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Determination of the Transport Rate of Xenobiotics and Nanomaterials Across the Placenta using the ex vivo Human Placental Perfusion Model
Published on: June 18, 2013
Physiologically-Based Pharmacokinetic Modeling to Investigate Piperaquine Exposure in Pregnant Women Using an
Sonia Khier1,2, Myriam El Gaaloul3, Joel Tarning4,5
1Institut des Biomolécules Max Mousseron (IBMM), CNRS, UMR5247, Montpellier University, Montpellier, France.
None:
Malaria in pregnancy poses significant risks to the mother, fetus, and neonate, necessitating effective treatment and prevention strategies. This study explored the application of an individualized ('virtual twin') physiologically-based pharmacokinetic (PBPK) modeling approach to the antimalarial drug piperaquine in pregnant women. The piperaquine PBPK model was previously developed and validated within Simcyp for a healthy non-pregnant population. Individualized profiles considering patient age, gestational age, and piperaquine dose were generated using the Simcyp pregnancy population for 36 patients from two clinical trials, including 12 Sudanese and 24 Thai pregnant women with malaria. Model predictive performance was assessed by comparing simulated piperaquine concentrations with observed clinical data. Mean body weight was higher in the virtual vs. the observed population, requiring adjustment of exposures to reflect the mg/kg dose in malaria patients. Results showed good model performance for exposures up to Day 3, with AUC predicted:observed ratios within 0.5 to 2.0 for all Sudanese women in the second (5/5) or third trimester (7/7) and for 14/16 Thai women in their second and 8/8 in their third trimester. The model overestimated clearance in the Sudanese population by ~1.7-fold, leading to underestimation of Day 7 plasma concentrations. This methodological investigation demonstrated the potential of an individualized PBPK approach to inform antimalarial drug pharmacokinetics in pregnancy. Further model refinement is needed to support dose adjustment, including verification of additional individualization factors against clinical data sets. This highlights the need for generating and sharing pharmacokinetic and disease-specific demographic data from pregnant women in malaria-endemic countries.
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