Predicting Drug Disposition during Pregnancy through Modeling and Simulation

Thesis (Ph.D.)--University of Washington, 2013Physiological and ADME changes during pregnancy can significantly affect drug pharmacokinetics and may necessitate dose adjustment. Since it is logistically impossible to delineate the changes in PK of all drugs administered to pregnant women, alternative approaches that can generalize across drugs and predict drug disposition in pregnancy are highly-desirable. We developed and validated a novel PBPK model, which integrates gestational time-dependent changes in maternal physiology and hepatic CYP enzyme activities, preclinical and clinical data, to predict maternal pharmacokinetics of drugs cleared primarily by CYP3A (midazolam, nifedipine, indinavir), CYP1A2 (theophylline), CYP2D6 (metoprolol, paroxetine, dextromethorphan, clonidine), CYP2C9 (phenytoin), and multiple CYP enzymes (glyburide, methadone). We showed that our PBPK model can quantitatively predict the disposition during pregnancy of drugs cleared via single and/or multiple CYP pathways , and allow generalization beyond model drugs studied (e.g. midazolam) to other drugs with well-characterized ADME characteristics (e.g. indinavir). Moreover, our PBPK approach allowed us to bridge knowledge gaps that are difficult to test through clinical studies, for example, discerning the site (hepatic, intestinal, or both) of CYP3A induction in pregnancy. The coupled maternal-fetal physiology imposes additional concerns for both efficacy and safety of treatment. Fetal exposure to drugs not only depends on maternal pharmacokinetics, but also depends on placental passage of drugs. In this research, the gestational time-dependent contribution of the placental efflux transporter, P-glycoprotein, to drug distribution into the fetus, was quantified with PET imaging in a physiologically relevant model, the pregnant macaque. Such knowledge can be incorporated into a comprehensive fetal model in the future to predict fetal exposure to drugs. Taken together, the presented modeling approach can identify drugs whose pharmacokinetics may be altered during pregnancy, guide rational PK study design, and support dose adjustment for pregnant women