KDD. 2025 Aug;2025(v1):2347-2358. doi: 10.1145/3690624.3709418. Epub 2025 Jul 20.

ABSTRACT

Drug repurposing identifies new therapeutic uses for existing drugs, reducing the time and costs compared to traditional de novo drug discovery. Most existing drug repurposing studies using real-world patient data often treat the entire population as homogeneous, ignoring the heterogeneity of treatment responses across patient subgroups. This approach may overlook promising drugs that benefit specific subgroups but lack notable treatment effects across the entire population, potentially limiting the number of repurposable candidates identified. To address this, we introduce STEDR, a novel drug repurposing framework that integrates subgroup analysis with treatment effect estimation. Our approach first identifies repurposing candidates by emulating multiple clinical trials on real-world patient data and then characterizes patient subgroups by learning subgroup-specific treatment effects. We deploy STEDR to Alzheimer's Disease (AD), a condition with few approved drugs and known heterogeneity in treatment responses. We emulate trials for over one thousand medications on a large-scale real-world database covering over 8 million patients, identifying 14 drug candidates with beneficial effects to AD in characterized subgroups. Experiments demonstrate STEDR's superior capability in identifying repurposing candidates compared to existing approaches. Additionally, our method can characterize clinically relevant patient subgroups associated with important AD-related risk factors, paving the way for precision drug repurposing.

PMID:40248108 | PMC:PMC12001032 | DOI:10.1145/3690624.3709418

Biomed Rep. 2025 Apr 9;22(6):95. doi: 10.3892/br.2025.1973. eCollection 2025 Jun.

ABSTRACT

Biofilm formation is a key factor in the persistence and recurrence of otorhinolaryngology (ORL) infections, driving antimicrobial resistance and treatment failure. Chronic conditions, such as rhinosinusitis, otitis media and tonsillitis, are linked to biofilm-producing pathogens, forming protective extracellular matrices that shield bacteria from immune defenses and antibiotics. The present review explores emerging pharmacological strategies to disrupt biofilm integrity and improve treatment outcomes. Strategies such as quorum sensing inhibitors, antibiofilm peptides, enzymatic dispersal agents, and drug repurposing can potentially disrupt biofilms and counter-resistance mechanisms. Furthermore, novel therapies (including nanotechnology-based drug delivery systems, phage therapy and immunomodulation) offer innovative alternatives for managing biofilm-associated infections. However, clinical implementation remains challenging. Future research should prioritize optimizing drug formulations, refining delivery techniques, and exploring synergistic combinations to enhance biofilm eradication. Implementing these innovative strategies can improve the management of chronic ORL infections, reducing recurrence rates and enhancing patient outcomes.

PMID:40247931 | PMC:PMC12001231 | DOI:10.3892/br.2025.1973

Antiviral Res. 2025 Apr 15:106167. doi: 10.1016/j.antiviral.2025.106167. Online ahead of print.

ABSTRACT

Cell-based drug repurposing screens have been a common approach to identifying compounds with antiviral properties. For Ebola virus (EBOV), such screens yield unexpectedly high hit rates. We investigated two mechanisms underlying the anti-EBOV activities of repurposed compounds. Phospholipidosis (PLD) is excessive accumulation of cellular lipids that confounds screens for SARS-CoV-2. We performed a meta-analysis of published screens and supplemented these with our own using infectious EBOV at biosafety level-4. A list of nearly 400 hit compounds from seven anti-EBOV screens was compiled. Most (61%) of these hits were predicted to induce PLD, and their anti-EBOV activities broadly correlated with PLD induction. PLD-inducing compounds did not inhibit infection by several other highly pathogenic viruses, suggesting that PLD was not a confounding factor for screens against Lassa, Crimean-Congo hemorrhagic fever, and Rift Valley fever viruses. Of four cells lines tested, HeLa cells were the least susceptible to PLD induction. In addition to PLD, many of the hit compounds identified disrupt cholesterol homeostasis. Previous research found inhibition of cholesterol synthesis by statins blocked EBOV infection. To understand if compounds inhibiting this mechanism could contribute to high hit rates, we further examined this pathway. We identified multiple additional inhibitors of cholesterol biosynthesis, that also blocked EBOV infection, albeit with varying potency and cytotoxicity across cell lines. EBOV inhibitors that acted through this mechanism were suppressed by the addition of exogenous cholesterol. Our findings help define the effects that contribute to anti-EBOV activities and hence facilitate the selection of lead molecules suitable for subsequent development.

PMID:40245950 | DOI:10.1016/j.antiviral.2025.106167

Mol Divers. 2025 Apr 17. doi: 10.1007/s11030-025-11187-6. Online ahead of print.

ABSTRACT

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease globally. A low-carbohydrate diet (LCD) offers benefits to MASLD patients, albeit its exact mechanism is not fully understood. Using public liver transcriptome data from MASLD patients before/after LCD intervention, we applied differential expression analysis and machine learning to identify key genes. We initially identified 162 differentially expressed genes in the GSE107650 dataset. Secondly, employing two machine learning algorithms, we found that PRAMENP, LEAP2, LOC105379013, and argininosuccinate synthetase 1 (ASS1) are potential hub genes. Additionally, protein-protein interaction and single-cell RNA location analyses suggested that ASS1 was the most crucial hub gene. Then, L1000CDS2 analysis of the gene-expression signatures was employed for drug repurposing studies. CGP71683, an appetite suppressant, was predicted to improve MASLD and may mimic the ASS1 expression pattern induced by an LCD. Molecular dynamics confirmed spontaneous, stable CGP71683-ASS1 complex formation. Overall, this work based on analysis of machine learning algorithms, essential gene identification, and drug repurposing studies suggested that ASS1 is an essential gene in MASLD and CGP71683 is a potential drug candidate for treating MASLD by targeting ASS1 and mimicking the beneficial effects of an LCD. However, due to the inherent limitations of a purely computational approach, further experimental investigation is necessary to validate the anticipated results.

PMID:40244373 | DOI:10.1007/s11030-025-11187-6

Int J Mol Sci. 2025 Mar 24;26(7):2931. doi: 10.3390/ijms26072931.

ABSTRACT

Cardiovascular disease remains the leading global cause of mortality, largely driven by atherosclerosis, a chronic inflammatory condition characterized by lipid accumulation and immune-cell infiltration in arterial walls. Macrophages play a central role by forming foam cells and secreting pro-atherogenic cytokines, such as TNF-α, IFN-γ, and IL-1β, which destabilize atherosclerotic plaques, expanding the lipid core and increasing the risk of thrombosis and ischemia. Despite the significant health burden of subclinical atherosclerosis, few targeted therapies exist. Current treatments, including monoclonal antibodies, are limited by high costs and immunosuppressive side effects, underscoring the urgent need for alternative therapeutic strategies. In this study, we employed in silico drug repositioning to identify multitarget inhibitors against TNF-α, IFN-γ, and IL-1β, leveraging a virtual screening of 2750 FDA-approved drugs followed by molecular dynamics simulations to assess the stability of selected cytokine-ligand complexes. This computational approach provides structural insights into potential inhibitors. Additionally, we highlight nutraceutical options, such as fatty acids (oleic, linoleic and eicosapentaenoic acid), which exhibited strong and stable interactions with key cytokine targets. Our study suggests that these bioactive compounds could serve as effective new therapeutic approaches for atherosclerosis.

PMID:40243563 | DOI:10.3390/ijms26072931

Int J Mol Sci. 2025 Mar 25;26(7):2955. doi: 10.3390/ijms26072955.

ABSTRACT

The development of novel therapeutics in neuro-oncology faces significant challenges, often marked by high costs and low success rates. Despite advances in molecular biology and genomics, targeted therapies have had limited impact on improving patient outcomes in brain tumors, particularly gliomas, due to the complex, multigenic nature of these malignancies. While significant efforts have been made to design drugs that target specific signaling pathways and genetic mutations, the clinical success of these rational approaches remains sparse. This review critically examines the landscape of neuro-oncology drug discovery, highlighting instances where serendipity has led to significant breakthroughs, such as the unexpected efficacy of repurposed drugs and off-target effects that proved beneficial. By exploring historical and contemporary cases, we underscore the role of chance in the discovery of impactful therapies, arguing that embracing serendipity alongside rational drug design may enhance future success in neuro-oncology drug development.

PMID:40243541 | DOI:10.3390/ijms26072955

Exp Ther Med. 2025 Apr 4;29(6):110. doi: 10.3892/etm.2025.12860. eCollection 2025 Jun.

ABSTRACT

TAM (TYRO3, AXL, MERTK) receptor tyrosine kinases (RTKs) have intrinsic roles in tumor cell proliferation, migration, chemoresistance, and suppression of antitumor immunity. The overexpression of TAM RTKs is associated with poor prognosis in various types of cancer. Single-target agents of TAM RTKs have limited efficacy because of an adaptive feedback mechanism resulting from the cooperation of TAM family members. This suggests that multiple targeting of members has the potential for a more potent anticancer effect. The present study used a deep-learning based drug-target interaction (DTI) prediction model called molecule transformer-DTI (MT-DTI) to identify commercially available drugs that may inhibit the three members of TAM RTKs. The results showed that fostamatinib, a spleen tyrosine kinase (Syk) inhibitor, could inhibit the three receptor kinases of the TAM family with an IC50 <1 µM. Notably, no other Syk inhibitors were predicted by the MT-DTI model. To verify this result, this study performed in vitro studies with various types of cancer cell lines. Consistent with the DTI results, this study observed that fostamatinib suppressed cell proliferation by inhibiting TAM RTKs, while other Syk inhibitors showed no inhibitory activity. These results suggest that fostamatinib could exhibit anticancer activity as a pan-TAM inhibitor. Taken together, these findings demonstrated that this artificial intelligence model could be effectively used for drug repurposing and repositioning. Furthermore, by identifying its novel mechanism of action, this study confirmed the potential for fostamatinib to expand its indications as a TAM inhibitor.

PMID:40242601 | PMC:PMC12001310 | DOI:10.3892/etm.2025.12860

Cancer Res Treat. 2025 Apr 15. doi: 10.4143/crt.2025.033. Online ahead of print.

ABSTRACT

PURPOSE: Triple-negative breast cancer (TNBC) is a particularly aggressive subtype of breast cancer, with approximately 30% of patients eventually developing brain metastases (BM), which result in poor outcomes. An understanding of the tumor microenvironment (TME) at both primary and metastatic sites offers insights into the mechanisms underlying BM and potential therapeutic targets.

MATERIALS AND METHOD: Spatial RNA sequencing (spRNA-seq) was performed on primary TNBC and paired BM tissues from three patients, one of whom had previously received immune checkpoint inhibitors before BM diagnosis. Specimen regions were categorized into tumor, proximal, and distal TME based on their spatial locations. Gene expression differences across these zones were analyzed, and immune cell infiltration was estimated using TIMER. A gene module analysis was conducted to identify key gene clusters associated with BM.

RESULTS: Distinct gene expression profiles were noted in the proximal and distal TMEs. In BM, the proximal TME exhibited neuronal gene expression, suggesting neuron-tumor interactions compared to tumor, and upregulation of epithelial genes compared to the distal TME. Immune cell analysis revealed dynamic changes in CD8+ T cells and macrophages across the tumor and TME zones. Gene module analysis identified five key modules, including one related to glycolysis, which correlated with patient survival. Drug repurposing analysis identified potential therapeutic targets, including VEGFA, RAC1, EGLN3, and CAMK1D.

CONCLUSION: This study provides novel insights into the transcriptional landscapes in TNBC BM using spRNA-seq, emphasizing the role of neuron-tumor interactions and immune dynamics. These findings suggest new therapeutic strategies and underscore the importance of further research.

PMID:40241579 | DOI:10.4143/crt.2025.033

BMC Biol. 2025 Apr 16;23(1):101. doi: 10.1186/s12915-025-02206-x.

ABSTRACT

BACKGROUND: Drug-disease association (DDA) prediction aims to identify potential links between drugs and diseases, facilitating the discovery of new therapeutic potentials and reducing the cost and time associated with traditional drug development. However, existing DDA prediction methods often overlook the global relational information provided by other biological entities, and the complex association structure between drug diseases, limiting the potential correlations of drug and disease embeddings.

RESULTS: In this study, we propose HNF-DDA, a subgraph contrastive-driven transformer-style heterogeneous network embedding model for DDA prediction. Specifically, HNF-DDA adopts all-pairs message passing strategy to capture the global structure of the network, fully integrating multi-omics information. HNF-DDA also proposes the concept of subgraph contrastive learning to capture the local structure of drug-disease subgraphs, learning the high-order semantic information of nodes. Experimental results on two benchmark datasets demonstrate that HNF-DDA outperforms several state-of-the-art methods. Additionally, it shows superior performance across different dataset splitting schemes, indicating HNF-DDA's capability to generalize to novel drug and disease categories. Case studies for breast cancer and prostate cancer reveal that 9 out of the top 10 predicted candidate drugs for breast cancer and 8 out of the top 10 for prostate cancer have documented therapeutic effects.

CONCLUSIONS: HNF-DDA incorporates all-pairs message passing and subgraph capture strategies into heterogeneous network embedding, enabling effective learning of drug and disease representations enriched with heterogeneous information, while also demonstrating significant potential for applications in drug repositioning.

PMID:40241152 | DOI:10.1186/s12915-025-02206-x

J Transl Med. 2025 Apr 16;23(1):448. doi: 10.1186/s12967-025-06431-4.

ABSTRACT

BACKGROUND: Oral Lichen Planus (OLP) is a chronic inflammatory disorder that may progress to Oral Squamous Cell Carcinoma (OSCC). Lipid metabolism dysregulation has been implicated in tumor development and immune response modulation. This study aims to explore the role of lipid metabolism, particularly the lipids diacylglycerol (DAG), triacylglycerol (TAG), and phosphatidylcholine (PC), in the progression from OLP to OSCC, and to identify potential therapeutic targets for prevention and treatment.

METHODS: We performed a Mendelian randomization (MR) analysis to investigate the causal relationships between lipid metabolism and the risk of OLP and OSCC. Differential gene expression analysis was conducted to identify key genes related to lipid metabolism. The interactions of lipid species and key genes were examined using drug databases (DrugBank, DGIdb, and TCMSP) to explore potential drug candidates. Enrichment analysis of signaling pathways, including PPAR signaling, was also conducted to understand the underlying mechanisms.

RESULTS: Our MR analysis revealed that DAG exerts a protective effect in OLP (OR < 1), but its role shifts to a risk factor in OSCC (OR > 1), potentially by altering the tumor immune microenvironment. TAG and PI dysregulation also plays a critical role in tumorigenesis. Gene expression analysis identified several key lipid metabolism-related genes, including SLC27A6, FABP3, FABP4, ADIPOQ, and PLIN1, whose expression differed between OLP and OSCC, highlighting their importance in tumor progression. These genes were enriched in the PPAR signaling pathway, suggesting its involvement in tumor growth and immune modulation. Potential drug candidates, such as palm acid (PA), Imatinib, and Curcumin, were identified through drug-repurposing strategies.

CONCLUSION: Lipid metabolism dysregulation plays a crucial role in the progression of OLP to OSCC. Targeting key lipid metabolism pathways and genes, such as DAG, TAG, PI, and the PPAR pathway, may offer promising strategies for early diagnosis and therapeutic intervention. This study provides novel insights into the molecular mechanisms of OLP-to-OSCC progression and suggests potential drug candidates, including natural compounds, for future clinical applications. Further research is needed to validate these findings in clinical settings.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40241125 | DOI:10.1186/s12967-025-06431-4

NPJ Parkinsons Dis. 2025 Apr 16;11(1):77. doi: 10.1038/s41531-025-00933-0.

ABSTRACT

Many drug targets in ongoing Parkinson's disease (PD) clinical trials have strong genetic links. While genome-wide association studies (GWAS) nominate regions associated with disease, pinpointing causal genes is challenging. Our aim was to prioritize additional druggable genes underlying PD GWAS signals. The polygenic priority score (PoPS) integrates genome-wide information from MAGMA gene-level associations and over 57,000 gene-level features. We applied PoPS to East Asian and European PD GWAS data and prioritized genes based on PoPS, distance to the GWAS signal, and non-synonymous credible set variants. We prioritized 46 genes, including well-established PD genes (SNCA, LRRK2, GBA1, TMEM175, VPS13C), genes with strong literature evidence supporting a mechanistic link to PD (RIT2, BAG3, SCARB2, FYN, DYRK1A, NOD2, CTSB, SV2C, ITPKB), and genes relatively unexplored in PD. Many hold potential for drug repurposing or development. We prioritized high-confidence genes with strong links to PD pathogenesis that may represent our next-best candidates for developing disease-modifying therapeutics.

PMID:40240380 | DOI:10.1038/s41531-025-00933-0

Annu Rev Biomed Data Sci. 2025 Apr 16. doi: 10.1146/annurev-biodatasci-101424-120950. Online ahead of print.

ABSTRACT

Drug target identification is the first step in drug development, and its importance is underscored by the fact that, even when using genetic evidence to improve success rates, only a small fraction of lead targets end up approved for use in the clinic. One of the reasons for this is the lack of in-depth understanding of the complexity of human diseases.In this review we argue that network-based approaches, which are able to capture relationships between relevant genes and proteins, and diverse data modalities have high potential for improving drug target identification and drug repurposing. We present the evolution of network-based methods that have been developed for this purpose and discuss the limitations of these approaches that are holding them back from making an impact in the clinic. We finish by presenting our recommendations for overcoming these limitations, for example, by leveraging emerging technologies such as artificial intelligence and knowledge graphs.

PMID:40239307 | DOI:10.1146/annurev-biodatasci-101424-120950

J Neurooncol. 2025 Apr 16. doi: 10.1007/s11060-025-05004-1. Online ahead of print.

ABSTRACT

PURPOSE: This retrospective study aims to show a real-life single-center experience with clinical management of relapsed pediatric ependymomas using results from comprehensive molecular profiling.

METHODS: Eight relapsed ependymomas were tested by whole exome sequencing, RNA sequencing, phosphoproteomic arrays, array comparative genome hybridization, and immunohistochemistry staining for PD-L1 expression and treated with an individualized approach implementing targeted inhibitors, immunotherapy, antiangiogenic metronomic treatment, or other agents. Treatment efficacy was evaluated using progression-free survival (PFS), overall survival (OS), survival after relapse (SAR), and PFS ratios.

RESULTS: Genomic analyses did not reveal any therapeutically actionable alterations. Surgery remained the cornerstone of patient treatment, supplemented by adjuvant radiotherapy. Empiric agents were chosen quite frequently, often involving drug repurposing. In six patients, prolonged PFS after relapse was seen because of immunotherapy, MEMMAT, or empiric agents and is reflected in the PFS ratio ≥ 1. The 5-year OS was 88%, the 10-year OS was 73%, the 2-year SAR was 88%, and the 5-year SAR was 66%.

CONCLUSION: We demonstrated the feasibility and good safety profile. Promising was the effect of immunotherapy on ZFTA-positive ependymomas. However, further research is required to establish the most effective approach for achieving sustained remission in these patients.

PMID:40238025 | DOI:10.1007/s11060-025-05004-1

Clin Transl Sci. 2025 Apr;18(4):e70214. doi: 10.1111/cts.70214.

ABSTRACT

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease. Its progressively expanding, fluid-filled renal cysts eventually lead to end-stage renal disease. Despite the relatively high prevalence, treatment options are currently limited to a single drug approved by the FDA and EMA. Here, we investigated human ADPKD patient-derived three-dimensional cyst cultures (3DCC) as an in vitro model for ADPKD and drug repurposing research. First, we analyzed the proteomes of 3DCC derived from healthy and diseased tissues. We then compared the protein expression profiles with those of reference tissues, mainly from the same patients. We quantified 290 proteins affecting drug disposition and proposed target proteins for drug treatment. Lastly, we investigated the functional response of the quantified target proteins after exposure to repurposing candidates in the 3DCC. Proteomic profiling of human 3DCC reflected previously reported pathophysiological alterations, including aberrant protein expression in inflammation and metabolic reprogramming. While the 3DCCs largely recapitulated the disease phenotype in vitro, drug transporter expression was reduced compared to in vivo conditions. Target proteins for proposed repurposing candidates showed similar expression in vitro and in tissues. Exposure to these repurposing candidates inhibited cyst swelling in vitro, supporting the suitability of the 3DCC for ADPKD drug screening. In summary, our results provide new insights into the ADPKD proteome and offer a starting point for further research to improve treatment options for affected individuals.

PMID:40235151 | DOI:10.1111/cts.70214

Sci Rep. 2025 Apr 15;15(1):12955. doi: 10.1038/s41598-025-97653-5.

ABSTRACT

Ionizing radiation induces DNA damage and impairs genomic integrity, leading to cell death and tissue injuries or carcinogenesis. Medical radiation protectors are essential and necessary. However, there are limited radioprotectors in clinics, which can't meet the growing demand for countering radiation emergencies. Traditional drug discovery approach has been proven expensive and risky. Computational drug repositioning provides an attractive strategy for radioprotector discovery. Here we constructed a systematic workflow to identify repositioning radioprotectors by comparison of biosimilarity between γ-ray and known medicines characterized by gene expression signatures from GEO and LINCS. Using enrichment scoring, medicines with negative scores were considered as candidates of revising or mitigating radiation injuries. Seven approved medicines were identified, and their targets enriched in steroid and estrogen metabolic, chemical carcinogenesis associated pathways. Lenalidomide, an approved medicine for multiple myeloma and anemia, was further verified as a promising potential radioprotector. It increases survival of mice after lethal doses of irradiation by alleviating bone marrow and intestinal injury in vivo, and inhibits apoptosis of cultured irradiated AHH- 1 and IEC- 6 cells in vitro. This study introduces rational drug repositioning to radiation medicine and provides viable candidates for radioprotective therapeutic regimens.

PMID:40234645 | DOI:10.1038/s41598-025-97653-5

Acta Pharmacol Sin. 2025 Apr 15. doi: 10.1038/s41401-025-01539-1. Online ahead of print.

ABSTRACT

Hyperlipidemia, a major risk factor for cardiovascular diseases, is associated with limitations in clinical lipid-lowering medications. Drug repurposing strategies expedite the research process and mitigate development costs, offering an innovative approach to drug discovery. This study employed systematic literature and guidelines review to compile a training set comprising 176 lipid-lowering drugs and 3254 non-lipid-lowering drugs. Multiple machine learning models were developed to predict the lipid-lowering potential of drugs. A multi-tiered validation strategy was implemented, encompassing large-scale retrospective clinical data analysis, standardized animal studies, molecular docking simulations and dynamics analyses. Through a comprehensive screening analysis utilizing machine learning, 29 FDA-approved drugs with lipid-lowering potential were identified. Clinical data analysis confirmed that four candidate drugs, with Argatroban as the representative, demonstrated lipid-lowering effects. In animal experiments, the candidate drugs significantly improved multiple blood lipid parameters. Molecular docking and dynamics simulations elucidated the binding patterns and stability of candidate drugs in interaction with related targets. We successfully identified multiple non-lipid-lowering drugs with lipid-lowering potential by integrating state-of-the-art machine learning techniques with multi-level validation methods, thereby providing new insights into lipid-lowering drugs, establishing a paradigm for AI-based drug repositioning research, and expanding the repertoire of lipid-lowering medications available to clinicians.

PMID:40234619 | DOI:10.1038/s41401-025-01539-1

Biomed Pharmacother. 2025 Apr 14;186:118047. doi: 10.1016/j.biopha.2025.118047. Online ahead of print.

ABSTRACT

In this study, we utilized the yeast two-hybrid system to screen for proteins interacting with USP24. Out of 250 such proteins, functional enrichment analysis using MetaCore™ indicated that 33 of them were involved in lung cancer progression. We then investigated gene expression and survival rates of these 33 proteins in lung cancer patients and cell lines through TCGA databases, Kaplan-Meier Plotter databases, and RNA-seq profile from A549/A549-T24 cells. By employing the patients' survival rate and gene expression profile of these 33 USP24-interacting proteins as gene signatures, we identified 10 potential drugs for inhibiting lung cancer progression or drug resistance via drug repurposing strategy using the Connectivity Map (CMap) database. Of these 10 drugs, six showed similar indicators in Clinical Trials, while the other four candidates (15-delta prostaglandin J2, Astemizole, Trifluoperazine, and 1,4-chrysenequinone) were chosen to evaluate their effect on re-sensitizing cytotoxicity of Taxol and Gefitinib in drug-resistant cancer cells. Experiments demonstrated that treatment with USP24-i-101 and Astemizole alone significantly inhibited drug resistance and re-sensitized the cytotoxicity of Taxol and Gefitinib in drug-resistant lung cancer cells. Notably, combination therapy with USP24-i-101and Astemizole re-sensitized the cytotoxicity of Taxol and Gefitinib in drug-resistant lung cancer, which could benefit in inhibiting drug resistance during cancer therapy.

PMID:40233501 | DOI:10.1016/j.biopha.2025.118047

Cent Nerv Syst Agents Med Chem. 2025 Apr 11. doi: 10.2174/0118715249350698250317041551. Online ahead of print.

ABSTRACT

BACKGROUND: The search for effective treatments for neurodegenerative diseases, particularly Alzheimer's disease, has been fraught with challenges. Alzheimer's disease accounts for 60-80% of dementia cases globally, affecting approximately about 50 million people. Currently, drug repurposing has emerged as a promising strategy in new drug development, attracting significant attention from regulatory agencies, such as the US FDA.

AIM: This study aimed to investigate the potential therapeutic role of dolutegravir in Alzheimer's disease (AD) treatment using a novel network pharmacology approach. Specifically, it explored the interaction of dolutegravir with key molecular targets involved in AD pathology, predicted its effects on relevant biological pathways, and evaluated its viability as a new therapeutic candidate.

OBJECTIVE: This study employed a network pharmacology framework to evaluate dolutegravir, an antiretroviral drug, as a potential treatment for Alzheimer's disease, shedding light on its possible therapeutic mechanisms.

METHOD: A network pharmacology approach was used to predict the drug targets of dolutegravir. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify interacting pathways. Additionally, protein- protein interaction (PPI) network analysis was conducted to assess key interactions and molecular docking studies were performed to evaluate the binding affinity of dolutegravir to the predicted targets.

RESULT: PPI network analysis revealed that dolutegravir interacted with several key targets, including BRAF, mTOR, MAPK1, MAPK3, NOS1, BACE1, CAPN1, CASP3, CASP7, CASP8, CHUK, IKBKB, PIK3CA, and PIK3CD. KEGG pathway analysis suggested that dolutegravir could influence amyloid-beta formation, amyloid precursor protein metabolism, and the cellular response to amyloid-beta. Molecular docking results showed the highest binding affinity of dolutegravir for PI3KCD (-8.5 kcal/mol) and MTOR (-8.7 kcal/mol).

CONCLUSION: The findings indicated that dolutegravir holds significant potential in modulating key pathways involved in Alzheimer's disease pathogenesis. These results provide a strong foundation for further investigations into the therapeutic efficacy and safety of dolutegravir in the treatment of Alzheimer's disease. The use of drug repurposing strategies, leveraging Dolutegravir's established pharmacological profile, offers a promising route for accelerated therapeutic development in AD.

PMID:40231534 | DOI:10.2174/0118715249350698250317041551

ACS Appl Bio Mater. 2025 Apr 14. doi: 10.1021/acsabm.5c00362. Online ahead of print.

ABSTRACT

Fruit spoilage caused by oxidation and microbial infection exacerbates resource wastage. Although starch films including chitosan possessed admirable biocompatibility owing to great biodegradability compared with conventional plastics, deficient antibacterial and antioxidant capacity restricted food shelf life. Herein, an environmentally friendly antibacterial film (CS/G-CDs) was constructed by carbon dots derived from Cirsii Herba (CDs), which was formed through high affinity resulting from hydrogen bonding between chitosan molecules and hydroxyl originating from CDs. The prepared CDs presented homogeneous and monodisperse spherical structures with an ultrasmall size, providing favorable conditions for uniform film formation. Encouragingly, the antioxidant capacity of CS/G-CDs increased 5.00-fold, followed by an antibacterial rate of up to 97.0%. Dramatically, CS/G-CDs revealed glorious UV shielding efficacy (99.9% for UVB and 98.2% for UVA), and its preservation time for blueberries was remarkably extended 8 days longer than that of the chitosan film. Overall, Chinese herb-derived antibacterial films exhibited magnified antibacterial/antioxidant properties and great biocompatibility, which provided a promising strategy for sustainable development of packaging materials.

PMID:40227972 | DOI:10.1021/acsabm.5c00362

Cancers (Basel). 2025 Mar 28;17(7):1144. doi: 10.3390/cancers17071144.

ABSTRACT

According to the European Federation of Pharmaceutical Industries and Association (EFPIA), a drug takes about 12-13 years from the first synthesis of a new active substance for the medicinal product to reach the market [...].

PMID:40227656 | DOI:10.3390/cancers17071144