Display options
Cite
Strigun A, Wahrheit J, Beckers S, et al. Metabolic profiling using HPLC allows classification of drugs according to their mechanisms of action in HL-1 cardiomyocytes. Toxicol Appl Pharmacol. 2011;252(2):183-91doi: 10.1016/j.taap.2011.02.008.
Strigun, A., Wahrheit, J., Beckers, S., Heinzle, E., & Noor, F. (2011). Metabolic profiling using HPLC allows classification of drugs according to their mechanisms of action in HL-1 cardiomyocytes. Toxicology and applied pharmacology, 252(2), 183-91. https://doi.org/10.1016/j.taap.2011.02.008
Strigun, Alexander, et al. "Metabolic profiling using HPLC allows classification of drugs according to their mechanisms of action in HL-1 cardiomyocytes." Toxicology and applied pharmacology vol. 252,2 (2011): 183-91. doi: https://doi.org/10.1016/j.taap.2011.02.008
Strigun A, Wahrheit J, Beckers S, Heinzle E, Noor F. Metabolic profiling using HPLC allows classification of drugs according to their mechanisms of action in HL-1 cardiomyocytes. Toxicol Appl Pharmacol. 2011 Apr 15;252(2):183-91. doi: 10.1016/j.taap.2011.02.008. Epub 2011 Feb 12. PMID: 21320520.
Copy Download .nbib Format: NLM
Share it on

Toxicol Appl Pharmacol. 2011 Apr 15;252(2):183-91. doi: 10.1016/j.taap.2011.02.008. Epub 2011 Feb 12.

Metabolic profiling using HPLC allows classification of drugs according to their mechanisms of action in HL-1 cardiomyocytes.

Toxicology and applied pharmacology

Alexander Strigun, Judith Wahrheit, Simone Beckers, Elmar Heinzle, Fozia Noor

Affiliations

  1. Biochemical Engineering Institute, Saarland University, Campus A1.5, D-66123 Saarbruecken, Germany.

PMID: 21320520 DOI: 10.1016/j.taap.2011.02.008

Abstract

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.

Copyright © 2011 Elsevier Inc. All rights reserved.

Substances

MeSH terms

Publication Types