Because of the heterogeneity of the human population, it is generally expected that there will be a broad range of observed susceptibilities to the biological effects of exposure to chemicals or drugs. Often it is possible to distinguish specific classes of individuals, such as infants or the elderly, who appear to be more susceptible to a specific effect. Non-cancer risk assessment often address this variability by dividing the experimentally determined acceptable exposure level by an uncertainty factor of 10 to protect sensitive individuals; cancer risk assessments typically do not address this issue in any quantitative fashion. Physiologically based pharmacokinetic (PBPK) modeling provides the capability to quantitatively describe the potential impact of pharmacokinetic factors on the variability of individual risk. In particular, PBPK models can be used to determine the impact of differences in key metabolism enzymes, whether due to multiple genotypic expression, such as cytochrome P450 polymorphisms, or just due to normal variation in enzyme activities within the general population. Other potential modulators of sensitivity which can be addressed quantitatively with a PBPK model include physical condition, level of activity, disease states, age, hormonal status, and interactions with other chemicals and drugs. In each case, the PBPK model provides a quantitative structure for determining the effect of these various factors on the relationship between the external (environmental) exposure and the internal (biologically effective) target tissue exposure. When coupled with Monte Carlo analysis, the PBPK model provides a method to assess the quantitative impact of these sources of variability on individual risk (as opposed to average population risk) by comparing model-predicted risks over the distribution of individual parameter values.