Clin Transl Med. 2025 Apr;15(4):e70290. doi: 10.1002/ctm2.70290.

ABSTRACT

Single-cell omics has emerged as a powerful tool for elucidating cellular heterogeneity in health and disease. Parallel advances in artificial intelligence (AI), particularly in pattern recognition, feature extraction and predictive modelling, now offer unprecedented opportunities to translate these insights into clinical applications. Here, we propose single-cell -omics-based Disease Predictor through AI (scDisPreAI), a unified framework that leverages AI to integrate single-cell -omics data, enabling robust disease and disease-stage prediction, alongside biomarker discovery. The foundation of scDisPreAI lies in assembling a large, standardised database spanning diverse diseases and multiple disease stages. Rigorous data preprocessing, including normalisation and batch effect correction, ensures that biological rather than technical variation drives downstream models. Machine learning pipelines or deep learning architectures can then be trained in a multi-task fashion, classifying both disease identity and disease stage. Crucially, interpretability techniques such as SHapley Additive exPlanations (SHAP) values or attention weights pinpoint the genes most influential for these predictions, highlighting biomarkers that may be shared across diseases or disease stages. By consolidating predictive modelling with interpretable biomarker identification, scDisPreAI may be deployed as a clinical decision assistant, flagging potential therapeutic targets for drug repurposing and guiding tailored treatments. In this editorial, we propose the technical and methodological roadmap for scDisPreAI and emphasises future directions, including the incorporation of multi-omics, standardised protocols and prospective clinical validation, to fully harness the transformative potential of single-cell AI in precision medicine.

PMID:40170267 | DOI:10.1002/ctm2.70290

Methods Mol Biol. 2025;2914:323-332. doi: 10.1007/978-1-0716-4462-1_22.

ABSTRACT

Selecting a fluid near an affected organ can improve the likelihood of identifying a biomarker panel from pathological tissue. Cerebrospinal fluid (CSF), in close contact with the brain, is a valuable source of biomarkers for neurological disorders due to the inaccessibility of brain tissue. Moreover, the altered CSF proteome identified in neurological diseases can facilitate the repurposing of drugs already used for other therapeutic purposes. In this context, Connectivity Map (CMap) is a valuable tool as it provides information on compounds and gene modifications that can be utilized to reverse specific pathological signatures. Analyzing CSF differential proteomics through the CMap framework offers an efficient and cost-effective approach to identifying potential novel therapies for neurodegenerative diseases.

PMID:40167927 | DOI:10.1007/978-1-0716-4462-1_22

ACS Biomater Sci Eng. 2025 Apr 1. doi: 10.1021/acsbiomaterials.5c00068. Online ahead of print.

ABSTRACT

Drug repurposing is an attractive route for finding new therapeutics for brain cancers such as glioblastoma. Local administration of drugs to brain tumors or the postsurgical resection cavity holds promise to deliver a high dose to the target site with minimal off-target effects. Drug delivery systems aim to sustain the release of the drug at the target site but typically exhibit drawbacks such as a poor safety profile, uncontrolled/rapid drug release, or poor control over synthesis parameters/material dimensions. Herein, we analyzed the antidepressant vortioxetine and showed in vitro that it causes a greater loss of viability in glioblastoma cells than it does to normal primary human astrocytes. We developed a new droplet microfluidic-based emulsion method to reproducibly produce vortioxetine-loaded poly(lactic-co-glycolic) acid (PLGA) microspheres with tight size control (36.80 ± 1.96 μm). The drug loading efficiency was around 90% when 9.1% (w/w) drug was loaded into the microspheres, and drug release could be sustained for three to 4 weeks. The vortioxetine microspheres showed robust antiglioblastoma efficacy in both 2D monolayer and 3D spheroid patient-derived glioblastoma cells, highlighting the potential of combining an antidepressant with sustained local delivery as a new therapeutic strategy.

PMID:40167528 | DOI:10.1021/acsbiomaterials.5c00068

Am J Med Genet C Semin Med Genet. 2025 Apr 1:e32138. doi: 10.1002/ajmg.c.32138. Online ahead of print.

ABSTRACT

Rare diseases impact approximately 1 in 10 people worldwide, and yet, less than 5% of all rare diseases currently have an approved treatment option available. This is due to many challenges unique to rare diseases, including small, diverse patient populations, the cost of drug development that is not proportionate to the number of patients who could potentially benefit from treatment, and difficulty with clinical trial design to validate new therapeutics. As a result, drug repurposing has become an increasingly promising option for finding treatment options for rare diseases. First described in 2018, Bachmann-Bupp Syndrome (BABS) is a rare neurodevelopmental disorder that is caused by gain-of-function variants in the ornithine decarboxylase (ODC1) gene and is characterized by developmental delay, hypotonia, and alopecia. Through collaboration and the use of a unique drug repurposing strategy, the first patient identified with BABS was treated with the repurposed drug eflornithine, also known as α-difluoromethylornithine (DFMO), in just 16 months. Currently, five additional patients with BABS are being treated with DFMO. This model of drug repurposing of an FDA-approved drug for use in another indication can serve as an example of what is possible in the scope of other rare diseases, specifically in other polyaminopathies.

PMID:40167220 | DOI:10.1002/ajmg.c.32138

Circulation. 2025 Apr 1. doi: 10.1161/CIRCULATIONAHA.121.058621. Online ahead of print.

ABSTRACT

BACKGROUND: Gene mutations are responsible for a sizeable proportion of cases of heart failure. However, the number of patients with any specific mutation is small. Repositioning of existing US Food and Drug Administration-approved compounds to target specific mutations is a promising approach to efficient identification of new therapies for these patients.

METHODS: The National Institutes of Health Library of Integrated Network-Based Cellular Signatures database was interrogated to identify US Food and Drug Administration-approved compounds that demonstrated the ability to reverse the transcriptional effects of LMNA knockdown. Top hits from this screening were validated in vitro with patient-specific induced pluripotent stem cell-derived cardiomyocytes combined with force measurement, gene expression profiling, electrophysiology, and protein expression analysis.

RESULTS: Several angiotensin receptor blockers were identified from our in silico screen. Of these, olmesartan significantly elevated the expression of sarcomeric genes and rate and force of contraction and ameliorated arrhythmogenic potential. In addition, olmesartan exhibited the ability to reduce phosphorylation of extracellular signal-regulated kinase 1 in LMNA-mutant induced pluripotent stem cell-derived cardiomyocytes.

CONCLUSIONS: In silico screening followed by in vitro validation with induced pluripotent stem cell-derived models can be an efficient approach to identifying repositionable therapies for monogenic cardiomyopathies.

PMID:40166828 | DOI:10.1161/CIRCULATIONAHA.121.058621

medRxiv [Preprint]. 2025 Mar 24:2025.03.19.25324271. doi: 10.1101/2025.03.19.25324271.

ABSTRACT

BACKGROUND: Randomized controlled trials (RCTs) provide the highest level of clinical evidence but are often limited by cost, time, and ethical constraints. Emulating RCTs using real-world data (RWD) offers a complementary approach to evaluate the treatment effect in a real clinical setting. This study aims to replicate clinical trials based on Mayo Clinic Platform (MCP) electronic health records (EHRs) and emulation frameworks. In this study, we address two key questions: (1) whether clinical trials can be feasibly replicated using the MCP, and (2) whether trial emulation produces consistent conclusions based on real clinical data compared to the original randomized controlled trials RCTs.

METHODS: We conducted a retrospective observational study with an adaption of trial emulation. To assess feasibility, we applied a refined filtering method to identify trials suitable for emulation. The emulation protocol was carefully designed on top of the original RCT protocol to balance scientific rigor and practical feasibility. To minimize potential selection bias and enhance comparability between groups, we employed propensity score matching (PSM) as a statistical adjustment method.

RESULTS: Based on our predefined search criteria targeting phase 3 trials focused on drug repurposing for heart failure patients, we initially identified 27 eligible trials. After a two-step manual review of the original eligibility criteria and extraction of the patient cohorts based on MCP visualizer, we further narrowed our selection to the WARCEF trial, as it provided an adequate sample size for the emulation within the MCP. The experiment compares the WARCEF trial and a simulation study on Aspirin vs. Warfarin. The original study (smaller sample) found no significant difference (HR = 1.016, p < 0.91). The simulation (larger sample) showed a slightly higher HR (1.161) with borderline significance (p < 0.052, CI: 0.999-1.350), suggesting a possible increased risk with Warfarin, though not conclusive.

CONCLUSION: RCT emulation enhances real-world evidence (RWE) for clinical decision-making but faces limitations from confounding, missing data, and cohort biases. Future research should explore machine learning-driven patient matching and scalable RCT emulation. This study supports the integration of RWE into evidence-based medicine.

PMID:40166580 | PMC:PMC11957179 | DOI:10.1101/2025.03.19.25324271

medRxiv [Preprint]. 2025 Mar 23:2025.03.22.25324223. doi: 10.1101/2025.03.22.25324223.

ABSTRACT

Drug development is a long and costly process, and repurposing existing drugs for use toward a different disease or condition may serve as a cost-effective alternative. As drug targets with genetic support have a doubled success rate, genetics-informed drug repurposing holds promise in translating genetic findings into therapeutics. In this study, we developed a Genetics Informed Network-based Drug Repurposing via in silico Perturbation (GIN-DRIP) framework and applied the framework to repurpose drugs for type-2 diabetes (T2D). In GIN-DRIP for T2D, it integrates multi-level omics data to translate T2D GWAS signals into a genetics-informed network that simultaneously encodes gene importance scores and a directional effect (up/down) of risk genes for T2D; it then bases on the GIN to perform signature matching with drug perturbation experiments to identify drugs that can counteract the effect of T2D risk alleles. With this approach, we identified 3 high-confidence FDA-approved candidate drugs for T2D, and validated telmisartan, an anti-hypertensive drug, in our EHR data with over 3 million patients. We found that telmisartan users were associated with a reduced incidence of T2D compared to users of other anti-hypertensive drugs and non-users, supporting the therapeutic potential of telmisartan for T2D. Our framework can be applied to other diseases for translating GWAS findings to aid drug repurposing for complex diseases.

PMID:40166562 | PMC:PMC11957187 | DOI:10.1101/2025.03.22.25324223

medRxiv [Preprint]. 2025 Mar 17:2025.03.17.25324106. doi: 10.1101/2025.03.17.25324106.

ABSTRACT

BACKGROUND: Complex diseases may share portions of their polygenic architectures which can be leveraged to identify drug targets with low off-target potential or repurposable candidates. However, the literature lacks methods which can make these inferences at scale using publicly available data.

METHODS: We introduce a Bayesian model to estimate the polygenic structure of a trait using only gene-based association test statistics from GWAS summary data and returns gene-level posterior risk probabilities (PRPs). PRPs were used to infer shared polygenicity between 496 trait pairs and we introduce measures that can prioritize drug targets with low off-target effects or drug repurposing potential.

RESULTS: Across 32 traits, we estimated that 69.5 to 97.5% of disease-associated genes are shared between multiple traits, and the estimated number of druggable genes that were only associated with a single disease ranged from 1 (multiple sclerosis) to 59 (schizophrenia). Estimating the shared genetic architecture of ALS with all other traits identified the KIT gene as a potentially harmful drug target because of its deleterious association with triglycerides, but also identified TBK1 and SCN11B as putatively safer because of their non-association with any of the other 31 traits. We additionally found 21 genes which are candidate repourposable targets for Alzheimer's disease (AD) (e.g., PLEKHA1, PPIB ) and 5 for ALS (e.g., GAK, DGKQ ).

CONCLUSIONS: The sets of candidate drug targets which have limited off-target potential are generally smaller compared to the sets of pleiotropic and putatively repurposable drug targets, but both represent promising directions for future experimental studies.

PMID:40166559 | PMC:PMC11957083 | DOI:10.1101/2025.03.17.25324106

Front Pharmacol. 2025 Mar 17;16:1537912. doi: 10.3389/fphar.2025.1537912. eCollection 2025.

ABSTRACT

BACKGROUND AND PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has devastated global health and the economy, underscoring the urgent need for extensive research into the mechanisms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral entry and the development of effective therapeutic interventions.

EXPERIMENTAL APPROACH: We established a cell line expressing human angiotensin-converting enzyme 2 (ACE2). We used it as a model of pseudotyped viral entry using murine leukemia virus (MLV) expressing SARS-CoV-2 spike (S) protein on its surface and firefly luciferase as a reporter. We screened an U.S. Food and Drug Administration (FDA)-approved compound library for inhibiting ACE2-dependent SARS-CoV-2 pseudotyped viral entry and identified several drug-repurposing candidates.

KEY RESULTS: We identified 18 drugs and drug candidates, including 14 previously reported inhibitors of viral entry and four novel candidates. Pyridoxal 5'-phosphate, Dovitinib, Adefovir dipivoxil, and Biapenem potently inhibit ACE2-dependent viral entry with inhibitory concentration 50% (IC50) values of 57nM, 74 nM, 130 nM, and 183 nM, respectively.

CONCLUSION AND IMPLICATIONS: We identified four novel FDA-approved candidate drugs for anti-SARS-CoV-2 combination therapy. Our findings contribute to the growing body of evidence supporting drug repurposing as a viable strategy for rapidly developing COVID-19 treatments.

PMID:40166473 | PMC:PMC11955658 | DOI:10.3389/fphar.2025.1537912

bioRxiv [Preprint]. 2025 Mar 18:2025.03.18.642440. doi: 10.1101/2025.03.18.642440.

ABSTRACT

Human topoisomerase III beta (TOP3B) is a type IA topoisomerase that can change the topology of DNA and RNA substrates via a phosphotyrosine covalent intermediate. TOP3B has been shown to be required for the efficient replication of certain positive-sense ssRNA viruses including Dengue. We applied molecular dynamics simulation combined with docking studies to identify potential inhibitors of TOP3B from a library comprised of drugs that are FDA-approved or undergoing clinical trials for potential drug repurposing. Topoisomerase activity assay of the top virtual screening hits showed that bemcentinib, a compound known to target the AXL receptor tyrosine kinase, can inhibit TOP3B relaxation activity. Additional small molecules that share the N5 , N3 -1 H -1,2,4-triazole-3,5-diamine moiety of bemcentinib were synthesized and tested for inhibition of TOP3B relaxation activity. Five of these molecules showed comparable IC 50 as bemcentinib for inhibition of TOP3B. However, these five molecules had less selectivity towards TOP3B inhibition versus bemcentinib when inhibition of the type IB human topoisomerase I was com-pared. These results suggest that exploration of tyrosine kinase inhibitors and their analogs may allow the identification of novel topoisomerase inhibitors.

PMID:40166181 | PMC:PMC11956937 | DOI:10.1101/2025.03.18.642440

Front Immunol. 2025 Mar 17;16:1537428. doi: 10.3389/fimmu.2025.1537428. eCollection 2025.

ABSTRACT

Pemphigoid diseases constitute a group of organ-specific autoimmune diseases characterized and caused by autoantibodies targeting autoantigens expressed in the skin and mucous membranes. Current therapeutic options are still based on unspecific immunosuppression that is associated with severe adverse events. Biologics, targeting the IL4-pathway or IgE are expected to change the treatment landscape of pemphigoid diseases. However, clinical studies demonstrated that targeting these pathways alone is most likely not sufficient to meet patient and healthcare partitioners expectations. Hence, model systems are needed to identify and validate novel therapeutic targets in pemphigoid diseases. These include pre-clinical animal models, in vitro and ex vivo model systems, hypothesis-driven drug repurposing, as well as exploitation of real-world-data. In this review, we will highlight the medical need for pemphigoid diseases, and in-depth discuss the advantages and disadvantages of the available pemphigoid disease model systems. Ultimately, we discuss how rapid translation can be achieved for the benefit of the patients.

PMID:40165962 | PMC:PMC11955494 | DOI:10.3389/fimmu.2025.1537428

Neuroscience. 2025 Mar 29:S0306-4522(25)00266-0. doi: 10.1016/j.neuroscience.2025.03.064. Online ahead of print.

ABSTRACT

Drug repositioning or drug reprofiling, involves identifying novel indications for approved and previously abandoned drugs in the treatment of other diseases. The traditional drug discovery process is tedious, time-consuming, risky, and challenging. Fortunately, the inception of the drug repositioning concept has expedited the process by using compounds with established safety profiles in humans, and thereby significantly reducing costs. Alzheimer's disease (AD) is a severe neurological disorder characterized by progressive degeneration of the brain with limited and less effective therapeutic interventions. Researchers have attempted to identify potential treatment of AD from existing drug however, the success of drug repositioning strategy in AD remains uncertain. This article briefly discusses the importance and effectiveness of drug repositioning strategies, the major obstacles in the development of drugs for Alzheimer's disease (AD), approaches to address these challenges, and the role of machine learning in identifying early markers of AD for improved management.

PMID:40164279 | DOI:10.1016/j.neuroscience.2025.03.064

Bioimpacts. 2024 Jun 26;15:29952. doi: 10.34172/bi.29952. eCollection 2025.

ABSTRACT

INTRODUCTION: Drug repurposing as a low-cost, time-saving, and often less risky strategy has been attractive for the treatment of coronavirus disease 2019 (COVID-19) during the pandemic. This trial aimed to evaluate the effectiveness of dolutegravir, an HIV-1 integrase inhibitor, in admitted patients with moderate COVID-19.

METHODS: This study was a randomized, double-blind, placebo-controlled clinical trial assessing the efficacy of dolutegravir in adults admitted to a hospital in Ghaemshahr, Mazandaran Province, Iran. Patients aged 18-80 years with early symptoms of moderate COVID-19, which was confirmed based on reverse transcription polymerase chain reaction (RT-PCR) and/or chest computed tomography (CT) scan, were considered to be included in this study. Patients were randomly assigned in a 1:1 ratio to receive 50 mg dolutegravir plus the standard treatment regimen or the same value of placebo plus the standard treatment regimen, daily for 7 days. The standard treatment regimen was remdesivir 200 mg on day 1 followed by 100 mg for five days or until discharge. The primary endpoint was recovery 10 days after the beginning of the study.

RESULTS: Between August 22 and October 23, 2021, of 120 patients who were enrolled, 93 patients were randomly assigned to receive 50 mg dolutegravir (n=46) or the placebo regimen (n=47). No significant difference was observed between the two intervention groups based on the obtained results including frequency of respiratory modes during the first five days of admission, respiratory rate, and O2 saturation during six time periods.

CONCLUSION: The results showed that in adult patients admitted to the hospital with moderate COVID-19, treatment with dolutegravir was not associated with improvement in clinical recovery. Larger randomized trials are required to provide more robust evidence about the effectiveness of dolutegravir.

PMID:40161930 | PMC:PMC11954744 | DOI:10.34172/bi.29952

Front Oncol. 2025 Mar 14;15:1519355. doi: 10.3389/fonc.2025.1519355. eCollection 2025.

ABSTRACT

The research and development of new anti-cancer drugs face challenges such as high costs, lengthy development cycles, and limited data on side effects. In contrast, the clinical safety and side effects of traditional drugs have been well established through long-term use. The development or repurposing of traditional drugs with potential applications in cancer treatment offers an economical, feasible, and promising strategy for new drug development. This article reviews the novel applications of traditional drugs in tumor immunotherapy, discussing how they can enhance tumor treatment efficacy through functional repositioning, while also reducing development time and costs. Recent advancements in cancer immunotherapy have revolutionized treatment options, but resistance to ICIs remains a significant challenge. Drug repurposing has emerged as a promising strategy to identify novel agents that can enhance the efficacy of immunotherapies by overcoming ICI resistance. A study suggests that drug repositioning has the potential to modulate immune cell activity or alter the tumor microenvironment, thereby circumventing the resistance mechanisms associated with immune checkpoint blockade. This approach provides a rapid and cost-effective pathway for identifying therapeutic candidates that can be quickly transitioned into clinical trials. To improve the effectiveness of tumor immunotherapy, it is crucial to explore systematic methods for identifying repurposed drug candidates. Methods such as high-throughput screening, computational drug repositioning, and bioinformatic analysis have been employed to efficiently identify potential candidates for cancer treatment. Furthermore, leveraging databases related to immunotherapy and drug repurposing can provide valuable resources for drug discovery and facilitate the identification of promising compounds. It focuses on the latest advancements in the use of antidiabetic drugs, antihypertensive agents, weight-loss medications, antifungal agents, and antiviral drugs in tumor immunotherapy, examining their mechanisms of action, clinical application prospects, and associated challenges. In this context, our aim is to explore these strategies and highlight their potential for expanding the therapeutic options available for cancer immunotherapy, providing valuable references for cancer research and treatment.

PMID:40161377 | PMC:PMC11949826 | DOI:10.3389/fonc.2025.1519355

AIMS Microbiol. 2025 Feb 5;11(1):126-149. doi: 10.3934/microbiol.2025007. eCollection 2025.

ABSTRACT

BACKGROUND: Drug repositioning has emerged as a promising strategy for assessing its antimicrobial efficacy in treating infectious diseases.

METHODS: Seventy-five samples were collected and investigated for the presence of Pseudomonas aeruginosa. Antibiotic resistance, hemolytic activity, twitching motility, and biofilm formation were assessed. lasI and lasR genes were detected using conventional PCR. Minimum inhibitory concentrations of paroxetine, fluoxetine, and levofloxacin were determined by broth micro-dilution. The fractional inhibitory concentration index was calculated to assess the interaction between fluoxetine/levofloxacin and paroxetine/levofloxacin combinations. Half the MIC values of the drugs were selected for inhibitory effect assessment for virulence factors. Antibacterial and healing effects of fluoxetine were investigated on 30 male albino rats using a digital camera, bacterial count, and histological examination.

RESULTS: Our 25 P. aeruginosa isolates were highly drug-resistant. 80%, 92%, and 80% of isolates were positive for twitching motility, hemolysis, and biofilm formation, respectively. 92% of isolates were positive for lasI gene and 96% for lasR gene. MICs of fluoxetine and paroxetine ranged from 32 to 512 µg/mL and MICs of levofloxacin ranged from 1 to 256 µg/mL. A synergistic outcome was observed in both combinations. Biofilm formation, twitching motility, and hemolysis were inhibited by paroxetine and fluoxetine in the majority of isolates. Fluoxetine/levofloxacin and paroxetine/levofloxacin combinations inhibited twitching motility, hemolysis, and biofilm formation in all isolates. Enhanced wound healing was observed in rats treated with fluoxetine and levofloxacin, with the fluoxetine/levofloxacin combination group demonstrating the most significant wound-healing effect. Bacterial count decreased in rats treated with levofloxacin, fluoxetine, and the levofloxacin/fluoxetine combination. Histological examination revealed higher wound healing in the levofloxacin-treated group than the fluoxetine group, and the combination treatment group displayed the fastest rate of wound healing.

CONCLUSIONS: Paroxetine and fluoxetine showed considerable antibacterial inhibitory effects against multi-drug resistant P. aeruginosa isolates. Fluoxetine showed significant improvement in anti-inflammatory effects and wound healing. To the best of our knowledge, this is the first Egyptian study to investigate the repurposing of paroxetine and fluoxetine as antibacterial agents. Further studies are needed to investigate their applicability as antibacterial agents as single agents or in combination with other antibiotics.

PMID:40161243 | PMC:PMC11950684 | DOI:10.3934/microbiol.2025007

Biochem Biophys Res Commun. 2025 Mar 25;760:151701. doi: 10.1016/j.bbrc.2025.151701. Online ahead of print.

ABSTRACT

The histone deacetylase group of proteins, which includes the sirtuins, contributes to several cellular processes and is intimately involved in cancer development. Sirtuins type two (SIRT2) is a constituent of the human sirtuin family, which modulates a range of biological functions and is recognized as a potential biomarker for numerous cancers. The impact of SIRT2 knockout on tumorigenesis is debated and varies with the type of tumor; nonetheless, pharmacological inhibition of SIRT2 results exclusively in growth inhibition of diverse cancer cell lines. As a result, SIRT2 regulation is thought to be a viable protein for treating cancer. Herein, the DrugBank database, containing >14,000 drug molecules, was repurposed to find potential anticancer medications that have the capacity to inhibit the SIRT2 protein utilizing in-silico techniques. In light of the experimental findings, the capability of AutoDock Vina1.1.2 software to anticipate the docking scores and poses of the SIRT2 inhibitors was assessed. SirReal2, a potential SIRT2 inhibitor, was the controller for this study. Notably, drugs with docking scores less than SiReal2 were chosen and introduced to molecular dynamics (MD) simulations, accompanied by binding affinities estimations utilizing the MM-GBSA approach. Interestingly, MM-GBSA calculations demonstrated that five drugs, namely DB11526, DB11977, DB15133, DB04739, and DB04632, revealed potential affinities as SIRT2 inhibitors exhibiting ΔGbinding less than -50.0 kcal/mol. The post-MD analyses were inspected for DB11526, DB11977, DB15133, DB04739, and DB04632, indicating excellent steadiness of these drugs bound to SIRT2 protein throughout the 200 ns MD. The ADMET features were also examined and were acceptable. These findings suggested that more attention should be paid to DB11526, DB11977, DB15133, DB04739, and DB04632 as SIRT2 inhibitors utilizing in-vitro/in-vivo assays to treat cancer disease.

PMID:40158406 | DOI:10.1016/j.bbrc.2025.151701

Neurotherapeutics. 2025 Mar 28:e00576. doi: 10.1016/j.neurot.2025.e00576. Online ahead of print.

ABSTRACT

Glioblastoma (GB) is an incurable cancer of the brain, and there is an urgent need to identify effective treatments. This may be achieved by either identifying new molecules or through drug repurposing. To ascertain the therapeutic potential of known GSK-3β and/or PDE7 inhibitors in GB, a drug screening was conducted using a Drosophila melanogaster glioma model. VP3.15, a dual inhibitor with anti-inflammatory and neuroprotective roles in multiple sclerosis, was selected for further investigation. VP3.15 demonstrated robust anti-tumor efficacy against a panel of human and mouse GB cells; however, its capacity to inhibit orthotopic growth was only observed in a wild-type PTEN cell line. The in vivo dependence on PTEN was further suggested with the results in fly gliomas. The analysis of the VP3.15-treated tissues revealed a notable reduction in the number of myeloid cells and in the degree of vascularization. Mechanistic studies indicate that VP3.15 diminishes the production of GAL9, a key molecule that stimulates pro-angiogenic macrophages. Our findings substantiate the pro-tumoral function of GSK-3β, which might depend on the PTEN genetic status. Furthermore, we have delineated the therapeutic potential of VP3.15, which acts through the inhibition of the supportive role of the GB microenvironment. This molecule could be safely and effectively utilized after PTEN characterization in GB patients.

PMID:40157890 | DOI:10.1016/j.neurot.2025.e00576

Daru. 2025 Mar 29;33(1):17. doi: 10.1007/s40199-025-00560-3.

ABSTRACT

BACKGROUND: Mebendazole (MBZ) is an anthelmintic drug that was repurposed as an anti-cancer agent.

OBJECTIVES: This study aimed at formulating MBZ into stearylamine tailored spanlastics dispersed in nanogel for enhancing MBZ anti-tumor efficacy against skin cancer.

METHODS: MBZ spanlastics were prepared by thin film hydration using 21 × 31 factorial design. The formulation variables were the total amount (mg) of Span 60 and Tween 80 in the formulations and the ratio between Span 60 and Tween 80.

RESULTS: Optimal spanlastics formulation was composed of 400 mg of Span 60 and Tween 80 in a ratio of 2:1 and showed EE% of 78 ± 2.9% and PS of 284.00 ± 35.36 nm. Stearylamine (20 mg) was added to the optimized formulation and showed acceptable positive charge (zeta potential = 47.53 ± 1.50 mV). It was dispersed in 30% Tetronic®1107 solution to form a nanogel. MBZ nanogel was assessed for their cytotoxic effect on cell proliferation against human malignant melanoma and epidermoid carcinoma cell lines and showed 38.70 ± 1.70% and 48.60 ± 0.50% (respectively) cell proliferation compared to the control group (100%). Finally, its permeation through Wistar rat skin was tested.

CONCLUSION: SA-spanlastics nanogel holds potential as an effective nanocarrier for boosting MBZ anti-cancer efficacy.

PMID:40156679 | DOI:10.1007/s40199-025-00560-3

BioData Min. 2025 Mar 28;18(1):27. doi: 10.1186/s13040-025-00442-z.

ABSTRACT

The integration of multi-omics data from diverse high-throughput technologies has revolutionized drug discovery. While various network-based methods have been developed to integrate multi-omics data, systematic evaluation and comparison of these methods remain challenging. This review aims to analyze network-based approaches for multi-omics integration and evaluate their applications in drug discovery. We conducted a comprehensive review of literature (2015-2024) on network-based multi-omics integration methods in drug discovery, and categorized methods into four primary types: network propagation/diffusion, similarity-based approaches, graph neural networks, and network inference models. We also discussed the applications of the methods in three scenario of drug discovery, including drug target identification, drug response prediction, and drug repurposing, and finally evaluated the performance of the methods by highlighting their advantages and limitations in specific applications. While network-based multi-omics integration has shown promise in drug discovery, challenges remain in computational scalability, data integration, and biological interpretation. Future developments should focus on incorporating temporal and spatial dynamics, improving model interpretability, and establishing standardized evaluation frameworks.

PMID:40155979 | DOI:10.1186/s13040-025-00442-z

BMC Cancer. 2025 Mar 28;25(1):560. doi: 10.1186/s12885-025-13845-2.

ABSTRACT

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, particularly due to advanced-stage metastasis. P-cadherin (CDH3), a potential therapeutic target, is highly expressed in CRC tissues and associated with poor prognosis and metastasis. However, the mechanisms underlying its role in CRC progression and its translational potential remain poorly understood.

MATERIALS AND METHODS: This study integrated multiple public databases (TCGA, HCMDB, UALCAN, HPA, UniProt, cBioPortal, and GEO) to evaluate CDH3 expression, construct a prognostic model, and perform functional and translational analyses. Immunohistochemistry was used to validate CDH3 protein expression in clinical samples. Additional analyses included correlations with clinicopathological parameters, immune infiltration (TIDE, TISIDB), functional enrichment (KEGG, GSEA), drug sensitivity (GSCA), and molecular docking (MOE). Single-cell sequencing (CancerSEA, HPA) was also conducted to explore CDH3's role at the single-cell level.

RESULTS: CDH3 expression was significantly elevated in CRC tissues and correlated with poor prognosis, recurrence, and metastasis. CDH3 expression was associated with the infiltration of resting immune cells, particularly dendritic cells, and enrichment analysis revealed its critical role in CRC metastasis through extracellular matrix (ECM) and local adhesion pathways. Notably, afatinib emerged as a promising candidate for targeting CDH3 via "drug repositioning," a process involving the repurposing of existing drugs for new therapeutic applications.

CONCLUSION: This study provides novel insights into CDH3's role in CRC metastasis and its potential as a therapeutic target. The translational potential of CDH3, including its integration with immunotherapy and drug repositioning strategies, offers a promising avenue for the treatment of metastatic CRC.

PMID:40155851 | DOI:10.1186/s12885-025-13845-2