The purpose of this modeling effort was to explore the effects of various processes and physiological parameters on the DDI involving competitive and time-dependent inhibition (TDI). Absorption and pharmacokinetics of both drugs were simulated using GastroPlus™ 7.0 (Simulations Plus, Inc., Lancaster, CA). The program’s Advanced Compartmental Absorption and Transit (ACATTM) model described the intestinal absorption, coupled with its PBPKPlus™ module for pharmacokinetic distribution and clearance. Human physiologies were generated by the program’s internal Population Estimates for Age-Related (PEAR) Physiology™ module. Tissue/plasma partition coefficients were calculated using a modified Rodgers algorithm based on tissue composition and in vitro and in silico physicochemical properties (ADMET Predictor™, Simulations Plus, Lancaster, CA). Metabolic clearances of both drugs in gut and liver were based on built-in in vitro values for the expression levels of 3A4 in each gut compartment and the average expression of 3A4 in liver. Literature in vitro values for enzyme kinetic constants for 3A4 metabolism of midazolam, diltiazem and diltiazem metabolite were used as reported. Renal secretion of diltiazem and its metabolites was estimated as fup*GFR and their residual clearances due to other metabolic processes were fitted to in vivo data. The PBPK models correctly described plasma concentration-time (Cp-time) profiles of midazolam, diltiazem and N-demethyldiltiazem for a variety of doses after i.v. and p.o. administration. Literature in vitro values for diltiazem inhibition constants were used as reported. Dynamic simulation within the DDI Module in GastroPlus was used to predict DDIs between the two drugs.
International Conference on Drug-Drug Interactions (DDI), June 2010, Seattle, Washington
By Viera Lukacova, Michael Bolger, Walter Woltosz