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Thresholds in chemical carcinogenesis

I F Purchase1, T R Auton

  • 1Zeneca Central Toxicology Laboratory, Cheshire, United Kingdom.

Regulatory Toxicology and Pharmacology : RTP
|December 1, 1995
PubMed
Summary
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Scientific analysis of the proposed uses of the T25 dose descriptor in chemical carcinogen regulation.

Archives of toxicology·2002

Carcinogenic risk assessment assumes no safe dose for chemical carcinogens. Scientific evidence and mechanistic data support a linear dose-response relationship, justifying uniform risk assessment methods.

Area of Science:

  • Toxicology
  • Risk Assessment
  • Carcinogenesis

Background:

  • Carcinogenic risk assessment commonly assumes no threshold dose for chemical carcinogens.
  • A threshold is defined as a dose below which no adverse effects are observed.
  • This assumption is based on the understanding of chemical carcinogenesis and dose-response relationships.

Purpose of the Study:

  • To analyze epidemiological and experimental data on chemical carcinogenesis.
  • To evaluate the scientific justification for the no-threshold assumption.
  • To advocate for consistent low-dose risk assessment methodologies across different toxicological endpoints.

Main Methods:

  • Analysis of epidemiological and experimental data, including large-scale studies like ED01.
  • Review of mechanistic data related to chemical carcinogenesis.

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  • Comparison of dose-response relationships for various toxicological endpoints.

    • Main Results:

    • No thresholds for chemical carcinogenesis have been demonstrated in human or animal studies.
    • Mechanistic data support a linear dose-response relationship at low doses.
    • Other toxicological endpoints also exhibit linear dose-response relationships at low doses.

    Conclusions:

    • The no-threshold concept in chemical carcinogenesis is scientifically supported.
    • Using differing low-dose risk assessment methods for various toxicological endpoints is not justified.
    • A symmetrical approach to risk assessment, with transparent assumptions, is recommended for balanced risk management.