bioRxiv [Preprint]. 2025 May 24:2025.05.23.655845. doi: 10.1101/2025.05.23.655845.

ABSTRACT

BACKGROUND: Medulloblastoma is the most common malignant pediatric brain tumor. Survival rates vary widely between subgroups, with an average overall survival of 70%. Recurrent medulloblastoma is highly aggressive, treatment-resistant, and usually fatal. In addition, current treatments are highly toxic to the developing brain and surviving patients suffer from lifelong side effects. Therefore, novel therapeutic options are urgently needed.

METHODS: To inform risk-based, personalized therapy, we developed a novel platform called DrugSeq, which allows predictions of drug sensitivities in patients across medulloblastoma subgroups. We used a perturbagen-response dataset to calculate transcriptional response signatures for each drug and compared this to patient medulloblastoma tumor gene expression. We then stratified patients by molecular subgroup and used an ANOVA analysis to identify drugs that selectively targeted each subgroup.

RESULTS: We found distinct differences in transcriptional profiles and predicted drug sensitivity for each medulloblastoma subgroup. We identified several kinase inhibitors, epigenetic inhibitors, and several drugs that have been investigated in drug repositioning studies for cancer.

CONCLUSIONS: We posit that DrugSeq may identify novel therapies and facilitate patient stratification in clinical trials, leading to more successful targeted medulloblastoma therapies that improve tumor response while minimizing late toxicities. This computational tool can also be used for other cancers to stratify patients based on any clinical or molecular feature.

KEY POINTS: DrugSeq calculates drug sensitivity for medulloblastoma tumors stratified by subgroup.DrugSeq platform may inform patient stratification strategies in clinical trials.

IMPORTANCE OF THE STUDY: Medulloblastoma is the most common malignant pediatric brain tumor. Current standard-of-care typically includes surgical resection, multi-agent chemotherapy, and radiation. However, survival rates vary widely between subgroups, ranging from 45 to 90%, depending on age and molecular features. In addition, surviving children frequently suffer from debilitating late side effects of therapy including neurocognitive impairment, epilepsy, stroke, subsequent cancer, endocrinopathies, and early mortality. Therefore, novel therapeutic options are urgently needed. However, a one-size-fits-all approach for therapy is unlikely to be effective given the well-characterized intertumor heterogeneity of medulloblastoma.

PMID:40475420 | PMC:PMC12139894 | DOI:10.1101/2025.05.23.655845

Front Pharmacol. 2025 May 22;16:1534989. doi: 10.3389/fphar.2025.1534989. eCollection 2025.

ABSTRACT

INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a chronic fibrotic lung disease with high mortality. Current therapies are very limited, with nintedanib and pirfenidone being the only non-invasive but non-curative interventions, ultimately bridging to lung transplantation.

METHODS: In silico modeling of dysregulated pathways in IPF and screening for putative interfering small molecules identified carvedilol as a promising anti-fibrotic agent. We validated drug-mediated effects on key features of fibroblast activation in functional assays and gene expression analyses in human embryonic lung fibroblasts (MRC-5). Precision-cut lung slices (PCLSs) generated from human lung tissue were assessed for secreted fibrotic markers' expression.

RESULTS: Treatment with carvedilol reduced metabolic activity, inhibited cell proliferation, and led to decreased migratory activity, as observed in scratch wound assays, in human lung fibroblasts. The functional profile was reflected at the transcriptional level as commonly known fibrotic marker genes, e.g., alpha smooth muscle actin and collagen 1, were robustly repressed. Proteomic profiling underlined a strong extracellular matrix interference with elevated syntheses of several collagen types and various integrins, which play a critical role in pro-fibrotic downstream signaling. Comparison of healthy and fibrotic lung tissue validated an upregulation of pro-fibrotic miR-21 secretion in the ex vivo PCLS model, which remained unchanged upon carvedilol therapy.

CONCLUSION: Herein, carvedilol demonstrated significant anti-fibrotic effects on human lung fibroblasts in vitro, thus presenting great potential as an anti-IPF treatment. In addition, miR-21 was validated as a secreted pro-fibrotic biomarker in the ex vivo PCLS model.

PMID:40474967 | PMC:PMC12137325 | DOI:10.3389/fphar.2025.1534989

J Chem Inf Model. 2025 Jun 5. doi: 10.1021/acs.jcim.5c00817. Online ahead of print.

ABSTRACT

The forecasting of drug-target interactions (DTIs) is a crucial element in the domain of drug repositioning. Current methodologies, primarily based on dual-branch architectures or graph neural networks (GNNs), typically model binary associations─specifically drug-target or target-disease relationships─thereby overlooking the directional dependencies and synergistic mechanisms intrinsic to tripartite drug-target-disease (GTD) interactions. To address this disparity, we present MTGNN (Multimodal Transformer Graph Neural Network), a comprehensive prediction framework designed to model GTD triplets directly. MTGNN specifically constructs a heterogeneous graph that incorporates direction-aware metapaths to capture biologically significant directional dependencies (e.g., drug → target → disease) and utilizes a dual-path Transformer architecture to integrate both the topological structure and semantic features of biomedical entities (drugs, targets, and diseases). A cross-attention technique is also implemented to dynamically align graph-based and modality-specific semantic representations, promoting improved cross-modal interaction. Comprehensive tests performed validate the effectiveness of MTGNN in precisely inferring GTD connections, exhibiting enhanced performance and generalization capacities. These findings highlight the efficacy of MTGNN as a formidable computational instrument for medication repositioning.

PMID:40474342 | DOI:10.1021/acs.jcim.5c00817

Microb Pathog. 2025 Jun 3:107781. doi: 10.1016/j.micpath.2025.107781. Online ahead of print.

ABSTRACT

The rise of multidrug-resistant (MDR) Pseudomonas aeruginosa remains an unresolved and substantial challenge to public health, which highlights an urgent need for newer therapeutic strategies. Despite the availability of innumerable antibiotics that effectively eliminate bacterial infections, their unregulated consumption and overexploitation has promoted the development of multidrug resistance by inducing selection pressure. As the world progresses into the post-antibiotic era, antivirulence therapies that exploit a 'disarm-don't kill approach' are gaining momentum as a promising alternative to existing antimicrobial regimens. In view of extensive research being conducted to explore alternate intervention strategies against P. aeruginosa, this review augments scientific literature on repurposing of Food and Drug Administration (FDA)-approved drugs as antivirulence agents, focusing on their ability to disarm quorum sensing (QS), suppress virulence factor production, and disrupt biofilm formation. Drugs from various categories, including but not limited to antifungals, antidiabetics, antihypertensives, antiparasitics, NSAIDs, and antibiotics have been reported to override QS circuitry and QS-regulated virulence pathways in P. aeruginosa through in vitro and in vivo studies. Further, pre-clinical studies with FDA-approved drugs have been substantiated by in silico analysis predicting strong binding affinities to key QS receptors of P. aeruginosa such as LasR, RhlR, and PqsR, underscoring their potential mechanisms of action. Besides, with the well-documented safety profiles, pharmacokinetics, and clinical efficacy of the existing drugs, this repurposing approach streamlines the drug development process, minimizes costs, and accelerates the transition to clinical application. This review underscores the transformative potential of drug repurposing as a cost-effective and sustainable solution to the escalating antimicrobial resistance crisis and advocates for further research to optimize and clinically validate these promising antivirulence therapies.

PMID:40473130 | DOI:10.1016/j.micpath.2025.107781

Comput Biol Med. 2025 Jun 3;194:110471. doi: 10.1016/j.compbiomed.2025.110471. Online ahead of print.

ABSTRACT

INTRODUCTION: The average survival time for High-Grade Serous Ovarian Cancer (HGSOC) is around 3.4 years post-diagnosis. The treatment options are limited, especially for relapsed patients, resistant to standard treatment. Therefore, novel drug candidates are needed.

OBJECTIVE: We propose a novel approach for predicting potential drug candidates by focusing on agents capable of reversing the effects of perturbed RNA network.

METHODS: The competing endogenous RNA (ceRNAs) network was constructed on differential expression (DE) of long non-coding RNAs (lncRNAs), protein-coding RNAs (mRNAs) and microRNAs (miRNAs) from the primary HGSOC tumour tissues. It allowed for identification of key perturbed axes of RNA regulation. The publicly available resources for drug repurposing were used to select candidates for in-vitro validation.

RESULTS: The phosphoinositide 3-kinase (PI3K) pathway, known to be involved in developing drug resistance in ovarian cancer, was identified as highly dependent from the coding and non-coding RNA interactions. PI3K pathway inhibitors, PI-103 and ZSTK474, were identified as drug candidates and their efficacy against HGSOC was confirmed in vitro. E2F1 and SNAI2 are essential transcription factors (TFs) known for regulating critical cancer pathways such as cell cycle repair or epithelial-mesenchymal transition (EMT). In our study, these TFs were identified as hub regulators within the ceRNA network.

CONCLUSION: Investigation of fine-tune regulation of RNA by non-coding RNAs and TFs uncovered a significant role of ceRNA network in cancer development, highlighting its integration with master regulatory pathways that drive tumor progression and sustainability. The drug repurposing workflow based on ceRNA-limited differentially expressed mRNAs allowed for effective prioritization of compounds with potential to be applied as treatment.

PMID:40472506 | DOI:10.1016/j.compbiomed.2025.110471

Comput Struct Biotechnol J. 2025 May 5;27:1838-1849. doi: 10.1016/j.csbj.2025.05.002. eCollection 2025.

ABSTRACT

Chagas disease is a neglected tropical zoonosis caused by the protozoan Trypanosoma cruzi. The two approved medications for treating this disease show variable efficacy in the chronic phase, highlighting the need for new therapeutic interventions. This study explores a bioinformatics-driven approach to drug discovery using AlphaFold-predicted protein structures. Starting from a virtual screening of approximately 30,000 compounds, 24 were experimentally tested, and two already approved drugs, pimecrolimus and ledipasvir, demonstrated significant antiparasitic activity. These compounds were predicted to target previously uncharacterized T. cruzi proteins, ledipasvir interacting with a calpain-like protein and pimecrolimus likely binding a mitotic cyclin. Molecular dynamics simulations showed that pimecrolimus remains stable in the predicted binding site, while ledipasvir exhibits a higher RMSD. While experimental validation of these targets is needed, these findings underscore the potential of integrating AlphaFold structures into drug discovery strategies to accelerate the identification of new compounds against Chagas disease and other neglected tropical diseases.

SUMMARY: We performed a virtual screening experiment with T. cruzi AlphaFold protein models and a compound collection of more than 30,000 compounds. We tested the top ranked compounds in an in vitro setting, and found two promising candidates for drug repurposing against Chagas disease: pimecrolimus and ledipasvir.

PMID:40470316 | PMC:PMC12136714 | DOI:10.1016/j.csbj.2025.05.002

Int J Pharm X. 2025 May 9;9:100336. doi: 10.1016/j.ijpx.2025.100336. eCollection 2025 Jun.

ABSTRACT

With advancements in drug repurposing, the search for effective and less harmful anticancer agents remains a critical endeavor. Raloxifene, although not a typical anticancer drug, holds promise in this context. However, its poor solubility poses a significant challenge to its therapeutic potential and formulation efficiency. Functional excipients such as cyclodextrins (e.g., β-cyclodextrin, hydroxy propyl β-cyclodextrin, and Captisol) and pH-modifying excipients (e.g., tartaric acid, azelaic acid, and arginine) were investigated to enhance solubility, dissolution, cytotoxicity and cellular uptakes employing Caco-2 cell lines through binary solid dispersions. In silico studies suggested the potential for salt formation with raloxifene-azelaic acid and raloxifene-tartaric acid, as well as inclusion complexes with cyclodextrins. Experimental results showed that pH-modifying excipients, particularly tartaric and azelaic acids, significantly improved solubility (up to an 800-fold increase), outperforming cyclodextrins (8-fold increase) compared to the untreated drug. Cytotoxicity studies on the human breast cancer (Michigan cancer foundation, MCF-7) cells revealed that raloxifene-tartaric acid significantly enhanced cell killing, achieving efficacy comparable to the standard anticancer drug Taxol. Additionally, both fluorescence-labeled raloxifene: hydroxy propyl β-cyclodextrin coprecipitated mixtures (Coppt) and raloxifene: tartaric acid Coppt exhibited concentration- and time-dependent cellular uptake, with mean fluorescence intensity increasing significantly at 24 h, indicating rapid internalization and sustained intracellular retention, especially at higher concentrations. More interestingly, the superior cellular uptake was in favor of the latter, indicating the pH-modifying excipient tartaric acid, and these findings correlated well with solubility and dissolution studies.

PMID:40470029 | PMC:PMC12136891 | DOI:10.1016/j.ijpx.2025.100336

Chem Biol Drug Des. 2025 Jun;105(6):e70138. doi: 10.1111/cbdd.70138.

ABSTRACT

Emergence of life-threatening fungal infections like systemic candidiasis concurrently with bacterial infections and limitations of current antifungal therapies warrant the discovery of novel inhibitors. We identified besifloxacin (BS), an FDA-approved antibacterial, as a potent antifungal inhibitor. A combination of besifloxacin with fluconazole showed a positive synergy (δ = 29.58) resulting in 80% inhibition of microbial growth. BS was able to reduce the MIC of FLC from 2 mg/L to 0.5 mg/L when used in combination. Additionally, in murine systemic Candida infection, BS reduced fungal load by 83% in mice kidneys at a dose of 100 mg/kg/day. The findings demonstrated the antifungal potential of BS, proposing its use in combination therapy with fluconazole to combat resistance through alternative mechanisms.

PMID:40468536 | DOI:10.1111/cbdd.70138

BMC Pharmacol Toxicol. 2025 Jun 4;26(1):116. doi: 10.1186/s40360-025-00958-4.

ABSTRACT

In this study, the structural, electronic, pharmacokinetic, and biological properties of molecule 2-(6-kloro-2-(4-klorofenil)imidazo[1,2-a]piridin-3-il)-N, N-dipropilasetamid (Alpidem), an imidazopyridine derivative anxiolytic known for its high BZ₁ (benzodiazepine-1) receptor affinity and low adverse effect profile, were comprehensively investigated by density functional theory (DFT) and in-silico methods. The alpidem molecule was optimized using the 6-311G(d, p) basis set with the B3LYP and B3PW91 methods; information on the stability and chemical reactivity of the structure was obtained via the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), molecular electrostatic potential (MEP) maps, natural bonding orbital (NBO) analysis, non-linear optical (NLO) properties, and Mulliken charge distributions. Comparative analysis of two different methods has shown that the results are consistent with each other and provide reliable data. In addition, the drug similarity, bioavailability score, bioactivity values, absorption, distribution, metabolism, and excretion (ADME) profiles of the Alpidem molecule were calculated, and it was determined that the Alpidem molecule has pharmacologically favorable properties. Within the scope of molecular docking analyses, its interactions with two different enzymes (PDB ID: 2Z5X and 4BDT) associated with Alzheimer's disease were evaluated. The binding energy values obtained were - 8.00 kcal/mol (2Z5X) and - 9.60 kcal/mol (4BDT), respectively, and the strong binding affinity, especially with the 4BDT protein, suggests that Alpidem may be a potential inhibitor candidate in Alzheimer's disease. This multi-level theoretical study demonstrates that Alpidem is a drug repurposing molecule not only as an anxiolytic but also in neurodegenerative diseases and provides important data that will shed light on experimental studies. The results of this multi-level theoretical study show that Alpidem is a drug repurposing molecule not only as an anxiolytic but also in neurodegenerative diseases and provides important data that will shed light on experimental studies.

PMID:40468388 | DOI:10.1186/s40360-025-00958-4

Int Immunopharmacol. 2025 Jun 3;161:114975. doi: 10.1016/j.intimp.2025.114975. Online ahead of print.

ABSTRACT

Severe bacterial infections (e.g., pneumonia, sepsis) serve as key contributors to acute lung injury (ALI), underscoring the necessity of concurrent anti-infective therapy. The pathogenesis of ALI primarily mediates through two intertwined pathological processes: oxidative stress and cytokine storm. Minocycline, a semisynthetic tetracycline derivative with established clinical applications, exhibits promising potential as a therapeutic candidate for ALI due to its anti-inflammatory and antioxidant pharmacological actions. This investigation employed the lipopolysaccharide (LPS)-induced ALI mice model and RAW264.7 cells inflammation model to evaluate the pulmonary protective effects of minocycline. Our findings demonstrated that minocycline ameliorated symptoms of ALI in LPS-induced mice, including attenuating inflammatory cell infiltration, suppressing cytokine storm, mitigating oxidative stress damage, alleviating pulmonary edema and reducing microvascular permeability. Parallel in vitro experiments revealed that minocycline exhibited inhibitory effects on inflammatory response and oxidative stress in LPS-stimulated RAW264.7 cells. These results suggested that minocycline attenuated cytokine storm and oxidative stress, thereby protecting mice against lung injury. Therefore, minocycline may offer superior benefits compared to other antibiotics for ALI patients infected with susceptible bacteria. While preclinical investigations have unveiled emerging clinical application prospects, rigorous clinical trials remain imperative to substantiate minocycline's therapeutic efficacy in human populations.

PMID:40466614 | DOI:10.1016/j.intimp.2025.114975

Eur J Med Chem. 2025 May 31;295:117816. doi: 10.1016/j.ejmech.2025.117816. Online ahead of print.

ABSTRACT

The unique characteristics of cancer are crucial for comprehending the processes underlying cancer initiation, development, and maintenance. These hallmarks guide the development of novel therapeutic strategies aimed at fundamental traits of cancer, resulting in more targeted therapies with the possibility for sustained effectiveness and minimized adverse effects. Drug repurposing, a novel approach that leverages the known safety and pharmacological properties of existing drugs, has surfaced as a viable alternative to traditional drug development. This method expedites the timescale for introducing novel medicines into clinical practice, often demonstrating reduced failure rates in clinical trials. Recent data substantiates the therapeutic efficacy of many repurposed medications in the management of oral squamous cell carcinomas (OSCC), a highly aggressive and treatment-resistant malignancy. Prominent instances include metformin, phenformin, propranolol, acetylsalicylic acid, celecoxib, itraconazole, statins, dihydroartemisinin, and methotrexate. These pharmaceuticals demonstrated diverse anticancer actions, rendering them valuable tools in the therapy of OSCC. This review provides a comprehensive overview of molecular signaling in the reprocessing of drugs for OSCC.

PMID:40466285 | DOI:10.1016/j.ejmech.2025.117816

PLoS One. 2025 Jun 4;20(6):e0323880. doi: 10.1371/journal.pone.0323880. eCollection 2025.

ABSTRACT

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a major risk factor for liver cirrhosis, yet effective prevention or treatment strategies remain limited. To address this, we utilized a signature-based in silico drug repositioning approach to identify potential therapeutics for MASLD that may reduce the risk of cirrhosis.

METHODS: We analyzed gene expression datasets to identify differentially expressed genes (DEGs) in MASLD and matched them to candidate drugs using L1000CDS2. We further validated potential drugs by cross-referencing with prescription data from the Korea National Health Insurance Service (NHIS). Participants who underwent health screenings between 2013 and 2014 were included. MASLD was diagnosed in individuals with hepatic steatosis (fatty liver index ≥60) and at least one cardiometabolic risk factor.

RESULTS: We identified 11 drug candidates and analyzed 49,555 MASLD patients (mean age: 63.0 years, SD: 8.6). Atenolol (SHR: 0.81; 95% CI: 0.72-0.92; P < 0.001), isosorbide dinitrate (SHR: 0.82; 95% CI: 0.73-0.93; P = 0.001), and valsartan (SHR: 0.52; 95% CI: 0.45-0.60; P < 0.001) were associated with a reduced risk of cirrhosis. Conversely, amlodipine-based combinations (SHR: 1.24; 95% CI: 1.11-1.39; P < 0.001), torasemide (SHR: 1.39; 95% CI: 1.24-1.56; P < 0.001), and valsartan-based combinations (SHR: 1.22; 95% CI: 1.09-1.37; P < 0.001) were linked to an increased risk.

CONCLUSIONS: Our findings suggest that antihypertensive drugs such as atenolol and isosorbide dinitrate may protect MASLD patients from cirrhosis, providing valuable insights for clinical applications and treatment strategies.

LIMITATIONS: This study is limited to drugs registered in the Korean NHIS, potentially excluding other relevant candidates. Additionally, the absence of dietary and genetic data in the NHIS database may introduce residual confounding. Lastly, as the study population consists solely of Korean adults, the findings may not be generalizable to other populations.

PMID:40465795 | DOI:10.1371/journal.pone.0323880

Naunyn Schmiedebergs Arch Pharmacol. 2025 Jun 3. doi: 10.1007/s00210-025-04317-2. Online ahead of print.

ABSTRACT

Psoriasis is a chronic inflammatory skin disorder that is triggered by immune-mediated, genetic, and environmental factors. Moxifloxacin is a fluoroquinolone antibiotic with extended non-expected anti-inflammatory and immune-modulating effects. This study aims to investigate the possible influence of two different concentrations of moxifloxacin emulgel on psoriasis induced via imiquimod in mice. Dividing 48 mice into six groups (8 mice for each group), all groups gated imiquimod to induce psoriasis (except group I) for 7 days. The induction group (Group II) received imiquimod cream for 7 days. The vehicle group obtained emulgel base for 7 days. The rest of the groups got calcipotriol 0.005% ointment, moxifloxacin 3% emulgel, and moxifloxacin 5% emulgel, respectively, once daily for a further 7 days after the induction period. Topical moxifloxacin had important anti-psoriatic activity by diminishing the Psoriasis Area Severity Index (PASI) scores and improving histological alterations during imiquimod application. Moreover, moxifloxacin significantly lowered the levels of inflammatory biomarkers like TGF-β, TNF-α, IL-17, IL-1β, IL-23, and VEFG while increasing levels of anti-inflammatory biomarkers IL-10 and IL-37. Moxifloxacin also suppressed oxidative indicators such as MDA and elevated antioxidant enzyme levels, such as catalase. Moxifloxacin has substantial anti-psoriatic action against imiquimod-induced psoriasis through its anti-proliferative and anti-inflammatory effects. Furthermore, moxifloxacin has a restorative effect on the histopathological alterations of mice's skin induced by imiquimod.

PMID:40459759 | DOI:10.1007/s00210-025-04317-2

Comput Struct Biotechnol J. 2025 May 1;27:1784-1799. doi: 10.1016/j.csbj.2025.04.042. eCollection 2025.

ABSTRACT

Epstein-Barr virus (EBV) is linked to various cancers like gastric carcinoma, nasopharyngeal carcinoma, and Burkitt's lymphoma, leading to around 200,000 deaths annually. Despite efforts, FDA-approved drugs to combat EBV infection are lacking. In this endeavor, we have developed an AI/ML based predictive algorithm "Anti-EBV" to find potential antivirals against EBV. We utilized small molecules from the ChEMBL database, which were experimentally tested for antiviral activity against EBV in lytic phase, in terms of IC50 /EC50 values. 17,968 molecular fingerprints and descriptors were computed for each molecule. Further, the best-performing 150 descriptors were used in the predictive model development. The molecules were then split into training/testing (T315) and independent validation (V35) datasets, followed by 10-fold cross validation to develop robust models. Various machine-learning techniques (MLTs) namely SVM, KNN, ANN, DNN, RF and XGBoost were used for predictive models development. SVM model achieved the best performance with Pearson's correlation coefficient (PCC) of 0.91 on T315 dataset and 0.95 on V35 dataset, respectively. These models were found to be robust by applicability domain, decoy dataset and chemical clustering analyses. The top-performing model was used to screen approved drugs from DrugBank, identifying potential repurposed drugs namely arzoxifene, succimer, abemaciclib and many more. To further validate these findings, top compounds were docked against key lytic proteins BZLF1 and BHRF1, demonstrating strong binding affinities for compounds like fluspirilene and suvorexant. This model is accessible as the "Anti-EBV" web server http://bioinfo.imtech.res.in/manojk/antiebv/ for antiviral prediction, making it the first AI/ML-based study for antiviral identification against EBV in lytic phase.

PMID:40458637 | PMC:PMC12127599 | DOI:10.1016/j.csbj.2025.04.042

BMC Bioinformatics. 2025 Jun 2;26(1):149. doi: 10.1186/s12859-025-06158-5.

ABSTRACT

BACKGROUND: The volume and complexity of biological data have significantly increased in recent years, often represented as network models continue to increase at a rapid pace. However, drug discovery in the context of complex phenotypes are hampered by the difficulties inherent in producing machine learning algorithms that can integrate molecular-genetic, biochemical, physiological, and other diverse datasets. Recent developments have expanded network analysis techniques, such as network embedding, to effectively explore multilayer network structures. Multilayer networks, which incorporate various nodes and connections in formats such as multiplex, heterogeneous, and bipartite networks, provide an effective framework for merging diverse and multi-scale biological data sources. However, current network embedding methods face challenges and limitations in addressing the heterogeneity and diversity of these networks. Therefore, there is an essential need for the development of new network embedding methods to manage the complexity and diversity of multi-omics biological information effectively.

RESULTS: Here, we report a universal multilayer network embedding method MultiXVERSE, which is to the best of our knowledge the first one capable of handling any kind of multilayer network. We applied it to a molecular-drug-disease multiplex-heterogeneous network. Our model made new predictions about a link between GABA and cancer that we verified experimentally in the Xenopus laevis model.

CONCLUSIONS: The development of MultiXVERSE represents a significant advancement in the integration and analysis of multilayer networks for biological research. By providing a universal, scalable framework for multilayer network embedding, MultiXVERSE enables the systematic exploration of molecular and phenotypic interactions across diverse biological contexts. Our experimental validation of the predicted link between GABA and cancer using Xenopus laevis underscores its capability to generate biologically meaningful hypotheses and accelerate breakthroughs in multi-omics research. Future directions include applying MultiXVERSE to additional multi-omics datasets and integrating it with high-throughput experimental pipelines for systematic hypothesis generation and validation, particularly in drug discovery. Beyond its biological applications, MultiXVERSE is a versatile tool that can be utilized for analyzing multilayer networks in a wide range of fields, including social sciences and other complex systems. By offering a universal framework, MultiXVERSE paves the way for novel insights and interdisciplinary collaborations in multilayer network research.

PMID:40457205 | DOI:10.1186/s12859-025-06158-5

Discov Oncol. 2025 Jun 2;16(1):987. doi: 10.1007/s12672-025-02321-9.

ABSTRACT

BACKGROUND: Dysregulation of cholesterol metabolism has been recognized as a critical driver in the pathogenesis of hepatic disorders, particularly hepatocellular carcinoma (HCC) and metabolic dysfunction-associated steatotic liver disease (MASLD). However, the causal relationships between circulating proteins involved in cholesterol homeostasis and the progression of these hepatopathologies remain insufficiently explored, warranting further mechanistic investigation.

METHODS: This study utilized Mendelian randomization (MR) to identify the role of cholesterol metabolism-related proteins in HCC and MASLD. We systematically investigated the causal associations of these proteins with HCC and MASLD and their roles in disease progression using circulating proteomic databases and bioinformatics tools. In addition, network-based drug repositioning techniques and molecular docking experiments were utilized to assess the interactions of the above biomarkers with known drugs to discover drugs with potential therapeutic effects.

RESULTS: MR analysis identified several proteins linked with significant risk for HCC and MASLD. Notably, apolipoprotein E (APOE) expression was significantly increased in tissues from HCC and MASLD patients, closely correlating with elevated disease risk. Meta-analysis demonstrated a significant causal relationship between APOE and increased risk of HCC (OR: 1.710, 95% CI 1.220-2.400; P < 0.01) and MASLD (OR: 1.490, 95% CI 1.280-1.740; P < 0.01). Additionally, network analysis revealed extensive interactions between APOE and other disease-related proteins, suggesting that APOE may contribute to liver disease progression through its influence on complex protein networks.

CONCLUSION: Our findings delineate a novel mechanistic involvement of cholesterol regulatory proteins, with APOE demonstrating pathogenic significance in both HCC and MASLD. This investigation substantially provides new insights into the molecular mechanisms of these liver diseases and highlights potential therapeutic targets.

PMID:40455174 | DOI:10.1007/s12672-025-02321-9

iScience. 2025 Apr 18;28(5):112488. doi: 10.1016/j.isci.2025.112488. eCollection 2025 May 16.

ABSTRACT

Steroid hormones regulate cell physiology and immune function, with dysregulated steroidogenesis promoting cancer progression by supporting tumor growth and suppressing anti-tumor immunity. Targeting CYP11A1, the first and rate-limiting enzyme in steroid biosynthesis, has shown promise in cancer therapy, but safe and effective inhibitors remain an unmet need. Undertaking in silico structure-based drug repurposing approach, we found posaconazole as an inhibitor of CYP11A1. The docking pose analysis showed that posaconazole can form multiple hydrogen bonds and hydrophobic interactions with the key residues at the binding site and the cofactor, stabilizing the protein-ligand complex. We validated its inhibition efficiency in cell-based assays. In a mouse model of lung metastasis, we demonstrated that posaconazole restricts metastasis by stimulating anti-tumor immunity. These findings highlight posaconazole's potential as a research tool to study steroidogenesis and as a candidate for further preclinical and clinical evaluation in pathologies associated with local steroidogenesis, such as steroidogenic tumors.

PMID:40454094 | PMC:PMC12124671 | DOI:10.1016/j.isci.2025.112488

J Alzheimers Dis. 2025 Jun 2:13872877251344572. doi: 10.1177/13872877251344572. Online ahead of print.

ABSTRACT

BackgroundAlzheimer's disease (AD) is a major neurodegenerative disorder with limited treatment options.ObjectiveThis study aimed to identify novel therapeutic targets for AD using proteome-wide Mendelian randomization (MR) and colocalization analyses.MethodsWe conducted a large-scale, proteome-wide MR analysis using data from two extensive genome-wide association studies (GWASs) of plasma proteins: the UK Biobank Pharma Proteomics Project (UKB-PPP) and the deCODE Health Study. We extracted genetic instruments for plasma proteins from these studies and utilized AD summary statistics from European Bioinformatics Institute GWAS Catalog. Colocalization analysis assessed whether identified associations were due to shared causal variants. Phenome-wide association studies and drug repurposing analyses were performed to assess potential side effects and identify existing drugs targeting the identified proteins.ResultsOur MR analysis identified significant associations between genetically predicted levels of 9 proteins in the deCODE dataset and 17 proteins in the UKB-PPP dataset with AD risk after Bonferroni correction. Four proteins (BCAM, CD55, CR1, and GRN) showed consistent associations across both datasets. Colocalization analysis provided strong evidence for shared causal variants between GRN, CR1, and AD. PheWAS revealed minimal potential side effects for CR1 but suggested possible pleiotropic effects for GRN. Drug repurposing analysis identified several FDA-approved drugs targeting CR1 and GRN with potential for AD treatment.ConclusionsThis study identifies GRN and CR1 as promising therapeutic targets for AD. These findings provide new directions for AD drug development, but further research and clinical trials are warranted to validate the therapeutic potential of these targets.

PMID:40452368 | DOI:10.1177/13872877251344572

Int J Pharm. 2025 May 30:125789. doi: 10.1016/j.ijpharm.2025.125789. Online ahead of print.

ABSTRACT

Integrating artificial intelligence (AI) into drug discovery has revolutionized pharmaceutical innovation, addressing the challenges of traditional methods that are costly, time-consuming, and suffer from high failure rates. By utilizing machine learning (ML), deep learning (DL), and natural language processing (NLP), AI enhances various stages of drug development, including target identification, lead optimization, de novo drug design, and drug repurposing. AI tools, such as AlphaFold for protein structure prediction and AtomNet for structure-based drug design, have significantly accelerated the discovery process, improved efficiency and reduced costs. Success stories like Insilico Medicine's AI-designed molecule for idiopathic pulmonary fibrosis and BenevolentAI's identification of baricitinib for COVID-19 highlight AI's transformative potential. Additionally, AI enables the exploration of vast chemical spaces, optimization of clinical trials, and the identification of novel therapeutic targets, paving the way for precision medicine. However, challenges such as limited data accessibility, integration of diverse datasets, interpretability of AI models, and ethical concerns remain critical hurdles. Overcoming these limitations through enhanced algorithms, standardized databases, and interdisciplinary collaboration is essential. Overall, AI continues to reshape drug discovery, reducing timelines, increasing success rates, and driving the development of innovative and accessible therapies for unmet medical needs.

PMID:40451590 | DOI:10.1016/j.ijpharm.2025.125789

Antiviral Res. 2025 May 30:106205. doi: 10.1016/j.antiviral.2025.106205. Online ahead of print.

ABSTRACT

The incidence of orthoflavivirus infections is on the rise, yet effective antivirals are unavailable for all members of this family. Additionally, new orthoflaviviruses are emerging, highlighting the need for antiviral strategies with a pan-flavivirus activity. In response, the Global Health Priority Box was screened, leading to the identification of a compound with pan-flavivirus activity. This hit compound demonstrated inhibition of viral replication, consistent efficacy across various cell lines, and maintained activity even at high multiplicity of infection. Importantly it has a high barrier to resistance and possibly acts through a novel mechanism of action. Due to these attributes and its favorable in vitro ADMET profile, compound MMV1791425 emerges as a promising candidate for the future development of a pan-flavivirus antiviral.

PMID:40451519 | DOI:10.1016/j.antiviral.2025.106205