There is a need in biological research to minimise the use of animal experimentation, but procedures to set exposure limits via the concept of the No Observed Adverse Effect Level (NOAEL) have traditionally needed large numbers of animals to investigate levels corresponding to low risks. Dynamic modelling has been proposed as an alternative method, but is deterministic in nature. A modelling structure that uses Monte Carlo simulation to introduce random variation into the parameters of these models was developed, in order to simulate the behaviour a population with interdividual variation. This approach to a mathematical model was then applied describing the deposition, retention and clearance in the lung of a poorly soluble dust of low toxicity (TiO2), and inflammation resulting from the presence of the dust. Population variation (either animal or human) was simulated by generating 1000 variable instances of the key parameter sets. Predictions of lung burden, lymph node burden and recruitment of the inflammatory cells PMN were found to be most strongly influenced by individual breathing rates, and variation was close to linear in all the important parameters.
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