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Results for “Drug Repositioning”

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PubMed2025

Identification of MMP14 and MKLN1 as colorectal cancer susceptibility genes and drug-repositioning candidates from a genome-wide association study.

BACKGROUND: Genome-wide association studies (GWAS) and subsequent functional interpretation have been used to identify susceptible genes and potential drug-repositioning candidates. This study aimed to identify genes associated with colorectal cancer (CRC) and potential drug-repositioning candidates. METHODS: Patients with CRC at Seoul National University Hospital (SNUH, discovery study) and Chonnam National University Hospital (CNUH, replication study) were included as case groups. The Korean Genome and Epidemiology Study (KoGES) participants were included as a control group. Single-nucleotide polymorphisms (SNPs) were extracted from blood-derived DNA (N = 409,063). A SNP-based logistic regression model was applied. Furthermore, post-GWAS analysis was conducted. Drug-repositioning candidates were identified using a pre-trained deep neural network and the druggability assessment tool. RESULTS: In the discovery study, we conducted a 1:3 age- and sex-matched case-control study that included 500 CRC cases (mean age 63.0 ± 7.15 years) and 1,500 healthy controls (mean age 62.9 ± 7.07 years), each group comprising 50% males and 50% females. The replication study enrolled 4,860 patients with CRC and 46,384 healthy controls. The two-stage GWAS revealed statistically significant associations among MKLN1 (rs75170436, 7q32.3, beta (log odds ratio) = - 0.90, Pmeta = 5.90 × 10-13), MMP14 (rs3751489, 14q11.2, beta (log odds ratio) = - 1.91, Pmeta = 2.31 × 10-12). Post-GWAS functional analysis revealed strong associations on two genes highlighting deleterious effects and increased gene expression. Drug-repositioning analysis identified GW0742 (PPARβ/δ agonist) with the highest binding score and druggability score for MMP14 with a reference allele (12.06, 0.85). CONCLUSIONS: Using GWAS, MKLN1 and MMP14 were found to be associated with CRC development and we identified GW0742 (PPARβ/δ agonist) as a potential drug-repositioning candidate for CRC based on MKLN1 and MMP14. These findings improve the understanding of CRC development and provide insights into novel therapeutic targets and candidates for CRC treatment.

Humans↗

PubMed2026

Leveraging bioinformatics approaches for drug repositioning in space radiation protection.

The health effects of space radiation, primarily Galactic Cosmic Rays (GCRs), on humans remain largely unknown, with potential cardiovascular consequences posing a significant threat to astronauts on long-duration spaceflight missions. Currently, there are no established pharmacological countermeasures for GCR exposure. Drug repositioning offers a promising strategy to accelerate pharmaceutical research in space medicine. This study leverages existing bioinformatics techniques to identify and prioritize potential drug candidates associated with proteomic perturbations following simulated GCR exposure using previously published murine cardiac proteomic data. A protein-protein interaction (PPI) network was constructed using the top differentially expressed proteins (DEPs) from murine heart tissue following exposure to 5-ion GCRs as seed nodes, focusing on experimentally supported interactions. Network topology, Markov clustering, and functional enrichment analyses were used to characterize biologically relevant proteins and pathways. Drug-protein interactions were predicted using Drugst.One and mapped to PPI clusters of interest to identify candidate drugs. Selected drug-macromolecule interactions were further explored using CB-Dock2 molecular docking and short-duration molecular dynamics simulations as hypothesis-generating structural assessments. Analysis of a key PPI network cluster consisting of several ATP synthase proteins identified 23 unique drug candidates. These analyses demonstrate a systematic approach for leveraging bioinformatics techniques to identify candidate molecular targets and generate pharmacological hypotheses in the context of space radiation countermeasures. Ultimately, this strategy introduces a hypothesis-generating framework for the prioritization of potential drug candidates for future computational characterization and experimental investigation against spaceflight stressors.

Animals↗

PubMed2026

Proteomic insights into Helicobacter pylori infection in stomach cells, revealing host response and host-targeted therapeutics repurposing.

BACKGROUND: Helicobacter pylori (H. pylori) is a globally prevalent gastric pathogen strongly associated with chronic gastritis, peptic ulcers, and gastric cancer. While bacterial factors have been extensively studied, host proteomic responses and their therapeutic potential remain largely underexplored. RESEARCH DESIGN AND METHODS: Current analyses employed a systematic proteomics-based data integration and harmonization approach (retrospective qualitative cohort study) to identify important differentially regulated host proteins. Proteomic datasets were curated from in vitro studies and analyzed for functional enrichment, protein-protein interaction networks, and hub protein identification. To explore therapeutic repurposing, drug repositioning was performed using the DrugBank database. RESULTS: Data summation describing protein differential regulation in human gastric cells as a result of the infection revealed 1672 perturbed host proteins. Bioinformatics analysis revealed 11 proteins including CSK, MET, RELA, MARK2, GRB2, FTO, PLCG1, CRKL, RPS5, RPS9, and RPS27A to be ideal host targets for therapeutic repurposing. Clinically approved drugs such as Dasatinib (targeting CSK) and Crizotinib (targeting MET) emerged as promising candidates due to favorable pharmacokinetics and known bioactivity. CONCLUSIONS: Host-directed therapeutics could offer alternative strategies to conventional antibiotic therapy, addressing challenges such as resistance and infection recurrence, providing a foundation for future experimental validation and development of host-targeted interventions for infection control.

Humans↗

PubMed2026

Single-cell expression quantitative trait locus Mendelian randomization reveals immune cell-specific causal regulatory networks and actionable targets in polycystic ovary syndrome.

ObjectiveTo systematically investigate whether the pathogenesis of polycystic ovary syndrome (PCOS) is causally related to dysregulated gene expression in specific immune cell subsets, and to evaluate the potential of these causal genes as actionable drug targets.MethodsThis study employed a two-sample Mendelian randomization (MR) framework using publicly available genome-wide association study (GWAS) summary statistics. The participant data included 797 PCOS cases and 140,558 controls (no direct patient recruitment was involved). Instrumental variables were derived from high-resolution immune cell-specific single-cell expression quantitative trait locus (sc-eQTL) data (OneK1K project) across 14 immune cell types. Primary analyses utilized the inverse-variance weighted (IVW) method. Shared causal variants were validated using Bayesian colocalization. Phenome-wide association analysis (PheWAS), external transcriptomic dataset validation (GSE8157), and DrugBank database screening were conducted for pleiotropy assessment and drug repositioning.ResultsMR analysis revealed genome-wide significant causal associations for GLIPR1 in non-classical monocytes (Mono NC) and XBP1 in CD4+ effector memory T cells (CD4 ET) with PCOS risk. Higher GLIPR1 expression was associated with a decreased PCOS risk (OR = 0.669, P = 4.34×10-6), whereas higher XBP1 expression was associated with an increased risk (OR = 1.406, P = 9.53×10-8). Colocalization analysis confirmed that GLIPR1 shares a causal variant with PCOS (PP.H4 = 96.73%). PheWAS and external validation confirmed the safety profile and significant upregulation (P = 0.03) of GLIPR1. Drug repositioning identified SOT-107, a Phase III protein therapy drug, as a potential interacting agent for GLIPR1.ConclusionsThis sc-eQTL MR study reveals immune cell-specific causal regulatory networks in PCOS. GLIPR1 in non-classical monocytes represents a high-confidence protective target, while XBP1 provides suggestive evidence for immune-mediated pathogenesis. The candidate drug SOT-107 highlights theoretical repositioning opportunities, though rigorous preclinical validation remains required.

Female↗

PubMed2026

Systematic Identification of Therapeutic Targets and Repurposed Drugs for Stroke: From Genome Causal Analysis to Multilevel Validation.

BACKGROUND: Stroke is a severe cerebrovascular disease characterized by narrow time windows and complications. This study aimed to identify novel drug targets and repurposed drugs for stroke. METHODS: This study used expression quantitative trait loci data from druggable genes in brain and blood as instrumental variables. Mendelian randomization, colocalization, and phenome-wide Mendelian randomization were applied to evaluate causal relationships and potential side effects, with stroke and ischemic stroke as primary outcomes. Preclinical validation used oxygen-glucose deprivation/reperfusion and middle cerebral artery occlusion/reperfusion models. Pharmacological and behavioral assessments evaluated the therapeutic potential of candidate targets and drugs. Additionally, proteomic sequencing was performed following GGCX (γ-glutamyl carboxylase) overexpression to explore its biological functions. RESULTS: Elevated GGCX expression in brain and blood was potentially causally associated with reduced risk of stroke and ischemic stroke, supported by colocalization evidence, although potential cardiovascular risks could not be excluded. Drug repositioning identified ifenprodil as a candidate agent that reduced infarction volume, improved motor and cognitive functions, and reversed GGCX downregulation in mice. Ifenprodil treatment and GGCX overexpression alleviated oxygen-glucose deprivation/reperfusion-induced injury and upregulated GGCX expression. Mechanistically, GGCX conferred neuroprotection by regulating protein homeostasis, suppressing inflammation, promoting metabolic recovery, and modulating nuclear transcriptional regulation. CONCLUSIONS: This study established a potential causal link between GGCX and stroke risk, particularly ischemic stroke. GGCX represents a promising therapeutic target for ischemic stroke. Targeted GGCX expression upregulation and drug repurposing, particularly ifenprodil, may offer novel therapeutic avenues. Further validation is warranted to assess clinical efficacy and safety.

Animals↗

PubMed2026

Leveraging the genetics of psychiatric disorders to prioritize potential drug targets and compounds.

Genetics can inform biologically relevant drug development and repurposing, which may improve patient care. Here, we leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds. We used the genome-wide association studies of four psychiatric disorders [attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] and genes encoding drug targets. We conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We conducted multiple molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder. We reveal candidate drug targets associated with a single or multiple disorders that implicate glutamate signaling. Drug prioritization indicated genetic support for psychotropic medications, including several top-ranked antipsychotics for schizophrenia. We also observed genetic support for commonly used psychotropics for psychiatric treatment (e.g., clozapine, duloxetine, and lithium). Revealed opportunities for drug repurposing included cholinergic drugs for ADHD, estrogen modulators for depression, and matrix metalloproteinases for ADHD and depression. Our findings indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission. Our extensive analyses highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders, providing novel opportunities for improving patient outcomes. Depicted is the series of analyses conducted to generate a list of prioritized drug targets and compounds. First pairings of genome-wide association study (GWAS) traits with drugs are generated using enrichment analyses. Next, a series of molecular trait analyses is conducted to generate and rank a list of potential drug targets for each GWAS trait. Finally, enrichment and molecular trait results are combined to generate a ranked list of prioritized drugs for each GWAS trait based on supporting genetic evidence. ADHD = Attention deficit hyperactivity disorder, BIP = Bipolar disorder, DEP = Depression, SCZ = Schizophrenia, DBP = Diastolic blood pressure, T2D = Type 2 diabetes, RNA = ribonucleic acid, XWAS = both transcriptome and proteome-wide association studies, MR = Mendelian randomization, coloc = colocalization.

Humans↗

PubMed2024

Drug repurposing in status epilepticus.

The treatment of status epilepticus (SE) has changed little in the last 20 years, largely because of the high risks and costs of new drug development for SE. Moreover, SE poses specific challenges to drug development, such as patient diversity, logistical hurdles, and the need for acute treatment strategies that differ from chronic seizure prevention. This has reduced the appetite of industry to develop new drugs in this area. Drug repurposing is an attractive approach to address this unmet need. It offers significant advantages, including reduced development time, lower costs, and higher success rates, compared to novel drug development. Here I demonstrate how novel methods integrating biological knowledge and computational methods can be applied to drug repurposing in status epilepticus. Biological approaches focus on addressing mechanisms underlying drug resistance in SE (using for example ketamine, tacrolimus and safinamide) and longer-term consequences (using for example omaveloxolone, celecoxib and losartan). Additionally, artificial intelligence platforms, such as ChatGPT, can rapidly generate promising drug lists, while in silico methods can analyze gene expression changes to predict molecular targets. Combining AI and in silico approaches has identified several candidate drugs, including metformin, sirolimus and riluzole, for SE treatment. Despite the promise of repurposing, challenges remain, such as intellectual property issues and regulatory barriers. Nonetheless, drug repurposing presents a viable solution to the high costs and slow progress of traditional drug development for SE. This paper is based on a presentation made at the 9th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures, in April 2024.

Animals↗

PubMed2026

Drug repurposing using transcriptomics: principles and unmet needs in cardiovascular disease.

Although cardiovascular disease is the leading cause of death globally, therapeutic development in this field is slow. Given the high cost of developing new drugs and running clinical trials for cardiovascular disease, repurposing of drugs with approved safety profiles is an attractive strategy for therapeutic development that can significantly reduce the time and cost investment before phase II clinical trials. In the era of "Omics," various new methods and several large databases have been developed to enable the use of transcriptomics data for drug repurposing. This review summarizes the principles and workflow of signature mapping, which forms the foundation of statistical models used for transcriptome-based drug repurposing. We highlight the features of different analysis pipelines and databases that have been developed for signature mapping. These analysis pipelines prioritize genes that are statistically important, an approach that fundamentally differs from the pharmacological approach of identifying disease-driving and therapeutically targetable pathways. Outcomes of signature mapping pipelines are sensitive to the quality of input data, and results are not always reproducible. Moreover, all widely used RNA-seq databases are derived from cancer research and lack high-quality molecular data for cardiovascular disease. These unmet needs call for interdisciplinary collaboration and large networks of cardiovascular research-oriented biobanks to create the databases needed for transcriptomic-based signature mapping for drug repurposing efforts.

Drug Repositioning↗

PubMed2025

Genomics-informed drug-repurposing strategy identifies two therapeutic targets for preventing liver disease associated with metabolic dysfunction.

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

Humans↗

PubMed2025

Network-based drug repurposing for psychiatric disorders using single-cell genomics.

Neuropsychiatric disorders lack effective treatments due to a limited understanding of the underlying cellular and molecular mechanisms. To address this, we integrated population-scale single-cell genomics data and analyzed 23 cell-type-level gene regulatory networks across schizophrenia, bipolar disorder, and autism. Our analysis revealed potential druggable transcription factors co-regulating known risk genes that converge into cell-type-specific co-regulated modules. We applied graph neural networks on those modules to prioritize novel risk genes and leveraged them in a network-based drug repurposing framework to identify 220 drug molecules with the potential for targeting specific cell types. We found evidence for 37 of these drugs in reversing disorder-associated transcriptional phenotypes. Additionally, we discovered 335 drug-cell quantitative trait loci (eQTLs), revealing genetic variation's influence on drug target expression at the cell-type level. Our results provide a single-cell network medicine resource that provides potential mechanistic insights for advancing treatment options for neuropsychiatric disorders.

Drug Repositioning↗

PubMed2026

Memantine and its analogs: Potential applications in cancer therapy.

Drug repurposing refers to the process of using an existing drug or drug candidate for a new treatment or medical condition for which it was not indicated before. The process of "drug repurposing" usually involves an FDA-approved entity which has undergone clinical development and have a with well-established safety and toxicity profile in patients. Several convergent studies show that repurposed drugs may present a promising strategy for the management and therapy of several human cancers. Memantine is used to combat dementia in moderate-to-severe Alzheimer's disease (AD) patients. Several convergent studies show that memantine (and its analogs) may have applications in multiple disease conditions associated with human cancers. Memantine and its related compounds have shown promise as neuroprotective agents, anti-fatigue agents and pain-relieving agents to alleviate the toxic side effects of radiation therapy and chemotherapy. Recent publications have revealed that memantine displays anti-cancer activity by exerting direct growth-suppressive activity on the primary tumor as modulating the genomic/cellular landscape of the tumor microenvironment. Currently, clinical trials are in progress, which aim to evaluate the potential applications of memantine in cancer treatment. The adamantane scaffold in memantine has proved to be a versatile tool for the discovery of synthetic memantine analogs with robust anti-cancer activity. The discovery of second-generation memantine analogs may have wider applications in combating cancer recurrence and addressing clinical challenges in the treatment of drug-resistant and metastatic cancers.

Humans↗

PubMed2026

Microglial PICALM: A novel genetic driver and therapeutic target in vascular dementia.

BACKGROUND: Vascular dementia (VaD) lacks well-defined genetic mechanisms. Cell-type-specific effects of GWAS loci remain unexplored. METHODS: We integrated single‑cell eQTL data (183 donors, eight cell types) with VaD GWAS (3624 cases, 475,484 controls) using Mendelian randomization and Bayesian colocalization, replicated in an independent cohort (2074 cases, 456,366 controls). Subtype, snRNA‑seq, cell‑cell communication, PheWAS, expression profiling, and drug prediction with BBB permeability assessment were performed. RESULTS: Microglial PICALM was the only robustly replicated signal (OR = 0.8334, p = 5.3 × 10⁻⁴; colocalization PP.H4 > 0.75). The effect was strongest in multiple infarctions dementia (OR = 0.7746). Exploratory snRNA-seq analysis (4 VaD vs. 4 controls; GSE282111) provided supporting evidence for microglial PICALM enrichment and downregulation (p < 0.001). PICALM‑high microglia showed enhanced neurovascular‑ and phagocytosis‑related communication (e.g., SPP1, GAS6, GRN). PheWAS revealed no pleiotropy. In silico drug repurposing prioritised three FDA-approved BBB-penetrant compounds (disopyramide, benzocaine, amantadine) as candidates warranting further mechanistic validation. CONCLUSIONS: Microglial PICALM is identified as a likely genetic determinant of VaD, especially in the multiple infarctions subtype. Upregulating PICALM may be associated with a neuroprotective microglial phenotype, highlighting PICALM as a candidate therapeutic target warranting further experimental validation.

Humans↗

PubMed2026

A reproducible computational transcriptomic framework for cell-type-resolved fibroinflammatory-AKT remodeling in human heart failure.

BACKGROUND: Human heart failure involves multicellular transcriptional remodeling, but public transcriptomic studies often remain disconnected from cell-type localization and perturbational interpretation. METHODS: We developed a reproducible computational workflow integrating human left-ventricular bulk transcriptomes, donor-level cell-type pseudobulk results from a human heart-failure single-cell/single-nucleus atlas, external snRNA-seq support, curated module scoring, focused ligand-receptor prioritization and LINCS/L1000 perturbational matching. RESULTS: Cross-cohort analysis identified 14,358 same-direction HF-associated genes, including 1633 replicated HF-up and 785 replicated HF-down genes. Donor-level pseudobulk analysis localized disease remodeling to cardiomyocyte, fibroblast and myeloid compartments. Activated fibroblast and inflammatory myeloid programs defined a fibroinflammatory remodeling axis connected to context-dependent AKT-associated transcriptional shifts. External snRNA-seq support was strongest for fibroblast activation and AKT-associated remodeling, with etiology-dependent heterogeneity across validation resources. L1000FWD screening prioritized safety-aware perturbational hypotheses, including glimepiride and simvastatin as interpretable candidates requiring experimental validation. CONCLUSIONS: This study provides a computational transcriptomic framework linking reproducible human HF signatures, cell-type-resolved fibroinflammatory remodeling and perturbational genomic prioritization without claiming drug efficacy or AKT causality.

Humans↗

PubMed2026

Mapping the immune-genetic architecture of Epstein-Barr virus-related phenotypes and multiple sclerosis through a single-cell genetic framework for target prioritization and pharmacologic hypothesis generation.

BACKGROUND: Multiple sclerosis (MS) is a severe neuroinflammatory disease causing substantial long-term disability. Strong epidemiologic evidence links Epstein-Barr virus (EBV) exposure with MS risk, but genetic evidence for immune target prioritization in EBV-related phenotypes remains limited. METHODS: We integrated single-cell cis-eQTL data from 14 immune cell types with GWASs of an EBV-related clinical phenotype and MS using a single-cell Mendelian randomization framework with colocalization analyses. Candidate eGenes were evaluated in independent cohorts. For multi-SNP instruments, we performed heterogeneity, pleiotropy, MR-Egger, weighted median, mode-based, and MR-PRESSO sensitivity analyses. We also conducted phenome-wide association analyses and queried DrugBank to annotate candidate compounds targeting prioritized genes. RESULTS: We prioritized 43 immune-cell-specific candidate eGenes with convergent genetic support, including 6 for the EBV-related phenotype and 37 for MS. SERPINB1 in NK cells was associated with increased risk of the EBV-related phenotype, whereas HLA-G was associated with decreased risk. For MS, APOM and MSH5 showed protective associations, while AHI1 showed cell-type-dependent, bidirectional associations across immune lineages. Colocalization and independent cohort evaluation supported these findings. Among FDR-significant multi-SNP associations, MR-Egger intercept tests did not indicate directional pleiotropy, although a small subset showed heterogeneity or MR-PRESSO signals. Phenome-wide analyses identified no significant adverse phenotypic associations among evaluable genes at the prespecified threshold. DrugBank annotation nominated sodium nitroprusside, fasudil, artenimol, and choline as hypothesis-generating compounds for experimental follow-up. CONCLUSIONS: This study provides a single-cell genetic framework for prioritizing immune-cell-specific candidate targets for EBV-related phenotypes and MS, and nominates genetically supported targets and pharmacologic hypotheses for experimental investigation.

Humans↗

PubMed2025

Novel approaches to clinical trial design in cancer neuroscience.

The emerging field of cancer neuroscience has revealed profound bidirectional interactions between the nervous system and cancer cells, identifying novel therapeutic vulnerabilities across diverse malignancies. This review examines the unique challenges and strategies for translating these insights into effective therapies. We propose innovative approaches to overcome these barriers through drug repurposing, enhanced biomarker development, and optimized trial designs. Repurposing neuroactive drugs with established safety profiles offers an accelerated path to clinical impact, particularly for targeting glutamatergic, adrenergic, and neurotrophic signaling pathways. Emphasizing mitigation of neurotoxicity and improved patient quality of life will be paramount moving forward. Repurposed agents that show preliminary potential for "dual use" (i.e., simultaneous toxicity mitigation and synergistic anti-tumor effects) are highlighted for special consideration. Master protocols and window-of-opportunity trials provide platforms to rapidly validate mechanisms while addressing patient-centered outcomes. By systematically addressing these foundational elements across disciplines, cancer neuroscience can translate its profound mechanistic insights into meaningful therapeutic advances for patients with treatment-resistant malignancies.

Humans↗

PubMed2024

The effect of fampridine on working memory: a randomized controlled trial based on a genome-guided repurposing approach.

Working memory (WM), a key component of cognitive functions, is often impaired in psychiatric disorders such as schizophrenia. Through a genome-guided drug repurposing approach, we identified fampridine, a potassium channel blocker used to improve walking in multiple sclerosis, as a candidate for modulating WM. In a subsequent double-blind, randomized, placebo-controlled, crossover trial in 43 healthy young adults (ClinicalTrials.gov, NCT04652557), we assessed fampridine's impact on WM (3-back d-prime, primary outcome) after 3.5 days of repeated administration (10 mg twice daily). Independently of baseline cognitive performance, no significant main effect was observed (Wilcoxon P = 0.87, r = 0.026). However, lower baseline performance was associated with higher working memory performance after repeated intake of fampridine compared to placebo (rs = -0.37, P = 0.014, n = 43). Additionally, repeated intake of fampridine lowered resting motor threshold (F(1,37) = 5.31, P = 0.027, R2β = 0.01), the non-behavioral secondary outcome, indicating increased cortical excitability linked to cognitive function. Fampridine's capacity to enhance WM in low-performing individuals and to increase brain excitability points to its potential value for treating WM deficits.

Adult↗

PubMed2025

Integrating explainable artificial intelligence with multiomics systems biology and electronic health record data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health records data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; 9 tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct subtissues (defined as clusters of samples within a brain tissue that share a specific expression pattern); and gene-gene coexpression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six Food and Drug Administration (FDA)-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large US de-identified insurance-claims database (n = 364 733), exposure to promethazine, one of the candidate drugs, was associated with a 57%-62% lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both P < .001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multiomics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Alzheimer Disease↗

PubMed2026

Large-scale pleiotropic analysis across cancers reveals shared genetic mechanisms and identifies novel functional genes.

Pleiotropic genetic loci have been increasingly reported in cancer, and identifying genetic variants with pleiotropic associations can reveal shared biological pathways influencing multiple cancers. Using summary statistics from genome-wide association studies for 37 cancer types (N = 433 836), we identified extensive genome-wide and local genetic correlations among cancers. Through pairwise pleiotropic analysis, we identified 75 243 significant pleiotropic single nucleotide polymorphisms (SNPs) across 372 cancer pairs, among which 3472 were lead SNPs with potential regulatory functions. Using FUMA and MAGMA, we identified 2527 pleiotropic risk loci and 4272 candidate pleiotropic genes. Notably, genes such as TERT (5p15.33), POU5F1B (8q24.21), and FANCA (16q24.3) exhibited widespread pleiotropy across multiple cancer types. Pathway enrichment analysis highlighted the critical roles of pigment synthesis, metabolism, and apoptosis in skin-related cancers, while cross-cancer enrichment analysis emphasized pathways related to apoptosis, chromatin structure, and intermediate filaments. We also identified 33 novel functional genes harboring previously unreported cancer risk variants. Drug-gene interaction analysis revealed several repositionable FDA-approved drugs. Importantly, drug sensitivity assays demonstrated that bosutinib and cobimetinib exhibited promising therapeutic potential in breast cancer cell lines. Finally, we developed the PleioCancer database (https://gonglab.hzau.edu.cn/PleioCancer/), providing a comprehensive resource for cancer pleiotropy research. These findings have important implications for carcinogenesis cancer, prevention and treatment.

Humans↗