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Animal models of neoplastic development.
1McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, 53706-1599, USA.
Developments in Biologicals
|January 5, 2002
Summary
Regulatory agencies rely on the two-year chronic bioassay for neoplastic development, but newer models like multistage and genetically-engineered mice offer advancements. A combination of models may enhance cancer prevention and therapy strategies.
Area of Science:
- Toxicology and Carcinogenesis
- Biomedical Research
Background:
- The traditional two-year chronic bioassay is the established regulatory standard for evaluating neoplastic development.
- This long-standing method, developed over 30 years ago, primarily focuses on potential chemical carcinogens.
- Despite its
Purpose of the Study:
- To review and compare various animal models for neoplastic development.
- To discuss the evolution of carcinogenicity testing methodologies.
- To highlight the potential of newer models in regulatory decision-making and cancer research.
Main Methods:
- Review of existing literature on animal models for carcinogenicity testing.
- Comparison of the chronic bioassay with shorter-term assays, multistage models, and genetically-engineered mouse models.
- Analysis of the historical development and current பயன்பாடு of these models.
Main Results:
- The two-year chronic bioassay remains the regulatory "gold standard" despite recognized deficiencies.
- Shorter-term assays and single-organ endpoint models have seen limited adoption in some regions.
- Multistage models and genetically-engineered mouse models (involving oncogenes and tumor suppressor genes) are emerging as valuable alternatives.
Conclusions:
- A combination of different animal model systems is likely to be most effective for practical and basic applications in cancer prevention and therapy.
- Further integration of advanced models may improve the accuracy and efficiency of regulatory assessments.
- The field is moving towards more sophisticated and potentially predictive carcinogenicity testing strategies.
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