Neurotherapeutics. 2025 Mar 20:e00575. doi: 10.1016/j.neurot.2025.e00575. Online ahead of print.

ABSTRACT

Neuroblastoma is an aggressive childhood cancer that arises from the sympathetic nervous system. Despite advances in treatment, high-risk neuroblastoma remains difficult to manage due to its heterogeneous nature and frequent development of drug resistance. Drug repurposing guided by single-cell analysis presents a promising strategy for identifying new therapeutic options. Here, we aim to characterize high-risk neuroblastoma subpopulations and identify effective repurposed drugs for targeted treatment. We performed single-cell transcriptomic analysis of neuroblastoma samples, integrating bulk RNA-seq data deconvolution with clinical outcomes to define distinct malignant cell states. Using a systematic drug repurposing pipeline, we identified and validated potential therapeutic agents targeting specific high-risk neuroblastoma subpopulations. Single-cell analysis revealed 17 transcriptionally distinct neuroblastoma subpopulations. Survival analysis identified a highly aggressive subpopulation characterized by elevated UBE2C/PTTG1 expression and poor patient outcomes, distinct from a less aggressive subpopulation with favorable prognosis. Drug repurposing screening identified Adavosertib as particularly effective against the aggressive subpopulation, validated using SK-N-DZ cells as a representative model. Mechanistically, Adavosertib suppressed cell proliferation through AKT/mTOR pathway disruption, induced G2/M phase cell cycle arrest, and promoted apoptosis. Further analysis revealed UBE2C and PTTG1 as key molecular drivers of drug resistance, where their overexpression enhanced proliferation, Adavosertib resistance, and cell migration. This study establishes a single-cell-based drug repurposing strategy for high-risk neuroblastoma treatment. Our approach successfully identified Adavosertib as a promising repurposed therapeutic agent for targeting specific high-risk neuroblastoma subpopulations, providing a framework for developing more effective personalized treatment strategies.

PMID:40118716 | DOI:10.1016/j.neurot.2025.e00575

Eur J Med Chem. 2025 Mar 15;290:117516. doi: 10.1016/j.ejmech.2025.117516. Online ahead of print.

ABSTRACT

Ferroptosis has emerged as a promising therapeutic approach for a wide range of diseases. However, limited chemical diversity and poor drug-like profiles have hindered the development of effective ferroptosis inhibitors for clinical use. Herein, we identified drug-like imidazo[1,2-a]pyridine-3-amine derivatives as innovative ferroptosis inhibitors for injury-related diseases by drug scaffold repositioning strategy. Our findings established that the selected compounds exhibited high radical scavenging and effective membrane retention, thereby leading to significant suppression of lipid peroxidation and ferroptosis at nanomolar concentrations. Notably, compound C18, with low cytotoxicity and favorable pharmacokinetics properties, demonstrated remarkable in vivo neuroprotection against ischemic brain injury in mice. In conclusion, our investigations not only engender potent ferroptosis inhibitors with novel structural characteristics that warrant further development, but also serve as a valuable case study for drug repurposing in the discovery of additional ferroptosis inhibitors.

PMID:40117856 | DOI:10.1016/j.ejmech.2025.117516

HGG Adv. 2025 Mar 20:100425. doi: 10.1016/j.xhgg.2025.100425. Online ahead of print.

ABSTRACT

Haploinsufficiency of FBXO11, encoding a ubiquitin ligase complex subunit, is associated with a variable neurodevelopmental disorder. So far, the underlying nervous-system related pathomechanisms are poorly understood, and specific therapies are lacking. Using a combined approach, we established an FBXO11-deficient human stem cell-based neuronal model using CRISPR/CAS9 and a Drosophila model using tissue specific knockdown techniques. We performed transcriptomic analyses on iPSC-derived neurons and molecular phenotyping in both models. RNA-sequencing revealed disrupted transcriptional networks related to processes important for neuronal development, such as differentiation, migration and cell signaling. Consistently, we found that loss of FBXO11 leads to neuronal phenotypes such as impaired neuronal migration and abnormal proliferation/differentiation balance in human cultured neurons and impaired dendritic development and behavior in Drosophila. Interestingly, application of three different proteasome activating substances could alleviate FBXO11-deficiency-associated phenotypes in both human neurons and flies. One of these substances is the long-approved drug Verapamil, opening the possibility of drug repurposing in the future. Our study shows the importance of FBXO11 for neurodevelopment and highlights the reversibility of related phenotypes, opening an avenue for potential development of therapeutic approaches through drug repurposing.

PMID:40114442 | DOI:10.1016/j.xhgg.2025.100425

Brief Bioinform. 2025 Mar 4;26(2):bbaf117. doi: 10.1093/bib/bbaf117.

ABSTRACT

The prediction of virus-drug associations (VDAs) is crucial for drug repositioning, contributing to the identification of latent antiviral drugs. In this study, we developed a graph-based integrated Gaussian kernel similarity (GiGs) method for predicting potential VDAs in drug repositioning. The GiGs model comprises three components: (i) collection of experimentally validated VDA information and calculation virus sequence, drug chemical structure, and drug side effect similarity; (ii) integration of viruses and drugs similarity based on the above information and Gaussian interaction profile kernel (GIPK); and (iii) utilization of similarity-constrained weight graph normalization matrix factorization to predict antiviral drugs. The GiGs model enhances correlation matrix quality through the integration of multiple biological data, improves performance via similarity constraints, and prevents overfitting and predicts missing data more accurately through graph regularization. Extensive experimental results indicated that the GiGs model outperforms five other advanced association prediction methods. A case study identified broad-spectrum drugs for treating highly pathogenic human coronavirus infections, with molecular docking experiments confirming the model's accuracy.

PMID:40112339 | DOI:10.1093/bib/bbaf117

Microbiol Spectr. 2025 Mar 20:e0298724. doi: 10.1128/spectrum.02987-24. Online ahead of print.

ABSTRACT

Candida albicans filamentation and biofilm formation are key virulence factors tied to tissue invasion and antifungal tolerance. Pilocarpine hydrochloride (PHCl), a muscarinic receptor agonist, inhibits biofilm maturation, although its mechanism remains unclear. We explored PHCl effects by analyzing sphingolipid and lipid composition and proteomics in treated C. albicans SC5314 biofilms. PHCl significantly decreased polar lipid and ergosterol levels in biofilms while inducing phytoceramide and glucosylceramide accumulation. PHCl also induced reactive oxygen species and early apoptosis. Proteomic analysis revealed that PHCl treatment downregulated proteins associated with metabolism, cell wall remodeling, and DNA repair in biofilms to levels comparable to those observed in planktonic cells. Consistent with ergosterol reduction, Erg2 was found to be reduced. Overall, PHCl disrupts key pathways essential for biofilm integrity, decreasing its stability and promoting surface detachment, underscoring its potential as a versatile antifungal compound.

IMPORTANCE: Candida albicans filamentation and biofilm formation represent crucial virulence factors promoting fungus persistence and drug tolerance. The common eukaryotic nature of mammalian cells poses significant limitations to the development of new active nontoxic compounds. Understanding the mechanism underlying PHCl inhibitory activity on yeast-hypha transition, biofilm adhesion, and maturation can pave the way to efficient drug repurposing in a field where pharmaceutical investment is lacking.

PMID:40111054 | DOI:10.1128/spectrum.02987-24

Cytoskeleton (Hoboken). 2025 Mar 20. doi: 10.1002/cm.22020. Online ahead of print.

ABSTRACT

Polo-like kinase 4 (PLK4) is a centrosome-specific kinase aberrantly expressed in cancers. Drugs inhibiting its catalytic kinase domain are under clinical phase-1/2 trials in patients with different leukemia types. However, the kinase domain of PLK4 shows structural similarity with other kinases. Therefore, drugs targeting the unique C-terminal polo-box domain (PBD) of PLK4 could provide better specificity. The knowledge of domain orientation in a full-length PLK4 structure is imperative for drug discovery. In this work, we utilized ab initio and threading approaches to predict the full-length structure of human PLK4, which was employed for virtually screening the ChEMBL library. Among the hit compounds targeting the unique regions in PLK4, we identified Alectinib, which affects centrosome numbers corresponding to PLK4 levels at centrosomes. The FT-IR analysis also confirmed Alectinib interaction with the PBD. Therefore, this work identifies a chemical scaffold that could be repurposed to target the unique regions of PLK4.

PMID:40110897 | DOI:10.1002/cm.22020

Front Res Metr Anal. 2025 Mar 5;10:1509502. doi: 10.3389/frma.2025.1509502. eCollection 2025.

ABSTRACT

OBJECTIVE: Literature-based Discovery (LBD) identifies new knowledge by leveraging existing literature. It exploits interconnecting implicit relationships to build bridges between isolated sets of non-interacting literatures. It has been used to facilitate drug repurposing, new drug discovery, and study adverse event reactions. Within the last decade, LBD systems have transitioned from using statistical methods to exploring deep learning (DL) to analyze semantic spaces between non-interacting literatures. Recent works explore knowledge graphs (KG) to represent explicit relationships. These works envision LBD as a knowledge graph completion (KGC) task and use DL to generate implicit relationships. However, these systems require the researcher to have domain-expert knowledge when submitting relevant queries for novel hypothesis discovery.

METHODS: Our method explores a novel approach to identify all implicit hypotheses given the researcher's search query and expedites the knowledge discovery process. We revise the KGC task as the task of predicting interconnecting vertex embeddings within the graph. We train our model using a similarity learning objective and compare our model's predictions against all known vertices within the graph to determine the likelihood of an implicit relationship (i.e., connecting edge). We also explore three approaches to represent edge connections between vertices within the KG: average, concatenation, and Hadamard. Lastly, we explore an approach to induce inductive biases and expedite model convergence (i.e., input representation scaling).

RESULTS: We evaluate our method by replicating five known discoveries within the Hallmark of Cancer (HOC) datasets and compare our method to two existing works. Our results show no significant difference in reported ranks and model convergence rate when comparing scaling our input representations and not using this method. Comparing our method to previous works, we found our method achieves optimal performance on two of five datasets and achieves comparable performance on the remaining datasets. We further analyze our results using statistical significance testing to demonstrate the efficacy of our method.

CONCLUSION: We found our similarity-based learning objective predicts linking vertex embeddings for single relationship closed discovery replication. Our method also provides a ranked list of linking vertices between a set of inputs. This approach reduces researcher burden and allows further exploration of generated hypotheses.

PMID:40110121 | PMC:PMC11920161 | DOI:10.3389/frma.2025.1509502

Juntendo Med J. 2025 Jan 17;71(1):26-31. doi: 10.14789/ejmj.JMJ24-0026-P. eCollection 2025.

ABSTRACT

Stroke remains a leading cause of mortality and morbidity globally, posing significant challenges to healthcare systems due to its impact on Activities of Daily Living, Quality of Life, and healthcare costs. Current treatments primarily focus on acute management through thrombolytic therapy and thrombectomy, but only a limited number of patients benefit, underscoring the need for effective therapies to aid chronic stroke recovery. Despite ongoing clinical trials, cell therapy faces substantial logistical and cost-related hurdles, limiting its widespread adoption. Strategies to minimalize post-stroke sequelae emphasize preventing cerebral infarction deterioration, utilizing predictive scoring systems for focused treatment, and exploring drug repositioning. The complex interplay within the Neurovascular Unit and Oligovascular Niche highlights the role of various cell types and neurotrophic factors in stroke pathophysiology and recovery phases. Notably, microglia and astrocytes exhibit dual phenotypes ─ either inflammatory or protective ─ depending on the environment, influencing neural damage or repair processes post-stroke. Mitochondrial therapy emerges as a promising avenue, leveraging the organelles' ability to migrate between cells and mitigate inflammatory responses. Studies suggest that mitochondria transferred from astrocytes or other sources could transform inflammatory astrocytes into protective ones, thereby promoting white matter integrity and potentially reducing dementia progression associated with stroke sequelae. In conclusion, addressing stroke's multifaceted challenges requires innovative therapeutic approaches targeting inflammatory mechanisms and enhancing neuroprotection. Early detection and intervention, coupled with advancements in mitochondrial therapy and understanding intercellular interactions, hold promise for improving stroke outcomes and reducing long-term neurological complications.

PMID:40109397 | PMC:PMC11915467 | DOI:10.14789/ejmj.JMJ24-0026-P

Adv Healthc Mater. 2025 Mar 20:e2404117. doi: 10.1002/adhm.202404117. Online ahead of print.

ABSTRACT

Ovarian cancer (OC) poses a significant clinical challenge due to its high recurrence rates and resistance to standard therapies, particularly in advanced stages where recurrence is common, and treatment is predominantly palliative. Personalized treatments, while effective in other cancers, remain underutilized in OC due to a lack of reliable biomarkers predicting clinical outcomes. Accordingly, precision medicine approaches are limited, with PARP inhibitors showing efficacy only in specific genetic contexts. Drug repurposing offers a promising, rapidly translatable strategy by leveraging existing pharmacological data to identify new treatments for OC. Patient-derived polyclonal spheroids, isolated from ascites fluid closely mimic the clinical behavior of OC, providing a valuable model for drug testing. Using these spheroids, a high-throughput drug screening pipeline capable of evaluating both cytotoxicity and anti-migratory properties of a diverse drug library, including FDA-approved, investigational, and newly approved compounds is developed. The findings highlight the importance of 3D culture systems, revealing a poor correlation between drug efficacy in traditional 2D models and more clinically relevant 3D spheroids. This approach has expedited the identification of promising candidates, such as rapamycin, which demonstrated limited activity as a monotherapy but synergized effectively with standard treatments like cisplatin and paclitaxel in vitro. In combination with platinum-based therapy, Rapamycin led to significant in vitro cytotoxicity and a marked reduction in tumor burden in a syngeneic in vivo model. This proof-of-concept study underscores the potential of drug repurposing to rapidly advance new treatments into clinical trials for OC, offering renewed hope for patients with advanced disease.

PMID:40109101 | DOI:10.1002/adhm.202404117

Inflammopharmacology. 2025 Mar 19. doi: 10.1007/s10787-025-01695-0. Online ahead of print.

ABSTRACT

Sporadic Alzheimer's disease is a progressive neurodegenerative disorder affecting the central nervous system. Its main two hallmarks are extracellular deposition of aggregated amyloid beta resulting in senile plaques and intracellular hyperphosphorylated tau proteins forming neuro-fibrillary tangles. As those processes are promoted by the glycogen synthase kinase-3 enzyme, GSK3 inhibitors may be of therapeutic value in SAD. GSK3 is also inhibited by the action of insulin on insulin signaling. Insulin receptor desensitization in the brain is hypothesized to cause inhibition of insulin signaling pathway that ultimately causes cognitive deficits seen in SAD. In extant research, induction of cognitive impairment is achieved by intracerebroventricular injection of streptozotocin-a diabetogenic compound that causes desensitization to insulin receptors in the brain leading to the appearance of most of the SAD signs and symptoms. Valproic acid -a histone deacetylase inhibitor and anti-epileptic drug-has been recently studied in the management of SAD as a possible GSK3 inhibitor. Accordingly, the aim of the present study is to explore the role of multiple VPA doses on the downstream effects of the insulin signaling pathway in ICV STZ-injected mice and suggest a possible mechanism of VPA action. ICV STZ-injected mice showed deficiency in short- and long-term memory as well as increased anxiety, as established by open field test, Modified Y-maze, Morris water maze, and elevated plus maze neurobehavioral tests.

PMID:40108007 | DOI:10.1007/s10787-025-01695-0

Biomol Biomed. 2025 Mar 18. doi: 10.17305/bb.2025.12058. Online ahead of print.

ABSTRACT

Cervical cancer presents a significant healthcare challenge due to recurrent disease and drug resistance, highlighting the urgent need for novel therapeutic strategies. Network pharmacology facilitates drug repurposing by elucidating multi-target mechanisms of action. Adefovir, an acyclic nucleotide analog, has shown promising potential in cervical cancer treatment, particularly in HeLa cells. In vitro studies have demonstrated that adefovir inhibits HeLa cell proliferation by enhancing apoptosis while maintaining a low cytotoxicity profile at therapeutic concentrations, making it an attractive candidate for further exploration. A combined network pharmacology and in vitro study was conducted to investigate the molecular mechanism of adefovir against cervical cancer. Potential gene targets for adefovir and cervical cancer were predicted using database analysis. Hub targets were identified, and protein-protein interaction (PPI) networks were constructed. Molecular docking assessed adefovir's binding affinity to key targets. In vitro cytotoxic assays, including 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and crystal violet assays, were performed using 96-well plates to evaluate anti-proliferative effects in HeLa cells. Apoptosis was assessed via p53 immunocytochemistry Enzyme-Linked Immunosorbent Assay (ELISA), while Vascular Endothelial Growth Factor ELISA (VEGF ELISA) was used to measure cell proliferation. Venn analysis identified 144 common targets between adefovir and cervical cancer. Network analysis revealed key hub targets involved in oncogenic pathways. Molecular docking demonstrated strong binding between adefovir and Mitogen-Activated Protein Kinase 3 (MAPK3) and SRC proteins. In vitro, adefovir significantly suppressed HeLa cell viability, with an Inhibitory Concentration 50 (IC50) of 7.8 μM, outperforming 5-Fluorouracil (5-FU). Additionally, it induced apoptosis via p53 activation and inhibited cell proliferation through VEGF suppression. These integrated computational and experimental findings suggest that adefovir exerts multi-targeted effects against cervical cancer. Its promising preclinical efficacy warrants further investigation as a potential alternative therapy.

PMID:40105884 | DOI:10.17305/bb.2025.12058

Endocrinology. 2025 Mar 19:bqaf055. doi: 10.1210/endocr/bqaf055. Online ahead of print.

ABSTRACT

Diabetes is the most expensive chronic disease in the U.S. with over $400 billion in annual costs and it affects over 38 million Americans. While major advances in drug treatment have been made for type 2 diabetes (T2D) and the often-associated obesity, there are still no approved and effective medications targeting beta cell loss or islet dysfunction, which is one of the major underlying causes of both, type 1 diabetes (T1D) and T2D. In addition, there are no oral medications for T1D approved in the U.S. more than a hundred years after the discovery of insulin and attractive therapeutic targets are only starting to emerge. As we celebrate the 75th anniversary of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), progress is finally being made in this area with NIDDK support. This mini-review follows the discovery of thioredoxin-interacting protein inhibitors as an example of a methodical approach to identify and develop an oral beta cell treatment for T1D. It further discusses how the initial molecular findings were translated into novel clinical treatment approaches that promote the patient's own islet health and beta cell function using drug repurposing as well as new drug discovery.

PMID:40105688 | DOI:10.1210/endocr/bqaf055

Hum Genomics. 2025 Mar 19;19(1):29. doi: 10.1186/s40246-025-00733-w.

ABSTRACT

BACKGROUND: There is a vast prevalence of mental disorders, but patient responses to psychiatric medication fluctuate. As food choices and daily habits play a fundamental role in this fluctuation, integrating machine learning with network medicine can provide valuable insights into disease systems and the regulatory leverage of lifestyle in mental health.

METHODS: This study analyzed coexpression network modules of MDD and PTSD blood transcriptomic profile using modularity optimization method, the first runner-up of Disease Module Identification DREAM challenge. The top disease genes of both MDD and PTSD modules were detected using random forest model. Afterward, the regulatory signature of two predominant habitual phenotypes, diet-induced obesity and smoking, were identified. These transcription/translation regulating factors (TRFs) signals were transduced toward the two disorders' disease genes. A bipartite network of drugs that target the TRFS together with PTSD or MDD hubs was constructed.

RESULTS: The research revealed one MDD hub, the CENPJ, which is known to influence intellectual ability. This observation paves the way for additional investigations into the potential of CENPJ as a novel target for MDD therapeutic agents development. Additionally, most of the predicted PTSD hubs were associated with multiple carcinomas, of which the most notable was SHCBP1. SHCBP1 is a known risk factor for glioma, suggesting the importance of continuous monitoring of patients with PTSD to mitigate potential cancer comorbidities. The signaling network illustrated that two PTSD and three MDD biomarkers were co-regulated by habitual phenotype TRFs. 6-Prenylnaringenin and Aflibercept were identified as potential candidates for targeting the MDD and PTSD hubs: ATP6V0A1 and PIGF. However, habitual phenotype TRFs have no leverage over ATP6V0A1 and PIGF.

CONCLUSION: Combining machine learning and network biology succeeded in revealing biomarkers for two notoriously spreading disorders, MDD and PTSD. This approach offers a non-invasive diagnostic pipeline and identifies potential drug targets that could be repurposed under further investigation. These findings contribute to our understanding of the complex interplay between mental disorders, daily habits, and psychiatric interventions, thereby facilitating more targeted and personalized treatment strategies.

PMID:40102990 | DOI:10.1186/s40246-025-00733-w

Nat Rev Drug Discov. 2025 Mar 18. doi: 10.1038/s41573-025-01164-x. Online ahead of print.

ABSTRACT

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

PMID:40102635 | DOI:10.1038/s41573-025-01164-x

IEEE J Biomed Health Inform. 2025 Mar 18;PP. doi: 10.1109/JBHI.2025.3552536. Online ahead of print.

ABSTRACT

Central nervous system (CNS) diseases such as glioblastoma (GBM), multiple sclerosis (MS), and Alzheimer's disease (AD) remain challenging due to their complexity and limited treatments. Conventional drug repurposing strategies often rely on bulk RNA sequencing data, which can overlook cellular heterogeneity and mask rare but critical cell populations. Here, we introduce scDrugLink, a computational method that integrates single-cell transcriptomic data with drug targets and perturbation signatures to improve repurposing. For each cell type, scDrugLink constructs a Drug2Cell matrix based on drug targets to estimate promotion/inhibition scores and derives sensitivity/resistance scores by reverse matching signatures and disease-associated genes. These scores are then "linked," yielding robust therapeutic rankings. In our study, we present a systematic evaluation of single-cell drug repurposing methods for CNS diseases. Applied to atlas data for GBM, MS, and AD, scDrugLink surpassed three state-of-the-art methods (ASGARD, DrugReSC, and scDrugPrio), achieving area under the receiver operating characteristic curve (AUC) ranges of 0.6286-0.7242 and area under the precision-recall curve (AUPRC) ranges of 0.3412-0.5484. It also ranked top when comparing AUC and AUPRC at the level of individual cell types. Moreover, applying the "linking" principle to baseline methods boosted their performance, on average improving AUC and AUPRC by 0.0160 and 0.0244, respectively. Despite the advancements, the complexity and heterogeneity of CNS diseases, along with incomplete drug data, indicate that further improvement is necessary. We discuss these challenges and suggest directions for enhancing single-cell drug repurposing in the future.

PMID:40100675 | DOI:10.1109/JBHI.2025.3552536

IEEE Trans Neural Netw Learn Syst. 2025 Mar 18;PP. doi: 10.1109/TNNLS.2025.3547300. Online ahead of print.

ABSTRACT

Predicting protein-ligand binding affinities is a critical problem in drug discovery and design. A majority of existing methods fail to accurately characterize and exploit the geometrically invariant structures of protein-ligand complexes for predicting binding affinities. In this study, we propose Geo-protein-ligand binding affinity (PLA), a geometric equivariant graph representation learning framework with local-global structure awareness, to predict binding affinity by capturing the geometric information of protein-ligand complexes. Specifically, the local structural information of 3-D protein-ligand complexes is extracted by using an equivariant graph neural network (EGNN), which iteratively updates node representations while preserving the equivariance of coordinate transformations. Meanwhile, a graph transformer is utilized to capture long-range interactions among atoms, offering a global view that adaptively focuses on complex regions with a significant impact on binding affinities. Furthermore, the multiscale information from the two channels is integrated to enhance the predictive capability of the model. Extensive experimental studies on two benchmark datasets confirm the superior performance of Geo-PLA. Moreover, the visual interpretation of the learned protein-ligand complexes further indicates that our model offers valuable biological insights for virtual screening and drug repositioning.

PMID:40100667 | DOI:10.1109/TNNLS.2025.3547300

Mol Divers. 2025 Mar 18. doi: 10.1007/s11030-025-11131-8. Online ahead of print.

ABSTRACT

The global impact of emerging and re-emerging viral agents during epidemics and pandemics leads to serious health and economic burdens. Among the major emerging or re-emerging viruses include SARS-CoV-2, Ebola virus (EBOV), Monkeypox virus (Mpox), Hepatitis viruses, Zika virus, Avian flu, Influenza virus, Chikungunya virus (CHIKV), Dengue fever virus (DENV), West Nile virus, Rhabdovirus, Sandfly fever virus, Crimean-Congo hemorrhagic fever (CCHF) virus, and Rift Valley fever virus (RVFV). A comprehensive literature search was performed to identify existing studies, clinical trials, and reviews that discuss drug repositioning strategies for the treatment of emerging and re-emerging viral infections using databases, such as PubMed, Scholar Google, Scopus, and Web of Science. By utilizing drug repositioning, pharmaceutical companies can take advantage of a cost-effective, accelerated, and effective strategy, which in turn leads to the discovery of innovative treatment options for patients. In light of antiviral drug resistance and the high costs of developing novel antivirals, drug repositioning holds great promise for more rapid substitution of approved drugs. Main repositioned drugs have included chloroquine, ivermectin, dexamethasone, Baricitinib, tocilizumab, Mab114 (Ebanga™), ZMapp (pharming), Artesunate, imiquimod, saquinavir, capmatinib, naldemedine, Trametinib, statins, celecoxib, naproxen, metformin, ruxolitinib, nitazoxanide, gemcitabine, Dorzolamide, Midodrine, Diltiazem, zinc acetate, suramin, 5-fluorouracil, quinine, minocycline, trifluoperazine, paracetamol, berbamine, Nifedipine, and chlorpromazine. This succinct review will delve into the topic of repositioned drugs that have been utilized to combat emerging and re-emerging viral pathogens.

PMID:40100484 | DOI:10.1007/s11030-025-11131-8

Mol Pharm. 2025 Mar 18. doi: 10.1021/acs.molpharmaceut.5c00191. Online ahead of print.

ABSTRACT

Statins have been reported for diverse pleiotropic activities, including antimicrobial and antibiofilm. However, due to the limited understanding of their mode of action, none of the statins have gained approval for antimicrobial or antibiofilm applications. In a recent drug repurposing study, we observed that two statins (i.e., Simvastatin and Lovastatin) interact stably with TasA(28-261), the principal extracellular matrix protein of Bacillus subtilis, and also induce inhibition of biofilm formation. Nevertheless, the underlying mechanism remained elusive. In the present study, we examined the impact of these statins on the physiological activity of TasA(28-261), specifically its interaction with TapA(33-253) and aggregation into the amyloid-like structure using purified recombinant TasA(28-261) and TapA(33-253) in amyloid detection-specific in vitro assays (i.e., CR binding and ThT staining assays). Results revealed that both statins interfered with amyloid formation by the TasA(28-261)-TapA(33-253) complex, while neither statin inhibited amyloid formation by lysozyme, a model amyloid-forming protein. Moreover, neither statin significantly altered the expressions of terminal regulatory genes (viz, sinR, sinI) and terminal effector genes (viz, tasA, tapA, and bslA) involved in biofilm formation by B. subtilis. While the intricate interplay between Simvastatin and Lovastatin with the diverse molecular constituents of B. subtilis biofilm remains to be elucidated conclusively, the findings obtained during the present study suggest that the underlying mechanism for Simvastatin- and Lovastatin-mediated inhibition of B. subtilis biofilm formation is manifested by interfering with the aggregation and amyloid formation by TasA(28-261)-TapA(33-253). These results represent one of the first experimental evidence for the underlying mechanism of antibiofilm activity of statins and offer valuable directions for future research to harness statins as antibiofilm therapeutics.

PMID:40100146 | DOI:10.1021/acs.molpharmaceut.5c00191

ChemMedChem. 2025 Mar 18:e202500084. doi: 10.1002/cmdc.202500084. Online ahead of print.

ABSTRACT

This study explores the anti-cancer potential of N-methylated open-ring derivatives of carborane-substituted diclofenac analogs. By N-methylation, the open-chain form could be trapped and cyclization back to lactam or amidine derivatives was inhibited. A small library of carborane- and phenyl-based secondary and tertiary arylamines bearing carboxylic acid or nitrile groups was synthesized and analyzed for their COX-affinity in vitro and in silico. The compounds were further evaluated against mouse adenocarcinoma (MC38), human colorectal carcinoma (HCT116) and human colorectal adenocarcinoma (HT29) cell lines and showed potent cytotoxicity. Additional biological assessments of the mode of action were performed using flow cytometric techniques and fluorescence microscopy. The data obtained revealed a common antiproliferative effect coupled with the induction of caspase-independent apoptosis and the specific effects of the compound on the phenotype of MC38 cells, resulting in impaired cell viability of MC38 cells and satisfactory selectivity exceeding the antitumor activity of diclofenac.

PMID:40099997 | DOI:10.1002/cmdc.202500084

BMC Vet Res. 2025 Mar 17;21(1):178. doi: 10.1186/s12917-025-04616-z.

ABSTRACT

Cystic echinococcosis (CE) is a neglected zoonotic disease that causes significant economic losses in livestock and poses health risks to humans, necessitating improved diagnostic and therapeutic strategies. This study investigates CE in donkeys using a multifaceted approach that includes molecular identification, gene expression analysis, serum biochemical profiling, histopathological and immunohistochemical examination, and in vitro drug efficacy evaluation. Molecular analysis of hydatid cyst protoscolices (HC-PSCs) from infected donkey livers and lungs revealed a high similarity to Echinococcus equinus (GenBank accession: PP407081). Additionally, gene expression analysis indicated significant increases (P < 0.0001) in interleukin 1β (IL-1β) and interferon γ (IFN-γ) levels in lung and liver homogenates. Serum biochemical analysis showed elevated aspartate transaminase (AST), alkaline phosphatase (ALP), and globulin levels, alongside decreased albumin compared to non-infected controls. Histopathological examination revealed notable alterations in pulmonary and hepatic tissues associated with hydatid cyst infection. Immunohistochemical analysis showed increased expression of nuclear factor kappa B (NF-κB), tumor necrosis factor-α (TNF-α), and toll-like receptor-4 (TLR-4), indicating a robust inflammatory response. In vitro drug evaluations revealed that Paroxetine (at concentrations of 2.5, and 5 mg/mL) demonstrated the highest efficacy among repurposed drugs against HC-PSCs, resulting in the greatest cell mortality. Colmediten followed closely in effectiveness, whereas both Brufen and Ator exhibited minimal effects. This study identifies Paroxetine as a promising alternative treatment for hydatidosis and provides a framework for investigating other parasitic infections and novel therapies.

PMID:40098107 | DOI:10.1186/s12917-025-04616-z