Front Pharmacol. 2025 Apr 8;16:1539032. doi: 10.3389/fphar.2025.1539032. eCollection 2025.

ABSTRACT

Background: Parkinson's disease (PD), a prevalent and progressive neurodegenerative disorder, currently lacks effective and satisfactory pharmacological treatments. Computational drug repurposing represents a promising and efficient strategy for drug discovery, aiming to identify new therapeutic indications for existing pharmaceuticals. Methods: We employed a drug-target network approach to computationally repurpose FDA-approved drugs from databases such as DrugBank. A literature review was conducted to select candidates not previously reported as pharmacoprotective against PD. Subsequent in vitro evaluation utilized Cell Counting Kit-8 (CCK8) assays to assess the neuroprotective effects of the selected compounds in the SH-SY5Y cell model of Parkinson's disease induced by 1-methyl-4-phenylpyridinium (MPP+). Furthermore, an in vivo mouse model of Parkinson's disease induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) was developed to investigate the mechanisms of action and therapeutic potential of the identified drug candidates. Results: Our approach identified 176 drug candidates, with 28 selected for their potential anti-Parkinsonian effects and lack of prior PD-related reporting. CCK8 assays showed significant neuroprotection in SH-SY5Y cells for Omaveloxolone and Cyproheptadine. In the MPTP-induced mouse model, Cyproheptadine inhibited interleukin-6 (IL-6) expression and prevented Tyrosine Hydroxylase (TH) downregulation via the MAPK/NFκB pathway, while Omaveloxolone alleviated TH downregulation, potentially through the Kelch-like ECH-associated protein 1 (KEAP1)-NF-E2-related factor 2 (Nrf2)/antioxidant response element (ARE) pathway. Both drugs preserved dopaminergic neurons and improved neurological deficits in the PD model. Conclusion: This study elucidates potential drug candidates for the treatment of Parkinson's disease through the application of computational repurposing, thereby underscoring its efficacy as a drug discovery strategy.

PMID:40264664 | PMC:PMC12011821 | DOI:10.3389/fphar.2025.1539032

Curr Drug Res Rev. 2025 Apr 21. doi: 10.2174/0125899775365699250408091801. Online ahead of print.

ABSTRACT

Computational drug repositioning has emerged as an efficient approach to discovering new indications for existing drugs, offering lower risk and cost compared to traditional drug discovery methods. Various computational approaches have been developed, including targetbased, gene-expression-based, phenome-based, and multi-omics-based methods. Recent advancements leverage diverse data sources, such as biomedical databases and online healthrelated information. Techniques incorporating drug structure and target information have shown promising results in predicting new drug indications. Despite significant progress, challenges remain, including data noise reduction, method ensemble, negative sample selection, and data sparseness. Overall, computational drug repositioning continues to be a valuable tool in drug discovery and development.

PMID:40264316 | DOI:10.2174/0125899775365699250408091801

Sci Rep. 2025 Apr 22;15(1):13975. doi: 10.1038/s41598-025-88388-4.

ABSTRACT

The 3C-like protease (3CLpro) is essential in the SARS-CoV-2 life cycle and a promising target for antiviral drug discovery, as no similar proteases exist in humans. This study aimed to identify effective SARS-CoV-2 inhibitors among FDA-approved drugs. Previous computational analysis revealed several drugs with high binding affinity to the 3CLpro active site. In vitro enzymatic assays confirmed that ten of these drugs effectively inhibited the enzyme. To evaluate their impact on viral replication, we used non-infectious SARS-CoV-2 sub-genomic replicons in lung and intestinal cells. Amcinonide, eltrombopag, lumacaftor, candesartan, and nelfinavir inhibited replication at low micromolar concentrations. Lumacaftor showed IC50 values of 964 nM in Caco-2 cells and 458 nM in Calu-3 cells, while candesartan had IC50 values of 714 nM and 1.05 µM, respectively. Furthermore, dual combination experiments revealed that amcinonide, pimozide, lumacaftor, and eltrombopag acted as potent inhibitors at nanomolar concentrations when combined with candesartan. This study highlights lumacaftor, candesartan, and nelfinavir as effective inhibitors of SARS-CoV-2 replication in vitro and emphasizes their potential for repurposing as antiviral treatments. These findings support future clinical trials and may lead to breakthroughs in COVID-19 treatment strategies.

PMID:40263343 | DOI:10.1038/s41598-025-88388-4

Mol Biol Rep. 2025 Apr 22;52(1):410. doi: 10.1007/s11033-025-10524-0.

ABSTRACT

Drug repurposing is the process of using currently approved drugs for a novel treatment or medical condition for which it was not previously indicated. Despite promising preclinical and clinical results, most of the newly designed senotherapeutic agents synthesized have limited clinical utility due to individual and organ-specific variations in aging phenotype and adverse side effects. All these limitations indicate that further clinical research is required to determine the effectiveness of repurposed senotherapeutic drug interventions, such as metformin, for age-related diseases. Metformin exerts diverse senotherapeutic effects on various aging tissues at different metabolic conditions. Although not exhibiting senolytic properties, metformin has effectively suppressed cellular senescence and senescence-associated secretory phenotype (SASP) in age-related diseases (ARDs). Targeting specific SASP-related signaling pathways with metformin may offer new therapeutic benefits to alleviate the detrimental effects of senescent cells accumulated in most common ARDs in the elderly. Metformin was also the first drug evaluated for its senescence-targeting effects in a large clinical trial named "Targeting Aging with Metformin (TAME)". In this review, we critically evaluate the literature to highlight senotherapeutic mechanisms in which metformin can be therapeutically repurposed for the prevention and treatment of ARDs.

PMID:40261556 | DOI:10.1007/s11033-025-10524-0

Int J Antimicrob Agents. 2025 Apr 19:107522. doi: 10.1016/j.ijantimicag.2025.107522. Online ahead of print.

ABSTRACT

RNA-binding proteins (RBPs) are essential regulators of cellular RNA processes, including RNA stability, translation, and post-translational regulation. During viral infections, RBPs are key regulators of the viral cycle due to their interaction with both host and viral RNAs. Herein we initially explore the roles of specific RBP families, namely heterogeneous nuclear ribonucleoproteins (hnRNPs), DEAD-box helicases, human antigen R (HuR), and the eukaryotic initiation factors of the eIF4F complex, in viral RNA replication, translation, and assembly. Next, we examine the potential of these RBPs as host-targeting antivirals against pandemic-prone RNA viruses that have been gaining momentum in recent years. Targeting RBPs could disrupt cellular homeostasis, leading to unintended effects on host cells; however, RBPs have been successfully targeted mainly in anticancer therapies, showcasing that their modulation can be safely achieved by drug repurposing. By disrupting key viral-RBP interactions or modulating RBP functions, such therapeutic interventions aim to inhibit viral propagation and restore normal host processes. Thus, conceivable benefits of targeting RBPs as alternative antiviral strategies include their broad-spectrum activity and potential for combination therapies with conventional antivirals, reduced or delayed resistance development, and concomitant enhancement of host immune responses. Our discussion also highlights the broader implications of leveraging host-directed therapies in an attempt to overcome viral resistance. Finally, we emphasize the need for continued innovation to refine these strategies for broad-spectrum antiviral applications.

PMID:40258479 | DOI:10.1016/j.ijantimicag.2025.107522

In Silico Pharmacol. 2025 Apr 17;13(2):67. doi: 10.1007/s40203-025-00347-z. eCollection 2025.

ABSTRACT

Morphine and heroin dependence are growing concerns worldwide. Drug dependence is one of the greatest challenges, and developing alternative therapeutic strategies is essential. Due to few treatment options in pain management, morphine, a potent analgesic, is widely prescribed, but it carries a high risk of abuse. For the management of drug dependence, we have limited treatment options available, therefore, strategies should be developed to manage drug-seeking behaviors in clinical settings. We tried to find any FDA-approved drug targeting µ-opioid receptors through the in-silico approach. We screened around 186 FDA-approved drugs; we observed several drugs showing better docking scores with good affinity. We found vilazodone, indinavir, and lorazepam as potential drugs based on their affinity and mechanism of action. Later, these drugs were screened against human µ-opioid (PDB ID:8EF6) and other novel drug targets (5HT1 and TLR-4) that are associated with morphine dependence. Following docking, density functional theory (DFT), molecular dynamics (MD), molecular mechanics, and general born surface area (MM-GBSA) were performed to calculate the stability and ligand-protein binding free energies. Vilazodone, indinavir and lorazepam showed promising docking, MD, the energy gap between the HOMO and LUMO chemical reactivity, and MM-GBSA results compared to morphine and naloxone. We propose that these three drugs have huge potential to reverse the morphine and heroin dependence in diseased subjects near future.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-025-00347-z.

PMID:40255259 | PMC:PMC12006581 | DOI:10.1007/s40203-025-00347-z

Microb Pathog. 2025 Apr 17:107586. doi: 10.1016/j.micpath.2025.107586. Online ahead of print.

ABSTRACT

Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis (Mtb) and is a growing public health problem worldwide. Within the innate immune response, we highlight the secretion of the antimicrobial peptide LL-37, which is crucial for Mtb elimination in infected cells. Previous reports have shown that CEBPα activation induces LL-37 independently of its main inducer, vitamin D, under endoplasmic reticulum (ER) stress. In this study, we report that infection with Mtb causes ER stress in pulmonary epithelial cells and macrophages. The stress induces the activation of CEBPα, which in turn promotes the LL-37 expression. Furthermore, the participation of CEBPα is necessary for the correct clearance of Mtb in an in vitro infection model. We identify candidate drugs (mycophenolic acid, indapamide, and glibenclamide) capable of activating CEBPα and promoting LL-37 through in silico assays. The effect of the drugs was corroborated by gene and protein expression analysis. Finally, we observed that treatment with these drugs improves bacterial clearance in infected cells. Our results lead us to suggest CEBPα as a potential therapeutic target as an adjuvant in the standard treatment of tuberculosis, seeking a reduction in treatment time, and thus a lower appearance of drug resistance.

PMID:40252936 | DOI:10.1016/j.micpath.2025.107586

Comput Biol Chem. 2025 Apr 15;118:108471. doi: 10.1016/j.compbiolchem.2025.108471. Online ahead of print.

ABSTRACT

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by the overexpression of poly-ADP ribose polymerase 1 (PARP1), a key enzyme in DNA repair. Targeting PARP1 with inhibitors presents a promising therapeutic strategy, particularly given the limited treatment options for TNBC. This study employed in silico methodologies to evaluate the pharmacokinetic and inhibitory potential of FDA-approved drugs and compounds derived from Onosma. dichroantha root extracts against PARP1. Virtual screening and molecular docking identified Midazolam, Olaparib, Beta-sitosterol, and 1-Hexyl-4-nitrobenzene as top candidates, exhibiting strong binding affinities of -10.6 kcal/mol, -9.9 kcal/mol, -6.83 kcal/mol, and -5.53 kcal/mol respectively. Molecular dynamics simulations (MDS) over 100 nanoseconds revealed that Beta-sitosterol formed the most stable complex with PARP1, demonstrating minimal structural deviations and robust hydrogen bonding. The Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) analysis further confirmed Beta-Sitosterol and Olaparib superior binding free energy (ΔGbind= -175.43 kcal/mol and -180.8 kcal/mol respectively), highlighting its potential as a potent PARP1 inhibitor. ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling indicated that Beta-Sitosterol adheres to Lipinski's Rule of Five, with high intestinal absorption (95.88 %) and blood-brain barrier permeability (0.824), despite low water solubility. Protein-protein interaction analysis identified key PARP1-associated proteins, including CASP3, CASP7, and XRCC1, suggesting broader therapeutic implications. These findings underscore the potential of Beta-Sitosterol as a novel PARP1 inhibitor for TNBC treatment, combining computational validation with favorable pharmacokinetic properties. The study also highlights the utility of drug repurposing and plant-derived compounds in developing targeted therapies for TNBC, paving the way for further preclinical and clinical investigations.

PMID:40252254 | DOI:10.1016/j.compbiolchem.2025.108471

Sci Rep. 2025 Apr 18;15(1):13445. doi: 10.1038/s41598-025-98629-1.

ABSTRACT

Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in clinical trials. Deep learning advancements, particularly generative models, offer potential solutions to these challenges. One promising algorithm is DrugGPT, a transformer-based model, that generates small molecules for input protein sequences. Although promising, it generates both chemically valid and invalid structures and does not incorporate the features of approved drugs, resulting in time-consuming and inefficient drug discovery. To address these issues, we introduce DrugGen, an enhanced model based on the DrugGPT structure. DrugGen is fine-tuned on approved drug-target interactions and optimized with proximal policy optimization. By giving reward feedback from protein-ligand binding affinity prediction using pre-trained transformers (PLAPT) and a customized invalid structure assessor, DrugGen significantly improves performance. Evaluation across multiple targets demonstrated that DrugGen achieves 100% valid structure generation compared to 95.5% with DrugGPT and produced molecules with higher predicted binding affinities (7.22 [6.30-8.07]) compared to DrugGPT (5.81 [4.97-6.63]) while maintaining diversity and novelty. Docking simulations further validate its ability to generate molecules targeting binding sites effectively. For example, in the case of fatty acid-binding protein 5 (FABP5), DrugGen generated molecules with superior docking scores (FABP5/11, -9.537 and FABP5/5, -8.399) compared to the reference molecule (Palmitic acid, -6.177). Beyond lead compound generation, DrugGen also shows potential for drug repositioning and creating novel pharmacophores for existing targets. By producing high-quality small molecules, DrugGen provides a high-performance medium for advancing pharmaceutical research and drug discovery.

PMID:40251288 | DOI:10.1038/s41598-025-98629-1

Int J Biol Macromol. 2025 Apr 16:143217. doi: 10.1016/j.ijbiomac.2025.143217. Online ahead of print.

ABSTRACT

Flaviviridae family comprises some of the most vulnerable viruses known for causing widespread outbreaks, high mortality rates, and severe long-term health complications in humans. Viruses like Dengue (DENV), Zika (ZIKV) and Hepatitis C (HCV) are endemic across the globe, especially in tropical and subtropical regions, infecting multiple tissues and leading to significant health crises. Investigating virus-host interactions across tissues can reveal tissue-specific drug targets and aid antiviral drug repurposing. In this study, we employed a multi-step computational approach to construct a comprehensive virus-human interactome by integrating virus-host protein-protein interactions (PPIs) with tissue-specific gene expression profiles to study key protein targets associated with Flaviviridae infections. Mapping drug-target predictions revealed druggable proteins - CCNA2 in peripheral blood mononuclear cells (PBMC) and EIF2S2, CDK7 and CARS in the liver, with Tamoxifen, Sirolimus, Entrectinib and L-cysteine as potential repurposable drugs, respectively. Further protein-ligand docking and molecular dynamics (MD) simulations were conducted to assess the stability of the complexes. These findings highlight common druggable human targets exploited by DENV, ZIKV and HCV, providing a foundation for broad-spectrum antiviral therapies. By focusing on shared host pathways and targets in viral replication, we propose promising drug candidates, supporting the development of unified therapeutic strategies against Flaviviridae viruses.

PMID:40250655 | DOI:10.1016/j.ijbiomac.2025.143217

RSC Adv. 2025 Apr 17;15(16):12331-12341. doi: 10.1039/d5ra01341k. eCollection 2025 Apr 16.

ABSTRACT

Atypical teratoid/rhabdoid tumor (AT/RT) is a rare and aggressive tumor of the primary central nervous system primarily affecting children. It typically originates in the cerebellum and brain stem and is associated with a low survival rate. While standard chemotherapy has been used as a primary treatment for AT/RTs, its success rate is unsatisfactory, and patients often experience severe side effects. Therefore, there is an urgent need to develop new and effective treatment strategies. One promising approach for identifying new therapies is drug repurposing. Although many FDA-approved drugs have been repurposed for various cancers, there have been no reports of such applications for AT/RTs. In this study, a library of 2130 FDA-approved drugs was screened using a high-throughput screening system against 2D traditional cultures and 3D spheroid cultures of AT/RT cell lines (BT-12 and BT-16). From this screening, colchicine, a non-chemotherapeutic agent, was identified as a promising candidate. It exhibited IC50 values of 0.016 and 0.056 μM against 2D BT-12 and 2D BT-16 cells, respectively, and IC50 values of 0.004 and 0.023 μM against 3D BT-12 and BT-16 spheroid cultures. Additionally, the cytotoxic effects of colchicine on human brain endothelial cells and human astrocytes were evaluated, and CC50 > 20 μM was observed, which is over two orders of magnitude higher than its effective concentrations in AT/RT cells, indicating considerably lower toxicity to normal brain cells and brain endothelial cells. In conclusion, colchicine shows significant potential to be repurposed as a treatment for AT/RTs, providing a safer and more effective therapeutic option for this rare and challenging disease.

PMID:40248220 | PMC:PMC12004362 | DOI:10.1039/d5ra01341k

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