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Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo
Published on: February 6, 2015
Interspecies differences in cancer susceptibility and toxicity
J G Hengstler1, B Van der Burg, P Steinberg
1Institute of Toxicology, University of Mainz, Germany.
Drug Metabolism Reviews
|November 27, 1999
Summary
Extrapolating animal toxicology to humans is complex. This review highlights significant interspecies differences in the metabolism and toxicity of various compounds, impacting human risk assessment.
Area of Science:
- Toxicology
- Comparative Metabolism
- Risk Assessment
Background:
- Extrapolation of toxicological data from laboratory animals to humans presents significant challenges.
- Understanding interspecies differences in chemical metabolism and toxicity is crucial for accurate human risk assessment.
Purpose of the Study:
- To review and analyze interspecies variations in the metabolism and toxicity of key chemical classes.
- To perform extrapolations to human toxicity and carcinogenicity where possible.
Main Methods:
- Literature review and analysis of existing toxicological data.
- Comparative assessment of metabolism and toxicity across species for selected compounds.
- Extrapolation of animal data to predict human health effects.
Main Results:
- Significant interspecies variations observed for heterocyclic amines, aflatoxin B1, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), endocrine disruptors, polycyclic aromatic hydrocarbons, tamoxifen, and digitoxin.
- Humans may be more susceptible to heterocyclic amines, while mice are resistant to aflatoxin B1 due to efficient glutathione conjugation.
- Humans appear less sensitive to TCDD and tamoxifen toxicity compared to laboratory animals like guinea pigs and rats.
Conclusions:
- Interspecies differences in metabolism and toxicity necessitate careful consideration in toxicological risk assessment.
- Human susceptibility varies greatly depending on the compound, with some posing lower risks than initially predicted from animal models.
- Further research is needed to refine extrapolation methods for more accurate human health risk predictions.
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