Early-Stage Prediction of Human Intestinal Metabolism From In Vitro Liver Microsomal Clearance

For reliable pharmacokinetic predictions at early stages in drug discovery, parameters that make up human oral bioavailability must be sufficiently accurate (ie, fraction absorbed through intestinal epithelia [fa], fraction escaping intestinal first-pass metabolism [Fg], and fraction escaping hepatic first-pass metabolism [Fh]). Although there are established methods for predicting human fa and Fh, those for predicting human Fg remain less reliable. To address this gap, we developed a simple nonlinear regression model based on in vitro unbound intrinsic clearance in human liver microsomes (CLint,mic,u) to predict in vivo human Fg for cytochrome P450 substrates expected to undergo intestinal metabolism. The nonlinear regression model predicted in vivo human Fg within 1.5- and 2-fold of observed values for 89% and 96% of compounds, respectively. Direct comparisons of the regression model to previously reported methods (Qgut and Advanced Dissolution, Absorption, and Metabolism models) revealed an improved prediction performance of the regression model over the Qgut model for 18 shared compounds (89% vs 78% within 1.5-fold, respectively), and over the Advanced Dissolution, Absorption, and Metabolism model for 22 shared compounds (82% vs 68% within 1.5-fold, respectively). A significant correlation between CLint,mic,u and in vivo fa × Fg was also observed in rats for compounds with high permeability and low efflux, further supporting the relationship between in vivo Fg and in vitro unbound intrinsic clearance from liver microsomes. Together with the human fa prediction via in vitro permeability assays, the nonlinear regression model established in this study will allow an early prediction of human oral absorption, involving both permeability limitation and intestinal metabolism, with relatively high-throughput assays. SIGNIFICANCE STATEMENT: Although previous studies have demonstrated a strong correlation between the human fraction escaping intestinal first-pass metabolism (Fg) and liver microsomal clearance, none have suggested this finding as a standalone model to predict in vivo human Fg. Here, this study proposes a high-throughput nonlinear regression model to predict in vivo human Fg using only in vitro unbound intrinsic clearance from liver microsomes at the early stages in drug discovery.