Application of PBPK modeling to predict human intestinal metabolism of CYP3A substrates - an evaluation and case...

First pass metabolism in the intestinal mucosa is a determinant of oral bioavailability of CYP3A substrates and so the prediction of intestinal availability (Fg) of potential drug candidates is important. Although intestinal metabolism can be modeled in commercial physiologically based pharmacokinetic (PBPK) software tools, a thorough evaluation of prediction performance is lacking. The current study evaluates the accuracy and precision of GastroPlus Fg predictions for 20 CYP3A substrates using in vitro and in silico input data for metabolic clearance and membrane permeation, and illustrates a potential impact of intestinal metabolism modeling on decision making in a drug Research and Development project. This analysis supports that CYP3A mediated metabolic clearance measured in human liver microsomes can be used to predict gut wall metabolism. Using values scaled from in vitro cell permeability as input for effective jejunal permeability resulted in good Fg prediction accuracy (no significant bias and ∼95% of predictions within 2 fold from in vivo estimated Fg), whereas simulations with in silico predicted permeability tended to overestimate gut metabolism (40% of Fg predictions under predicted more than 2 fold) ±2 fold range as an estimate of imprecision in metabolic clearance and permeability inputs propagated to >5 and <2 fold ranges of predicted Fg for compounds with <30% and >75% in vivo Fg, respectively, suggesting lower precision of predictions for high extraction compounds. Furthermore, parameter sensitivity analysis suggests that limitations in solubility or dissolution may either decrease Fg by preventing saturation of metabolism or increase Fg by shifting the site of absorption towards the colon where expression of CYP3A is low. The case example illustrates how, when accounting for the associated uncertainty in predicted pharmacokinetics and linking to predictive models for efficacy, PBPK modeling of intestinally metabolized compounds can support decision making in drug Research and Development.