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

ACS Chem Neurosci. 2025 May 31. doi: 10.1021/acschemneuro.5c00082. Online ahead of print.

ABSTRACT

Caspase-3-dependent apoptosis is believed to contribute to the brain injury of ischemic stroke, and a caspase-3 inhibitor has been repeatedly reported to reduce the brain injury of ischemic stroke. However, currently recognized caspase-3 inhibitors are still only used as a research tool, and none of them is available in the clinic to treat brain injury of ischemic stroke. Based on the concept of drug repositioning and bioinformatics techniques, we have identified Cerdulatinib, a multitargeted tyrosine kinase inhibitor to treat tumors and immune-related diseases in the clinic, as a potential caspase-3 inhibitor. This study aims to explore the effect of Cerdulatinib on brain injury from ischemic stroke and the underlying mechanisms. In mice with ischemic stroke, Cerdulatinib significantly decreased infarct volume and improved sensorimotor function, memory ability, and cognitive function. In nerve cells exposed to hypoxia, Cerdulatinib increased cell viability and decreased LDH release. Mechanistically, Cerdulatinib inhibited the protein level of cleaved caspase-3 and the activity of caspase-3, resulting in a decrease in brain cell apoptosis. Based on these results, we conclude that Cerdulatinib can protect the brain against ischemic injury by reducing apoptosis, which is related to the suppression of caspase-3 cleavage and caspase-3 activity. This study may extend the clinical indications of Cerdulatinib in the treatment of patients with an ischemic stroke.

PMID:40448621 | DOI:10.1021/acschemneuro.5c00082

Spectrochim Acta A Mol Biomol Spectrosc. 2025 May 27;342:126487. doi: 10.1016/j.saa.2025.126487. Online ahead of print.

ABSTRACT

Drug repositioning and reuse is a cost-effective strategy for the development of new drugs, and drug co-crystal is a fast and effective technical means. Acetylsalicylic acid is a BCS II drug, which has the limitations of high permeability and low solubility, and the safety and efficacy of the drug have been greatly affected. Co-crystallization with other forming agents is considered to be a promising technical means, which can not only increase the solubility, but also improve the dissolution rate and stability. In this paper, the cocrystal of acetylsalicylic acid and arginine was prepared by grinding method. The physical and chemical characterization of the raw material, the mixture and the obtained cocrystal was carried out by XRD, terahertz spectroscopy (THz-TDS) and Raman spectroscopy (Raman). The obvious difference was observed on the characteristic peaks of the cocrystal, which proved the formation of the cocrystal. Understanding the basic properties of lattice vibration during the eutectic process is challenging, yet it can be accomplished through theoretical calculations. By employing density-functional theory (DFT) calculations, the molecular configurations and vibration spectra of the two drug cocrystals can be obtained, enabling a deeper understanding of the vibration modes of drug molecules in the low-frequency range. Moreover, this study demonstrates the sensitivity of terahertz time-domain spectroscopy (TDS) technology in detecting intermolecular hydrogen-bond interactions in drug cocrystals. When comparing cocrystal molecules with active pharmaceutical ingredient (API) molecules, it is found that cocrystals possess better binding energy, driven by intermolecular hydrogen bonds and dispersion forces.

PMID:40446719 | DOI:10.1016/j.saa.2025.126487

PLoS One. 2025 May 30;20(5):e0324146. doi: 10.1371/journal.pone.0324146. eCollection 2025.

ABSTRACT

Drug-target interactions (DTIs) play a critical role in drug discovery and repurposing. Deep learning-based methods for predicting drug-target interactions are more efficient than wet-lab experiments. The extraction of original and substructural features from drugs and proteins plays a key role in enhancing the accuracy of DTI predictions, while the integration of multi-feature information and effective representation of interaction data also impact the precision of DTI forecasts. Consequently, we propose a drug-target interaction prediction model, SSCPA-DTI, based on substructural subsequences and a cross co-attention mechanism. We use drug SMILES sequences and protein sequences as inputs for the model, employing a Multi-feature information mining module (MIMM) to extract original and substructural features of DTIs. Substructural information provides detailed insights into molecular local structures, while original features enhance the model's understanding of the overall molecular architecture. Subsequently, a Cross-public attention module (CPA) is utilized to first integrate the extracted original and substructural features, then to extract interaction information between the protein and drug, addressing issues such as insufficient accuracy and weak interpretability arising from mere concatenation without interactive integration of feature information. We conducted experiments on three public datasets and demonstrated superior performance compared to baseline models.

PMID:40445972 | DOI:10.1371/journal.pone.0324146

Front Pharmacol. 2025 May 13;16:1599297. doi: 10.3389/fphar.2025.1599297. eCollection 2025.

ABSTRACT

Major depressive disorder (MDD) is a serious neuropsychiatric condition that affects millions of people worldwide, causing significant psychological distress and lifestyle deterioration. The serotonin transporter, which plays a critical role in regulating the uptake of serotonin (5-HT) back into presynaptic cells, is a primary target for antidepressants. Though selective serotonin reuptake inhibitors (SSRIs) are still the pharmacologic treatment of choice, alternative methods remain in demand to enhance the efficacy of treatment and offer more therapeutic options. Drug repurposing provides an efficient solution to speed up antidepressant research because it identifies existing FDA-approved medications that might inhibit the serotonin transporter. A virtual screening method was integrated into the study that examined 3620 FDA-approved drugs to discover new repurposed serotonin transporter-inhibiting molecules. The binding affinity, structural stability, and inhibitory potential were assessed using molecular docking and molecular dynamics (MD) simulations. Among the screened compounds, Flunarizine, a well-known calcium channel blocker, emerged as a promising serotonin transporter inhibitor due to its strong and stable binding configuration within the transporter's active site. Detailed molecular docking studies revealed that Flunarizine formed key interactions with critical residues of the serotonin transporter, suggesting its potential as an effective modulator. Subsequent 500-nanosecond MD simulations further confirmed the stability of the serotonin transporter-Flunarizine complex, demonstrating minimal structural deviations and maintaining crucial dynamic properties throughout the simulation trajectory. These findings highlight Flunarizine's potential for repurposing as a novel therapeutic agent targeting serotonin transport modulation. The study provides a solid foundation for further preclinical and clinical investigations into the antidepressant repurposing of Flunarizine.

PMID:40444039 | PMC:PMC12120357 | DOI:10.3389/fphar.2025.1599297

BMC Med. 2025 May 30;23(1):321. doi: 10.1186/s12916-025-04127-6.

ABSTRACT

BACKGROUND: Insulin is known to be associated with a higher risk of coronary artery disease (CAD), but molecular mechanisms remain unclear. This study aimed to explore protein-mediated pathways linking fasting insulin to CAD using Mendelian randomization (MR).

METHODS: This MR study examined the association between fasting insulin and CAD using genome-wide association study (GWAS) data from MAGIC and CARDIoGRAMplusC4D. To investigate underlying mechanisms, a two-step proteome-wide MR analysis was conducted. First, associations of fasting insulin with 2940 circulating proteins were assessed using GWAS of proteomics from UKB-PPP. Proteins affected by insulin were then analyzed for their association with CAD risk. Proteins selected in both steps were considered as potential mediators. Sensitivity analyses to test whether associations are robust to pleiotropy and replication using other GWAS data, including GWAS of proteomics from deCODE and GWAS of CAD from FinnGen Biobank, were performed.

RESULTS: Genetically predicted insulin was associated with a higher risk of CAD (odds ratio 1.79, 95% confidence interval 1.34 to 2.40). At a false discovery rate of 0.05, insulin affected 355 proteins, ten of which were both increased by insulin and linked to a higher risk of CAD. After sensitivity and replication analyses, PLA2G7, GZMA, LDLR, AGRP, and HHEX were identified as reliable mediators. Mediation analyses using non-pleiotropic instruments showed that PLA2G7, GZMA, LDLR, and AGRP explained 19.50%, 6.91%, 19.31%, and 29.66% of insulin's total effect on CAD, respectively.

CONCLUSIONS: This study identified five protein mediators linking insulin to CAD. These proteins could be considered as potential targets to mitigate insulin-related cardiovascular risk, providing novel insights for drug repurposing.

PMID:40442727 | DOI:10.1186/s12916-025-04127-6

J Biomed Res. 2025 May 30:1-12. doi: 10.7555/JBR.39.20250166. Online ahead of print.

ABSTRACT

The comorbidity of skin and gastrointestinal tract (GIT) diseases, primarily driven by the gut-skin axis (GSA), is well-known. However, the genetic contribution to the GSA remains unclear. Here, using genome-wide association study (GWAS) summary statistics from European populations, we performed genome-wide pleiotropic analysis to investigate the shared genetic basis and causal associations between skin and GIT diseases. We observed extensive genetic correlations and overlaps between skin and GIT diseases. A total of 298 pleiotropic loci were identified, 75 of which were colocalized, and 61 exhibited pleiotropic effects across multiple trait pairs, including 2p16.1 ( PUS10), 6p21.32 ( HLA-DRB1), 10q21.2 ( ZNF365), and 19q13.11 ( SLC7A10). Additionally, five novel loci were identified based on the pleiotropic analysis, with RORA at 15q22.2 validated by the latest inflammatory bowel disease GWAS. Gene-based analysis found 394 unique pleiotropic genes, which were enriched in GSA-associated tissues and immune system, whereas protein-protein interaction analysis further revealed the GPCR-cAMP, chromatin remodeling, JAK-STAT, and HLA-mediated immunity pathways coregulate GSA comorbidity. Notably, the JAK-STAT pathway showed strong potential in drug repurposing, with Adalimumab targeting TNF and Ustekinumab targeting IL-12B already used to treat both skin and GIT diseases. Finally, Mendelian randomization analysis suggested five significant causal associations, and subsequent mediation analysis introduced three potential microbiota-GIT-skin pathways. Taken together, our study suggested that the shared genetic factors between skin and GIT diseases are widely distributed across the genome. These findings will improve our understanding of the genetic basis of GSA and offer significant implications for simultaneously treating skin and GIT diseases.

PMID:40441863 | DOI:10.7555/JBR.39.20250166

Exp Parasitol. 2025 May 27:108966. doi: 10.1016/j.exppara.2025.108966. Online ahead of print.

ABSTRACT

Leishmaniasis is a parasitic disease caused by protozoa of the genus Leishmania, the conventional treatments are expensives, high adverse reactions and long-term parenteral administration This study aimed to evaluate the therapeutic potential of the antiviral Oseltamivir (Osv) in microemulsion in the topical treatment of cutaneous leishmaniasis in BALB/c mice infected with Leishmania major. After infection, the mice were divided into five groups (Control, Amphotericin B 3%, Osv 0.5%, Osv 1% and Osv 1%+Amphotericin B 1.5%) and treated for 21 days. Clinical parameters, such as body weight and lesion size, in addition to parasite load, hematological, biochemical and histopathological analyses were evaluated. A significant reduction in the parasite load was observed in the groups treated with Oseltamivir and Amphotericin B (70% to 76.5%), when compared to the control group (95%). Clinical evaluation showed fewer lesions in the treatment groups compared to the control group. Although Amphotericin B alone caused liver and kidney toxicity, treatment with Oseltamivir, alone or in combination with Amphotericin B, did not show any toxicity. In histopathological examination, the groups treated with Oseltamivir showed lower degrees of histopathological alterations. Thus, Oseltamivir, as monotherapy or in combination with Amphotericin B, proved to be effective and safe, representing a promising alternative in the treatment of cutaneous leishmaniasis.

PMID:40441373 | DOI:10.1016/j.exppara.2025.108966

Drug Discov Today. 2025 May 27:104390. doi: 10.1016/j.drudis.2025.104390. Online ahead of print.

ABSTRACT

Drug repurposing leverages existing drugs for new therapeutic uses, offering significant opportunities but facing challenges such as financial and regulatory barriers and the need for robust evidence for an efficient clinical development plan. This paper examines the critical steps in drug repurposing and their role in improving study success rates. It also highlights the support infrastructure provided by the University College London (UCL) Repurposing Therapeutic Innovation Network (TIN), as a partnership model to address these challenges through diverse expertise, enterprise insight, and tailored guidance. By fostering collaborations and offering structured support, the Repurposing TIN aims to accelerate repurposing efforts and deliver patient benefits. We invite potential collaborators to join us in advancing drug repurposing through innovative and strategic approaches.

PMID:40441598 | DOI:10.1016/j.drudis.2025.104390

Regul Toxicol Pharmacol. 2025 May 27:105864. doi: 10.1016/j.yrtph.2025.105864. Online ahead of print.

ABSTRACT

Modified new drugs are pivotal in advancing innovative therapies through repurposing existing therapeutic agents. The regulatory framework, including the pertinent regulations and policies, plays a crucial role in shaping the development and evolution of these drugs. This retrospective study systematically compared the regulatory approvals of modified new drugs via the 505(b)(2) new drug application (NDA) pathway in the United States (US) and Class 2 NDA pathway in China from 2017 to 2023, which focused on distinctions in registration classifications, availability, therapeutic indications, dosage forms, modifications, clinical advantages and clinical study designs. The findings indicate that the US has more detailed and comprehensive classification systems, as well as a higher number of approvals (417 vs. 99). Moreover, the modified new drugs approved in China still exhibit significant gaps in indication distribution, dosage forms, and modifications compared to those in the US. Notably, a greater proportion of confirmatory clinical studies were conducted for Class 2 NDAs (81.4%) than 505(b)(2) NDAs (41.0%), with a significant difference in the use of active controls (48.6% in China vs. 26.4% in the US, P=0.002). Additionally, the combination of emerging technologies in modified new drugs presents both technical and regulatory challenges for authorities. It raises worthwhile questions about how regulators will evaluate medical products developed with entirely new technologies. Therefore, it is recommended that Chinese regulators refine registration classifications, reassess the positioning of modified new drugs, and expand the definition of clinical advantage within the policy and regulatory framework. These measures are essential for addressing unmet medical needs and fostering a conducive ecosystem for the advancement of modified new drugs.

PMID:40441284 | DOI:10.1016/j.yrtph.2025.105864

Front Med (Lausanne). 2025 May 14;12:1576029. doi: 10.3389/fmed.2025.1576029. eCollection 2025.

ABSTRACT

BACKGROUND: Metabolic-associated fatty liver disease (MAFLD) is a prevalent chronic liver condition with significant health implications. Fermented Cordyceps Preparation (FCP) has shown promise in managing metabolic disorders, prompting interest in its potential for MAFLD prevention. There is, however, a lack of large-scale clinical evidence regarding its preventive efficacy and long-term safety.

AIM: We aimed to assess the preventive efficacy and safety of FCP, as regards combatting MAFLD.

METHODS: Propensity score matching was used to select 343 FCP users and 1372 non-users with metabolic syndrome, (MS) as recorded in EMR. These two groups were followed for 750 days, to track the incidence of MAFLD. The Kaplan Meier method was used to calculate the cumulative risk of MAFLD events in each subgroup. A Multiple linear regression model was used to compare the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST), as between the two groups.

RESULTS: Compared with non-users, FCP users were associated with a 26% decreased risk of MAFLD (hazard ratio 0.74, 95% confidence interval 0.56-0.97). During the follow-up, the changes in both ALT and AST, were insignificantly different between the two groups.

CONCLUSION: These findings highlight the potential of FCP in MAFLD prevention and offer insight into its safety profile, suggesting avenues for further clinical validation and drug repurposing efforts.

PMID:40438375 | PMC:PMC12116537 | DOI:10.3389/fmed.2025.1576029

Sci Rep. 2025 May 28;15(1):18722. doi: 10.1038/s41598-025-02773-7.

ABSTRACT

The COVID-19 pandemic has initiated a global health emergency, with an exigent need for an effective cure. Progressively, drug repurposing is emerging as a promising solution for saving time, cost, and labor. However, the number of drug candidates that have been identified for the treatment of COVID-19 is still insufficient, so more effective and thorough drug exploration strategies are required. In this study, we joined the molecular docking with machine learning approaches to find some prospective therapeutic candidates for COVID-19 treatment. We screened the 5903 approved drugs for their inhibition by targeting the replicating enzyme 3CLpro of SARS-CoV-2. Molecular docking is used to calculate the binding affinities of these drugs towards 3CLpro. We employed several machine learning approaches for QSAR modeling to explore some potential drugs with high binding affinities. Our outcomes demonstrated that the Decision Tree Regression (DTR) model, with the best scores of R² and RMSE, is the most suitable model to explore the potential drugs. We shortlisted six favorable drugs with their respective Zinc IDs (3873365, 85432544, 203757351, 85536956, 8214470, and 261494640) within the range of -15 kcal/mol to -13 kcal/mol. We further examined the physiochemical and pharmacokinetic properties of these most potent drugs. Our study provides an efficient framework to explore the potential drugs against COVID-19 and establishes the impending combination of molecular docking with machine learning approaches to accelerate the identification of potential therapeutic candidates. Our verdicts contribute to the larger goal of finding effective cures for COVID-19, which is an acute global health challenge. The outcomes of our study provide valuable insights into potential therapeutic candidates for COVID-19 treatment.

PMID:40436944 | DOI:10.1038/s41598-025-02773-7

mSystems. 2025 May 28:e0019625. doi: 10.1128/msystems.00196-25. Online ahead of print.

ABSTRACT

Human microbiome plays a crucial role in host health and disease by mediating the impact of environmental factors on clinical outcomes. Mediation analysis is a valuable tool for dissecting these complex relationships. However, existing approaches are primarily designed for cross-sectional studies. Modern clinical research increasingly utilizes long follow-up periods, leading to complex data structures, particularly in metagenomic studies. To address this limitation, we introduce a novel mediation framework based on structural equation modeling that leverages linear mixed-effects models using penalized quasi-likelihood estimation with a debiased lasso. We applied this framework to a 16S rRNA sputum microbiome data set collected from patients with cystic fibrosis over 10 years to investigate the mediating role of the microbiome in the relationship between clinical states, disease aggressiveness phenotypes, and lung function. We identified richness as a key mediator of lung function. Specifically, Streptococcus was found to be significantly associated with mediating the decline in lung function on treatment compared to exacerbation, while Gemella was associated with the decline in lung function on recovery. This approach offers a powerful new tool for understanding the complex interplay between microbiome and clinical outcomes in longitudinal studies, facilitating targeted microbiome-based interventions.

IMPORTANCE: Understanding the mechanisms by which the microbiome influences clinical outcomes is paramount for realizing the full potential of microbiome-based medicine, including diagnostics and therapeutics. Identifying specific microbial mediators not only reveals potential targets for novel therapies and drug repurposing but also offers a more precise approach to patient stratification and personalized interventions. While traditional mediation analyses are ill-equipped to address the complexities of longitudinal metagenomic data, our framework directly addresses this gap, enabling robust investigation of these increasingly common study designs. By applying this framework to a decade-long cystic fibrosis study, we have begun to unravel the intricate relationships between the sputum microbiome and lung function decline across different clinical states, yielding insights that were previously unknown.

PMID:40434093 | DOI:10.1128/msystems.00196-25

Br J Clin Pharmacol. 2025 May 28. doi: 10.1002/bcp.70113. Online ahead of print.

ABSTRACT

This review highlights the role of reverse transcriptase (RT) inhibition in cellular regulation associated with non-terminal repeat retrotransposons and endogenous retroviruses. Based on their pleiotropic characteristics, RT inhibitors (RTIs) are discussed as potential anticancer agents. Both the nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs) display cytotoxicity in cancer cells which are likely mediated by endogenous RT inhibition and not necessarily by differing molecular structures. Three features of RTIs are evident in inducing cytotoxicity in cancer cells. Firstly, NRTIs and NNRTIs induce cell cycle arrest. Secondly, they suppress transposable elements, inhibit long interspersed nuclear elements (LINE)-1, with RTI key in cytotoxicity in cancer cells. Thirdly, the cyclic GMP-AMP-synthase-stimulator of interferon genes (cGAS-STING) pathway can be activated by LINE-1-derived cytoplasmic DNA with promotion of p21-dependent cell cycle arrest and cell-mediated immune response. This suggests that RTIs induce DNA strand breaks with incomplete retrotransposition, initiate cell cycle arrest and an immune response. Additionally, poly (ADP-ribose) polymerase 1 and 2 (PARP1, PARP2) and its relationship with DNA methylation is highlighted in the context of LINE-1 retrotransposition. There is a need to examine the relationship between PARP1, PARP2 and mutated BRCA proteins in normal and abnormal LINE-1 retrotransposition. This review explores how efavirenz and related RT inhibitors suppress endogenous reverse transcriptase, providing a basis for preclinical evaluation of RT inhibitors as potential repurposed drugs for cancer treatment.

PMID:40432477 | DOI:10.1002/bcp.70113