Metabolic profiling using HPLC allows classification of drugs according to their mechanisms of action in XXXX cardiomyocytes.

@entity1522 @entity8253 @entity37462 @entity880 @entity1370 @entity413 @entity2388
Along with hepatotoxicity, @entity1370 side effects remain one of the major reasons for drug withdrawals and boxed warnings. Prediction methods for @entity1370 are insufficient. High content screening comprising of not only electrophysiological characterization but also cellular molecular alterations are expected to improve the @entity1370 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 @entity37462 cardiomyocytes after exposure to drugs from different classes (analgesics, antimetabolites, @entity8253 , 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 @entity413 , @entity880 , @entity2388 and @entity1522 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.
