Biologically Motivated Cancer Risk Models

Thorslund, Todd W.; Brown, Charles; Charnley, Gail

doi:10.1111/j.1539-6924.1987.tb00974.x

Cited by 112 publications

(41 citation statements)

References 8 publications

(2 reference statements)

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“…Whereas the growth of cells that have undergone the first stage in the two-stage clonal expansion model is exponential, Bogen (12) argued that geometric growth appeared to provide a better representation of certain data sets. In this paper; the two-stage model with exponential cell growth will be studied, with the terminology of Thorslund et al (10) …”

Section: Two-stage Clonal Expansion Modelmentioning

confidence: 99%

“…The purpose of this paper is to investigate the currently popular two-stage clonal expansion model (8,9) with respect to its ability to describe the EDO, discontinued dosing data. The success of such modeling would perhaps enable the characterization of 2-AAF-induced liver and bladder tumorigenesis in terms of initiation, promotion, and completion (progression) (10). At any rate, successful prediction of bladder tumor responses for discontinued dosing groups by the two-stage clonal expansion model would add to its growing acceptance for carcinogenesis dose-response modeling.…”

Section: Introductionmentioning

confidence: 99%

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Two-stage and Weibull models for carcinogenesis applied to the ED01 discontinued dosing data.

Kodell

Felton

1991

Environ Health Perspect

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The two-stage clonal expansion model for a single, less-than-lifetime period of dosing is formulated and applied to the liver and bladder tumor data from the EDMo study. The model successfully predicts liver tumor incidence for time points beyond termination of dosing with 2-acetylaminofluorene, but it is unsuccessful for bladder tumor incidence. A discontinued dosing version of the Weibull model is proposed and is shown to predict successfully both liver and bladder tumor incidences for time points after termination of dosing.

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Section: Two-stage Clonal Expansion Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-stage and Weibull models for carcinogenesis applied to the ED01 discontinued dosing data.

Kodell

Felton

1991

Environ Health Perspect

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“…The PBPK model-based risk assessments have used these models to estimate tissue dose but still rely on LMS approach as the response model. Biologically based response models are also being developed for use in risk assessment (28,29). Fully linked dosimetry-response simulation models promise to integrate a diversity of pharmacokinetic, mechanistic, and tumor progression studies into a unitary description ofchemical carcinogenesis (30,31).…”

Section: Issues Surrounding the Use Of Pbpk Models In Risk Assessmentmentioning

confidence: 99%

Physiologically based pharmacokinetics and cancer risk assessment.

Andersen

Krishnan

1994

Environ Health Perspect

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Physiologically based pharmacokinetic (PBPK) modeling involves mathematically describing the complex interplay of the critical physicochemical and biological determinants involved in the disposition of chemicals. In this approach, the body is divided into a number ofbiologically relevant tissue compartments, arranged in an anatomically accurate manner, anddefined with appropriae physiologicalcharacteristic The extapolation ofpharmaconeicbehavior ofeas from high dose to low dose for various exposure routes and species is possible with this approach because these models are developed by integrating quantitative information on the critical determinnts ofcemi disposition undera biological modeling framework. The principal application of PBPK models is in the prediction of tissue dosimetry of toxic moiety (e.g., parent chemical, reactive metabolite, macromolecular adduct) of a chemical. Such an application has been demonstrated with dichloromethane, a liver and lung carcinogen in the B6C3F~mouse. The PBPK model-based risk assessment approach estimated a cancer risk to people of3.7x10-8 for a lifetime inhalation exposure of 1 Ag/m3, which is lower by more than two orders of magnitude than that calculated by the U.S. Environmental Protection Agency using the linearized multistage model (for low-dose extrapolation) and body surface correction factor (for interspecies scaling).The capability of predicting the target tissue exposure to toxic moiety in people with PBPK models should help reduce the uncertainty associated with the extrapolation procedures adopted in conventional dose-response assessment.

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“…The basic reason we used this model is that it has strong biological support [see Tan (4)]. Because the two-stage model and its extensions are complex enough to involve stochastic proliferation and differentiation of initiated cells and yet simple enough to be applicable to many data sets, this model has been suggested as the basic model for assessing risk of environmental agents (6).…”

mentioning

confidence: 99%

Monte Carlo Results for the 3-Poly Test for Animal Carcinogenicity Experiments

Tan¹,

Zhang²

1996

Environmental Health Perspectives

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(4)]. Because the two-stage model and its extensions are complex enough to involve stochastic proliferation and differentiation of initiated cells and yet simple enough to be applicable to many data sets, this model has been suggested as the basic model for assessing risk of environmental agents (6).We will briefly describe the model and how Monte Carlo data can be generated; we will then apply the 3-poly methods to the data. To assess the efficiency and robustness of the test, we will compute the Monte Carlo size and the power of the test. Finally, we will discuss the results and some of the issues relevant to the method. The Model and Generation of Monte Carlo DataTo generate Monte Carlo studies, animal carcinogenicity experiments were carried out in which healthy animals were exposed to different dose levels of the carcinogen at to-0 and are followed up until time tM = tk.In the group of animals exposed to the carcinogen with dose level z, let a,(jlz) and a2(jIlz) denote the numbers of animals which died at time j without and with cancerous tumors, respectively; similarly, in the group of animals exposed to the carcinogen with dose level z, let b1(z) and b2(z) be the numbers of terminal sacrifices without and with cancerous tumors, respectively.To generate Monte Carlo data from such an experiment, we assign D as the random variable for the time to death, Tas the random variable for the time to the onset of cancer tumors, and Z as the variable for the dose level. Given Z=z, let o0(tjz), O(tls,z), (t > s), and ko(tlz) be the incidence func- For j < t < j+1, the survival functions associated with cx0(tl z), P,0(tl s,z), and ko(tl z) are given respectively by Bo(tli)for j >i, andwhere a (i) = J1 a. (xlz)dx, f3z(jls) = j0 t(xs x for j-12 s, and AZW = .f17lo(xIzkPc Assume that 300(tls,z) = 00(tli,z) for i-l < s < i and that during one time unit, the event of death and the event of developing cancerous tumors are independent of each other. Then Pz(tIs) = Pz(t Ii) for i-I < s < i < t, and the probabilities for generating

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Biologically Motivated Cancer Risk Models

Abstract: A two-stage dose response model is proposed for use in cancer risk assessment. The model assumes that transformation probabilities and cellular dynamics are exposure- and time-dependent.

Cited by 112 publications

References 8 publications

Two-stage and Weibull models for carcinogenesis applied to the ED01 discontinued dosing data.

Two-stage and Weibull models for carcinogenesis applied to the ED01 discontinued dosing data.

Physiologically based pharmacokinetics and cancer risk assessment.

Monte Carlo Results for the 3-Poly Test for Animal Carcinogenicity Experiments

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