Literature DB >> 2050075

Biological bases for cancer dose-response extrapolation procedures.

J D Wilson1.   

Abstract

The Moolgavkar-Knudson theory of carcinogenesis of 1981 incorporates the viable portions of earlier multistage theories and provides the basis for both the linearized multistage and biologically based dose-response extrapolation methodologies. This theory begins with the premise that cancer occurs because irreversible genetic changes (mutations) are required for transformation of normal cells to cancer cells; incidence data are consistent with only two critical changes being required, but a small contribution from three or higher mutation pathways cannot be ruled out. Events or agents that increase the rate of cell division also increase the probability that one of these critical mutations will occur by reducing the time available for repair of DNA lesions before mitosis. The DNA lesions can occur from background causes or from treatment with mutagenic agents. Thus, the equations describing incidence as a function of exposure to carcinogenic agents include two separate terms, one accounting for mutagenic and one for mitogenic stimuli. At high exposures these interact, producing synergism and high incidence rates, but at low exposures they are effectively independent. The multistage models that are now used include only terms corresponding to the mutagenic stimuli and thus fail to adequately describe incidence at high dose rates. Biologically based models attempt to include mitogenic effects, as well; they are usually limited by data availability.

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Year:  1991        PMID: 2050075      PMCID: PMC1519475          DOI: 10.1289/ehp.90-1519475

Source DB:  PubMed          Journal:  Environ Health Perspect        ISSN: 0091-6765            Impact factor:   9.031


  14 in total

1.  Uncertainty estimates for low-dose-rate extrapolations of animal carcinogenicity data.

Authors:  H Guess; K Crump; R Peto
Journal:  Cancer Res       Date:  1977-10       Impact factor: 12.701

2.  A stochastic two-stage model for cancer risk assessment. II. The number and size of premalignant clones.

Authors:  A Dewanji; D J Venzon; S H Moolgavkar
Journal:  Risk Anal       Date:  1989-06       Impact factor: 4.000

3.  Biologically based models for cancer risk assessment: a cautionary note.

Authors:  S Moolgavkar; A Dewanji
Journal:  Risk Anal       Date:  1988-03       Impact factor: 4.000

4.  Experimental design of bioassays for screening and low dose extrapolation.

Authors:  D W Gaylor; J J Chen; R L Kodell
Journal:  Risk Anal       Date:  1985-03       Impact factor: 4.000

Review 5.  Carcinogenesis modeling: from molecular biology to epidemiology.

Authors:  S H Moolgavkar
Journal:  Annu Rev Public Health       Date:  1986       Impact factor: 21.981

6.  A general probabilistic model of carcinogenesis: analysis of experimental urinary bladder cancer.

Authors:  R E Greenfield; L B Ellwein; S M Cohen
Journal:  Carcinogenesis       Date:  1984-04       Impact factor: 4.944

7.  A new protocol and its rationale for the study of initiation and promotion of carcinogenesis in rat liver.

Authors:  V R Potter
Journal:  Carcinogenesis       Date:  1981       Impact factor: 4.944

8.  Proliferative and genotoxic cellular effects in 2-acetylaminofluorene bladder and liver carcinogenesis: biological modeling of the ED01 study.

Authors:  S M Cohen; L B Ellwein
Journal:  Toxicol Appl Pharmacol       Date:  1990-06-01       Impact factor: 4.219

9.  A model-free approach to low-dose extrapolation.

Authors:  D Krewski; D Gaylor; M Szyszkowicz
Journal:  Environ Health Perspect       Date:  1991-01       Impact factor: 9.031

10.  The age distribution of cancer and a multi-stage theory of carcinogenesis.

Authors:  P ARMITAGE; R DOLL
Journal:  Br J Cancer       Date:  1954-03       Impact factor: 7.640

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  1 in total

1.  Trends in quantitative cancer risk assessment.

Authors:  S C Morris
Journal:  Environ Health Perspect       Date:  1991-01       Impact factor: 9.031

  1 in total

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