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Amiodarone is known to induce hepatic injury in some recipients. We applied an untargeted metabolomics approach to identify endogenous metabolites with potential as biomarkers for amiodarone-induced liver injury. Oral amiodarone administration for 1 week in rats resulted in significant elevation of acylcarnitines and phospholipids in the liver. Hepatic short-and medium-chain acylcarnitines were dramatically increased in a dose-dependent manner, while the serum levels of these acylcarnitines did not change substantially. In addition, glucose levels were significantly increased in both the serum and liver. Gene expression profiling showed that the hepatic mRNA levels of Cpt1, Cpt2, and Acat1 were significantly suppressed, whereas those of Acot1, Acly, Acss2, and Acsl3 were increased. These results suggest that hepatic acylcarnitines and glucose levels might be increased due to disruption of mitochondrial function and suppression of glucose metabolism. Perturbation of energy metabolism might be associated with amiodarone-induced hepatotoxicity. K E Y W O R D Sacylcarnitine, amiodarone, hepatotoxicity, LC/MSMS, metabolomics

Amiodarone is a class III anti-arrhythmic benzofuran derivative extensively utilized in treatment of life-threatening ventricular and supraventricular arrhythmias. However, amiodarone also produces adverse side effects including liver injury due to its metabolites rather than parent drug. The purpose of the present study was to identify metabolites of amiodarone in the plasma and urine of rats administered the drug by using an untargeted metabolomics approach. Drug metabolites were profiled by ultra-performance liquid chromatography-linked electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) and results subjected to multivariate data analysis. A total of 49 amiodarone metabolites were identified and their structures were characterized by tandem mass spectrometry. Amiodarone metabolites are presumed to be generated via five major types of metabolic reactions including N-desethylation, hydroxylation, carboxylation (oxo/hydroxylation), de-iodination, and glucuronidation. Data demonstrated that an untargeted metabolomics approach appeared to be a reliable tool for identifying unknown metabolites in a complex biological matrix.

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