis or acute toxicity in the T. molitor model. These findings support alexidine as a strong candidate for antifungal drug repurposing, especially for treatingC. hemeulonii sensu stricto infections. Its efficacy in inhibiting growth and biofilm formation, combined with a positive safety profile, underscores its potential for clinical development as an antifungal therapy.

PMID:40191372 | PMC:PMC11966325 | DOI:10.1021/acsomega.4c11382

RSC Adv. 2025 Apr 4;15(14):10622-10633. doi: 10.1039/d4ra09055a. eCollection 2025 Apr 4.

ABSTRACT

Peroxisome proliferator-activated receptors (PPARs) are ligand-activated nuclear receptors with a crucial regulatory role in carbohydrate and lipid metabolism and are emerging druggable targets in "metabolic syndrome" (MetS) and cancers. However, there is a need to identify ligands that can activate specific PPAR subtypes, particularly PPARβ/δ, which is less studied compared with other PPAR isoforms (α and γ). Herein, using the drug-repurposing approach, the ZINC database of clinically approved drugs was screened to target the PPARβ/δ receptor through high-throughput-virtual-screening, followed by molecular docking and molecular dynamics (MD) simulation. The top-scoring ligands were subjected to drug-likeness analysis. The hit molecule was tested in an in vitro model of NAFLD (non-alcoholic fatty liver disease). The top five ligands with strong binding affinity towards PPARβ/δ were canagliflozin > empagliflozin > lumacaftor > eprosartan > dapagliflozin. RMSD/RMSF analysis demonstrated stable protein-ligand complexation (PLC) by the top-scoring ligands with PPARβ/δ. In silico ADMET prediction analysis revealed favorable pharmacokinetic profiles of these top five ligands. Canagliflozin showed significant (P < 0.001) dose-dependent decrease in lipid accumulation and the associated oxidative stress-inflammatory response, suggesting its promising anti-steatotic potential. These outcomes pave the way for further validation and development of PPAR activity-modulating therapeutics.

PMID:40190631 | PMC:PMC11970364 | DOI:10.1039/d4ra09055a

BMC Med. 2025 Apr 7;23(1):200. doi: 10.1186/s12916-025-04022-0.

ABSTRACT

BACKGROUND: Influenza A virus (IAV) is a major cause of seasonal and global pandemics, posing serious health risks. Repositioning approved drugs offers an efficient antiviral strategy, particularly as calcium (Ca2⁺) is crucial for IAV infection, making Ca2⁺ channel blockers (CCBs) promising candidates for antiviral agents.

METHODS: The in vitro antiviral activity of cilnidipine was evaluated using MTT assays, qRT-PCR, plaque assays, and western blotting. Mechanistic studies involved time-of-addition, viral internalization, pseudovirus neutralization, and HA (hemagglutinin) syncytium assays. For in vivo analysis, BALB/c mice were intranasally infected to evaluate the effects of cilnidipine on viral titer, lung index, pulmonary inflammatory mediators, and survival rate.

RESULTS: In vitro, cilnidipine exhibits antiviral activity against IAV during the early stages of infection. It disrupts clathrin- and caveolin-mediated endocytosis to inhibit the internalization of IAV and interacts with the viral HA2 subunit to impede virus membrane fusion. Additionally, cilnidipine suppresses the PI3K-AKT and p38 MAPK pathways activated by IAV infections. In vivo, cilnidipine reduces virus titers and lung index, ameliorates lung pathology, and inhibits pulmonary inflammatory mediator expression, improving survival rates.

CONCLUSIONS: These findings highlight the promising anti-IAV properties of cilnidipine both in vitro and in vivo, suggesting its potential as a clinical agent for emergencies against influenza outbreaks.

PMID:40189517 | DOI:10.1186/s12916-025-04022-0

J Microbiol Methods. 2025 Apr 4:107125. doi: 10.1016/j.mimet.2025.107125. Online ahead of print.

ABSTRACT

Conventional clinical microbiological techniques are enhanced by the introduction of artificial intelligence (AI). Comprehensive data processing and analysis enabled the development of curated datasets that has been effectively used in training different AI algorithms. Recently, a number of machine learning (ML) and deep learning (DL) algorithms are developed and evaluated using diverse microbiological datasets. These datasets included spectral analysis (Raman and MALDI-TOF spectroscopy), microscopic images (Gram and acid fast stains), and genomic and protein sequences (whole genome sequencing (WGS) and protein data banks (PDBs)). The primary objective of these algorithms is to minimize the time, effort, and expenses linked to conventional analytical methods. Furthermore, AI algorithms are incorporated with quantitative structure-activity relationship (QSAR) models to predict novel antimicrobial agents that address the continuing surge of antimicrobial resistance. During the COVID-19 pandemic, AI algorithms played a crucial role in vaccine developments and the discovery of new antiviral agents, and introduced potential drug candidates via drug repurposing. However, despite their significant benefits, the implementation of AI encounters various challenges, including ethical considerations, the potential for bias, and errors related to data training. This review seeks to provide an overview of the most recent applications of artificial intelligence in clinical microbiology, with the intention of educating a wider audience of clinical practitioners regarding the current uses of machine learning algorithms and encouraging their implementation. Furthermore, it will discuss the challenges related to the incorporation of AI into clinical microbiology laboratories and examine future opportunities for AI within the realm of infectious disease epidemiology.

PMID:40188989 | DOI:10.1016/j.mimet.2025.107125

Pharm Dev Technol. 2025 Apr 5:1-33. doi: 10.1080/10837450.2025.2487575. Online ahead of print.

ABSTRACT

The COVID-19 pandemic exposed the fragility of today's marketed treatments for respiratory infections. As a primary site of infection, the upper airways may represent a key access route for the control and treatment for these conditions. The present study aims to explore and identify, through a patent review, the novelty of therapies for COVID-19 that use the intranasal route for drug administration. A search was carried out in Wipo and Espacenet, using the descriptors "COVID-19 OR SARS-CoV 2" AND "treatment OR therapy" AND NOT "vaccine OR immunizing" and the classification "A61K9/0043". Of the 151 patents identified, we excluded 73 duplicates, and 36 documents that meet the criteria adopted for exclusion (not nasally administered formulations, vaccines, post COVID-19 treatments, uncertain route of administration or form). We identified 78 unique patents on patent databases, of which 42 were selected for this review. The documents revealed the use of the intranasal pathway not only for drug repositioning but also for using plant-derived and biological molecules. Overall, the new formulations explore a variety of known drugs and natural products incorporated in drug carrier systems and devices for drug delivery and administration. Thus, the intranasal route remains a promising strategy for drug delivery, offering direct access to the primary infection site and warranting further exploration.

PMID:40186505 | DOI:10.1080/10837450.2025.2487575

Biofouling. 2025 Apr 4:1-18. doi: 10.1080/08927014.2025.2486250. Online ahead of print.

ABSTRACT

Urinary infections caused by Staphylococcus aureus are commonly associated with urinary catheterization and often result in severe complications. Given this problem, the objective of the study was to investigate the preventive action of promethazine (PMT) against the formation of methicillin-resistant Staphylococcus aureus (MRSA) biofilms when impregnated in urinary catheters. For this purpose, techniques such as broth microdilution, checkerboard, impregnation on urinary catheter fragments, flow cytometry assays and scanning electron microscopy were employed. PMT exhibited antimicrobial activity with Minimum Inhibitory Concentration (MIC) values ranging from 171 to 256 µg/mL, predominantly additive interaction in combination with oxacillin (OXA) and vancomycin (VAN), and a reduction in cell viability of biofilms formed and forming by methicillin-sensitive and -resistant S. aureus. Morphological alterations, damage to the membrane, and genetic material of cells treated with promethazine were also observed. The results demonstrated that PMT can be classified as a promising antimicrobial agent for use in the antibacterial coating of long-term urinary devices.

PMID:40183686 | DOI:10.1080/08927014.2025.2486250

J Virol. 2025 Apr 4:e0223424. doi: 10.1128/jvi.02234-24. Online ahead of print.

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) generally hijacks the cellular machinery of host cells for survival. However, how SARS-CoV-2 employs the host's deubiquitinase to facilitate virus replication remains largely unknown. In this study, we identified the host deubiquitinase USP22 as a crucial regulator of the expression of SARS-CoV-2 nucleocapsid protein (SARS-CoV-2 NP), which is essential for SARS-CoV-2 replication. We demonstrated that SARS-CoV-2 NP proteins undergo ubiquitination-dependent degradation in host cells, while USP22 interacts with SARS-CoV-2 NP and downregulates K63-linked polyubiquitination of SARS-CoV-2 NP, thereby protecting SARS-CoV-2 NP from degradation. Importantly, we further revealed that sulbactam, an antibiotic, can reduce USP22 protein levels, eventually promoting the degradation of SARS-CoV-2 NP in vitro and in vivo. This study reveals the mechanism by which SARS-CoV-2-encoded NP protein employs host deubiquitinase for virus survival and provides a potential strategy to fight against SARS-CoV-2 infection.IMPORTANCESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid protein (SARS-CoV-2 NP) plays a pivotal role in viral infection by binding to viral RNA, stabilizing the viral genome, and promoting replication. However, the interactions between SARS-CoV-2 NP and host intracellular proteins had not been elucidated. In this study, we provide evidence that SARS-CoV-2 NP interacts with the deubiquitinase USP22 in host cells, which downregulates SARS-CoV-2 NP ubiquitination. This reduction in ubiquitination effectively prevents intracellular degradation of SARS-CoV-2 NP, thereby enhancing its stability, marking USP22 as a potential target for antiviral strategies. Additionally, our findings indicate that sulbactam significantly decreases the protein levels of USP22, thereby reducing SARS-CoV-2 NP levels. This discovery suggests a novel therapeutic pathway in which sulbactam could be repurposed as an antiviral agent, demonstrating how certain antibiotics might contribute to antiviral treatment. This work thus opens avenues for drug repurposing and highlights the therapeutic potential of targeting host pathways to inhibit viral replication.

PMID:40183543 | DOI:10.1128/jvi.02234-24

Acta Myol. 2025 Mar;44(1):23-27. doi: 10.36185/2532-1900-1026.

ABSTRACT

OBJECTIVES: Non-dystrophic myotonias (NDM) are rare diseases due to mutations in the voltage-gated sodium (Nav1.4) and chloride (ClC-1) channels expressed in skeletal muscle fibers. We provide an up-to-date review of pharmacological treatments available for NDM patients and experimental studies aimed at identifying alternative treatments and at better understanding the mechanisms of actions.

METHODS: Literature research was performed using PubMed and ClinicalTrial.gov.

RESULTS: Today, the sodium channel blocker mexiletine is the drug of choice for treatment of NDM. Alternative drugs include other sodium channel blockers and the carbonic anhydrase inhibitor acetazolamide. Preclinical studies suggest that activators of ClC-1 channels or voltage-gated potassium channels may have antimyotonic potential.

CONCLUSIONS: An increasing number of antimyotonic drugs would help to design a precision therapy to address personalized treatment of myotonic individuals.

PMID:40183437 | DOI:10.36185/2532-1900-1026

Neuromolecular Med. 2025 Apr 3;27(1):24. doi: 10.1007/s12017-025-08847-z.

ABSTRACT

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that leads to motor neuron degeneration, muscle weakness, and respiratory failure. Despite ongoing research, effective treatments for ALS are limited. This study aimed to apply network biology and machine learning (ML) techniques to identify novel repurposed drug candidates for ALS. In this study, we conducted a meta-analysis using 4 transcriptome data in ALS patients (including motor neuron and muscle tissue) and healthy controls. Through this analysis, we uncovered common shared differentially expressed genes (DEGs) separately for motor neurons and muscle tissue. Using common DEGs as proxies, we identified two distinct clusters of highly clustered differential co-expressed cluster genes: the 'Muscle Tissue Cluster' for muscle tissue and the 'Motor Neuron Cluster' for motor neurons. We then evaluated the performance of the nodes of these two modules to distinguish between diseased and healthy states with ML algorithms: KNN, SVM, and Random Forest. Furthermore, we performed drug repurposing analysis and text-mining analyses, employing the nodes of clusters as drug targets to identify novel drug candidates for ALS. The potential impact of the drug candidates on the expression of cluster genes was predicted using linear regression, SVR, Random Forest, Gradient Boosting, and neural network algorithms. As a result, we identified five novel drug candidates for the treatment of ALS: Nilotinib, Trovafloxacin, Apratoxin A, Carboplatin, and Clinafloxacin. These findings highlight the potential of drug repurposing in ALS treatment and suggest that further validation through experimental studies could lead to new therapeutic avenues.

PMID:40180646 | DOI:10.1007/s12017-025-08847-z

Interdiscip Sci. 2025 Apr 3. doi: 10.1007/s12539-025-00698-3. Online ahead of print.

ABSTRACT

Identification of drug-target interactions (DTIs) is critical for drug discovery and drug repositioning. However, most DTI methods that extract features from drug molecules and protein entities neglect specific substructure information of pharmacological responses, which leads to poor predictive performance. Moreover, most existing methods are based on molecular graphs or molecular descriptors to obtain abstract representations of molecules, but combining the two feature learning methods for DTI prediction remains unexplored. Therefore, a new ASCS-DTI framework for DTI prediction is proposed, which utilizes a substructure attention mechanism to flexibly capture substructures of compounds at different grain sizes, allowing the important substructure information of each molecule to be learned. Additionally, the framework combines three different molecular fingerprinting information to comprehensively characterize molecular representations. A stacked convolutional coding module processes the sequence information of target proteins in a multi-scale and multi-level view. Finally, multi-modal fusion of molecular graph features and molecular fingerprint features, along with multi-modal information encoding of DTIs, is performed by the feature fusion module. The method outperforms six advanced baseline models on different benchmark datasets: Biosnap, BindingDB, and Human, with a significant improvement in performance, particularly in maintaining strong results across different experimental settings.

PMID:40178777 | DOI:10.1007/s12539-025-00698-3

Front Oncol. 2025 Mar 19;15:1450602. doi: 10.3389/fonc.2025.1450602. eCollection 2025.

ABSTRACT

AIM: Statins have been shown to improve the possibility of a pathological complete response (pCR) in patients with locally advanced rectal cancer when given in combination with neo-adjuvant chemo-radiation (NACTRT) in observational studies. The primary objective of this phase II randomized controlled trial (RCT) is to determine the impact of rosuvastatin in improving pCR rates in patients with locally advanced rectal cancer who are undergoing NACTRT. The secondary objectives are to compare adverse events, postoperative morbidity and mortality, disease-free survival (DFS), and overall survival in the two arms and to identify potential prognostic and predictive factors determining outcomes. If the study is positive, we plan to proceed to a phase III RCT with 3-year DFS as the primary endpoint.

METHODS: This is a prospective, randomized, open-label phase II/III study. The phase II study has a sample size of 316 patients (158 in each arm) to be accrued over 3 years to have 288 evaluable patients. The standard arm will receive NACTRT while the intervention group will receive 20 mg rosuvastatin orally once daily along with NACTRT for 6 weeks followed by rosuvastatin alone for 6-10 weeks until surgery. All patients will be reviewed after repeat imaging by a multidisciplinary tumor board at 12-16 weeks after starting NACTRT and operable patients will be planned for surgery. The pathological response rate, tumor regression grade (TRG), and post-surgical complications will be recorded.

CONCLUSION: The addition of rosuvastatin to NACTRT may improve the oncological outcomes by increasing the likelihood of pCR in patients with locally advanced rectal cancer undergoing NACTRT. This would be a low-cost, low-risk intervention that could potentially lead to the refinement of strategies, such as "watch and wait", in a select subgroup of patients.

CLINICAL TRIAL REGISTRATION: Clinical Trials Registry of India, identifier CTRI/2018/11/016459.

PMID:40177244 | PMC:PMC11961435 | DOI:10.3389/fonc.2025.1450602