Physiologically based pharmacokinetic modeling of drug disposition in rat and human: a fuzzy arithmetic approach

Probabilistic methods are insufficient for dealing with the vagueness inherent in human judgment of minimal data available during early drug development. We sought to use fuzzy set theory as a basis for quantifying and propagating vague judgment in a physiologically based pharmacokinetic (PBPK) model for diazepam disposition.

First, using diazepam distribution data in rat tissues and fuzzy regression, we estimated fuzzy rat tissue-to-plasma partition coefficients (Kp's). We scaled the coefficients prior to human PBPK modeling. Next, we constructed the fuzzy set of hepatic intrinsic clearance (CLint) by integrating CLint values measured in vitro from human hepatocytes. Finally, we used these parameters, and other physiological and biochemical information, to predict human diazepam disposition. We compared the simulated plasma kinetics with published concentration-time profiles.

We successfully identified rat Kp's by fuzzy regression. The predicted rat tissue concentration-time contours enveloped the animal tissue distribution data. For the human PBPK model, the mean in vivo plasma concentrations were contained in the simulated concentration-time envelopes.

We present a novel computational approach for handling information paucity in PBPK models using fuzzy arithmetic. Our methodology can model the vagueness associated with human perception and interpretation of minimal drug discovery data.