Physiologically-based pharmacokinetic (PBPK) models for prediction of saquinavir effect on midazolam pharmacodynamics
Poster Jun 07, 2012
Viera Lukacova, Haiying Zhou, Walter S. Woltosz, Michael B. Bolger
Purpose: To predict the drug-drug interaction effect of saquinavir on midazolam pharmacodynamics (PD).
Methods:The absorption and pharmacokinetics (PK) of midazolam and saquinavir were simulated using a beta version of GastroPlusTM8.0 (Simulations Plus, Inc., Lancaster, CA). Independent saquinavir and midazolam models were previously validated by comparing the simulated plasma concentration-time (Cp-time) profiles with experimental data for intravenous (i.v.) and oral (p.o.) administration for each drug, as well as by comparing the prediction of the observed drug-drug interaction (DDI) of saquinavir on midazolam PK. Models were fitted for several reported PD responses after midazolam administration based on the drug’s unbound Cp-time profiles. The fitted PD models were then used with the earlier prediction of saquinavir’s effect on midazolam PK to estimate the drug-drug interaction effect of saquinavir on midazolam PD.
Results: Baseline PD responses for midazolam (i.e., without saquinavir), such as the digit symbol substitution test (DSST) and the Maddox Wing Test (MWT), were equally well described by several different types of direct or indirect PD models, each resulting in a different prediction of the magnitude of saquinavir’s effect on midazolam PD. Several models predicted the maximum response within 20% of the observed maximum response; however, the prediction error was as high as 40% with other models. In the absence of PD response data for different dose levels of midazolam, there was no obvious most relevant baseline PD model for each response.
During early drug discovery, the study of metabolism plays an essential role in determining which drug candidates move forward into development and later stages. As an alternative to traditional Data Dependent Acquisition (DDA), the use of MSE/All Ions Fragmentation (AIF) has become common in metabolite identification workflows for the analysis of metabolic hot spots. Here we present a solution for analysis of MSE/AlF in metID studies.READ MORE