Establishing data-derived adjustment factors from published pharmaceuticalclinical trial data

In non-cancer risk assessment the goal traditionally has been to protect th e majority of people by setting limits that account for interindividual var iability in the human population. The Environmental Protection Agency (EPA) has assigned a default uncertainty factor (?UF) of 10 to account for inter individual variability in response to toxic agents in the general populatio n. Previous studies have suggested that it is appropriate to equally divide this factor into sub-factors of 3.2 each for variability in human pharmaco kinetics (PK) and pharmacodynamics (PD). As an extension of this model, one can envision using scientific data from the literature to modify the defau lt sub-factors with compound-specific adjustment factors (AFs) and to creat e new and more scientifically based defaults. In this paper, data from publ ished clinical trials on six pharmaceutical compounds were used to further illustrate how to calculate and interpret data-derived AFs. The clinical tr ial data were analyzed for content and the reported mean and standard devia tion values for two key PK parameters, area under the curve of blood concen tration by time (AUC) and peak plasma concentration (C-max), were evaluated . The mean PK values for each study were subsequently analyzed for variabil ity within the population (unimodal distributions) and for the presence of potentially susceptible subpopulations (bimodal distributions). A method ba sed on the proportion of the population covered was applied and data-derive d AFs were calculated for these six compounds. Our results showed that, of the 15 possible data-derived AFs calculated using unimodal and bimodal dist ributions, only three exceeded a value of 3.2. This study further illustrat es the value of calculating data-derived values when sufficient PK data are available.