Front Med (Lausanne). 2025 Jan 28;12:1492709. doi: 10.3389/fmed.2025.1492709. eCollection 2025.

ABSTRACT

OBJECTIVE: To systematically map the knowledge landscape and development trends in artificial intelligence (AI) applications for antimicrobial resistance (AMR) research through bibliometric analysis, providing evidence-based insights to guide future research directions and inform strategic decision-making in this dynamic field.

METHODS: A comprehensive bibliometric analysis was performed using the Web of Science Core Collection database for publications from 2014 to 2024. The analysis integrated multiple bibliometric approaches: VOSviewer for visualization of collaboration networks and research clusters, CiteSpace for temporal evolution analysis, and quantitative analysis of publication metrics. Key bibliometric indicators including co-authorship patterns, keyword co-occurrence, and citation impact were analyzed to delineate research evolution and collaboration patterns in this domain.

RESULTS: A collection of 2,408 publications was analyzed, demonstrating significant annual growth with publications increasing from 4 in 2014 to 549 in 2023 (22.7% of total output). The United States (707), China (581), and India (233) were the leading contributors in international collaborations. The Chinese Academy of Sciences (53), Harvard Medical School (43), and University of California San Diego (26) were identified as top contributing institutions. Citation analysis highlighted two major breakthroughs: AlphaFold's protein structure prediction (6,811 citations) and deep learning approaches to antibiotic discovery (4,784 citations). Keyword analysis identified six enduring research clusters from 2014 to 2024: sepsis, artificial neural networks, antimicrobial resistance, antimicrobial peptides, drug repurposing, and molecular docking, demonstrating the sustained integration of AI in antimicrobial therapy development. Recent trends show increasing application of AI technologies in traditional approaches, particularly in MALDI-TOF MS for pathogen identification and graph neural networks for large-scale molecular screening.

CONCLUSION: This bibliometric analysis shows the importance of artificial intelligence in enhancing the progress in the discovery of antimicrobial drugs especially toward the fight against AMR. From enhancing the fast, efficient and predictive performance of drug discovery methods, current AI capabilities have revealed observable potential to be proactive in combating the ever-growing challenge of AMR worldwide. This study serves not only an identification of current trends, but also, and especially, offers a strategic approach to further investigations.

PMID:39935800 | PMC:PMC11810743 | DOI:10.3389/fmed.2025.1492709

Clin Pharmacol Ther. 2025 Feb 11. doi: 10.1002/cpt.3583. Online ahead of print.

ABSTRACT

We evaluated whether drugs approved for other indications that also target metabolic drivers of Alzheimer's disease and related dementia (ADRD) pathogenesis are associated with delayed onset of ADRD. Using routinely collected healthcare data from two population-based data sources from the US (Medicare) and UK (CPRD), we conducted active comparator, new-user cohort studies. Four alternate analytic and design specifications were implemented: (1) an as-treated follow-up approach, (2) an as-started follow-up approach incorporating a 6-month induction period, (3) incorporating a 6-month symptom to diagnosis period to account for misclassification of ADRD onset, and (4) identifying ADRD through symptomatic prescriptions and diagnosis codes. Of the 10 drug pairs evaluated, hydrochlorothiazide vs. dihydropyridine CCBs showed meaningful reductions in 3 out of 4 analyses that addressed specific biases including informative censoring, reverse causality, and outcome misclassification (pooled hazard ratios [95% confidence intervals] across Medicare and CPRD: 0.81 [0.75-0.88] in Analysis 1, 0.98 [0.92-1.06] in Analysis 2, 0.83 [0.75-0.91] in Analysis 3, 0.75 [0.65-0.85] in Analysis 4). Amiloride vs. triamterene, although less precise, also suggested a potential reduction in risk in 3 out of 4 analyses (0.86 [0.66-1.11] in Analysis 1, 0.98 [0.79-1.23] in Analysis 2, 0.74 [0.54-1.00] in Analysis 3, 0.61 [0.36-1.05] in Analysis 4). Other analyses suggested likely no major differences in risk (probenecid, salbutamol, montelukast, propranolol/carvedilol, and anastrozole) or had limited precision precluding a definitive conclusion (semaglutide, ciloztozol, levetiracetam). Future replication studies should be considered to validate our findings.

PMID:39935003 | DOI:10.1002/cpt.3583

Cancer Metastasis Rev. 2025 Feb 11;44(1):31. doi: 10.1007/s10555-025-10249-3.

ABSTRACT

Cellular plasticity and the ability to avoid terminal differentiation are hallmarks of cancer. Here, we review the evidence that tumor cells themselves can take on properties of neurons of the central nervous system, which can regulate tumor growth and metastasis. We discuss recent evidence that axon guidance molecules and regulators of electrical activity and synaptic transmission, such as ion channels and neurotransmitters, can drive the oncogenic and invasive properties of tumor cells from a range of cancers. We also review how FDA-approved treatments for neurological disorders are being tested in pre-clinical models and clinical trials for repurposing as anti-cancer agents, offering the potential for new therapies for cancer patients that can be accessed more quickly.

PMID:39934425 | DOI:10.1007/s10555-025-10249-3

Cancer Immunol Immunother. 2025 Feb 11;74(3):104. doi: 10.1007/s00262-025-03955-y.

ABSTRACT

The response rates of PD-1/PD-L1 blockade in cancer immunotherapy are relatively low, necessitating the development of novel immune checkpoint inhibitors. Compared with other immune checkpoints, VISTA interacts with its ligand PSGL-1 only under acidic conditions in the tumor microenvironment to suppress the function of CD8+ T cells. On the other hand, drug repurposing offers advantages such as time efficiency and high safety. However, the development of VISTA/PSGL-1 inhibitor based on drug repurposing is still infancy. Here, by screening a library of marketed drugs, we identified Chidamide had a strong binding affinity toward VISTA (KD = 5 nM) and blocked VISTA/PSGL-1 under acidic conditions, thereby significantly enhancing the function of CD8+ T cells and inhibiting the tumor growth in immunocompetent murine CT26 tumor model. This study represents the first discovery of Chidamide as VISTA/PSGL-1 blocker for cancer immunotherapy.

PMID:39932560 | DOI:10.1007/s00262-025-03955-y

Curr Drug Discov Technol. 2025 Feb 10. doi: 10.2174/0115701638364199250123062248. Online ahead of print.

ABSTRACT

Drug design and development are crucial areas of study for chemists and pharmaceutical companies. Nevertheless, the significant expenses, lengthy process, inaccurate delivery, and limited effectiveness present obstacles and barriers that affect the development and exploration of new drugs. Moreover, big and complex datasets from clinical trials, genomics, proteomics, and microarray data also disrupt the drug discovery approach. The integration of Artificial Intelligence (AI) into drug design is both timely and crucial due to several pressing challenges in the pharmaceutical industry, including the escalating costs of drug development, high failure rates in clinical trials, and the in-creasing complexity of disease biology. AI offers innovative solutions to address these challenges, promising to improve the efficiency, precision, and success rates of drug discovery and development. Artificial intelligence (AI) and machine learning (ML) technology are crucial tools in the field of drug discovery and development. More precisely, the field has been revolutionized by the utilization of deep learning (DL) techniques and artificial neural networks (ANNs). DL algorithms & ML have been employed in drug design using various approaches such as physiochemical activity, polyphar-macology, drug repositioning, quantitative structure-activity relationship, pharmacophore modeling, drug monitoring and release, toxicity prediction, ligand-based virtual screening, structure-based vir-tual screening, and peptide synthesis. The use of DL and AI in this field is supported by historical evidence. Furthermore, management strategies, curation, and unconventional data mining aided as-sistance in modern modeling algorithms. In summary, the progress made in artificial intelligence and deep learning algorithms offers a promising opportunity for the development and discovery of effec-tive drugs, ultimately leading to significant benefits for humanity. In this review, several tools and algorithmic programs have been discussed which are being used in drug design along with the de-scriptions of the patents that have been granted for the use of AI in this field, which constitutes the main focus of this review and differentiates it fromalready published materials.

PMID:39931986 | DOI:10.2174/0115701638364199250123062248

Curr Opin Pediatr. 2025 Feb 11. doi: 10.1097/MOP.0000000000001448. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: Neonatal and infantile epilepsies represent a diverse group of disorders with significant neurodevelopmental impact, necessitating early diagnosis, and tailored treatment. Recent advancements in genetic research, phenotyping, and therapeutic development have reshaped the understanding and management of these conditions, making this review both timely and relevant.

RECENT FINDINGS: Next-generation sequencing has emerged as a cornerstone for diagnosing neonatal and infantile epilepsies, offering high diagnostic yields and enabling identification of etiology-specific phenotypes. Precision therapies, including sodium channel blockers, ganaxolone, and mammalian target of rapamycin (mTOR) inhibitors, target specific molecular mechanisms. Early initiation of treatment in conditions with a high risk of progressing to epilepsy, like vigabatrin in tuberous sclerosis complex, lower the incidence of infantile spasms and improve developmental outcomes. Drug repurposing has also provided effective options, such as fenfluramine in Dravet syndrome, with promising outcomes. Gene-based therapies, including antisense oligonucleotides and gene replacement, represent the new frontier for addressing the root causes of these disorders.

SUMMARY: The integration of genetic and molecular advancements is transforming the management of neonatal and infantile epilepsies, fostering precision-driven care. Continued research and innovation are essential to refine these strategies, optimize patient outcomes, and establish new standards of care.

PMID:39931929 | DOI:10.1097/MOP.0000000000001448

J Med Virol. 2025 Feb;97(2):e70229. doi: 10.1002/jmv.70229.

ABSTRACT

The main protease (Mpro) of SARS-CoV-2 is an attractive drug target for antivirals, as this enzyme plays a key role in virus replication. Drug repurposing is a promising option for the treatment of coronavirus disease 2019 (COVID-19). Recently, a number of FDA-approved drugs have been identified as Mpro inhibitors, but stringent hit validation is lacking. In this study, we rigorously reevaluated the in vitro inhibition of the Mpro enzyme by repurposed drugs via combined experiments, including the fluorescence resonance energy transfer (FRET) assay, fluorescence polarization (FP) assay, and protease biosensor cleavage assay. Our results from a set of in vitro assays revealed that boceprevir is a potential Mpro inhibitor, but other repurposed drugs, including atazanavir, dipyridamole, entrectinib, ethacridine, glecaprevir, hydroxychloroquine, ivermectin, meisoindigo, pelitinib, raloxifene, roxatidine acetate, saquinavir, teicoplanin, thonzonium bromide, and valacyclovir, are not. Our research highlights that the use of candidate Mpro inhibitors from primary screening warrants further comprehensive studies before the reporting of new findings.

PMID:39930936 | DOI:10.1002/jmv.70229

Sci Rep. 2025 Feb 10;15(1):4979. doi: 10.1038/s41598-025-87676-3.

ABSTRACT

Hepatocellular Carcinoma (HCC) is a malignancy with high mortality rates and limited treatment options. This study aimed to unearth the repurposable potential of FDA-approved drugs against specific genetic targets governing the HCC pathological pathways. The transcriptomics microarray datasets were explored to retrieve the HCC specific differentially expressed genes, and the significant genes were fed in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database to capture the protein-protein interactions, which were visualized in Cytoscape. This revealed CCNA2, a cell cycle regulator, as a potential target, which mediates its action by interacting with CDK1 and CDK2. Further, with the intention of identifying inhibitors for CDK1 and CDK2, a drug library was created, and the drugs were virtually screened against their respective targets via molecular docking and dynamics studies. This captured the binding affinity of Steviolbioside towards CDK1 and Etravirine and Fludarabine towards CDK2. In vitro, validation confirmed the cytotoxic potential of Etravirine and Fludarabine in Huh-7 cell lines. Further, enzymatic assays, gene expression analysis, and cell cycle analysis signified the anti-proliferative potential of Etravirine in Huh-7 cells via inhibition of CDK2. In this drug repurposing venture, Etravirine, a non-nucleoside reverse transcriptase inhibitor indicated for the treatment of HIV, emerged as a promising candidate for HCC treatment. The findings warrant further preclinical and clinical investigations to ascertain the repurposable potential of Etravirine against HCC, particularly in patients with viral infections.

PMID:39929880 | DOI:10.1038/s41598-025-87676-3

Cell Death Discov. 2025 Feb 10;11(1):56. doi: 10.1038/s41420-025-02328-9.

ABSTRACT

The phenomenon of cell death has garnered significant scientific attention in recent years, emerging as a pivotal area of research. Recently, novel modalities of cellular death and the intricate interplay between them have been unveiled, offering insights into the pathogenesis of various diseases. This comprehensive review delves into the intricate molecular mechanisms, inducers, and inhibitors of the underlying prevalent forms of cell death, including apoptosis, autophagy, ferroptosis, necroptosis, mitophagy, and pyroptosis. Moreover, it elucidates the crosstalk and interconnection among the key pathways or molecular entities associated with these pathways, thereby paving the way for the identification of novel therapeutic targets, disease management strategies, and drug repurposing.

PMID:39929794 | DOI:10.1038/s41420-025-02328-9

Neurogenetics. 2025 Feb 10;26(1):27. doi: 10.1007/s10048-025-00806-x.

ABSTRACT

Alzheimer's disease (AD) is a slow brain degeneration disorder in which the accumulation of beta-amyloid precursor plaque and an intracellular neurofibrillary tangle of hyper-phosphorylated tau proteins in the brain have been implicated in neurodegeneration. In this study, we identified the most important genes that are unique and sensitive in the entorhinal region of the brain to target AD effectively. At first, microarrays data are selected and constructed protein-protein interaction network (PPIN) and gene regulatory network (GRN) from differentially expressed genes (DEGs) using Cytoscape software. Then, networks analysis was performed to determine hubs, bottlenecks, clusters, and signaling pathways in AD. Finally, critical genes were selected as targets for repurposing drugs. Analyzing the constructed PPIN and GRN identified CD44, ELF1, HSP90AB1, NOC4L, BYSL, RRP7A, SLC17A6, and RUVBL2 as critical genes that are dysregulated in the entorhinal region of AD suffering patients. The functional enrichment analysis revealed that DEG nodes are involved in the synaptic vesicle cycle, glutamatergic synapse, PI3K-Akt signaling pathway, retrograde endocannabinoid signaling, endocrine and other factor-regulated calcium reabsorption, ribosome biogenesis in eukaryotes, and nicotine addiction. Gentamicin, isoproterenol, and tumor necrosis factor are repurposing new drugs that target CD44, which plays an important role in the development of AD. Following our model validation using the existing experimental data, our model based on previous experimental reports suggested critical molecules and candidate drugs involved in AD for further investigations in vitro and in vivo.

PMID:39928227 | DOI:10.1007/s10048-025-00806-x

Front Oncol. 2025 Jan 24;14:1536406. doi: 10.3389/fonc.2024.1536406. eCollection 2024.

ABSTRACT

INTRODUCTION: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer resistant to endocrine and targeted therapies. Immune checkpoint inhibitors (ICIs) have shown significant efficacy in various cancers. Taraxacum officinale, commonly known as dandelion, has traditionally been used to treat breast-related diseases and is recognized for its beneficial composition and low side effects. FDA-approved drugs, having undergone rigorous validation for their safety, efficacy, and quality, provide a foundation for drug repurposing research. Researchers may explore FDA-approved drugs targeting the potential target NANOS1 for TOE (Taraxacum officinale extract) treatment to develop innovative therapeutic strategies. In this context, Dig (Digoxin) and AA (Algestone acetophenide) have been identified as potential drug candidates for further exploration of their therapeutic effects and application potential in targeting NANOS1.

METHODS: RNA sequencing (RNA-seq) was employed to identify potential targets for triple-negative breast cancer (TNBC) from TOE. Bioinformatics tools, including bc-GenExMiner v4.8, the Human Protein Atlas, and the TIMER database, were utilized for target identification. Molecular docking studies assessed FDA-approved drugs interacting with these targets, with Dig and AA selected as candidate drugs. The therapeutic efficacy of Dig and AA in combination with PD-1 inhibitors was evaluated using the 4T1 mouse model. Flow cytometry was applied to assess lymphocyte infiltration in the tumor immune microenvironment. RNA-seq analysis after target silencing by small interfering RNA (siRNA) was performed, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Validation of findings was conducted through quantitative PCR and Western blot analysis.

RESULTS: TOE inhibited TNBC cell growth, migration, and invasion, as assessed by CCK-8 and transwell assays. RNA-seq indicated the effects may be due to NANOS1 down-regulation. Survival analysis showed lower NANOS1 expression correlated with better prognosis. Immunoinfiltration analysis indicated a negative correlation between NANOS1 levels and activated NK cells. Molecular docking identified Dig and AA as high-affinity binders of NANOS1. Animal experiments showed Dig and PD-1 inhibitor combination enhanced immunotherapy efficacy for TNBC.

DISCUSSION: The findings from this study suggest that TOE may offer a novel therapeutic approach for TNBC by targeting NANOS1, a protein whose down-regulation is associated with improved patient outcomes. The negative correlation between NANOS1 and activated NK cells highlights the potential role of the immune system in TNBC pathogenesis and response to treatment. The identification of Dig as potential drugs targeting NANOS1 provides a new direction for drug repurposing in TNBC. The synergistic effect of Dig and PD-1 inhibition observed in animal models is promising and warrants further investigation into the role of immunotherapy in TNBC treatment. Overall, this study identifies NANOS1 as a new target for TNBC therapy and suggests a combination therapy approach that could enhance immunotherapy effectiveness and improve patient outcomes.

PMID:39927118 | PMC:PMC11802438 | DOI:10.3389/fonc.2024.1536406

Drug Dev Res. 2025 Feb;86(1):e70057. doi: 10.1002/ddr.70057.

ABSTRACT

Acne is a skin disease that impacts 9.4% of the world's population. Available treatments for managing acne include retinoid-like drugs, antibiotics, corticosteroids, photo, and radiotherapy. Howevere, the aforementioned treatments have certain limitations such as possibility of developing skin cancer from tetracycline, doxycycline, and corticosteroids, microbial resistance to antibiotics, and deadly side effects, and so forth. Repurposing of existing therapeutics having excellent safety profile can be promising way to treat acne efficiently. The repurposed drugs and phytoceuticals from diverse classes have demonstrated promising effects in treating acne. These repurposed drugs have displayed antiacne effectiveness by targeting single or multiple signaling pathways. Various repurposed therapeutics undergoing clinical trials at different phases demonstrated their safety and efficacy in treating acne. Despite being a very good, safe, and less time-consuming strategy, drug repurposing (DR) faces multiple challenges such as lack of regulatory guidelines, preservation of intellectual property, and clinical validation of claimed therapeutic indication. DR appears to be a viable approach and is likely to offer effective treatment at a reasonable cost in alleviating acne.

PMID:39925109 | DOI:10.1002/ddr.70057

J Alzheimers Dis. 2025 Feb 9:13872877251314777. doi: 10.1177/13872877251314777. Online ahead of print.

ABSTRACT

BACKGROUND: Nearly two-thirds of Alzheimer's disease (AD) patients are women. Therapeutics for women are critical to lowering their elevated risk of developing this major cause of adult dementia. Moreover, targeting epigenetic processes such as histone acetylation that regulate multiple cellular pathways is advantageous given the multifactorial nature of AD. Histone acetylation takes part in memory consolidation, and its disruption is linked to AD.

OBJECTIVE: Determine whether the investigational drug RG2833 has repurposing potential for AD. RG2833 is a histone deacetylase HDAC1/3 inhibitor that is orally bioavailable and permeates the blood-brain-barrier.

METHODS: RG2833 effects were determined on cognition, transcriptome, and AD-like pathology in 11-month TgF344-AD female and male rats. Treatment started early in the course of pathology when therapeutic intervention is predicted to be most effective.

RESULTS: RG2833-treatment of 11-month TgF344-AD rats: (1) Significantly improved hippocampal-dependent spatial memory in females but not males. (2) Upregulated expression of immediate early genes, such as Arc, Egr1 and c-Fos, and other genes involved in synaptic plasticity and memory consolidation in females. Remarkably, out of 17,168 genes analyzed for each sex, no significant changes in gene expression were detected in males at p < 0.05, false discovery rate <0.05, and fold-change equal or > 1.5. (3) Failed to improve amyloid beta accumulation and microgliosis in female and male TgF344-AD rats.

CONCLUSIONS: Our study highlights the potential of histone-modifying therapeutics such as RG2833 to improve cognitive behavior and drive the expression of immediate early, synaptic plasticity and memory consolidation genes, especially in female AD patients.

PMID:39924842 | DOI:10.1177/13872877251314777

Antiviral Res. 2025 Feb 7:106101. doi: 10.1016/j.antiviral.2025.106101. Online ahead of print.

ABSTRACT

The global response to the COVID-19 pandemic, caused by the novel SARS-CoV-2 virus, has seen an unprecedented surge in the development of antiviral therapies. Traditional antiviral strategies have primarily focused on direct-acting antivirals (DAAs), which specifically target viral components. In recent years, increasing attention was given to an alternative approach aiming to exploit host cellular pathways or immune responses to inhibit viral replication, which has led to development of so-called host-targeted antivirals (HTAs). The emergence of SARS-CoV-2 and COVID-19 has promoted a boost in this field. Numerous HTAs have been tested and demonstrated their potential against SARS-CoV-2 through in vitro and in vivo studies. However, in striking contrast, only a limited number have successfully progressed to advanced clinical trial phases (2-4), and even less have entered clinical practice. This review aims to explore the current landscape of HTAs targeting SARS-CoV-2 that have reached phase 2-4 clinical trials. Additionally, it will delve into the challenges faced in the development of HTAs and in gaining regulatory approval and market availability.

PMID:39923941 | DOI:10.1016/j.antiviral.2025.106101

J Mol Graph Model. 2025 Jan 31;136:108967. doi: 10.1016/j.jmgm.2025.108967. Online ahead of print.

ABSTRACT

Matrix metalloproteinases (MMPs) are involved in different pathophysiological conditions like cancer, COPD, asthma, and inflammatory diseases. Among these MMPs, macrophage metalloelastase is one of the prime targets for COPD, and cancer. Therefore, to combat such diseases, potent novel macrophage metalloelastase inhibitors can be considered. Here, the classification-based molecular modeling was performed on large data of macrophage metalloelastase inhibitors that identified dibenzofuran, and diphenyl ether groups as important substructures contributing towards potent macrophage metalloelastase inhibition. This information was further implicated in repurposing marketed drugs through fragment-based and molecular docking-based virtual screening with molecular dynamics (MD) simulation-based stability validation and DFT calculations. This study identified fosamprenavir and tirofiban as promising hits that can exhibit potent macrophage metalloelastase inhibition which was also validated by the MD simulation and DFT-based calculations. Therefore, this study not only revealed these repurposed drugs as effective macrophage metalloelastase inhibitors but also opened up a horizon in developing novel potent macrophage metalloelastase inhibitors for the management of cancer and COPD in the future.

PMID:39923554 | DOI:10.1016/j.jmgm.2025.108967

Mol Pharmacol. 2025 Jan;107(1):100010. doi: 10.1016/j.molpha.2024.100010. Epub 2024 Dec 12.

ABSTRACT

Chemokine receptors CCR3, CCR4, and CCR5 are G protein-coupled receptors implicated in diseases like cancer, Alzheimer's, asthma, human immunodeficiency virus (HIV), and macular degeneration. Recently, CCR3 and CCR4 have emerged as potential stroke targets. Although only the CCR5 antagonist maraviroc is US Food and Drug Administration-approved (for HIV), we curated data on CCR3, CCR4, and CCR5 antagonists from ChEMBL to develop and validate machine learning models. The top 5-fold cross-validation statistics for these models were high for both classification and regression models for CCR3 (receiver operating characteristic [ROC], 0.94; R2 = 0.8), CCR4 (ROC, 0.98; R2 = 0.57), and CCR5 (ROC, 0.96; R2 = 0.78). The models for CCR3/4 were used to screen a small library of US Food and Drug Administration-approved drugs and 17 were initially tested in vitro against both CCR3/4 receptors. A promising compound lapatinib, a dual tyrosine kinase inhibitor, was identified as an antagonist for CCR3 (IC50, 0.7 μM) and CCR4 (IC50, 1.8 μM). Additional testing also identified it as an CCR5 antagonist (IC50, 0.9 μM), and it showed moderate in vitro HIV I inhibition. We demonstrated how machine learning can be used to identify molecules for repurposing as antagonists for G protein-coupled receptors such as CCR3, CCR4, and CCR5. Lapatinib may represent a new orally available chemical probe for these 3 receptors, and it provides a starting point for further chemical optimization for multiple diseases impacting human health. SIGNIFICANCE STATEMENT: We describe the building of machine learning models for the chemokine receptors CCR3, CCR4, and CCR5 trained on data from the ChEMBL database. Using these models, we identified lapatinib as a potent inhibitor of CCR3, CCR4, and CCR5. Our study illustrates the potential of machine learning in identifying molecules for repurposing as antagonists for G protein-coupled receptors, including CCR3, CCR4, and CCR5, which have various therapeutic applications.

PMID:39919162 | DOI:10.1016/j.molpha.2024.100010

PLoS Pathog. 2025 Feb 7;21(2):e1012562. doi: 10.1371/journal.ppat.1012562. eCollection 2025 Feb.

ABSTRACT

Infection by the liver fluke, Fasciola hepatica, places a substantial burden on the global agri-food industry and poses a significant threat to human health in endemic regions. Widespread resistance to a limited arsenal of chemotherapeutics, including the frontline flukicide triclabendazole (TCBZ), renders F. hepatica control unsustainable and accentuates the need for novel therapeutic target discovery. A key facet of F. hepatica biology is a population of specialised stem cells which drive growth and development - their dysregulation is hypothesised to represent an appealing avenue for control. The exploitation of this system as a therapeutic target is impeded by a lack of understanding of the molecular mechanisms underpinning F. hepatica growth and development. Wnt signalling pathways govern a myriad of stem cell processes during embryogenesis and drive tumorigenesis in adult tissues in animals. Here, we identify five putative Wnt ligands and five Frizzled receptors in liver fluke transcriptomic datasets and find that Wnt/β-catenin signalling is most active in juveniles, the most pathogenic life stage. FISH-mediated transcript localisation revealed partitioning of the five Wnt ligands, with each displaying a distinct expression pattern, consistent with each Wnt regulating the development of different cell/tissue types. The silencing of each individual Wnt or Frizzled gene yielded significant reductions in juvenile worm growth and, in select cases, blunted the proliferation of neoblast-like cells. Notably, silencing FhCTNNB1, the key effector of the Wnt/β-catenin signal cascade led to aberrant development of the neuromuscular system which ultimately proved lethal - the first report of a lethal RNAi-induced phenotype in F. hepatica. The absence of any discernible phenotypes following the silencing of the inhibitory Wnt/β-catenin destruction complex components is consistent with low destruction complex activity in rapidly developing juvenile worms, corroborates transcriptomic expression profiles and underscores the importance of Wnt signalling as a key molecular driver of growth and development in early-stage juvenile fluke. The putative pharmacological inhibition of Wnt/β-catenin signalling using commercially available inhibitors phenocopied RNAi results and provides impetus for drug repurposing. Taken together, these data functionally and chemically validate the targeting of Wnt signalling as a novel strategy to undermine the pathogenicity of juvenile F. hepatica.

PMID:39919127 | DOI:10.1371/journal.ppat.1012562

J Mov Disord. 2025 Feb 7. doi: 10.14802/jmd.25008. Online ahead of print.

ABSTRACT

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder and is characterized by progressive dopaminergic and non-dopaminergic neuronal loss and the presence of Lewy bodies, which are primarily composed of aggregated α-synuclein. Despite advancements in symptomatic therapies, such as dopamine replacement and deep brain stimulation, no disease-modifying therapies (DMTs) have been identified to slow or arrest neurodegeneration in PD. Challenges in DMT development include disease heterogeneity, the absence of reliable biomarkers, and the multifaceted pathophysiology of PD, encompassing neuroinflammation, mitochondrial dysfunction, lysosomal impairment, and oxidative stress. Drug repositioning and repurposing strategies using existing drugs for new therapeutic applications offer a promising approach to accelerate the development of DMTs for PD. These strategies minimize time, cost, and risk by using compounds with established safety profiles. Prominent candidates include glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, ambroxol, calcium channel blockers, statins, iron-chelating agents, c-Abl inhibitors, and memantine. Although preclinical and early clinical studies have demonstrated encouraging results, numerous phase III trials have yielded unfavorable outcomes, elucidating the complexity of PD pathophysiology and the need for innovative trial designs. This review evaluates the potential of prioritized repurposed drugs for PD, focusing on their mechanisms, preclinical evidence, and clinical trial outcomes, and highlights the ongoing challenges and opportunities in this field.

PMID:39914809 | DOI:10.14802/jmd.25008

Comput Biol Med. 2025 Feb 5;187:109744. doi: 10.1016/j.compbiomed.2025.109744. Online ahead of print.

ABSTRACT

Non-small-cell lung cancer (NSCLC) is one of the most malignant tumors and the leading cause of death from cancer worldwide and is increasing at a massive rate every year. Most NSCLC patients are diagnosed at advanced stages, which is associated with a poor prognosis and a very low 5-year survival rate. Therefore, the purpose of this study is to investigate molecular markers for early diagnosis, prognosis and therapy of NSCLC through the integration of bioinformatics and statistical methods. A total of 93 overlapping differentially expressed genes (oDEGs) were identified between NSCLC and normal samples through Linear Models for Microarray (LIMMA) and Significance Analysis of Microarrays (SAM) methods. Six top-degree oDEGs (CCNA2, CDC6, AURKA, CCNB1, MKI67, and PRC1) were identified as key genes (KGs) through the protein-protein interaction (PPI) network. The predictive accuracy analysis of the identified KGs revealed an accuracy of 96.92 %, with a sensitivity of 91.73 % and a specificity of 98.15 %. KGs associated with 3 molecular functions (MFs), 5 cellular components (CCs), 3 biological processes (BPs), and 4 pathways were identified through FunRich software. Analysis of expression levels using the UALCAN database revealed that KGs are significantly associated with potential early diagnostic biomarkers. Survival analysis using the GEPIA database demonstrated that the KGs possessed strong prognostic power for NSCLC. Finally, seven repurposed candidate drugs ENTRECTINIB, SORAFENIB, CHEMBL1765740, TOZASERTIB, NERVIANO, AZD-1152-HQPA, and SELICICLIB were proposed through molecular docking analysis. In conclusion, the findings of this study have the potential to significantly impact the early diagnosis, prognosis, and treatment of NSCLC.

PMID:39914199 | DOI:10.1016/j.compbiomed.2025.109744

Comput Biol Chem. 2025 Jan 31;115:108364. doi: 10.1016/j.compbiolchem.2025.108364. Online ahead of print.

ABSTRACT

OBJECTIVE: Development of novel drug solutions has always been an expensive endeavour, hence drug repurposing as an approach has gained popularity in recent years. In this review we intend to examine one of the most unique computational methods for drug repurposing, that being knowledge graphs.

METHOD: Through literature review we looked at the application of knowledge graphs in medicine, specifically at its use in drug repurposing. We also looked at literature embedding methods, integration of machine learning models and approaches to completion of knowledge graphs.

RESULT: After filtering 43 papers were used for analysis. Timeline, country distribution, application areas of knowledge graph was highlighted. General trends in the use of knowledge graphs for drug repurposing and any shortcomings of the approach was discussed.

CONCLUSION: This approach has gained popularity only very recently; hence it is in a nascent phase.

PMID:39914071 | DOI:10.1016/j.compbiolchem.2025.108364