Imsea: A novel metabolite set enrichment analysis strategy to decipher drug interactions
Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatme...
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2023
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my.utm.1050212024-04-01T07:49:35Z http://eprints.utm.my/105021/ Imsea: A novel metabolite set enrichment analysis strategy to decipher drug interactions Wang, Yongpei Liu, Xingxing Dong, Liheng Cheng, Kian-Kai Lin, Caigui Wang, Xiaomin Dong, Jiyang Deng, Lingli Raftery, Daniel TP Chemical technology Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics. American Chemical Society 2023 Article PeerReviewed Wang, Yongpei and Liu, Xingxing and Dong, Liheng and Cheng, Kian-Kai and Lin, Caigui and Wang, Xiaomin and Dong, Jiyang and Deng, Lingli and Raftery, Daniel (2023) Imsea: A novel metabolite set enrichment analysis strategy to decipher drug interactions. Analytical Chemistry, 95 (15). pp. 6203-6211. ISSN 0003-2700 http://dx.doi.org/10.1021/acs.analchem.2c04603 DOI : 10.1021/acs.analchem.2c04603 |
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TP Chemical technology Wang, Yongpei Liu, Xingxing Dong, Liheng Cheng, Kian-Kai Lin, Caigui Wang, Xiaomin Dong, Jiyang Deng, Lingli Raftery, Daniel Imsea: A novel metabolite set enrichment analysis strategy to decipher drug interactions |
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Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics. |
format |
Article |
author |
Wang, Yongpei Liu, Xingxing Dong, Liheng Cheng, Kian-Kai Lin, Caigui Wang, Xiaomin Dong, Jiyang Deng, Lingli Raftery, Daniel |
author_facet |
Wang, Yongpei Liu, Xingxing Dong, Liheng Cheng, Kian-Kai Lin, Caigui Wang, Xiaomin Dong, Jiyang Deng, Lingli Raftery, Daniel |
author_sort |
Wang, Yongpei |
title |
Imsea: A novel metabolite set enrichment analysis strategy to decipher drug interactions |
title_short |
Imsea: A novel metabolite set enrichment analysis strategy to decipher drug interactions |
title_full |
Imsea: A novel metabolite set enrichment analysis strategy to decipher drug interactions |
title_fullStr |
Imsea: A novel metabolite set enrichment analysis strategy to decipher drug interactions |
title_full_unstemmed |
Imsea: A novel metabolite set enrichment analysis strategy to decipher drug interactions |
title_sort |
imsea: a novel metabolite set enrichment analysis strategy to decipher drug interactions |
publisher |
American Chemical Society |
publishDate |
2023 |
url |
http://eprints.utm.my/105021/ http://dx.doi.org/10.1021/acs.analchem.2c04603 |
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1797905886080925696 |