Stochasticity in Physiologically Based Kinetics Models: Implications for Cancer Risk Assessment

In case of low-dose exposure to a substance, its concentration in cells is likely to be stochastic. Assessing the consequences of this stochasticity in toxicological risk assessment requires the coupling of macroscopic dynamics models describing whole-body kinetics with microscopic tools designed to...

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Bibliographic Details
Published in:Risk analysis 2009-08, Vol.29 (8), p.1182-1191
Main Authors: Péry, Alexandre Roger Raymond, Bois, Frederic Yves
Format: Article
Language:English
Subjects:
Algorithms
Biology
Butadiene
Butadienes - toxicity
Cancer
Computer Simulation
Dose-Response Relationship, Drug
Environmental Sciences
Health risk assessment
Humans
Kinetics
Life Sciences
Liver Neoplasms - chemically induced
Liver Neoplasms - diagnosis
Medical research
neoplasms
Neoplasms - chemically induced
Neoplasms - diagnosis
PBPK
Pharmacokinetics
Risk
Risk Assessment
Risk management
Software
Stochastic models
Stochastic Processes
stochasticity
Studies
Systems Biology
Tissue Distribution
Toxicity
Toxicology
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Summary:In case of low-dose exposure to a substance, its concentration in cells is likely to be stochastic. Assessing the consequences of this stochasticity in toxicological risk assessment requires the coupling of macroscopic dynamics models describing whole-body kinetics with microscopic tools designed to simulate stochasticity. In this article, we propose an approach to approximate stochastic cell concentration of butadiene in the cells of diverse organs. We adapted the dynamics equations of a physiologically based pharmacokinetic (PBPK) model and used a stochastic simulator for the system of equations that we derived. We then coupled kinetics simulations with a deterministic hockey stick model of carcinogenicity. Stochasticity induced substantial modifications relative to dose-response curve, compared with the deterministic situation. In particular, there was nonlinearity in the response and the stochastic apparent threshold was lower than the deterministic one. The approach that we developed could easily be extended to other biological studies to assess the influence of stochasticity at macroscopic scale for compound dynamics at the cell level.
ISSN:0272-4332
1539-6924
DOI:10.1111/j.1539-6924.2009.01242.x