دورية أكاديمية
Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing
| العنوان: | Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing |
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| المؤلفون: | Belinda B. Garana, James H. Joly, Alireza Delfarah, Hyunjun Hong, Nicholas A. Graham |
| المصدر: | BMC Bioinformatics, Vol 24, Iss 1, Pp 1-25 (2023) |
| بيانات النشر: | BMC, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Biology (General) |
| مصطلحات موضوعية: | Drug repurposing, Mechanism of action, Precision medicine, Targeted therapeutics, Enrichment analysis, Gene expression analysis, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5 |
| الوصف: | Abstract Background There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates. Results First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors. Conclusions DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA . |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1471-2105 |
| Relation: | https://doaj.org/toc/1471-2105 |
| DOI: | 10.1186/s12859-023-05343-8 |
| URL الوصول: | https://doaj.org/article/c5683b1f35d2401da9e0440268b4fdb6 |
| رقم الأكسشن: | edsdoj.5683b1f35d2401da9e0440268b4fdb6 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 14712105 |
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| DOI: | 10.1186/s12859-023-05343-8 |