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See detailDoxorubicin increases oxidative metabolism in HL-1 cardiomyocytes as shown by 13C metabolic flux analysis.
Strigun, Alexander; Wahrheit, Judith; Niklas, Jens et al

in Toxicological sciences : an official journal of the Society of Toxicology (2012), 125(2), 595-606

Doxorubicin (DXR), an anticancer drug, is limited in its use due to severe cardiotoxic effects. These effects are partly caused by disturbed myocardial energy metabolism. We analyzed the effects of ... [more ▼]

Doxorubicin (DXR), an anticancer drug, is limited in its use due to severe cardiotoxic effects. These effects are partly caused by disturbed myocardial energy metabolism. We analyzed the effects of therapeutically relevant but nontoxic DXR concentrations for their effects on metabolic fluxes, cell respiration, and intracellular ATP. (13)C isotope labeling studies using [U-(13)C(6)]glucose, [1,2-(13)C(2)]glucose, and [U-(13)C(5)]glutamine were carried out on HL-1 cardiomyocytes exposed to 0.01 and 0.02 muM DXR and compared with the untreated control. Metabolic fluxes were calculated by integrating production and uptake rates of extracellular metabolites (glucose, lactate, pyruvate, and amino acids) as well as (13)C-labeling in secreted lactate derived from the respective (13)C-labeled substrates into a metabolic network model. The investigated DXR concentrations (0.01 and 0.02 muM) had no effect on cell viability and beating of the HL-1 cardiomyocytes. Glycolytic fluxes were significantly reduced in treated cells at tested DXR concentrations. Oxidative metabolism was significantly increased (higher glucose oxidation, oxidative decarboxylation, TCA cycle rates, and respiration) suggesting a more efficient use of glucose carbon. These changes were accompanied by decrease of intracellular ATP. We conclude that DXR in nanomolar range significantly changes central carbon metabolism in HL-1 cardiomyocytes, which results in a higher coupling of glycolysis and TCA cycle. The myocytes probably try to compensate for decreased intracellular ATP, which in turn may be the result of a loss of NADH electrons via either formation of reactive oxygen species or electron shunting. [less ▲]

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See detailMetabolic profiling using HPLC allows classification of drugs according to their mechanisms of action in HL-1 cardiomyocytes.
Strigun, Alexander; Wahrheit, Judith; Beckers, Simone et al

in Toxicology and applied pharmacology (2011), 252(2), 183-91

Along with hepatotoxicity, cardiotoxic side effects remain one of the major reasons for drug withdrawals and boxed warnings. Prediction methods for cardiotoxicity are insufficient. High content screening ... [more ▼]

Along with hepatotoxicity, cardiotoxic side effects remain one of the major reasons for drug withdrawals and boxed warnings. Prediction methods for cardiotoxicity are insufficient. High content screening comprising of not only electrophysiological characterization but also cellular molecular alterations are expected to improve the cardiotoxicity prediction potential. Metabolomic approaches recently have become an important focus of research in pharmacological testing and prediction. In this study, the culture medium supernatants from HL-1 cardiomyocytes after exposure to drugs from different classes (analgesics, antimetabolites, anthracyclines, antihistamines, channel blockers) were analyzed to determine specific metabolic footprints in response to the tested drugs. Since most drugs influence energy metabolism in cardiac cells, the metabolite "sub-profile" consisting of glucose, lactate, pyruvate and amino acids was considered. These metabolites were quantified using HPLC in samples after exposure of cells to test compounds of the respective drug groups. The studied drug concentrations were selected from concentration response curves for each drug. The metabolite profiles were randomly split into training/validation and test set; and then analysed using multivariate statistics (principal component analysis and discriminant analysis). Discriminant analysis resulted in clustering of drugs according to their modes of action. After cross validation and cross model validation, the underlying training data were able to predict 50%-80% of conditions to the correct classification group. We show that HPLC based characterisation of known cell culture medium components is sufficient to predict a drug's potential classification according to its mode of action. [less ▲]

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