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Metabolic Profiling as a Tool for Understanding Mechanisms of Toxicity

Clarke, Christopher J., Haselden, John N.·2008·Toxicologic Pathology

Abstract

Metabolic profiling (metabolomics/metabonomics) is the measurement in biological systems of the complement of low-molecular-weight metabolites and their intermediates that reflects the dynamic response to genetic modification and physiological, pathophysiological, and/or developmental stimuli. The measurement and interpretation of the endogenous metabolite profile from a biological sample (typically urine, serum, or biological tissue extract) have provided many opportunities to investigate the changes induced by external stimuli (e.g., drug treatment) or enhance our knowledge of inherent biological variation within subpopulations. This article will focus on the basic principles of metabolic profiling and how the tools (nuclear magnetic resonance [NMR], liquid chromatography–mass spectrometry [LC-MS]) can be applied in toxicology and pathology. Metabolic profiling can complement conventional methodologies and other “omics” technologies in investigating preclinical drug development issues. Case studies will illustrate the value of metabolic profiling in improving our understanding of phospholipidosis and peroxisome proliferation. A key message will be that metabolic profiling offers huge potential to highlight biomarkers and mechanisms in support of toxicology and pathology investigations in preclinical drug development.

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Metadata

Authors
Clarke, C. J., & Haselden, J. N.
Venue
Toxicologic Pathology
Published
2008-01
Volume
36
Issue
1
Pages
140-147
ISSN
  • 0192-6233 (ISSN-L)
  • 1533-1601
OpenAlex
W1981434053
Cited by
216
References
0
FWCI
5.34
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Metabolic Profiling as a Tool for Understanding Mechanisms of Toxicity. https://app.cochat.ai/papers/10.1177/0192623307310947
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Clarke, C. J., & Haselden, J. N. (2008). Metabolic Profiling as a Tool for Understanding Mechanisms of Toxicity. Toxicologic Pathology. https://doi.org/10.1177/0192623307310947

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.