Novel insight into the etiology of ischemic stroke gained by integrative multiome-wide association study.

Stroke, characterized by sudden neurological deficits, is the second leading cause of death worldwide. Although genome-wide association studies (GWAS) have successfully identified many genomic regions associated with ischemic stroke (IS), the genes underlying risk and their regulatory mechanisms remain elusive. Here, we integrate a large-scale GWAS (N = 1 296 908) for IS together with molecular QTLs data, including mRNA, splicing, enhancer RNA (eRNA), and protein expression data from up to 50 tissues (total N = 11 588). We identify 136 genes/eRNA/proteins associated with IS risk across 60 independent genomic regions and find IS risk is most enriched for eQTLs in arterial and brain-related tissues. Focusing on IS-relevant tissues, we prioritize 9 genes/proteins using probabilistic fine-mapping TWAS analyses. In addition, we discover that blood cell traits, particularly reticulocyte cells, have shared genetic contributions with IS using TWAS-based pheWAS and genetic correlation analysis. Lastly, we integrate our findings with a large-scale pharmacological database and identify a secondary bile acid, deoxycholic acid, as a potential therapeutic component. Our work highlights IS risk genes/splicing-sites/enhancer activity/proteins with their phenotypic consequences using relevant tissues as well as identify potential therapeutic candidates for IS.

Molecular bases of comorbidities: present and future perspectives.

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

MiRAGE: mining relationships for advanced generative evaluation in drug repositioning.

MOTIVATION: Drug repositioning, the identification of new therapeutic uses for existing drugs, is crucial for accelerating drug discovery and reducing development costs. Some methods rely on heterogeneous networks, which may not fully capture the complex relationships between drugs and diseases. However, integrating diverse biological data sources offers promise for discovering new drug-disease associations (DDAs). Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. However, the challenge lies in effectively integrating different biological data sources to identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms. RESULTS: In response to this challenge, we present MiRAGE, a novel computational method for drug repositioning. MiRAGE leverages a three-step framework, comprising negative sampling using hard negative mining, classification employing random forest models, and feature selection based on feature importance. We evaluate MiRAGE on multiple benchmark datasets, demonstrating its superiority over state-of-the-art algorithms across various metrics. Notably, MiRAGE consistently outperforms other methods in uncovering novel DDAs. Case studies focusing on Parkinson's disease and schizophrenia showcase MiRAGE's ability to identify top candidate drugs supported by previous studies. Overall, our study underscores MiRAGE's efficacy and versatility as a computational tool for drug repositioning, offering valuable insights for therapeutic discoveries and addressing unmet medical needs.

RepurposeDrugs: an interactive web-portal and predictive platform for repurposing mono- and combination therapies.

RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications and clinical trials across 25 indication categories, including neoplasms and cardiovascular conditions. The current version comprises 4314 compounds (approved, terminated or investigational) and 161 drug combinations linked to 1756 indications/conditions, totaling 28 148 drug-disease pairs. By leveraging data on both approved and failed indications, RepurposeDrugs provides ML-based predictions for the approval potential of new drug-disease indications, both for mono- and combinatorial therapies, demonstrating high predictive accuracy in cross-validation. The validity of the ML predictor is validated through a number of real-world case studies, demonstrating its predictive power to accurately identify repurposing candidates with a high likelihood of future approval. To our knowledge, RepurposeDrugs web-portal is the first integrative database and ML-based predictor for interactive exploration and prediction of both single-drug and combination approval likelihood across indications. Given its broad coverage of indication areas and therapeutic options, we expect it accelerates many future drug repurposing projects.

DRONet: effectiveness-driven drug repositioning framework using network embedding and ranking learning.

As one of the most vital methods in drug development, drug repositioning emphasizes further analysis and research of approved drugs based on the existing large amount of clinical and experimental data to identify new indications of drugs. However, the existing drug repositioning methods didn't achieve enough prediction performance, and these methods do not consider the effectiveness information of drugs, which make it difficult to obtain reliable and valuable results. In this study, we proposed a drug repositioning framework termed DRONet, which make full use of effectiveness comparative relationships (ECR) among drugs as prior information by combining network embedding and ranking learning. We utilized network embedding methods to learn the deep features of drugs from a heterogeneous drug-disease network, and constructed a high-quality drug-indication data set including effectiveness-based drug contrast relationships. The embedding features and ECR of drugs are combined effectively through a designed ranking learning model to prioritize candidate drugs. Comprehensive experiments show that DRONet has higher prediction accuracy (improving 87.4% on Hit@1 and 37.9% on mean reciprocal rank) than state of the art. The case analysis also demonstrates high reliability of predicted results, which has potential to guide clinical drug development.

Validation of transcriptome signature reversion for drug repurposing in oncology.

Transcriptome signature reversion (TSR) has been extensively proposed and used to discover new indications for existing drugs (i.e. drug repositioning, drug repurposing) for various cancer types. TSR relies on the assumption that a drug that can revert gene expression changes induced by a disease back to original, i.e. healthy, levels is likely to be therapeutically active in treating the disease. Here, we aimed to validate the concept of TSR using the PRISM repurposing data set, which is-as of writing-the largest pharmacogenomic data set. The predictive utility of the TSR approach as it has currently been used appears to be much lower than previously reported and is completely nullified after the drug gene expression signatures are adjusted for the general anti-proliferative downstream effects of drug-induced decreased cell viability. Therefore, TSR mainly relies on generic anti-proliferative drug effects rather than on targeting cancer pathways specifically upregulated in tumor types.

Drug repositioning based on weighted local information augmented graph neural network.

Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug-disease associations, they often overlook the relevance between different node embeddings. Consequently, we propose a novel weighted local information augmented graph neural network model, termed DRAGNN, for drug repositioning. Specifically, DRAGNN firstly incorporates a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. To prevent excessive embedding of information in a limited vector space, we omit self-node information aggregation, thereby emphasizing valuable heterogeneous and homogeneous information. Additionally, average pooling in neighbor information aggregation is introduced to enhance local information while maintaining simplicity. A multi-layer perceptron is then employed to generate the final association predictions. The model's effectiveness for drug repositioning is supported by a 10-times 10-fold cross-validation on three benchmark datasets. Further validation is provided through analysis of the predicted associations using multiple authoritative data sources, molecular docking experiments and drug-disease network analysis, laying a solid foundation for future drug discovery.

Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study.

BACKGROUND: Male-pattern baldness (MPB) is the most common cause of hair loss in men. It can be categorized into three types: type 2 (T2), type 3 (T3), and type 4 (T4), with type 1 (T1) being considered normal. Although various MPB-associated genetic variants have been suggested, a comprehensive study for linking these variants to gene expression regulation has not been performed to the best of our knowledge. RESULTS: In this study, we prioritized MPB-related tissue panels using tissue-specific enrichment analysis and utilized single-tissue panels from genotype-tissue expression version 8, as well as cross-tissue panels from context-specific genetics. Through a transcriptome-wide association study and colocalization analysis, we identified 52, 75, and 144 MPB associations for T2, T3, and T4, respectively. To assess the causality of MPB genes, we performed a conditional and joint analysis, which revealed 10, 11, and 54 putative causality genes for T2, T3, and T4, respectively. Finally, we conducted drug repositioning and identified potential drug candidates that are connected to MPB-associated genes. CONCLUSIONS: Overall, through an integrative analysis of gene expression and genotype data, we have identified robust MPB susceptibility genes that may help uncover the underlying molecular mechanisms and the novel drug candidates that may alleviate MPB.

MREDTA: A BERT and transformer-based molecular representation encoder for predicting drug-target binding affinity.

Drug-target binding affinity (DTA) prediction is vital for drug repositioning. The accuracy and generalizability of DTA models remain a major challenge. Here, we develop a model composed of BERT-Trans Block, Multi-Trans Block, and DTI Learning modules, referred to as Molecular Representation Encoder-based DTA prediction (MREDTA). MREDTA has three advantages: (1) extraction of both local and global molecular features simultaneously through skip connections; (2) improved sensitivity to molecular structures through the Multi-Trans Block; (3) enhanced generalizability through the introduction of BERT. Compared with 12 advanced models, benchmark testing of KIBA and Davis datasets demonstrated optimal performance of MREDTA. In case study, we applied MREDTA to 2034 FDA-approved drugs for treating non-small-cell lung cancer (NSCLC), all of which act on mutant EGFRT790M protein. The corresponding molecular docking results demonstrated the robustness of MREDTA.

Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations.

COVID-19 is an infectious disease caused by SARS-CoV-2 leading to the ongoing global pandemic. Infected patients developed a range of respiratory symptoms, including respiratory failure, as well as other extrapulmonary complications. Multiple comorbidities, including hypertension, diabetes, cardiovascular diseases, and chronic kidney diseases, are associated with the severity and increased mortality of COVID-19. SARS-CoV-2 infection also causes a range of cardiovascular complications, including myocarditis, myocardial injury, heart failure, arrhythmias, acute coronary syndrome, and venous thromboembolism. Although a variety of methods have been developed and many clinical trials have been launched for drug repositioning for COVID-19, treatments that consider cardiovascular manifestations and cardiovascular disease comorbidities specifically are limited. In this review, we summarize recent advances in drug repositioning for COVID-19, including experimental drug repositioning, high-throughput drug screening, omics data-based, and network medicine-based computational drug repositioning, with particular attention on those drug treatments that consider cardiovascular manifestations of COVID-19. We discuss prospective opportunities and potential methods for repurposing drugs to treat cardiovascular complications of COVID-19.

Revamped role for approved drug: integrative computational and biophysical analysis of saquinavir's peptidyl arginine deiminase 4 inhibition for rheumatoid arthritis.

The pursuit of novel therapeutics is a complex and resource-intensive endeavor marked by significant challenges, including high costs and low success rates. In response, drug repositioning strategies leverage existing FDA-approved compounds to predict their efficacy across diverse diseases. Peptidyl arginine deiminase 4 (PAD4) plays a pivotal role in protein citrullination, a process implicated in the autoimmune pathogenesis of rheumatoid arthritis (RA). Targeting PAD4 has thus emerged as a promising therapeutic approach. This study employs computational and enzyme inhibition strategies to identify potential PAD4-targeting compounds from a library of FDA-approved drugs. In silico docking analyses validated the binding interactions and orientations of screened compounds within PAD4's active site, with key residues such as ASP350, HIS471, ASP473, and CYS645 participating in crucial hydrogen bonding and van der Waals interactions. Molecular dynamics simulations further assessed the stability of top compounds exhibiting high binding affinities. Among these compounds, Saquinavir (SQV) emerged as a potent PAD4 inhibitor, demonstrating competitive inhibition with a low IC50 value of 1.21 ± 0.04 µM. In vitro assays, including enzyme kinetics and biophysical analyses, highlighted significant changes in PAD4 conformation upon SQV binding, as confirmed by circular dichroism spectroscopy. SQV induced localized alterations in PAD4 structure, effectively occupying the catalytic pocket and inhibiting enzymatic activity. These findings underscore SQV's potential as a therapeutic candidate for RA through PAD4 inhibition. Further validation through in vitro and in vivo studies is essential to confirm SQV's therapeutic benefits in autoimmune diseases associated with dysregulated citrullination.

DrugReAlign: a multisource prompt framework for drug repurposing based on large language models.

Drug repurposing is a promising approach in the field of drug discovery owing to its efficiency and cost-effectiveness. Most current drug repurposing models rely on specific datasets for training, which limits their predictive accuracy and scope. The number of both market-approved and experimental drugs is vast, forming an extensive molecular space. Due to limitations in parameter size and data volume, traditional drug-target interaction (DTI) prediction models struggle to generalize well within such a broad space. In contrast, large language models (LLMs), with their vast parameter sizes and extensive training data, demonstrate certain advantages in drug repurposing tasks. In our research, we introduce a novel drug repurposing framework, DrugReAlign, based on LLMs and multi-source prompt techniques, designed to fully exploit the potential of existing drugs efficiently. Leveraging LLMs, the DrugReAlign framework acquires general knowledge about targets and drugs from extensive human knowledge bases, overcoming the data availability limitations of traditional approaches. Furthermore, we collected target summaries and target-drug space interaction data from databases as multi-source prompts, substantially improving LLM performance in drug repurposing. We validated the efficiency and reliability of the proposed framework through molecular docking and DTI datasets. Significantly, our findings suggest a direct correlation between the accuracy of LLMs' target analysis and the quality of prediction outcomes. These findings signify that the proposed framework holds the promise of inaugurating a new paradigm in drug repurposing.

HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies.

BACKGROUND: The identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects. However, earlier research has primarily centered on extracting and utilizing representations of drugs, like molecular structure or interaction graphs, often neglecting the inherent biomedical semantics of drugs and side effects. RESULTS: To address the previously mentioned issue, we introduce a hybrid multi-modal fusion framework (HMMF) for predicting drug side effect frequencies. Considering the wealth of biological and chemical semantic information related to drugs and side effects, incorporating multi-modal information offers additional, complementary semantics. HMMF utilizes various encoders to understand molecular structures, biomedical textual representations, and attribute similarities of both drugs and side effects. It then models drug-side effect interactions using both coarse and fine-grained fusion strategies, effectively integrating these multi-modal features. CONCLUSIONS: HMMF exhibits the ability to successfully detect previously unrecognized potential side effects, demonstrating superior performance over existing state-of-the-art methods across various evaluation metrics, including root mean squared error and area under receiver operating characteristic curve, and shows remarkable performance in cold-start scenarios.

Fusing graph transformer with multi-aggregate GCN for enhanced drug-disease associations prediction.

BACKGROUND: Identification of potential drug-disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data. RESULTS: In this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug-disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug-drug, drug-disease, and disease-disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation. CONCLUSIONS: Rigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug-disease association prediction, which is beneficial for drug repositioning and drug safety research.

Phospholipid metabolic adaptation promotes survival of IDH2 mutant acute myeloid leukemia cells.

Genetic mutations in the isocitrate dehydrogenase (IDH) gene that result in a pathological enzymatic activity to produce oncometabolite have been detected in acute myeloid leukemia (AML) patients. While specific inhibitors that target mutant IDH enzymes and normalize intracellular oncometabolite level have been developed, refractoriness and resistance has been reported. Since acquisition of pathological enzymatic activity is accompanied by the abrogation of the crucial WT IDH enzymatic activity in IDH mutant cells, aberrant metabolism in IDH mutant cells can potentially persist even after the normalization of intracellular oncometabolite level. Comparisons of isogenic AML cell lines with and without IDH2 gene mutations revealed two mutually exclusive signalings for growth advantage of IDH2 mutant cells, STAT phosphorylation associated with intracellular oncometabolite level and phospholipid metabolic adaptation. The latter came to light after the oncometabolite normalization and increased the resistance of IDH2 mutant cells to arachidonic acid-mediated apoptosis. The release of this metabolic adaptation by FDA-approved anti-inflammatory drugs targeting the metabolism of arachidonic acid could sensitize IDH2 mutant cells to apoptosis, resulting in their eradication in vitro and in vivo. Our findings will contribute to the development of alternative therapeutic options for IDH2 mutant AML patients who do not tolerate currently available therapies.

Rafoxanide negatively modulates STAT3 and NF-κB activity and inflammation-associated colon tumorigenesis.

In the colorectal cancer (CRC) niche, the transcription factors signal transducer and activator of transcription 3 (STAT3) and nuclear factor-κB (NF-κB) are hyperactivated in both malignant cells and tumor-infiltrating leukocytes (TILs) and cooperate to maintain cancer cell proliferation/survival and drive protumor inflammation. Through drug repositioning studies, the anthelmintic drug rafoxanide has recently emerged as a potent and selective antitumor molecule for different types of cancer, including CRC. Here, we investigate whether rafoxanide could negatively modulate STAT3/NF-κB and inflammation-associated CRC. The antineoplastic effect of rafoxanide was explored in a murine model of CRC resembling colitis-associated disease. Cell proliferation and/or STAT3/NF-κB activation were evaluated in colon tissues taken from mice with colitis-associated CRC, human CRC cells, and CRC patient-derived explants and organoids after treatment with rafoxanide. The STAT3/NF-κB activation and cytokine production/secretion were assessed in TILs isolated from CRC specimens and treated with rafoxanide. Finally, we investigated the effects of TIL-derived supernatants cultured with or without rafoxanide on CRC cell proliferation and STAT3/NF-κB activation. The results showed that rafoxanide restrains STAT3/NF-κB activation and inflammation-associated colon tumorigenesis in vivo without apparent effects on normal intestinal cells. Rafoxanide markedly reduces STAT3/NF-κB activation in cultured CRC cells, CRC-derived explants/organoids, and TILs. Finally, rafoxanide treatment impairs the ability of TILs to produce protumor cytokines and promote CRC cell proliferation. We report the novel observation that rafoxanide negatively affects STAT3/NF-κB oncogenic activity at multiple levels in the CRC microenvironment. Our data suggest that rafoxanide could potentially be deployed as an anticancer drug in inflammation-associated CRC.

Tree-based scan statistics to generate drug repurposing hypotheses: a test case using sodium-glucose cotransporter-2 inhibitors.

Most drug repurposing studies using real-world data focused on validating, instead of generating, hypotheses. We used tree-based scan statistics to generate repurposing hypotheses for sodium-glucose cotransporter-2 inhibitors (SGLT2i). We used an active-comparator, new-user design to create a 1:1 propensity-score matched cohort of SGLT2i and dipeptidyl peptidase-4 inhibitors (DPP4i) initiators in the MerativeTM MarketScan® Research Databases. Tree-based scan statistics were estimated across an ICD-10-CM-based hierarchical outcome tree using incident outcomes identified from hospital and outpatient diagnoses. We used an adjusted P≤0.01 as the threshold for statistical alert to prioritize associations for evaluation as repurposing signals. We varied the analyses by tree size, scanning level, and clinical settings for outcomes. There were 80,510 matched SGLT2i-DPP4i initiator pairs with 215,333 outcomes among SGLT2i initiators and 223,428 outcomes among DPP4i initiators. There were 18 prioritized associations, which included chronic kidney disease (P=0.0001), an expected signal, and anemia (P=0.0001). Heart failure (P=0.0167), another expected signal, was identified slightly beyond the statistical alert threshold. Narrowing the outcome tree, scanning at different tree levels, and including outcomes from different clinical settings influenced the scan statistics. We identified signals aligning with recently approved indications of SGLT2i, plus potential repurposing signals supported by existing evidence but requiring future validation.

Can letrozole be repurposed for the treatment of visceral leishmaniasis?

Visceral leishmaniasis, caused by Leishmania infantum in New World countries, is the most serious and potentially fatal form of leishmaniasis, if left untreated. There are currently no effective prophylactic measures, and therapeutic options are limited. Therefore, we investigated whether the aromatase inhibitor letrozole (LET), which is already used to treat breast cancer, has an antileishmanial activity and/or immunomodulatory potential and therefore may be used to treat L. infantum infection. LET was active against L. infantum promastigote and amastigote life cycle stages in an in vitro infection model using human THP-1 cell-derived macrophages. In human peripheral blood leukocytes ex vivo, LET reduced the internalized forms of L. infantum by classical monocytes and activated neutrophils. Concomitantly, LET stimulated the production of IL-12/TNF-α and decreased the production of IL-10/TGF-ß by peripheral blood phagocytes, while in T and B cells, it promoted the production of TNF-α/IFN-γ and decreased that of IL-10. In a murine infection model, LET significantly reduced the parasite load in the liver after just 5 days and in the spleen after 15 days. During in vivo treatment with LET, the production of TNF-α/IFN-γ also increased. In addition, the proportion of developing granulomas decreased and that of mature granulomas increased in the liver, while there was no significant change in organ architecture in the spleen. Based on these data, repositioning of LET may be promising for the treatment of visceral leishmaniasis in humans.

Integration of National Health Insurance claims data and animal models reveals fexofenadine as a promising repurposed drug for Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a common and costly progressive neurodegenerative disease of unclear etiology. A disease-modifying approach that can directly stop or slow its progression remains a major unmet need in the treatment of PD. A clinical pharmacology-based drug repositioning strategy is a useful approach for identifying new drugs for PD. METHODS: We analyzed claims data obtained from the National Health Insurance Service (NHIS), which covers a significant portion of the South Korean population, to investigate the association between antihistamines, a class of drugs commonly used to treat allergic symptoms by blocking H1 receptor, and PD in a real-world setting. Additionally, we validated this model using various animal models of PD such as the 6-hydroxydopmaine (6-OHDA), α-synuclein preformed fibrils (PFF) injection, and Caenorhabditis elegans (C. elegans) models. Finally, whole transcriptome data and Ingenuity Pathway Analysis (IPA) were used to elucidate drug mechanism pathways. RESULTS: We identified fexofenadine as the most promising candidate using National Health Insurance claims data in the real world. In several animal models, including the 6-OHDA, PFF injection, and C. elegans models, fexofenadine ameliorated PD-related pathologies. RNA-seq analysis and the subsequent experiments suggested that fexofenadine is effective in PD via inhibition of peripheral immune cell infiltration into the brain. CONCLUSION: Fexofenadine shows promise for the treatment of PD, identified through clinical data and validated in diverse animal models. This combined clinical and preclinical approach offers valuable insights for developing novel PD therapeutics.