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Drug development is a long and costly process, and repurposing existing drugs for use toward a different disease or condition may serve as a cost-effective alternative. As drug targets with genetic support have a doubled success rate, genetics-informed drug repurposing holds promise in translating genetic findings into therapeutics. In this study, we developed a Genetics Informed Network-based Drug Repurposing via in silico Perturbation (GIN-DRIP) framework and applied the framework to repurpose drugs for type-2 diabetes (T2D). In GIN-DRIP for T2D, it integrates multi-level omics data to translate T2D GWAS signals into a genetics-informed network that simultaneously encodes gene importance scores and a directional effect (up/down) of risk genes for T2D; it then bases on the GIN to perform signature matching with drug perturbation experiments to identify drugs that can counteract the effect of T2D risk alleles. With this approach, we identified 3 high-confidence FDA-approved candidate drugs for T2D, and validated telmisartan, an anti-hypertensive drug, in our EHR data with over 3 million patients. We found that telmisartan users were associated with a reduced incidence of T2D compared to users of other anti-hypertensive drugs and non-users, supporting the therapeutic potential of telmisartan for T2D. Our framework can be applied to other diseases for translating GWAS findings to aid drug repurposing for complex diseases.
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http://dx.doi.org/10.1101/2025.03.22.25324223 | DOI Listing |
IEEE J Biomed Health Inform
September 2025
Drug-target interaction (DTI) identification is of great significance in drug development in various areas, such as drug repositioning and potential drug side effects. Although a great variety of computational methods have been proposed for DTI prediction, it is still a challenge in the face of sparsely correlated drugs or targets. To address the impact of data sparsity on the model, we propose a multi-view neighborhood-enhanced graph contrastive learning approach (MneGCL), which is based on graph clustering according to the adjacency relationship in various similarity networks between drugs or targets, to fully exploit the information of drugs and targets with few corrections.
View Article and Find Full Text PDFRSC Med Chem
August 2025
Pharmaceutical Organic Chemistry Department, Faculty of Pharmacy, Suez Canal University 4.5 Km the Ring Road Ismailia 41522 Egypt.
Protein kinases are central regulators of cell signaling and play pivotal roles in a wide array of diseases, most notably cancer and autoimmune disorders. The clinical success of kinase inhibitors-such as imatinib and osimertinib-has firmly established kinases as valuable drug targets. However, the development of selective, potent inhibitors remains challenging due to the conserved nature of the ATP-binding site, off-target effects, resistance mutations, and patient-specific variability.
View Article and Find Full Text PDFNAR Cancer
September 2025
Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany.
Personalized treatment selection is crucial for cancer patients due to the high variability in drug response. While actionable mutations can increasingly inform treatment decisions, most therapies still rely on population-based approaches. Here, we introduce neural interaction explainable AI (NeurixAI), an explainable and highly scalable deep learning framework that models drug-gene interactions and identifies transcriptomic patterns linked with drug response.
View Article and Find Full Text PDFFront Pharmacol
August 2025
School of Health Management, Zhejiang Pharmaceutical University, Ningbo, China.
Background: Acute and long-term mental health disorders correlate with coronavirus disease 2019 (COVID-19). The underlying mechanisms responsible for the coexistence of COVID-19 and depression remain unclear, and more research is needed to find hub genes and effective therapies. The main objective of this study was to evaluate gene-expression profiles and, identify key genes, and discovery potential therapeutic agents for co-occurrence in COVID-19 and major depressive disorder (MDD).
View Article and Find Full Text PDFOncol Res
September 2025
Development and Related Diseases of Women and Children Key Laboratory of Sichuan Province, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second Hospital, Sichuan University, Chengdu, 610041, China.
Objectives: Ovarian cancer, a leading cause of gynecological malignancy-related mortality, is characterized by limited therapeutic options and a poor prognosis. Although pyrimethamine has emerged as a promising candidate demonstrating efficacy in treating various tumors, the precise mechanisms of its antitumor effects remain obscure. This study was specifically designed to investigate the mode of action underlying the antitumor effects of pyrimethamine in preclinical settings.
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