Eur J Pharmacol. 2025 Jul 30:178007. doi: 10.1016/j.ejphar.2025.178007. Online ahead of print.

ABSTRACT

The cognitive decline associated with ageing is the most critical health issue affecting elderly individuals, and there is still a lack of effective interventions available. This study was designed to identify a drug capable of ameliorating age-related cognitive decline and the underlying mechanisms. Utilizing data mining of multisource databases and drug repositioning approaches based on transcriptome similarity, the cardiovascular drug ranolazine (Ran), was identified as a potential candidate with similar effects to those of resveratrol (RSV). Network pharmacology analysis predicted that Ran's effects on cognitive decline through the PI3K/AKT/mTOR signalling pathway. These predictions were subsequently verified using a combination of molecular, cellular, and tissue experiments, animal models of ageing induced by D-galactose, and omics studies. The results revealed that Ran extended the lifespan of Caenorhabditis elegans (C. elegans), improved the head swinging ability of ageing C. elegans, and alleviated mitochondrial membrane potential (MMP) damage in ageing hippocampal neuronal cells (HT22). In ageing rats, Ran not only enhanced spatial memory,exploratory behaviors and motor ability,but also alleviated mitochondrial structural damage in hippocampus and medial prefrontal cortex (mPFC). Notably, Ran alleviated age-related cognitive decline by regulating mitochondrial autophagy in hippocampus and mPFC through the PI3K/AKT/mTOR signalling pathway, rather its conventional mechanism of regulating fatty acid metabolism. In summary, this study reveals Ran's previously unrecognized role in alleviating age-related cognitive decline for the first time. These findings provide new options for the treatment of age-related cognitive decline and broaden the potential clinical applications of Ran.

PMID:40749868 | DOI:10.1016/j.ejphar.2025.178007

Schizophr Res. 2025 Jul 31;284:67-76. doi: 10.1016/j.schres.2025.07.022. Online ahead of print.

ABSTRACT

BACKGROUND: The reduced life expectancy observed in individuals with schizophrenia (SCZ) underscores the urgent need for novel therapeutic targets. Emerging evidence suggests that astrocytic dysfunction and immune-inflammatory processes contribute to SCZ pathophysiology. Low-dose methotrexate (MTX), an established immunomodulatory drug used for non-neurological conditions, demonstrated antipsychotic potential in early SCZ. However, the underlying mechanisms remain unknown.

AIMS: This study aimed to identify molecular targets of MTX within SCZ-relevant pathways, focusing on astrocytic and neuroimmune networks using in silico bioinformatics analysis and a rodent model for SCZ.

METHODS: In silico analysis: A bioinformatics-based approach was employed, integrating cognitive computing-generated knowledge graphs, modular co-expression analysis, and gene-drug interaction databases with a machine learning-based text-mining tool. Rodent model: The NMDA receptor antagonist MK-801-induced mouse model for SCZ was used to assess MTX's therapeutic potential, compared to clozapine. Behavioural outcomes (sociability and cognition) and astrocytic reactivity (GFAP expression in the medial prefrontal cortex, mPFC) were evaluated.

RESULTS: In silico: MTX preferentially targeted hub genes within astrocyte and NFκB modules. In astrocytes, MTX modulated NT5E, a key regulator of adenosine metabolism. In the NFκB module, MTX interacted with IL-6 and STAT3, both involved in immune regulation. Rodent model: MK-801 administration impaired sociability and cognition, accompanied by increased GFAP-positive astrocytes in the prelimbic mPFC, both of which were prevented by MTX. The neuroimmunomodulator effect of MTX was comparable to clozapine in both the in silico and rodent model.

CONCLUSION: Our findings provide mechanistic insights into the potential antipsychotic-like effects of low-dose MTX in SCZ, involving astrocytic modulation and immune regulation. Further experimental medicine studies in clinical populations stratified by astrocytic and neuroinflammatory biomarkers are warranted to validate these findings.

PMID:40749304 | DOI:10.1016/j.schres.2025.07.022

Integr Biol (Camb). 2025 Jan 8;17:zyaf015. doi: 10.1093/intbio/zyaf015.

ABSTRACT

BACKGROUND: Diabetes coexisting with infections (DCI) significantly increases the risk of severe outcomes and mortality in patients. This study proposes that RPL7P1, an uncharacterized pseudogene, plays a role in the pathogenesis of DCI.

METHODS: Using a multifaceted approach, we employed experimental datasets from ENCODE to identify key genes. Drug repositioning was performed using gene network analysis with z-scores and the ROCR package. Network expansion was facilitated by NetworkAnalyst's core algorithms, with disease validation through the NHGRI GWAS Catalog. Cytoscape was utilized for network visualization.

RESULTS: Our findings reveal RPL7P1's potential involvement in DCI through modulation of CBL and STXBP3 by sequestering hsa-miR-144-3p, interaction with IGF2BP2 protein, and crosstalk with ATP6V1E1 RNA. Single-cell profiling pinpointed endothelial cells as a potential signaling nexus. Therapeutic agents targeting the RPL7P1-centric network showed promise in managing infections in diabetic patients. Additionally, we identified key molecular players, the m6A modification of RPL7P1, and its role in cuproptosis-a novel form of cell death.

CONCLUSION: This research elucidates the potential role of the novel pseudogene RPL7P1 in DCI, highlighting the importance of pseudogenes in complex diseases and providing novel insights into the epigenetic modulation of diabetes complicated by infections. Insight Box Diabetes and infections often coexist, complicating our understanding of their shared mechanisms. To address this, we employed a comprehensive suite of bioinformatics methods, including pathway mapping, ceRNA analysis, PPI evaluation, single-cell dissection, network modeling, and drug repositioning strategies. Our research revealed that RPL7P1 modulates signaling pathways by sponging miR-144-3p, interacting with IGF2BP2 and ATP6V1E1 RNA, and influencing cuproptosis-a novel form of cell death-through m6A modification, a key RNA modification. We identified endothelial cells as key mediators of the RPL7P1 network in the liver. Additionally, our integrative approach uncovered five potential therapeutic drugs targeting the RPL7P1 network. These findings provide novel insights into coexisting diabetes and infections, underscoring the value of multidisciplinary strategies in uncovering coexisting disease mechanisms and treatment opportunities.

PMID:40748624 | DOI:10.1093/intbio/zyaf015

Mol Neurobiol. 2025 Aug 1. doi: 10.1007/s12035-025-05233-5. Online ahead of print.

ABSTRACT

Huntington's disease (HD) is an inherited neurodegenerative condition that typically appears later in life, marked by progressive motor impairments, cognitive deterioration, and a range of psychiatric disturbances. Crucial to HD pathogenesis is the aberrant activation of caspase-6 (CASP6), which leads to the cleavage of mutant huntingtin (mHTT) protein, generating a toxic N-terminal fragment. CASP6 activity is elevated in human HD brains and is associated with the progression of neuropathology. This study aims to investigate the impact of deleterious mutations in CASP6 on the catalytic activity and stability of the protein. Identification of such mutations may be implicated in developing improved diagnostic approaches. Out of 225 single-point amino acid substitutions screened, 13 were found to be pathogenic and tend to form CASP6 aggregates. Among them, one mutation, F55V, was found in the highly conserved region, which may affect CASP6 function. We further employed a drug repurposing approach to identify potential CASP6 inhibitors for therapeutic development against HD. The integrated virtual screening and MD simulations analysis has suggested two repurposed drugs, Risperidone and Lorpiprazole, against CASP6. These results highlighted that targeting of CASP6 is a promising strategy for therapeutic management of HD and offers a starting point for designing novel interventions. As psychiatric symptoms in HD share neuropathological mechanisms with major psychiatric disorders, targeting CASP6 may provide insights into therapeutic strategies for both. These findings highlight CASP6 as a promising target for HD therapy and broader research in neuropsychiatric disorders. While this study is limited to computational modelling, it offers a valuable starting point for downstream experimental research.

PMID:40748431 | DOI:10.1007/s12035-025-05233-5

J Chem Inf Model. 2025 Aug 1. doi: 10.1021/acs.jcim.5c00507. Online ahead of print.

ABSTRACT

AI-driven drug discovery relies on multiscale data integration. However, the veterinary drug development field currently lacks systematic integration of drug-disease-target information. To address this gap, we constructed the Veterinary DrugBank (https://ys.yuhoutech.com/vp/#/home, VDB). VDB integrates multisource authoritative resources (PubChem, VSDB, FDA Green Book, TTD, and Papich Handbook of Veterinary Drugs) to systematically characterize 891 clinical veterinary drugs. This includes their fundamental attributes, structural/physicochemical properties, and 271 unique therapeutic targets, with the drugs demonstrating efficacy across >50 anmial species. The database annotates 956 ATCvet-coded indications and 904 ICD-11-classified diseases, thereby providing a robust foundation for AI-driven drug discovery. Porcine epidemic diarrhea virus (PEDV) causes high mortality in neonatal piglets and substantial economic losses, threatening global swine production. To address the urgent need for anti-PEDV therapeutics, we demonstrate VDB's drug repurposing utility using PEDV as a case study. Given the genomic and target similarities between PEDV and Coronaviridae family members, we conducted a similarity assessment between veterinary drugs and over 400 anticoronavirus compounds. We identified two antiparasitic agents, Eprinomectin (VDB00307) and Selamectin (VDB00726), which exhibited antiviral activity against PEDV. This study demonstrates the potential of drug repurposing in veterinary medicine and provides data support for AI-driven drug design, thereby accelerating the development of novel veterinary drugs.

PMID:40747889 | DOI:10.1021/acs.jcim.5c00507

Biofouling. 2025 Aug 1:1-11. doi: 10.1080/08927014.2025.2540534. Online ahead of print.

ABSTRACT

The aim was to evaluate the antibacterial activity of diazepam against methicillin-susceptible (MSSA) and methicillin-resistant (MRSA) strains of Staphylococcus aureus and its possible mechanism of action. The broth microdilution assay was used to determine the minimum inhibitory concentration (MIC) of diazepam. A checkerboard assay was used to evaluate the interaction of diazepam with different antibiotics. Colorimetric assays with MTT were used to evaluate the effect of diazepam against the biofilms by MSSA and MRSA. Flow cytometry and fluorescence microscopy were used to evaluate the possible mechanism of action of diazepam against MRSA. Diazepam had a MIC of 256 µg/mL. It only had indifferent interactions with the analyzed antibiotics. Diazepam significantly reduced the viability of MSSA and MRSA biofilms. Diazepam caused fragmentation of bacterial DNA and carbonylation of proteins, resulting in reduced cell viability. Therefore, diazepam has in vitro antimicrobial activity against planktonic and biofilm strains of MRSA and MSSA.

PMID:40746247 | DOI:10.1080/08927014.2025.2540534

Mol Genet Metab. 2025 Jul 19;146(1-2):109195. doi: 10.1016/j.ymgme.2025.109195. Online ahead of print.

ABSTRACT

BACKGROUND: Congenital Disorders of Glycosylation (CDG) are a rapidly expanding group of inherited metabolic diseases caused by defects in glycosylation. Although over 190 genetic defects have been identified, effective treatments remain available for only a few. We hypothesized that integrative analysis of multi-omics datasets from individuals with various CDG could uncover common molecular signatures and highlight shared therapeutic targets.

METHODS: We compiled all publicly available RNA sequencing, proteomics and glycoproteomics datasets from patients with PMM2-CDG, ALG1-CDG, SRD5A3-CDG, NGLY1-CDDG, ALG13-CDG and PGM1-CDG, spanning different tissues, including induced cardiomyocytes, human cortical organoids, fibroblasts, and lymphoblasts. Differential expression and glycosylation analyses were performed, followed by Gene Set Enrichment Analysis (GSEA) to identify commonly dysregulated pathways. We then applied the EMUDRA drug prediction algorithm to prioritize candidate compounds capable of reversing these shared molecular signatures.

RESULTS: We identified four glycoproteins with consistent differential glycosylation across all eight glycoproteomics datasets. Six glycosylation sites and glycan structures were recurrently altered across CDG and showed partial correction with treatment. Pathway analysis revealed shared disruptions in autophagy, vesicle trafficking, and mitochondrial function. EMUDRA predicted several repurposable drug classes, including muscle relaxants, antioxidants, beta-adrenergic agonists, antibiotics, and NSAIDs, that could reverse key pathway abnormalities, particularly those involving autophagy and N-glycosylation.

CONCLUSION: Most dysregulated pathways were shared across CDG, suggesting the potential for common therapeutic strategies. Several candidate drugs targeting these shared abnormalities emerged from integrative analysis and warrant validation in future in vitro studies.

PMID:40743674 | DOI:10.1016/j.ymgme.2025.109195

Vet Parasitol. 2025 Jul 29;338:110558. doi: 10.1016/j.vetpar.2025.110558. Online ahead of print.

ABSTRACT

We report on the in vitro and in vivo success of Imidocarb dipropionate (IDP), an aromatic diamidine compound, as an effective drug against Surra-causing organism, Trypanosoma evansi. IDP is a clinically acclaimed drug for the treatment of babesiosis and anaplasmosis. We have also investigated cytotoxic effect of IDP on equine Peripheral mononuclear cells (PBMCs) and Vero cell line. The data generated indicate that the IC50 of IDP (2.73 µM) was significantly lower than its cytotoxic concentration (32.66 and 80.70 µM against PBMCs and Vero cell line, respectively). We have recorded significant downregulation in mRNA expression of genes responsible for cellular regulatory activities such as cell division, glycolysis, nucleic acid synthesis, immune evasion strategy and redox homeostasis. During mice experimentation, dose of 20 mg/kg body weight was found to prolong the survival period of T. evansi infected mice by 20 days following two subsequent intra-peritoneal injections administered 72 h apart. Hematological, biochemical and histopathological investigations showed the effect of IDP on mice kidney and liver at four times multiples of the therapeutic dosage. Further, clinical trials on higher-order organisms are required before recommendation of IDP against T. evansi in the clinical host.

PMID:40743659 | DOI:10.1016/j.vetpar.2025.110558

Ann Med. 2025 Dec;57(1):2539311. doi: 10.1080/07853890.2025.2539311. Epub 2025 Jul 31.

ABSTRACT

INTRODUCTION: Observational studies have shown that methotrexate, a conventional synthetic disease-modifying antirheumatic drug (csDMARD), is associated with lower arterial blood pressure (BP) and may reduce cardiovascular risk in rheumatoid arthritis (RA). However, it remains unclear whether a cause-and-effect relationship exists between the use of methotrexate and blood pressure reduction.

PATIENTS AND METHODS: We conducted a controlled comparative study of treatment-naïve newly diagnosed RA patients commenced on subcutaneous methotrexate (Group 1, n = 31, age 57 ± 15 years, 65% females, Disease Activity Score-28 - C-reactive protein, DAS28-CRP = 4.7 ± 1.2) or the DMARD comparator sulfasalazine (Group 2, n = 31, 54 ± 17 years, 61% females, DAS28-CRP = 5.0 ± 0.8). Clinic systolic (SBP, primary study endpoint) and diastolic BP (DBP) and augmentation index (AIx, a marker of arterial stiffness) were measured at baseline and after one and six months of treatment (ClinicalTrials.gov: NCT03254589).

RESULTS: After six months, compared to Group 2, Group 1 patients had significantly lower SBP (-7.4 mmHg, 95% CI -14.0 to -0.8, p = 0.03). By contrast, there were no significant between-group differences in DBP (p = 0.18), AIx (p = 0.85), or DAS28-CRP (p = 0.16). A significant effect of single nucleotide polymorphisms (SNPs) rs1801133 (methyl tetrahydrofolate reductase) and rs2231142 (ATP-binding cassette subfamily G member 2) on BP changes during methotrexate treatment was observed.

CONCLUSIONS: This is the first comparative study showing that methotrexate significantly reduces SBP in RA. This effect did not coincide with significant changes in arterial stiffness or disease activity. Further research is warranted to investigate the mechanisms underpinning the SBP-lowering effects of methotrexate, the role of specific SNPs, and whether such effects may account for reduced cardiovascular risk in patients with RA.

PMID:40742006 | DOI:10.1080/07853890.2025.2539311

Front Immunol. 2025 Jul 16;16:1632154. doi: 10.3389/fimmu.2025.1632154. eCollection 2025.

ABSTRACT

Migraines are among the most common neurological disorders, disabling nearly one in seven people worldwide, whereas glioblastoma (GBM) is the most aggressive primary brain tumour, with median survival scarcely beyond 15 months. Historically considered distinct, these conditions are increasingly linked by trigeminal nerve-driven neurogenic inflammation. Activation of trigeminovascular afferents provokes antidromic release of calcitonin gene-related peptide (CGRP), substance P (SP), and pituitary adenylate cyclase-activating polypeptide (PACAP); beyond mediating migraine pain, these peptides remodel vasculature, immune infiltrates, and extracellular matrix to facilitate GBM invasion. Pre-clinical studies show CGRP and SP up-regulate matrix-metalloproteinases and integrins, while PACAP modulates cAMP-MAPK signalling, collectively promoting perivascular migration and temozolomide resistance. Epidemiological analyses report higher migraine antecedents in patients later diagnosed with brain tumours, and high-resolution MRI frequently localises GBM spread along trigeminal pathways, underscoring anatomical plausibility. Emerging therapeutics mirror these insights: aprepitant (an NK1-receptor antagonist) triggers GBM apoptosis, gepant-class CGRP blockers curb invasive phenotypes, and radiolabelled SP analogues deliver focal alpha-therapy. These discoveries facilitate more precise pathogenetic characterisation, reduce diagnostic uncertainty, and expedite translational drug development. This review synthesises current evidence on trigeminal neurogenic inflammation as a mechanistic conduit between migraine biology and GBM progression, mapping cellular circuits, molecular crosstalk, and translational interventions. By integrating neurobiology, oncology, and pharmacology, we aim to delineate diagnostic blind spots, spotlight drug-repurposing opportunities, and chart a roadmap toward personalised strategies that simultaneously alleviate migraine burden and restrain glioblastoma aggressiveness.

PMID:40740778 | PMC:PMC12307418 | DOI:10.3389/fimmu.2025.1632154

Front Immunol. 2025 Jul 16;16:1606874. doi: 10.3389/fimmu.2025.1606874. eCollection 2025.

ABSTRACT

Colorectal cancer (CRC) continues to rise in global incidence and remains a leading cause of cancer-related mortality. Immunogenic cell death (ICD) has emerged as a critical modulator of tumor microenvironment (TME) dynamics; however, its prognostic implications and therapeutic potential in CRC require systematic characterization. Through the integrative analysis of single-cell RNA sequencing and bulk transcriptomic data, 11 ICD-related genes with prognostic significance were identified in CRC. A comprehensive computational framework was then employed to evaluate 101 machine learning combinations, ultimately constructing an optimized 11-gene ICD-related signature (ICDRS) by integrating StepCox [forward] and RSF. The ICDRS exhibited strong predictive performance for overall survival in CRC patients across the training and validation datasets. Notably, the ICDRS-based nomogram achieved outstanding time-dependent AUCs (>0.90) for 1- to 3-year survival prediction. Multidimensional analysis revealed significant associations between ICDRS-derived risk score and distinct immune infiltration patterns, immunotherapy response and TME characteristics. Furthermore, a novel macrophage subtype, SPP1+/SLC11A1+, was discovered and characterized by high infiltration levels. Drug repurposing analysis indicated Olaparib as a potential therapeutic candidate for high-risk CRC patients. Therefore, this study establishes ICDRS as a promising tool for CRC prognosis and immunotherapy, with future validation studies planned to guide personalized treatment strategies.

PMID:40740776 | PMC:PMC12307400 | DOI:10.3389/fimmu.2025.1606874

J Cancer. 2025 Jul 11;16(10):3163-3179. doi: 10.7150/jca.116402. eCollection 2025.

ABSTRACT

Bladder cancer is characterized by a high recurrence rate and aggressive behavior, with frequent emergence of chemoresistance. Current treatments such as surgery, chemotherapy, and immunotherapy have limited efficacy, underscoring the urgent need for effective early diagnostic biomarkers and novel targeted therapies. Results: In this study, we integrated plasma proteomic data from the UK Biobank Pharma Proteomics Project (UKB-PPP) and the Icelandic deCODE study with genome-wide association study (GWAS) data. We employed two-sample Mendelian randomization (MR), Bayesian colocalization analysis, and SMR/HEIDI tests to systematically identify potential plasma protein targets associated with bladder cancer risk. A total of 199 plasma proteins were found to be significantly associated with bladder cancer risk, among which five proteins (SLURP1, LY6D, WFDC1, NOV, and GSTM3) emerged as core candidate targets. Further validation showed that NOV and GSTM3 demonstrated robust causal associations with bladder cancer across multiple analytical methods, and molecular docking analysis revealed that these two proteins can bind to estrogen/progestin hormone-regulating drugs. Conclusions: Our study identified multiple plasma proteins with causal links to bladder cancer and revealed their potential roles in tumor immune evasion, antioxidant defenses, and tumor metabolism. These findings provide new insights into bladder cancer biology and offer potential targets for precision therapy and drug repositioning.

PMID:40740240 | PMC:PMC12305583 | DOI:10.7150/jca.116402

Sci Rep. 2025 Jul 30;15(1):27878. doi: 10.1038/s41598-025-06974-y.

ABSTRACT

Dengue virus (DENV) has emerged as a formidable global health challenge, with a surging incidence rate across the world. Despite numerous research initiatives aimed at developing effective antiviral therapy, no clinically proven drug or vaccine has been identified to combat all four genetically diverse serotypes of DENV. Therefore, comparative analysis of repurposed drugs and phytocompounds against all DENV serotypes is critical in the search for an effective long-term solution to this menacing disease. 93 phytocompounds and 15 drugs were shortlisted from the literature and screened using DataWarrior 5.5.0, from which 10 phytocompounds and 10 drugs were selected for further analysis. Molecular docking was performed by using AutoDockVina tool. Toxicity and drug likeness activity of standard drugs and phytoconstituents was done by using online servers. The current study showed that among all the selected phytoconstituents, lupiwighteone showed the best binding energy, favorable pharmacokinetics and no toxicity with all the selected serotypes of DENV. The MD simulation result supported the stability of lupiwighteone in complexes with NS3, NS5 and E-Protein. This study identifies lupiwighteone as a promising antiviral candidate with favorable drug-like properties against DENV-2, DENV-3, and DENV-4 serotypes. Furthermore, in vitro and in vivo study is required for the validation of antiviral activity of Lupiwighteone against dengue virus.

PMID:40738908 | DOI:10.1038/s41598-025-06974-y

Brief Bioinform. 2025 Jul 2;26(4):bbaf387. doi: 10.1093/bib/bbaf387.

ABSTRACT

In the last two decades, numerous in silico methods have been developed for drug repurposing, to accelerate and reduce the risks about early drug development. Particularly, following Connectivity Map, dozens of distinct data-driven methods have been implemented to find candidates from the comparison of differential transcriptomic signatures. Interestingly, there have been multiple proposals to integrate available knowledge using systems biology databases and adapted algorithms from the network biology research field. Despite their similarities, these methods have been formulated inconsistently over the years, even if some of them are fundamentally similar. The aim of this review is to reconcile these integrative methods, focusing on elucidating their common structures while underlining the specificities of their strategies. To achieve this, we classified those methods into two main categories, provided schematic workflow representations, and presented a homogenized formulation for each.

PMID:40736744 | DOI:10.1093/bib/bbaf387

J Biomol Struct Dyn. 2025 Jul 30:1-22. doi: 10.1080/07391102.2025.2530092. Online ahead of print.

ABSTRACT

Antibiotic resistance poses a significant global health threat, necessitating the development of novel antibacterial strategies. Riboswitches, particularly those sensing purines, have emerged as promising targets for antibiotic development. In an unconventional approach, we explore the repurposing of antiviral compounds as potential antibacterial agents targeting riboswitches. This study employs a comprehensive computational framework to investigate the binding of 2'-deoxyguanosine (2'-dG) and 10 antiviral analogs to the 2'-dG-II riboswitch. We integrated structural similarity analysis, molecular docking, molecular dynamics simulations and molecular mechanics/generalized Born surface area (MM/GBSA) free energy calculations to elucidate the molecular basis of ligand recognition and assess the feasibility of antiviral compounds as riboswitch-targeting antibacterials. Our results revealed that the guanine moiety's binding orientation is conserved across all compounds, while interactions involving sugar or sugar-like moieties significantly influence binding stability and specificity. Key nucleotides U22, C51 and C78 play crucial roles in ligand recognition across all complexes. Notably, the antiviral compounds lagociclovir and valganciclovir demonstrated higher binding affinities than the native ligand, with unique interaction patterns. The MM/GBSA analysis provided binding free energies consistent with the experimental data for known compounds, validating our computational approach. This study offers detailed insights into the adaptability of the 2'-dG-II riboswitch binding pocket and identifies promising candidates for riboswitch-targeting antibiotics, contributing to the broader efforts to combat antibiotic resistance through computational drug discovery and repurposing strategies.

PMID:40736044 | DOI:10.1080/07391102.2025.2530092

Viruses. 2025 Jul 10;17(7):972. doi: 10.3390/v17070972.

ABSTRACT

(1) Background: The highly pathogenic avian influenza virus H5N1 clade 2.3.4.4b is an emerging threat that poses a great risk to the poultry industry. A few human cases have been linked to the infection with this clade in many parts of the world, including the USA. Unfortunately, there are no specific vaccines or antiviral drugs that could help prevent and treat the infection caused by this virus in birds. Our major objective is to identify/repurpose some (novel/known) antiviral compounds that may inhibit viral replication by targeting some key viral proteins. (2) Methods: We used state-of-the-art machine learning tools such as molecular docking and MD-simulation methods from Biovia Discovery Studio (v24.1.0.321712). The key target proteins such as hemagglutinin (HA), neuraminidase (NA), Matrix-2 protein (M2), and the cap-binding domain of PB2 (PB2/CBD) homology models were validated through structural assessment via DOPE scores, Ramachandran plots, and Verify-3D metrics, ensuring reliable structural representations, confirming their reliability for subsequent in silico approaches. These approaches include molecular docking followed by molecular dynamics simulation for 50 nanoseconds (ns), highlighting the structural stability and compactness of the docked complexes. (3) Results: Molecular docking revealed strong binding affinities for both sofosbuvir and GS441524, particularly with the NA and PB2/CBD protein targets. Among them, GS441524 exhibited superior interaction scores and a greater number of hydrogen bonds with key functional residues of NA and PB2/CBD. The MM-GBSA binding free energy calculations further supported these findings, as GS441524 displayed more favorable binding energies compared to several known standard inhibitors, including F0045S for HA, Zanamivir for NA, Rimantadine and Amantadine for M2, and PB2-39 for PB2/CBD. Additionally, 50 ns molecular dynamics simulations highlighted the structural stability and compactness of the GS441524-PB2/CBD complex, further supporting its potential as a promising antiviral candidate. Furthermore, hydrogen bond monitor analysis over the 50 ns simulation confirmed persistent and specific interactions between the ligand and proteins, suggesting that GS441524 may effectively inhibit the NA, and PB2/CBD might potentially disrupt PB2-mediated RNA synthesis. (4) Conclusions: Our findings are consistent with previous evidence supporting the antiviral activity of certain nucleoside analog inhibitors, including GS441524, against various coronaviruses. These results further support the potential repurposing of GS441524 as a promising therapeutic candidate against H5N1 avian influenza clade 2.3.4.4b. However, further functional studies are required to validate these in silico predictions and support the inhibitory action of GS441524 against the targeted proteins of H5N1, specifically clade 2.3.4.4b.

PMID:40733589 | DOI:10.3390/v17070972

Viruses. 2025 Jul 4;17(7):945. doi: 10.3390/v17070945.

ABSTRACT

Rabies, a viral encephalitis caused by rabies virus (RABV), is 100% fatal upon the onset of symptoms. Effective post-exposure prophylaxis (PEP) measures are available, but they are often difficult to access in low-income countries. WHO estimates about 59,000 deaths due to rabies globally, and the majority are contributed by developing countries. Hence, developing drugs for the treatment of post-symptomatic rabies is an urgent and unmet demand. It is worth noting that previous efforts regarding antiviral strategies, such as small-interfering RNA, antibodies and small-molecule inhibitors, against the rabies virus have failed to show efficacy in pre-clinical studies, especially when the virus has reached the central nervous system (CNS). Therefore, drug repurposing seems to be an alternative tool for the development of new anti-rabies drugs. We validated and used a high-throughput, FITC-conjugated antibody-based flow cytometry assay to expedite the identification of repurposable new drug candidates against the RABV. The assay was validated using ribavirin and salinomycin as reference compounds, which showed EC50 values of 10.08 µM and 0.07 µM, respectively. We screened a SelleckChem library comprising 3035 FDA-approved compounds against RABV (CVS-11) at 10 µM concentration. Five compounds (clofazimine, tiamulin, difloxacin, harringtonine and homoharringtonine) were active against RABV, with greater than 90% inhibition. Homoharringtonine (HHT) identified in the present study is active against laboratory-adapted RABV (CVS-11) and clinical isolates of RABV, with an average EC50 of 0.3 µM in both BHK-21 and Neuro-2a cell lines and exhibits post-entry inhibition.

PMID:40733562 | DOI:10.3390/v17070945

Viruses. 2025 Jun 23;17(7):881. doi: 10.3390/v17070881.

ABSTRACT

On 11 March 2020, the World Health Organisation (WHO) declared a global pandemic caused by the SARS-CoV-2 coronavirus that earlier in February 2020 the WHO had named COVID-19 (coronavirus disease 2019). There were neither drugs nor vaccines that were known to be effective against the virus, stimulating an urgent worldwide search for treatments. An evaluation of existing drugs by 'repurposing' was initiated followed by a transition to de novo drug discovery. Repurposing of an already approved drug may accelerate the introduction of that drug into clinical use by circumventing early, including preclinical studies otherwise essential for a de novo drug and reducing development costs. Early in the pandemic three drugs were identified as repurposing candidates for the treatment of COVID-19: (i) hydroxychloroquine, an anti-malarial also used to treat rheumatoid arthritis and lupus; (ii) ivermectin, an antiparasitic approved for both human and veterinary use; (iii) remdesivir, an anti-viral originally developed to treat hepatitis C. The scientific evidence, both for and against the efficacy of these three drugs as treatments for COVID-19, vied with public demand and politicization as unqualified opinions clashed with evidence-based medicine. To quote Hippocrates, "There are in fact two things, science and opinion; the former begets knowledge, the latter ignorance".

PMID:40733499 | DOI:10.3390/v17070881

Molecules. 2025 Jul 16;30(14):2992. doi: 10.3390/molecules30142992.

ABSTRACT

Cancer remains one of the most formidable challenges to human health; hence, developing effective treatments is critical for saving lives. An important strategy involves reactivating tumor suppressor genes, particularly p53, by targeting their negative regulator MDM2, which is essential in promoting cell cycle arrest and apoptosis. Leveraging a drug repurposing approach, we screened over 24,000 clinically tested molecules to identify new MDM2 inhibitors. A key innovation of this work is the development and application of a selective cleaning algorithm that systematically filters assay data to mitigate noise and inconsistencies inherent in large-scale bioactivity datasets. This approach significantly improved the predictive accuracy of our machine learning model for pIC50 values, reducing RMSE by 21.6% and achieving state-of-the-art performance (R2 = 0.87)-a substantial improvement over standard data preprocessing pipelines. The optimized model was integrated with structure-based virtual screening via molecular docking to prioritize repurposing candidate compounds. We identified two clinical CB1 antagonists, MePPEP and otenabant, and the statin drug atorvastatin as promising repurposing candidates based on their high predicted potency and binding affinity toward MDM2. Interactions with the related proteins MDM4 and BCL2 suggest these compounds may enhance p53 restoration through multi-target mechanisms. Quantum mechanical (ONIOM) optimizations and molecular dynamics simulations confirmed the stability and favorable interaction profiles of the selected protein-ligand complexes, resembling that of navtemadlin, a known MDM2 inhibitor. This multiscale, accuracy-boosted workflow introduces a novel data-curation strategy that substantially enhances AI model performance and enables efficient drug repurposing against challenging cancer targets.

PMID:40733263 | DOI:10.3390/molecules30142992

Molecules. 2025 Jul 16;30(14):2985. doi: 10.3390/molecules30142985.

ABSTRACT

Coronavirus disease 2019 (COVID-19) produced devastating health and economic impacts worldwide. While progress has been made in vaccine development, effective antiviral treatments remain limited, particularly those targeting the papain-like protease (PLpro) of SARS-CoV-2. PLpro plays a key role in viral replication and immune evasion, making it an attractive yet underexplored target for drug repurposing. In this study, we combined machine learning, molecular dynamics, and molecular docking to identify potential PLpro inhibitors in existing drugs. We performed long-timescale molecular dynamics simulations on PLpro-ligand complexes at two known binding sites, followed by structural clustering to capture representative structures. These were used for molecular docking, including a training set of 127 compounds and a library of 1107 FDA-approved drugs. A random forest model, trained on the docking scores of the representative conformations, yielded 76.4% accuracy via leave-one-out cross-validation. Applying the model to the drug library and filtering results based on prediction confidence and the applicability domain, we identified five drugs as promising candidates for repurposing for COVID-19 treatment. Our findings demonstrate the power of integrating computational modeling with machine learning to accelerate drug repurposing against emerging viral targets.

PMID:40733251 | DOI:10.3390/molecules30142985