BMC Geriatr. 2025 May 9;25(1):321. doi: 10.1186/s12877-025-05982-x.

ABSTRACT

BACKGROUND: In addition to conventional symptomatic treatment drugs, anti-amyloid beta antibody drugs are expected to benefit patients with Alzheimer's disease (AD). However, issues such as side effects and high costs persist, and new preventive and therapeutic drugs are desired. Meanwhile, information on the diagnosis and symptomatic treatment of AD accumulated during daily clinical practice is stored as real-world data and is considered a powerful means of discovering unknown factors that could provide clues for new prevention and treatment approaches for AD through comprehensive exploration.

METHODS: We used anonymized hospital information system data from a tertiary care and academic hospital in Japan, spanning from 1981 to 2016, to search for potential suppressive factors for AD onset and to verify the validity of the discovered factors. We initially conducted a comprehensive search for candidate suppressive factors for AD and verified them using the inverse probability weighting (IPW) method with propensity scores.

RESULTS: From the comprehensive search, we identified glycyrrhizic acid (GA), a component of licorice, a traditional medicine with anti-inflammatory, antioxidant, antibacterial, and antiaging properties, as a candidate suppressing factor for AD. The IPW method showed that the odds ratio of developing AD in the GA group was 0.642 (95% confidence interval: 0.566-0.727) compared with the non-GA group after adjustment.

CONCLUSIONS: This is the first human study to suggest that GA may be a factor that can suppress the onset of AD. Additionally, our method could be a promising tool for drug repositioning that applies existing drugs already used in clinical settings with well-known side effects to diseases different from their original use.

PMID:40346511 | DOI:10.1186/s12877-025-05982-x

Adv Sci (Weinh). 2025 May 8:e2415698. doi: 10.1002/advs.202415698. Online ahead of print.

ABSTRACT

Peripheral T-cell lymphoma (PTCL) is a heterogeneous group of mature T-cell malignancies with poor prognosis. Therefore, improved therapies are urgently required to improve patient outcomes. In this study, metabolic inhibitor drug screening reveals that quinacrine elicits excellent antitumor activity both in vitro and in vivo by downregulating intracellular arginine levels in PTCL. Single-cell transcriptomic analyses reveal aberrant arginine metabolism in patients with PTCL, characterized by excessive solute carrier family 3 member 2 (SLC3A2) mediated arginine uptake preferentially in tumor cells. High SLC3A2 expression predicts poor outcomes in PTCL, as SLC3A2-mediated arginine uptake promotes the malignant behaviors of tumor cells and induces tumor immune escape, thereby fueling tumor progression. Mechanistically, high arginine levels induce global metabolic changes, including enhanced oxidative phosphorylation by promoting nascent RNA synthesis. This work identifies structure-specific recognition protein 1 (SSRP1), which upregulates SLC3A2, as a co-transcription factor with JUNB. Quinacrine disrupts SLC3A2-mediated arginine transport by targeting SSRP1. Combining quinacrine with histone deacetylase inhibitors is a promising therapeutic strategy for PTCL.

PMID:40344476 | DOI:10.1002/advs.202415698

J Cancer Res Clin Oncol. 2025 May 9;151(5):157. doi: 10.1007/s00432-025-06185-y.

ABSTRACT

PURPOSE: Drug repurposing may be an efficient strategy for identifying new cancer treatments. Tranexamic acid (TXA), an antifibrinolytic agent that affects the plasminogen-plasmin pathway, may have potential anticancer effects by influencing tumor cell proliferation, angiogenesis, inflammation, immune response, and tissue remodeling-all crucial processes contributing to tumor progression and metastasis.

OBJECTIVE: Evaluate TXA's anticancer effects across in vitro, animal, and clinical studies to assess its potential as a repurposed cancer drug.

METHODS: The study was designed as a PRISMA-compliant systematic review and meta-analysis. The literature search was conducted in MEDLINE, EMBASE, Web of Science, and the Cochrane Library. In vitro, animal, and clinical studies investigating the anticancer effects of TXA or epsilon-aminocaproic acid (EACA) were included. Animal and clinical studies were critically appraised, and studies with a low risk of bias were included in the meta-analysis.

RESULTS: Of 4367 identified records, 38 articles were included, collectively reporting findings from 41 in vitro studies, 34 animal studies (n = 843 animals), and seven clinical studies (n = 91 patients). The meta-analysis included nine animal studies and showed a tumor growth reduction in animals treated with TXA compared to controls with a standardized mean difference of - 1.0 (95%CI - 1.5; - 0.4) (p = 0.0002). Equivalently, the majority of in vitro studies reported reduced proliferation, viability, and invasiveness in TXA-exposed tumor cell lines. The clinical studies were considerably susceptible to bias, rendering any conclusions futile.

CONCLUSIONS: TXA shows promise as a repurposed cancer drug, revealing an overall reduction in tumor growth, viability, and invasiveness in animal and in vitro studies.

PMID:40343490 | DOI:10.1007/s00432-025-06185-y

Sci Rep. 2025 May 8;15(1):16114. doi: 10.1038/s41598-025-01303-9.

ABSTRACT

The growing prevalence of antibiotic resistance in multidrug-resistant Gram-negative bacteria (MDR-GNB), exacerbated by the misuse of antibiotics, presents a critical global health challenge. Colistin, a last-resort antibiotic for severe MDR-GNB infections, has faced diminishing efficacy due to the emergence of colistin-resistant (COL-R) strains. This study evaluates the potential of penfluridol (PF), an antipsychotic drug with notable antibacterial and antibiofilm properties, to restore colistin activity against COL-R GNB in vitro. PF alone exhibited limited antibacterial activity against COL-R GNB; however, its combination with colistin demonstrated strong synergistic effects, significantly reducing colistin's minimum inhibitory concentrations (MICs) by 4-128 times. Time-kill assays confirmed the combination's superior bactericidal activity compared to either agent alone. Membrane permeability assays revealed that PF enhanced colistin's ability to disrupt bacterial membranes, likely by facilitating colistin binding to lipopolysaccharide. Furthermore, PF significantly inhibited the development of colistin resistance over a 30-day resistance development assay. In addition to its antibacterial effects, PF exhibited notable antibiofilm activity. The combination of PF and colistin effectively inhibited biofilm formation and eradicated mature biofilms in most of the tested COL-R GNB strains. These findings mark the first report of PF's synergistic interaction with colistin against GNB biofilms, offering a promising strategy to combat biofilm-associated infections. Overall, the colistin/PF combination holds potential as an effective therapeutic strategy to enhance colistin efficacy, delay resistance development, and manage biofilm-associated infections in MDR-GNB.

PMID:40341530 | DOI:10.1038/s41598-025-01303-9

Psychol Med. 2025 May 9;55:e137. doi: 10.1017/S0033291725000819.

ABSTRACT

BACKGROUND: The Hierarchical Taxonomy of Psychopathology (HiTOP) and Research Domain Criteria (RDoC) frameworks emphasize transdiagnostic and mechanistic aspects of psychopathology. We used a multi-omics approach to examine how HiTOP's psychopathology spectra (externalizing [EXT], internalizing [INT], and shared EXT + INT) map onto RDoC's units of analysis.

METHODS: We conducted analyses across five RDoC units of analysis: genes, molecules, cells, circuits, and physiology. Using genome-wide association studies from the companion Part I article, we identified genes and tissue-specific expression patterns. We used drug repurposing analyses that integrate gene annotations to identify potential therapeutic targets and single-cell RNA sequencing data to implicate brain cell types. We then used magnetic resonance imaging data to examine brain regions and circuits associated with psychopathology. Finally, we tested causal relationships between each spectrum and physical health conditions.

RESULTS: Using five gene identification methods, EXT was associated with 1,759 genes, INT with 454 genes, and EXT + INT with 1,138 genes. Drug repurposing analyses identified potential therapeutic targets, including those that affect dopamine and serotonin pathways. Expression of EXT genes was enriched in GABAergic, cortical, and hippocampal neurons, while INT genes were more narrowly linked to GABAergic neurons. EXT + INT liability was associated with reduced gray matter volume in the amygdala and subcallosal cortex. INT genetic liability showed stronger causal effects on physical health - including chronic pain and cardiovascular diseases - than EXT.

CONCLUSIONS: Our findings revealed shared and distinct pathways underlying psychopathology. Integrating genomic insights with the RDoC and HiTOP frameworks advanced our understanding of mechanisms that underlie EXT and INT psychopathology.

PMID:40340892 | DOI:10.1017/S0033291725000819

J Transl Med. 2025 May 8;23(1):516. doi: 10.1186/s12967-025-06535-x.

ABSTRACT

BACKGROUND: Long COVID is a major public health burden causing a diverse array of debilitating symptoms in tens of millions of patients globally. In spite of this overwhelming disease prevalence, staggering cost, severe impact on patients' lives and intense global research efforts, study of the disease has proved challenging due to its complexity. Genome-wide association studies (GWAS) have identified only four loci potentially associated with the disease, although these results did not statistically replicate between studies. A previous combinatorial analysis study identified a total of 73 genes that were highly associated with two long COVID cohorts in the predominantly (> 91%) white European ancestry Sano GOLD population, and we sought to reproduce these findings in the independent and ancestrally more diverse All of Us (AoU) population.

METHODS: We assessed the reproducibility of the 5343 long COVID disease signatures from the original study in the AoU population. Because the very small population sizes provide very limited power to replicate findings, we initially tested whether we observed a statistically significant enrichment of the Sano GOLD disease signatures that are also positively correlated with long COVID in the AoU cohort after controlling for population substructure.

RESULTS: For the Sano GOLD disease signatures that have a case frequency greater than 5% in AoU, we consistently observed a significant enrichment (77-83%, p < 0.01) of signatures that are also positively associated with long COVID in the AoU cohort. These encompassed 92% of the genes identified in the original study. At least five of the disease signatures found in Sano GOLD were also shown to be individually significantly associated with increased long COVID prevalence in the AoU population. Rates of signature reproducibility are strongest among self-identified white patients, but we also observe significant enrichment of reproducing disease associations in self-identified black/African-American and Hispanic/Latino cohorts. Signatures associated with 11 out of the 13 drug repurposing candidates identified in the original Sano GOLD study were reproduced in this study.

CONCLUSION: These results demonstrate the reproducibility of long COVID disease signal found by combinatorial analysis, broadly validating the results of the original analysis. They provide compelling evidence for a much broader array of genetic associations with long COVID than previously identified through traditional GWAS studies. This strongly supports the hypothesis that genetic factors play a critical role in determining an individual's susceptibility to long COVID following recovery from acute SARS-CoV-2 infection. It also lends weight to the drug repurposing candidates identified in the original analysis. Together these results may help to stimulate much needed new precision medicine approaches to more effectively diagnose and treat the disease. This is also the first reproduction of long COVID genetic associations across multiple populations with substantially different ancestry distributions. Given the high reproducibility rate across diverse populations, these findings may have broader clinical application and promote better health equity. We hope that this will provide confidence to explore some of these mechanisms and drug targets and help advance research into novel ways to diagnose the disease and accelerate the discovery and selection of better therapeutic options, both in the form of newly discovered drugs and/or the immediate prioritization of coordinated investigations into the efficacy of repurposed drug candidates.

PMID:40340717 | DOI:10.1186/s12967-025-06535-x

J Med Chem. 2025 May 9. doi: 10.1021/acs.jmedchem.5c00021. Online ahead of print.

ABSTRACT

Aging induces dysfunction and increases the risk of chronic diseases in the elderly, positioning the development of antiaging drugs to the forefront of research. Drug repurposing offers an efficient strategy for identifying antiaging lead compounds. In this study, we employed phenotypic screening and discovered that enrofloxacin could extend the lifespan in Caenorhabditis elegans. Based on these findings, we conducted rational drug design to eliminate its antibacterial activity while maintaining the lifespan-extending effect, with the goal of developing safe and novel antiaging compounds. Consequently, JX10 exhibited minimal antibacterial activity and competent antiaging effects in C. elegans, senescent cells, and aged mice. In terms of its mechanism, JX10 acted as a senomorphic agent by suppressing the expression of p38 MAPK and NF-κB. Furthermore, JX10 demonstrated favorable safety and pharmacokinetic properties, supporting the feasibility of JX10 as a promising candidate with the potential for therapeutic interventions in aging and aging-related diseases.

PMID:40340340 | DOI:10.1021/acs.jmedchem.5c00021

J Clin Endocrinol Metab. 2025 May 8:dgaf276. doi: 10.1210/clinem/dgaf276. Online ahead of print.

ABSTRACT

BACKGROUND: Observational studies have shown association between polycystic ovary syndrome (PCOS) and asthma-related traits. However, whether this association is genetically driven or arises from observational biases remains unclear.

METHODS: This study integrated data from 10,074 PCOS cases and asthma-related traits obtained from UK Biobank and FinnGen cohorts. Global and local genetic architectures were examined using pleiotropic analysis under the composite null hypothesis, Functional Mapping and Annotation of Genetic Associations, and fine-mapping credible set analysis. Drug database mining was employed to identify pleiotropic genes as potential therapeutic targets. Tissue and cell enrichment analyses were conducted to uncover shared biological mechanisms.

RESULTS: We identified 3 novel significant genetic loci for asthma subtypes (2 for allergic asthma and 1 for childhood asthma). A positive overall genetic correlation between PCOS and asthma-related traits was observed. We discovered 5 pleiotropic causal regions encompassing 13 genes, with ERBB3 emerging as a potential central gene contributing to the shared pathophysiology of PCOS and asthma-related traits. Additionally, drug repositioning analysis suggested anakinra and artenimol as potential therapeutic candidates for PCOS and asthma comorbidity. Linkage disequilibrium score for the specific expression of genes analysis, along with transcriptome-wide association study, further identified gene expression patterns at the tissue/cell level in hypothalamo-pituitary, exocrine/endocrine, respiratory, and urogenital systems.

CONCLUSIONS: Our findings provide novel insights into the genetic basis and biological processes underlying the association between PCOS and asthma-related traits, warranting evaluation of whether PCOS-specific asthma risk assessment could improve clinical outcomes.

PMID:40339110 | DOI:10.1210/clinem/dgaf276

J Clin Invest. 2025 May 8:e181394. doi: 10.1172/JCI181394. Online ahead of print.

ABSTRACT

The activated JAK2/STAT pathway is characteristic of myeloproliferative neoplasms (MPNs). Pleckstrin-2 (PLEK2) signalosome is downstream of the JAK2/STAT5 pathway and plays an important role in MPN development. The detailed molecular composition of this signalosome is unclear. Here, we revealed peptidylprolyl isomerase-like 2 (PPIL2) as a critical component of the complex in regulating human and murine erythropoiesis. PPIL2 was a direct target of STAT5 and was upregulated in MPN patients and a Jak2V617F MPN mouse model. Mechanistically, PPIL2 interacted with and catalyzed p53 polyubiquitination and proteasome-mediated degradation to promote cell growth. Ppil2 deficiency, or inhibition by cyclosporin A, led to a marked upregulation of p53 in vivo and ameliorated myeloproliferative phenotypes in Jak2V617F mice. Cyclosporin A also markedly reduced JAK2 mutated erythroid and myeloid proliferation in an induced pluripotent stem cell-derived human bone marrow organoid model. Our findings revealed PPIL2 as a critical component of the PLEK2 signalosome in driving MPN pathogenesis through negatively regulating p53, thus providing a target and an opportunity for drug repurposing by using cyclosporin A to treat MPNs.

PMID:40338661 | DOI:10.1172/JCI181394

Curr Med Chem. 2025 May 7. doi: 10.2174/0109298673367163250417065816. Online ahead of print.

ABSTRACT

BACKGROUND: Developing new COVID-19 antivirals requires understanding viral proteins, oxidative stress, and drug repositioning. Safety assessments of organochalcogen molecules derived from AZT in Caenorhabditis elegans offer promising prospects for new treatments.

OBJECTIVE: In this work, we evaluated the safety and antioxidant effect of eight organochalcogen AZT-derivatives using the free-living nematode C. elegans through chronic exposure [48h]. In addition, we used in silico computational modelling analyses to predict protein targets for these compounds.

METHODS: This study used survival, litter size, brood size as toxicological and safety parameters, subcellular localization of DAF-16, expression of SOD-3 and GST-4, and ROS levels to evaluate the antioxidant effects and target prediction by similarity set approach [SEA], protein-protein interaction [PPI] network analysis, and comparative phylogenetic analysis to predict protein targets for these compounds.

RESULTS: The molecules were safe at concentrations of 1-500 μM. AZT, R3a, and R3f promoted DAF-16 nuclear translocation without affecting SOD-3 levels. R3f reduced GST-4 levels, while R3a increased ROS levels. In silico analyses identified 16 human protein targets of AZT and its derivatives, linked to nucleotide metabolism, DNA replication, and anti-inflammatory pathways, showing high homology to C. elegans.

CONCLUSION: We hypothesize that Se and Te atom insertion may alter pharmacological properties by modulating DAF-16, GST-4, and ROS-related pathways. in silico data suggest these derivatives are promising for antiviral activity, targeting nucleotide metabolism and DNA replication while also potentially modulating the anti-inflammatory response, an appealing feature for COVID-19 treatment.

PMID:40337965 | DOI:10.2174/0109298673367163250417065816

Nucleic Acids Res. 2025 May 8:gkaf399. doi: 10.1093/nar/gkaf399. Online ahead of print.

ABSTRACT

Enzymes with buried active sites utilize molecular tunnels to exchange substrates, products, and solvent molecules with the surface. These transport mechanisms are crucial for protein function and influence various properties. As proteins are inherently dynamic, their tunnels also vary structurally. Understanding these dynamics is essential for elucidating structure-function relationships, drug discovery, and bioengineering. Caver Web 2.0 is a user-friendly web server that retains all Caver Web 1.0 functionalities while introducing key improvements: (i) generation of dynamic ensembles via automated molecular dynamics with YASARA, (ii) analysis of dynamic tunnels with CAVER 3.0, (iii) prediction of ligand trajectories in multiple snapshots with CaverDock 1.2, and (iv) customizable ligand libraries for virtual screening. Users can assess protein flexibility, identify and characterize tunnels, and predict ligand trajectories and energy profiles in both static and dynamic structures. Additionally, the platform supports virtual screening with FDA/EMA-approved drugs and user-defined datasets. Caver Web 2.0 is a versatile tool for biological research, protein engineering, and drug discovery, aiding the identification of strong inhibitors or new substrates to bind to the active sites or tunnels, and supporting drug repurposing efforts. The server is freely accessible at https://loschmidt.chemi.muni.cz/caverweb.

PMID:40337920 | DOI:10.1093/nar/gkaf399

Int J Biol Macromol. 2025 May 5:143959. doi: 10.1016/j.ijbiomac.2025.143959. Online ahead of print.

ABSTRACT

Drug repositioning refers to new medical application exploration for existing drugs. Heparins, beyond their well-known anticoagulant properties widely used in clinics, present the capacity to carry biosignal molecules that is responsible for other properties such as anti-inflammatory, angiogenesis. Thus, heparins interaction with different biosignal molecules such as cytokines and growth factors have recently drawn attention and have promoted heparin repositioning as an active excipient with useful applications as drug-delivery systems and biomaterial-based tissue engineering scaffolds. Indeed, biomaterial heparinization can further help in their formulation such as in self-assembled heparin-based hydrogels or nanoparticles, and improve their biocompatibility. Moreover, the capacity of heparin to carry biosignal molecules enables the direct functionalization of heparinized biomaterial for tissue engineering. Both heparin characteristics namely the biosignal molecule carrying and biomaterial heparinization are reviewed here along their combination for biomaterial functionalization in tissue engineering applications.

PMID:40334894 | DOI:10.1016/j.ijbiomac.2025.143959

PLoS One. 2025 May 7;20(5):e0322546. doi: 10.1371/journal.pone.0322546. eCollection 2025.

ABSTRACT

Proteins are fundamental biomolecules composed of one or more chains of amino acids. They are essential for all living organisms, contributing to various biological functions and regulatory processes. Alterations in protein structures and functions are closely linked to diseases, emphasizing the need for in-depth study. A thorough understanding of these associations is crucial for developing targeted and more effective therapeutic strategies.Computational analyses of biomedical data facilitate the identification of specific patterns in proteins associated with diseases, providing novel insights into their biological roles. This study introduces a computational approach designed to detect relevant sequence patterns within proteins. These patterns, characterized by specific amino acid arrangements, can be critical for protein functionality. The proposed methodology was applied to proteins targeted by drugs used in lung cancer treatment, a disease that remains the leading cause of cancer-related mortality worldwide. Given that non-small cell lung cancer represents 85-90% of all lung cancer cases, it was selected as the primary focus of this study.Significant sequence patterns were identified, establishing connections between drug-target proteins and proteins associated with lung cancer. Based on these findings, a novel computational framework was developed to extend this pattern-based analysis to proteins linked to other diseases. By employing this approach, relationships between lung cancer drug-target proteins and proteins associated with four additional cancer types were uncovered. These associations, characterized by shared amino acid sequence features, suggest potential opportunities for drug repurposing. Furthermore, validation through an extensive literature review confirmed biological links between lung cancer drug-target proteins and proteins related to other malignancies, reinforcing the potential of this methodology for identifying new therapeutic applications.

PMID:40334012 | DOI:10.1371/journal.pone.0322546

Molecules. 2025 Apr 13;30(8):1736. doi: 10.3390/molecules30081736.

ABSTRACT

Alzheimer's disease (AD) affects an increasing number of people as the human population ages. The main pathological feature of AD, amyloid plaques, consists of the key protein amyloid-β and other co-deposited proteins. These co-deposited proteins and their protein interactors could hold some additional functional insights into AD pathophysiology. For this work, proteins found on amyloid plaques were collected from the AmyCo database. A protein-protein and protein-drug interaction network was constructed with data from the IntAct and DrugBank databases, respectively. In total, there were 12 proteins co-deposited on amyloid plaques that reportedly interact with 513 other proteins and are targets of 72 drugs. These drugs were shown to be almost entirely distinct from the panel of drugs currently approved by the FDA for AD and their corresponding protein targets. In conclusion, this work demonstrates the potential for drug repurposing of drugs that target proteins found in amyloid plaques.

PMID:40333680 | DOI:10.3390/molecules30081736

Int J Mol Sci. 2025 Apr 16;26(8):3761. doi: 10.3390/ijms26083761.

ABSTRACT

Novel drug discovery and repositioning remain critical challenges in biomedical research, requiring accurate prediction of drug-target interactions (DTIs). We propose the CPDP framework, which builds upon existing biomedical representation models and integrates contrastive learning with multi-dimensional representations of proteins and drugs to predict DTIs. By aligning the representation space, CPDP enables GNN-based methods to achieve zero-shot learning capabilities, allowing for accurate predictions of unseen drug data. This approach enhances DTI prediction performance, particularly for novel drugs not included in the BioHNs dataset. Experimental results demonstrate CPDP's high accuracy and strong generalization ability in predicting novel biological entities while maintaining effectiveness for traditional drug repositioning tasks.

PMID:40332398 | DOI:10.3390/ijms26083761

BMC Chem. 2025 May 6;19(1):119. doi: 10.1186/s13065-025-01468-4.

ABSTRACT

Trichinellosis represents great public health and economic problems worldwide. Moreover, the development of parasitic resistance against conventional anthelminthic treatment led to the urgent search for new therapeutic strategies, including drug repurposing. Bisphosphonates have been used to inhibit the growth of many parasites and have also emerged as promising candidates for the treatment of cryptosporidiosis and amoebic liver abscess. Alendronate is a second-generation bisphosphonate that is widely used for the treatment and prevention of osteoporosis. Till date, there is not enough data on the effect of this drug on Trichinella spiralis and it is unknown whether the regular use of this drug in osteoporotic patients may alter the course of the infection. ALN showed a significant lethal effect on both adult worms and juveniles, with severe tegumental damage in the form of fissures in the cuticle, widening of the hypodermal gland, and flattening of the cuticular annulation, ending with the appearance of multiple vesicles and large cauliflower masses. Molecular docking outcomes unveiled the potential inhibition of ALN against T. spiralis surface proteins (i.e., Ts-SP, Ts-PPase, Ts-MAPRC2, Ts-TS, Ts-MIF, etc.), with promising results confirmed its ability to defeat T. spiralis via targeting its surface proteins. Moreover, molecular dynamics simulation, through the analysis of RMSD, RMSF, RG, SASA and cluster analysis, proved the prolonged effective inhibition of ALN on T. spiralis inorganic pyrophosphatase, as an essential surface protein required for molting and developmental process of intestinal larval stages. Thus, ALN might be a valuable drug candidate for the treatment of trichinellosis and warrant further investigation in animal models of disease.

PMID:40329381 | DOI:10.1186/s13065-025-01468-4

Naunyn Schmiedebergs Arch Pharmacol. 2025 May 6. doi: 10.1007/s00210-025-04212-w. Online ahead of print.

ABSTRACT

Inflammatory bowel disease (IBD) is a chronic inflammatory condition of the gastrointestinal tract with a multifactorial etiology. Given the limitations and adverse effects of current therapies, there is a need for novel therapeutic approaches. Drug repurposing presents a promising opportunity to utilize medications with known safety and pharmacological profiles for alternative colitis treatment. Emerging evidence suggests the renin-angiotensin system (RAS) plays a significant role in the colitis pathophysiology. Angiotensin-converting enzyme (ACE) inhibitors may offer therapeutic potential by modulating pro-inflammatory cytokines and reducing oxidative stress. This study aims to evaluate the efficacy of lisinopril (LIS) in a 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis model in Wistar rats. Colitis was induced in Wistar rats via a single intracolonic TNBS dose (100 mg/kg). Treatment groups received oral interventions for 5 days: 5-aminosalicylic acid (5-ASA; 25.5 mg/kg), LIS (10 mg/kg), or LIS (20 mg/kg). Efficacy was evaluated using the disease activity score rate (DASR), colon/body weight ratio (CBWR), and colon length, diameter, and pH. Colonic tissue was analyzed macroscopically and histopathologically. Inflammatory biomarkers interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), oxidative stress markers glutathione (GSH), and malondialdehyde (MDA), as well as C-reactive protein (CRP) and complete blood count (CBC), were measured. LIS significantly reduced colitis severity, decreasing DASR and CBWR, while restoring colon dimensions and pH. LIS showed potent anti-colitic effects by suppressing TNF-α and IL-6 levels, reducing MDA, and increasing GSH. LIS restored RBC and WBC levels while normalizing CRP and hemoglobin levels. Histopathological and macroscopic analyses confirmed colonic protection with minimal detrimental effects on the stomach and liver. LIS, particularly at 20 mg/kg, exhibited dose-dependent anti-inflammatory, antioxidant, and tissue-protective effects, showing promise as a therapeutic agent for colitis treatment.

PMID:40328912 | DOI:10.1007/s00210-025-04212-w

PLoS One. 2025 May 6;20(5):e0319865. doi: 10.1371/journal.pone.0319865. eCollection 2025.

ABSTRACT

Rare diseases affect more than 30 million individuals, with the majority facing limited treatment options, elevating the urgency to innovative therapeutic solutions. Addressing these medical challenges necessitates an exploration of novel treatment modalities. Among these, drug repurposing emerges as a promising avenue, offering both potential and risk mitigation. To achieve this goal, we primarily focused on developing predictive models that harness cutting-edge computational techniques to uncover latent relationships between gene targets and chemical compounds towards drug repurposing. Building upon our previous investigation, where we successfully identified gene targets for compounds from the Tox21 in vitro assays, our endeavor expanded to a systematic prediction of potential targets for drug repurposing employing machine learning models built on diverse algorithms such as Support Vector Classifier, K-Nearest Neighbors, Random Forest, and Extreme Gradient Boosting. These models were trained on comprehensive biological activity profile data to predict the relationship between 143 gene targets and over 6000 compounds. Our models demonstrated high accuracy (>0.75), with predictions further validated by using public experimental datasets. Furthermore, several findings were evaluated via case studies. By elucidating these connections, we aim to streamline the drug repurposing process, ultimately catalyzing the discovery of more effective therapeutic interventions for rare diseases.

PMID:40327632 | DOI:10.1371/journal.pone.0319865

IEEE J Biomed Health Inform. 2025 May 6;PP. doi: 10.1109/JBHI.2025.3567452. Online ahead of print.

ABSTRACT

Drug-Gene Interaction (DGI) is crucial for drug discovery and personalized medicine. The continuous development of genomics and drug repositioning has brought increasing attention to the complex relations between drugs and genes. However, traditional biological experiments are time-consuming and costly, which makes it challenging to efficiently explore the multi-relational interactions between drugs and genes. Therefore, computational approaches aim to develop efficient schemes for predicting drug-gene relations to reduce the search space and experimental costs. Existing computational methods often suffer from data scarcity and poor generalization, which pose significant challenges for practical applications. To address these issues, we propose a novel multi-relation DGI prediction method based on molecular structure-driving and high-low-order attention denoising framework. Our approach captures molecular structural information through both atom and bond channels with a drug feature encoder. For network structure, we enhance both high- and low-order channels: the low-order channel leverages graph convolutional networks, while the high-order channel employs hypergraph-based message propagation. Additionally, we adopt consistency information loss and inter-channel attention mechanism to refine high- and low-order features. Experimental results on three drug-gene datasets demonstrate the superior performance of our model, particularly on sparse datasets DrugBank and DGIdb, with F1 improvements of 4.06% and 5.67%, respectively. Our implementations will be publicly available at: https://github.com/jianruichen/D-MAC.

PMID:40327470 | DOI:10.1109/JBHI.2025.3567452

Naunyn Schmiedebergs Arch Pharmacol. 2025 May 6. doi: 10.1007/s00210-025-04233-5. Online ahead of print.

ABSTRACT

Since the onset of the COVID-19 pandemic in March 2020, researchers worldwide have sought effective drugs to prevent and manage SARS-CoV-2 and its spectrum of symptoms. Ivermectin, originally developed as an anthelmintic for controlling parasitic infections in humans and animals, has drawn attention based on the hypothesis that it inhibits viral replication. In Austria, ivermectin usage peaked in November 2021, following promotion by the right-wing Freedom Party of Austria (FPÖ) as an alternative treatment to vaccination, resonating strongly within anti-vaccine and skeptical communities. The topic is also very present in the United States of America due to the re-election of D. Trump as US President and the designation of R. Kennedy as the United States' Secretary of Health and Human Services. To critically examine the controversial use of ivermectin for COVID-19 and publication trends during the pandemic, this study analysed all publications listed in PubMed from 1 January 2020 to 31 December 2022 using the keywords 'ivermectin' and 'COVID-19', resulting in a dataset of 353 publications. These publications were assessed for scientific quality, methodological rigour and bias, with particular focus on the influence of social and political dynamics on publication practices, as well as the prevalence of preprints, citation trends and the role of funding sources. Our study shows that many highly cited studies on ivermectin display methodological weaknesses and data gaps, contributing to the propagation of hypotheses lacking substantial empirical support. This analysis underscores the necessity of rigorous quality control during crises and highlights the long-term risks posed to scientific databases and public health by methodologically deficient research.

PMID:40327060 | DOI:10.1007/s00210-025-04233-5