medRxiv [Preprint]. 2025 Feb 21:2025.02.18.25321035. doi: 10.1101/2025.02.18.25321035.

ABSTRACT

Identification of drug-repurposing targets with genetic and biological support is an economically and temporally efficient strategy for improving treatment of diseases. We employed a cross-disciplinary approach to identify potential treatments for metabolic dysfunction associated steatotic liver disease (MASLD) using humans as a model organism. We identified 212 putative causal genes associated with MASLD using data from a large multi-ancestry genetic association study, of which 158 (74.5%) are novel. From this set we identified 57 genes that encode for druggable protein targets, and where the effects of increasing genetically predicted gene expression on MASLD risk align with the function of that drug on the protein target. These potential targets were then evaluated for evidence of efficacy using Mendelian randomization, pathway analysis, and protein structural modeling. Using these approaches, we present compelling evidence to suggest activation of FADS1 by icosopent ethyl as well as S1PR2 by fingolimod could be promising therapeutic strategies for MASLD.

PMID:40034783 | PMC:PMC11875238 | DOI:10.1101/2025.02.18.25321035

Res Sq [Preprint]. 2025 Feb 17:rs.3.rs-6025759. doi: 10.21203/rs.3.rs-6025759/v1.

ABSTRACT

Background Gene-environment interactions may enhance our understanding of hypertension. Our previous study highlighted the importance of considering psychosocial factors in gene discovery for blood pressure (BP) but was limited in statistical power and population diversity. To address these challenges, we conducted a multi-population genome-wide association study (GWAS) of BP accounting for gene-depressive symptomatology (DEPR) interactions in a larger and more diverse sample. Results Our study included 564,680 adults aged 18 years or older from 67 cohorts and 4 population backgrounds (African (5%), Asian (7%), European (85%), and Hispanic (3%)). We discovered seven novel gene-DEPR interaction loci for BP traits. These loci mapped to genes implicated in neurogenesis ( TGFA , CASP3 ), lipid metabolism ( ACSL1 ), neuronal apoptosis ( CASP3 ), and synaptic activity ( CNTN6 , DBI ). We also identified evidence for gene-DEPR interaction at nine known BP loci, further suggesting links between mood disturbance and BP regulation. Of the 16 identified loci, 11 loci were derived from African, Asian, or Hispanic populations. Post-GWAS analyses prioritized 36 genes, including genes involved in synaptic functions ( DOCK4 , MAGI2 ) and neuronal signaling ( CCK , UGDH , SLC01A2 ). Integrative druggability analyses identified 11 druggable candidate gene targets, including genes implicated in pathways linked to mood disorders as well as gene products targeted by known antihypertensive drugs. Conclusions Our findings emphasize the importance of considering gene-DEPR interactions on BP, particularly in non-European populations. Our prioritized genes and druggable targets highlight biological pathways connecting mood disorders and hypertension and suggest opportunities for BP drug repurposing and risk factor prevention, especially in individuals with DEPR.

PMID:40034430 | PMC:PMC11875294 | DOI:10.21203/rs.3.rs-6025759/v1

J Transl Med. 2025 Mar 3;23(1):258. doi: 10.1186/s12967-025-06293-w.

ABSTRACT

Pentamidine (PTM) is an aromatic diamidine administered for infectious diseases, e.g. sleeping sickness, malaria, and Pneumocystis jirovecii pneumonia. Due to similarities of cellular mechanisms between human cells and such infections, PTM has also been proposed for repurposing in non-infectious diseases such as cancer. Indeed, by modulating different signaling pathways such as PI3K/AKT, MAPK/ERK, p53, PD-1/PD-L1, etc., PTM has been shown to inhibit different properties of cancer, including proliferation, invasion, migration, hypoxia, and angiogenesis, while inducing anti-tumor immune responses and apoptosis. Given the promising implications of PTM for cancer treatment, however, the clinical translation of PTM in cancer is not without certain challenges. In fact, clinical trials have shown that systemic administration of PTM can be concurrent with serious adverse effects, e.g. hypoglycemia. Therefore, to reduce the administered doses of PTM, lower the risk of adverse effects, and prevent any potential drug resistance, while maintaining the anti-tumor efficacy, two main strategies have been suggested. One is combination therapy that employs PTM in conjunction with other anti-cancer modalities, such as chemotherapy and radiotherapy, and attacks tumor cells with significant additive or synergistic anti-tumor effects. The other is developing PTM-loaded nanocarrier drug delivery systems e.g. pegylated liposomes, chitosan-coated niosomes, squalene-based nanoparticles, hyaluronated lipid-polymer hybrid nanoparticles, etc., that offer enhanced pharmacokinetic characteristics, including increased bioavailability, sit-targeting, and controlled/sustained drug release. This review highlights the anti-tumor properties of PTM that favor its repurposing for cancer treatment, as well as, PTM-based combination therapies and nanocarrier delivery systems which can enhance therapeutic efficacy and simultaneously reduce toxicity.

PMID:40033361 | DOI:10.1186/s12967-025-06293-w

Comput Biol Med. 2025 Mar 2:109919. doi: 10.1016/j.compbiomed.2025.109919. Online ahead of print.

NO ABSTRACT

PMID:40032537 | DOI:10.1016/j.compbiomed.2025.109919

Pulm Pharmacol Ther. 2025 Mar 1:102348. doi: 10.1016/j.pupt.2025.102348. Online ahead of print.

NO ABSTRACT

PMID:40032240 | DOI:10.1016/j.pupt.2025.102348

IEEE J Biomed Health Inform. 2024 Nov 13;PP. doi: 10.1109/JBHI.2024.3497591. Online ahead of print.

ABSTRACT

Prediction of drug-target interactions (DTIs) is one of the crucial steps for drug repositioning. Identifying DTIs through bio-experimental manners is always expensive and time-consuming. Recently, deep learning-based approaches have shown promising advancements in DTI prediction, but they face two notable challenges: (i) how to explicitly capture local interactions between drug-target pairs and learn their higher-order substructure embeddings; (ii) How to filter out redundant information to obtain effective embeddings for drugs and targets. Results: In this study, we propose a novel approach, termed DSANIB, to infer potential interactions between drugs and targets. DSANIB comprises two primary components: (1) DSAN component: The Inter-view Attention Network Module explicitly learns the local interactions between drugs and targets, while the Intra-view Attention Network Module aggregates information from local interaction features to obtain their higher-order substructure embeddings. (2) Information Bottleneck (IB) component: DSANIB adopts the IB strategy, which could retain relevant information while minimizing the redundant features to obtain their discriminative representations. Extensive experimental results demonstrate that DSANIB outperforms other SOTA prediction models. In addition, visualization of drug and target embeddings learned through DSANIB could provide interpretable insights for the prediction results. Availability: The source code has been made publicly available on GitHub https://github.com/Zzz-Soar/DSANIB.

PMID:40030194 | DOI:10.1109/JBHI.2024.3497591

Expert Opin Drug Discov. 2025 Mar 3. doi: 10.1080/17460441.2025.2474661. Online ahead of print.

ABSTRACT

INTRODUCTION: Identifying treatments that can alter the natural history of amyotrophic lateral sclerosis (ALS) is challenging. For years, drug discovery in ALS has relied upon traditional approaches with limited success. Drug repurposing, where clinically approved drugs are reevaluated for other indications, offers an alternative strategy that overcomes some of the challenges associated with de novo drug discovery. Whilst not a new concept, the potential of drug repurposing in ALS is yet to be fully realized.

AREAS COVERED: In this review, the authors discuss the challenge of drug discovery in ALS and specifically examine the potential of drug repurposing for the identification of new effective treatments. The authors consider a broad range of approaches, from screening in experimental models to computational approaches, and outline some general principles for pre-clinical and clinical research to help bridge the translational gap. Literature was reviewed from original publications, press releases and clinical trials.

EXPERT OPINION: Despite the remaining challenges, drug repurposing offers the opportunity to improve therapeutic options for ALS patients. Nevertheless, stringent pre-clinical research will be necessary to identify the most promising compounds while innovative experimental medicine studies will also be paramount to bridge the aforementioned translational gap. The authors further highlight the importance of combining expertise across academia, industry and wider stakeholders, which will be key in the successful delivery of repurposed therapies to the clinic.

PMID:40029669 | DOI:10.1080/17460441.2025.2474661

Biochem Pharmacol. 2025 Feb 28:116839. doi: 10.1016/j.bcp.2025.116839. Online ahead of print.

ABSTRACT

Dengue viruses (DENV) pose significant health threats, with no approved antiviral drugs currently available, creating an urgent need for new therapies. This study screened FDA-approved drugs for their antiviral ability against DENV and identified three promising candidates: darunavir (DRV), domperidone, and tetracycline. DRV demonstrated the highest efficacy against three DENV serotypes, with half-maximal effective concentrations (EC50) below 1 µM, surpassing the performance of tetracycline and domperidone. It effectively blocked DENV envelope (E) protein attachment to two type cells with EC50 values less than 0.2 μM. Domperidone reduced DENV-2 attachment to TE671 cells (EC50 = 3.08 μM) but was less effective in BHK-21 cells, while tetracycline inhibited NS3 protease (IC50 = 1.12 μM). Among DRV's structurally related drugs, fosamprenavir (FPV) significantly reduced DENV infectivity and virus yield, with EC50 values below 0.5 µM. In vivo, DRV at 1, 2, and 5 mg/kg achieved 100 % survival in suckling mice, compared to 83.5 % with FPV. Real-time RT-PCR showed DRV more effectively reduced DENV-2 RNA in mouse brains than FPV. Molecular docking showed DRV and FPV bind tightly to the DENV-2 E protein's N-octyl-β-D-glucoside (βOG) hydrophobic pocket, with DRV forming stronger interactions than FPV. Chimeric DENV-2 single-round infectious particle tests confirmed DRV's effective targeting of this pocket, though mutations at K128, L198, Q200, I270, and T280 reduced its efficacy. These findings highlight DRV as a potent antiviral agent against DENV, targeting the E protein's βOG hydrophobic pocket, with the potential for rapid deployment in treating and preventing infections.

PMID:40024350 | DOI:10.1016/j.bcp.2025.116839

Inflammopharmacology. 2025 Feb 28. doi: 10.1007/s10787-025-01682-5. Online ahead of print.

ABSTRACT

Resistant strains of Plasmodium spp. pose a great threat to healthcare. Drug repurposing is a smart, and an effective way to look for new alternatives for different ailments including malaria. Protein tyrosine kinases (PTKs) play a crucial role in growth, maturation as well as differentiation of Plasmodium and this study explores antimalarial activity of PTKs inhibitor gefitinib using in silico and experimental approaches. The drug showed considerable inhibitory activity against P. falciparum 3D7 (IC50 0.49 µg/mL) and RKL-9 (IC50 0.83 µg/mL) strains. Isobologram analysis revealed substantial synergism between gefitinib and artesunate. Gefitinib illustrated highest negative D-score towards phosphoethanolamine methyltransferase followed by PfPK5 and CDPK1. Its acute toxicity was 4 g/kg. Gefitinib (100 mg/kg) exhibited a dose-dependent curative activity against P. berghei with 91.09% chemo-suppression and the combination of gefitinib 100 mg/kg and AS 50 mg/kg exhibited complete parasite clearance with no recrudescence which was also evidenced by cytokine analysis, biochemical as well as histopathological studies. At length, gefitinib illustrated considerable antiplasmodial action by targeting phosphoethanolamine methyltransferase, PfPK5 and CDPK1. The combination of gefitinib (100 mg/kg) and AS (50 mg/kg) holds promise for malaria treatment. Further, research is being done to evaluate its pharmacokinetic properties.

PMID:40019687 | DOI:10.1007/s10787-025-01682-5

Clin Exp Rheumatol. 2025 Feb;43(2):309-315. doi: 10.55563/clinexprheumatol/dyjcsn. Epub 2025 Feb 26.

ABSTRACT

OBJECTIVES: This paper aims to provide insight into the challenges and opportunities of conducting an investigator-led, international, multicentre clinical trial for Inclusion Body Myositis (IBM), a rare inflammatory myopathy.

METHODS: An international, multicentre, randomised, controlled trial of a repurposed drug (sirolimus) was initiated based on promising results from a mono-centric pilot study. The progress of the trial was analysed to identify key challenges encountered and solutions developed.

RESULTS: This large, collaborative study has presented a mosaic of challenges and opportunities, many ubiquitous with investigator-led trials. Key challenges have included securing adequate funding, coordinating manufacture of placebo, negotiating international contracts, managing limited study budgets and delays linked to the COVID-19 pandemic. Alongside these challenges, the study team have found opportunities for creative and effective solutions, including the flexibility of building study databases, optimising digital data capture and harnessing patient involvement.

CONCLUSIONS: Instrumental to the progress of the trial has been the collaboration between site teams, patient partnership and adaptability.

PMID:40018747 | DOI:10.55563/clinexprheumatol/dyjcsn

In Silico Pharmacol. 2025 Feb 25;13(1):35. doi: 10.1007/s40203-025-00314-8. eCollection 2025.

ABSTRACT

The primary objective of this study is to harness computer-aided drug repurposing (CADR) techniques to identify existing FDA-approved drugs that can potentially disrupt the assembly of the Hepatitis B Virus (HBV) core protein (HBcAg), an essential process in the virus's life cycle. By targeting this critical step, our study aims to expand the repertoire of therapeutic options for managing chronic Hepatitis B infection, a major global health challenge. Utilizing a combination of computational methods, including the CavityPlus server for ability to analyze druggable protein cavities and extract pharmacophore features and LigandScout for pharmacophore-based virtual screening of a vast library of FDA-approved drugs was conducted. Molecular dynamic simulation (MDS) was employed to evaluate the stability of HBcAg, complexed with Heteroaryldihydropyrimidine (HAP) and statins exhibiting particularly strong binding energies and conformational compatibility. Our approach focused on identifying pharmacophore features that align with known HBcAg inhibitors. The study identified several promising candidates, including Ciclopirox olamine, Voriconazole, Enasidenib, and statins, demonstrating potential interactions with HBc protein residues. Molecular docking further validated these interactions. The significance of these findings lies in their potential to offer new, effective therapeutic strategies for HBV treatment, particularly as alternatives to current therapies that often suffer from issues of viral resistance and adverse side effects. MDS analysis verified the robustness of HAP and statins by showing a high level of binding energies and compatibility with HBcAg. Our results provide a foundation for further experimental validation and underscore the utility of computer-aided drug repurposing as a rapid, cost-effective approach to drug discovery in antiviral research. This study contributes to our understanding of HBV biology and opens avenues for developing novel anti-HBV therapies based on repurposed drugs. The highlighted compound may also enhance the challenges of drug resistance when used as a combination therapy.

PMID:40018383 | PMC:PMC11861453 | DOI:10.1007/s40203-025-00314-8

Sci Rep. 2025 Feb 27;15(1):7035. doi: 10.1038/s41598-024-83555-5.

ABSTRACT

The relationship between ischemic stroke (IS) and pyroptosis centers on the inflammatory response elicited by cerebral tissue damage during an ischemic stroke event. However, an in-depth mechanistic understanding of their connection remains limited. This study aims to comprehensively analyze the gene expression patterns of pyroptosis-related differentially expressed genes (PRDEGs) by employing integrated IS datasets and machine learning techniques. The primary objective was to develop classification models to identify crucial PRDEGs integral to the ischemic stroke process. Leveraging three distinct machine learning algorithms (LASSO, Random Forest, and Support Vector Machine), models were developed to differentiate between the Control and the IS patient samples. Through this approach, a core set of 10 PRDEGs consistently emerged as significant across all three machine learning models. Subsequent analysis of these genes yielded significant insights into their functional relevance and potential therapeutic approaches. In conclusion, this investigation underscores the pivotal role of pyroptosis pathways in ischemic stroke and identifies pertinent targets for therapeutic development and drug repurposing.

PMID:40016488 | DOI:10.1038/s41598-024-83555-5

Sci Rep. 2025 Feb 28;15(1):7109. doi: 10.1038/s41598-025-91096-8.

ABSTRACT

Computational approaches complement traditional in-vitro or in-vivo assays, significantly accelerating the drug discovery process by increasing the probability of identifying promising lead compounds. In this study, the apoptotic compounds were assessed for antimycobacterial activity and immunomodulatory potential in infected THP-1 macrophage cells. The antimycobacterial activity of the apoptotic compounds was evaluated using the minimum inhibitory concentration (MIC) assay. The immunomodulatory potential of the apoptotic compounds was determined on mycobacterial-infected THP-1 and non-infected THP-1 macrophage cells. The potential binding dynamics of the compounds against InhA were predicted using molecular docking, molecular dynamics, and MM-GBSA binding free energies. The in-vitro MIC assay showed that cepharanthine (CEP) had the highest antimycobacterial activity against Mycobacterium smegmatis mc2155 and Mycobacterium tuberculosis H37Rv, with MICs of 3.1 and 1.5 µg/mL, respectively, followed by CP-31398 dihydrochloride hydrate (DIH) (MICs = 6.2 and 3.1 µg/mL, respectively), marinopyrrole A (MAR) (MICs = 25 and 12.5 µg/mL, respectively), and nutlin-3a (NUT) (MICs = 50 and 25 µg/mL, respectively). MICs for the rest of the drugs were > 200 µg/mL against both M. smegmatis mc2155 and M. tuberculosis H37Rv. Furthermore, the growth of M. smegmatis mc2155 in infected THP-1 macrophage cells treated with DIH, CEP, carboxyatractyloside potassium salt (CAR), and NUT was inhibited by the mentioned drugs. Cytokine profiling showed that DIH optimally regulated the secretion of IL-1β and TNF-α which potentially enhanced the clearance of the intracellular pathogen. Molecular dynamics simulations showed that NUT, MAR, 17-(allylamino)-17-demethoxygeldanamycin (17-AAG), and BV02 strongly bind to InhA. However, 17-AAG and BV02 did not show significant activity in-vitro. This study highlights the importance of probing already existing chemical scaffolds as a starting point for discovery of therapeutic agents against M. tuberculosis H37Rv using both pathogen and host directed approaches. The integration of molecular dynamics simulations provides valuable insights into potential scaffold modifications to enhance the affinity.

PMID:40016256 | DOI:10.1038/s41598-025-91096-8

Arterioscler Thromb Vasc Biol. 2025 Feb 27. doi: 10.1161/ATVBAHA.124.322145. Online ahead of print.

ABSTRACT

BACKGROUND: FXIa (coagulation factor XIa) is considered as a promising antithrombotic target with reduced hemorrhagic liabilities. The objective of this study was to identify a small-molecule inhibitor of FXIa as a potential low-hemorrhagic anticoagulant.

METHODS: A high-throughput virtual screening was conducted using a drug repurposing library with the catalytic domain of FXIa as the bait. The identified inhibitor's anticoagulant activity was evaluated in vitro and in both arterial and venous murine thrombotic models. The dependency of the inhibitor on FXIa was further examined using FXI-/- mice. Hemorrhagic risks were subsequently evaluated in models of both localized and major bleeding.

RESULTS: Virtual screening led to the identification of montelukast, a commonly used antiasthmatic drug, as a potent and specific FXIa inhibitor (IC50, 0.17 μmol/L). MK exhibited anticoagulant effects comparable to those of 2 mostly prescribed anticoagulants (warfarin and apixaban) in both arterial and venous thrombotic models. Notably, in stark contrast to the pronounced hemorrhagic risks of warfarin and apixaban, MK did not measurably increase the tendency of localized or major bleeding. Furthermore, MK did not prolong the time to arterial thrombotic occlusion in FXI-/- mice, while effectively inhibited arterial occlusion induced by the reinfusion of recombinant FXIa, confirming that MK's anticoagulant activity is mediated by plasma FXIa. Additionally, MK ameliorated inflammation levels and mitigated pulmonary microthrombus formation in a septic mouse model. Moreover, combination therapy with MK enhanced the antithrombotic effects of antiplatelets without an obvious increase of hemorrhage.

CONCLUSIONS: This proof-of-concept study suggests the potent low-hemorrhage antithrombotic effect of MK by targeting FXIa and unveiling a new therapeutic application of MK.

PMID:40013360 | DOI:10.1161/ATVBAHA.124.322145

Front Oncol. 2025 Feb 11;15:1550672. doi: 10.3389/fonc.2025.1550672. eCollection 2025.

NO ABSTRACT

PMID:40012550 | PMC:PMC11861434 | DOI:10.3389/fonc.2025.1550672

BMJ Open. 2025 Feb 26;15(2):e091495. doi: 10.1136/bmjopen-2024-091495.

ABSTRACT

INTRODUCTION: Wolfram syndrome (WFS1-Spectrum Disorder) is an ultra-rare monogenic form of progressive neurodegeneration and diabetes mellitus. In common with most rare diseases, there are no therapies to slow or stop disease progression. Sodium valproate, an anticonvulsant with neuroprotective properties, is anticipated to mediate its effect via alteration of cell cycle kinetics, increases in p21cip1 expression levels and reduction in apoptosis and increase in Wolframin protein expression. To date, there have been no multicentre randomised controlled trials investigating the efficacy of treatments for neurodegeneration in patients with Wolfram syndrome.

METHODS AND ANALYSIS: TREATWOLFRAM is an international, multicentre, double-blind, placebo-controlled, randomised clinical trial designed to investigate whether 36-month treatment with up to 40 mg/kg/day of sodium valproate will slow the rate of loss of visual acuity as a biomarker for neurodegeneration in patients with Wolfram syndrome. Patients who satisfied the eligibility criteria were randomly assigned (2:1) to receive two times per day oral gastro-resistant sodium valproate tablets up to a maximum dose of 800 mg 12 hourly or sodium valproate-matched placebo. Using hierarchical repeated measures analyses with a 5% significance level, 80% power and accounting for an estimated 15% missing data rate, a sample size of 70 was set. The primary outcome measure, visual acuity, will be centrally reviewed and analysed on an intention-to-treat population.

ETHICS AND DISSEMINATION: The protocol was approved by the National Research Ethics Service (West of Scotland; 18/WS/0020) and by the Medicines and Healthcare products Regulatory Agency. Recruitment into TREATWOLFRAM started in January 2019 and ended in November 2021. The treatment follow-up of TREATWOLFRAM participants is ongoing and due to finish in November 2024. Updates on trial progress are disseminated via Wolfram Syndrome UK quarterly newsletters and at family conferences for patient support groups. The findings of this trial will be disseminated through peer-reviewed publications and international presentations.

TRIAL REGISTRATION NUMBER: NCT03717909.

PMID:40010822 | DOI:10.1136/bmjopen-2024-091495

Phytomedicine. 2025 Feb 14;139:156518. doi: 10.1016/j.phymed.2025.156518. Online ahead of print.

ABSTRACT

BACKGROUND: Drug discovery from nature has a long, ethnopharmacologically-based background. Today, natural resources are undeniably vital reservoirs of active molecules or drug leads. Advances in (bio)informatics and computational biology emphasized the role of herbal medicines in the drug discovery pipeline.

PURPOSE: This review summarizes bioinformatic approaches applied in recent drug discovery from nature.

STUDY DESIGN: It examines advancements in molecular networking, pathway analysis, network pharmacology within a systems biology framework and AI for assessing the therapeutic potential of herbal preparations.

METHODS: A comprehensive literature search was conducted using Pubmed, SciFinder, and Google Database. Obtained data was analyzed and organized in subsections: AI, systems biology integrative approach, network pharmacology, pathway analysis, molecular networking, structure-based virtual screening.

RESULTS: Bioinformatic approaches is now essential for high-throughput data analysis in drug target identification, mechanism-based drug discovery, drug repurposing and side-effects prediction. Large datasets obtained from "omics" approaches require bioinformatic calculations to unveil interactions, and patterns in disease-relevant conditions. These tools enable databases annotations, pattern-matching, connections discovery, molecular relationship exploration, and data visualisation.

CONCLUSION: Despite the complexity of plant metabolites, bioinformatic approaches assist in characterization of herbal preparations and selection of bioactive molecule. It is perceived as powerful tool for uncovering multi-target effects and potential molecular mechanisms of compounds. By integrating multiple networks that connect gene-disease, drug-target and gene-drug-target, drug discovery from natural sources is experiencing a remarkable comeback.

PMID:40010031 | DOI:10.1016/j.phymed.2025.156518

CNS Neurosci Ther. 2025 Feb;31(2):e70294. doi: 10.1111/cns.70294.

ABSTRACT

OBJECTIVE: To identify and validate causal protein targets that may serve as potential therapeutic interventions for both the onset and progression of Parkinson's disease (PD) through integrative proteomic and genetic analyses.

METHOD: We utilized large-scale plasma and brain protein quantitative trait loci (pQTL) datasets from the deCODE Health study and the Religious Orders Study/Rush Memory and Aging Project (ROS/MAP), respectively. Proteome-wide association studies (PWAS) were conducted using the OTTERS framework for plasma proteins and the FUSION tool for brain proteins, examining associations with PD onset and three progression phenotypes: composite, motor, and cognitive. Significant protein associations (FDR-corrected p < 0.05) from PWAS were further validated using summary-based Mendelian randomization (SMR), colocalization analyses, and reverse Mendelian randomization (MR) to establish causality. Phenome-wide Mendelian randomization (PheW-MR) was performed to assess potential side effects across 679 disease traits when targeting these proteins to reduce PD-related phenotype risk by 20%. Additionally, we conducted cellular distribution-based clustering using gene expression data from the Allen Brain Atlas (ABA) to explore the distribution of key proteins across brain regions, constructed protein-protein interaction (PPI) networks via the STRING database to explore interactions among proteins, and evaluated the druggability of identified targets using the DrugBank database to identify opportunities for drug repurposing.

RESULT: Our analyses identified 25 candidate proteins associated with PD phenotypes, including 16 plasma proteins linked to PD progression (10 cognitive, 4 motor, and 3 composite) and 9 plasma proteins associated with PD onset. Notably, GPNMB was implicated in both plasma and brain tissues for PD onset. PheW-MR revealed predominantly beneficial side effects for the identified targets, with 83.7% of associations indicating positive outcomes and 16.3% indicating adverse effects. Cellular clustering categorized candidate targets into three distinct expression profiles across brain cell types using ABA. PPI network analysis highlighted one key interaction cluster among the proteins for PD cognitive progression and PD onset. Druggability assessment revealed 15 out of 25 proteins had repurposing opportunities for PD treatment.

CONCLUSION: We have identified 25 causal protein targets associated with the onset and progression of PD, providing new insights into the research and development of treatment strategies for PD.

PMID:40008429 | DOI:10.1111/cns.70294

Front Pharmacol. 2025 Feb 11;16:1525029. doi: 10.3389/fphar.2025.1525029. eCollection 2025.

ABSTRACT

Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. Specifically, a graph regularized approach is applied to integrate multiple drug and disease similarity information, which can effectively eliminate noise data and obtain integrated similarity features of drugs and diseases. Then, topological feature representations of drugs and diseases are learned from constructed biomedical knowledge graphs (KGs) which encompasses known drug-related and disease-related interactions. Next, the similarity features and topological features are fused by utilizing an attention-based feature fusion method. Finally, drug-disease associations are predicted using the graph convolutional network. Experimental results demonstrate that KGRDR achieves better performance when compared with the state-of-the-art drug-disease prediction methods. Moreover, case study results further validate the effectiveness of KGRDR in predicting novel drug-disease interactions.

PMID:40008124 | PMC:PMC11850324 | DOI:10.3389/fphar.2025.1525029

Heliyon. 2025 Feb 5;11(3):e42476. doi: 10.1016/j.heliyon.2025.e42476. eCollection 2025 Feb 15.

ABSTRACT

Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance. In contrast, deep learning excels at extracting features from raw sequences but often overlooks essential biological context features, hindering effective binding prediction. Additionally, these models struggle to capture global and local feature distributions efficiently in protein sequences and drug SMILES. Previous state-of-the-art models, like transformers and graph-based approaches, face scalability and resource efficiency challenges. Transformers struggle with scalability, while graph-based methods have difficulty handling large datasets and complex molecular structures. In this paper, we introduce InceptionDTA, a novel drug-target binding affinity prediction model that leverages CharVec, an enhanced variant of Prot2Vec, to incorporate both biological context and categorical features into protein sequence encoding. InceptionDTA utilizes a multi-scale convolutional architecture based on the Inception network to capture features at various spatial resolutions, enabling the extraction of both local and global features from protein sequences and drug SMILES. We evaluate InceptionDTA across a range of benchmark datasets commonly used in drug-target binding affinity prediction. Our results demonstrate that InceptionDTA outperforms various sequence-based, transformer-based, and graph-based deep learning approaches across warm-start, refined, and cold-start splitting settings. In addition to using CharVec, which demonstrates greater accuracy in absolute predictions, InceptionDTA also includes a version that employs simple label encoding and excels in ranking and predicting relative binding affinities. This versatility highlights how InceptionDTA can effectively adapt to various predictive requirements. These results emphasize the promise of our approach in expediting drug repurposing initiatives, enabling the discovery of new drugs, and contributing to advancements in disease treatment.

PMID:40007773 | PMC:PMC11850134 | DOI:10.1016/j.heliyon.2025.e42476