In non-cancer risk assessment the goal traditionally has been to protect the majority of people by setting limits that account for interindividual variability in the human population. The Environmental Protection Agency (EPA) has assigned a default uncertainty factor (?UF) of 10 to account for interindividual variability in response to toxic agents in the general population. Previous studies have suggested that it is appropriate to equally divide this factor into sub-factors of 3.2 each for variability in human pharmacokinetics (PK) and pharmacodynamics (PD). As an extension of this model, one can envision using scientific data from the literature to modify the default sub-factors with compound-specific adjustment factors (AFs) and to create new and more scientifically based defaults. In this paper, data from published clinical trials on six pharmaceutical compounds were used to further illustrate how to calculate and interpret data-derived AFs. The clinical trial data were analyzed for content and the reported mean and standard deviation values for two key PK parameters, area under the curve of blood concentration by time (AUC) and peak plasma concentration (Cmax), were evaluated. The mean PK values for each study were subsequently analyzed for variability within the population (unimodal distributions) and for the presence of potentially susceptible sub-populations (bimodal distributions). A method based on the proportion of the population covered was applied and data-derived AFswere calculated for these six compounds. Our results showed that, of the 15 possible data-derived AFscalculated using unimodal and bimodal distributions, only three exceeded a value of 3.2. This study further illustrates the value of calculating data-derived values when sufficient PK data are available.
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