The Use of ROC Analysis for the Qualitative Prediction of Human Oral Bioavailability from Animal Data

PURPOSE: To develop and evaluate a tool for the qualitative prediction of human oral bioavailability (F(human)) from animal oral bioavailability (F(animal)) data employing ROC analysis and to identify the optimal thresholds for such predictions. METHODS: A dataset of 184 compounds with known F(human) and F(animal) in at least one species (mouse, rat, dog and non-human primates (NHP)) was employed. A binary classification model for F(human) was built by setting a threshold for high/low F(human) at 50%. The thresholds for high/low F(animal) were varied from 0 to 100 to generate the ROC curves. Optimal thresholds were derived from ‘cost analysis’ and the outcomes with respect to false negative and false positive predictions were analyzed against the BDDCS class distributions. RESULTS: We successfully built ROC curves for the combined dataset and per individual species. Optimal F(animal) thresholds were found to be 67% (mouse), 22% (rat), 58% (dog), 35% (NHP) and 47% (combined dataset). No significant trends were observed when sub-categorizing the outcomes by the BDDCS. CONCLUSIONS: F(animal) can predict high/low F(human) with adequate sensitivity and specificity. This methodology and associated thresholds can be employed as part of decisions related to planning necessary studies during development of new drug candidates and lead selection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11095-013-1193-2) contains supplementary material, which is available to authorized users.