Physiologically Based Pharmacokinetic Modeling of Renally Cleared Drugs in Pregnant Women
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Since pregnant women are considerably underrepresented in clinical trials, information on optimal dosing in pregnancy is widely lacking. Physiologically based pharmacokinetic (PBPK) modeling may provide a method for predicting pharmacokinetic changes in pregnancy to guide subsequent in vivo pharmacokinetic trials in pregnant women, minimizing associated risks.
The goal of this study was to build and verify a population PBPK model that predicts the maternal pharmacokinetics of three predominantly renally cleared drugs (namely cefazolin, cefuroxime, and cefradine) at different stages of pregnancy. It was further evaluated whether the fraction unbound (f u) could be estimated in pregnant women using a proposed scaling approach.
Based on a recent literature review on anatomical and physiological changes during pregnancy, a pregnancy population PBPK model was built using the software PK-Sim®/MoBi®. This model comprised 27 compartments, including nine pregnancy-specific compartments. The PBPK model was verified by comparing the predicted maternal pharmacokinetics of cefazolin, cefuroxime, and cefradine with observed in vivo data taken from the literature. The proposed scaling approach for estimating the f u in pregnancy was evaluated by comparing the predicted f u with experimentally observed f u values of 32 drugs taken from the literature.
The pregnancy population PBPK model successfully predicted the pharmacokinetics of cefazolin, cefuroxime, and cefradine at all tested stages of pregnancy. All predicted plasma concentrations fell within a 2-fold error range and 85% of the predicted concentrations within a 1.25-fold error range. The f u in pregnancy could be adequately predicted using the proposed scaling approach, although a slight underestimation was evident in case of drugs bound to α1-acidic glycoprotein.
Pregnancy population PBPK models can provide a valuable tool to predict a priori the pharmacokinetics of predominantly renally cleared drugs in pregnant women. These models can ultimately support informed decision making regarding optimal dosing regimens in this vulnerable special population
The authors would like to thank Kirstin Thelen (Bayer AG) and Christoph Niederalt (Bayer AG) for valuable discussions.
Compliance with Ethical Standards
This publication and the work involved were funded by Bayer AG.
Conflict of interest
Andre Dallmann is a PhD student at the University of Münster and is employed on a grant from Bayer AG. Ibrahim Ince, Juri Solodenko, Michaela Meyer, Stefan Willmann, and Thomas Eissing were employed by Bayer AG during preparation of this manuscript and are potential stock holders of Bayer AG. Georg Hempel has received a research grant from Bayer AG since 2008.
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