Physiologically-based pharmacokinetic (PBPK) modeling and simulation have become an integral part of the drug development process. This approach is a mathematical technique using a series of differential equations to predict the pharmacokinetic behavior of drug molecules in humans and animals. The main application of the model to date is its use to translate in vitro data to predict and assess possible drug interactions at the level of biotransformation arising from inhibition or induction of metabolizing enzymes. These models provide numerous advantages over static models, as they include both drug-specific physico-chemical properties and system-specific physiologic factors, thus being able to predict pharmacokinetic behavior as much as possible and to predict possible drug interactions with high probability. That is why these models have already received regulatory approval and are routinely used to predict cytochrome P450-mediated drug interactions.
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