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Mechanistic Framework to Predict Maternal-Placental-Fetal Pharmacokinetics of Nifedipine Employing Physiologically Based Pharmacokinetic Modeling Approach.

Marya Antônya Werdan RomãoLeonardo PintoRicardo Carvalho CavalliGeraldo DuarteNatalia Valadares De MoraesKhaled AbduljalilFernanda de Lima Moreira
Published in: Journal of clinical pharmacology (2024)
Nifedipine is used for treating mild to severe hypertension and preventing preterm labor in pregnant women. Nevertheless, concerns about nifedipine fetal exposure and safety are always raised. The aim of this study was to develop and validate a maternal-placental-fetal nifedipine physiologically based pharmacokinetic (PBPK) model and apply the model to predict maternal, placental, and fetal exposure to nifedipine at different pregnancy stages. A nifedipine PBPK model was verified with nonpregnant data and extended to the pregnant population after the inclusion of the fetoplacental multicompartment model that accounts for the placental tissue and different fetal organs within the Simcyp Simulator version 22. Model parametrization involved scaling nifedipine transplacental clearance based on Caco-2 permeability, and fetal hepatic clearance was obtained from in vitro to in vivo extrapolation encompassing cytochrome P450 3A7 and 3A4 activities. Predicted concentration profiles were compared with in vivo observations and the transplacental transfer results were evaluated using 2-fold criteria. The PBPK model predicted a mean cord-to-maternal plasma ratio of 0.98 (range, 0.86-1.06) at term, which agrees with experimental observations of 0.78 (range, 0.59-0.93). Predicted nifedipine exposure was 1.4-, 2.0-, and 3.0-fold lower at 15, 27, and 39 weeks of gestation when compared with nonpregnant exposure, respectively. This innovative PBPK model can be applied to support maternal and fetal safety assessment for nifedipine at various stages of pregnancy.
Keyphrases
  • pregnant women
  • pregnancy outcomes
  • birth weight
  • preterm infants
  • preterm birth
  • machine learning
  • endothelial cells
  • early onset
  • big data
  • drug induced