PBPK Modeling of Lamotrigine and Efavirenz during Pregnancy: Implications for Personalized Dosing and Drug-Drug Interaction Management
This study aimed to model the pharmacokinetics of lamotrigine (LTG) and efavirenz (EFV) in pregnant women using physiologically based pharmacokinetic (PBPK) and pregnancy-specific PBPK (p-PBPK) models. For lamotrigine, the adult PBPK model demonstrated accurate predictions for pharmacokinetic parame...
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Veröffentlicht in: | Pharmaceutics 2024-09, Vol.16 (9), p.1163 |
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Sprache: | eng |
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Zusammenfassung: | This study aimed to model the pharmacokinetics of lamotrigine (LTG) and efavirenz (EFV) in pregnant women using physiologically based pharmacokinetic (PBPK) and pregnancy-specific PBPK (p-PBPK) models. For lamotrigine, the adult PBPK model demonstrated accurate predictions for pharmacokinetic parameters. Predictions for the area under the curve (AUC) and peak plasma concentration (Cmax) generally agreed well with observed values. During pregnancy, the PBPK model accurately predicted AUC and Cmax with a prediction error (%PE) of less than 25%. The evaluation of the EFV PBPK model revealed mixed results. While the model accurately predicted certain parameters for non-pregnant adults, significant discrepancies were observed in predictions for higher doses (600 vs. 400 mg) and pregnant individuals. The model's performance during pregnancy was poor, indicating the need for further refinement to account for genetic polymorphism. Gender differences also influenced EFV pharmacokinetics, with lower exposure levels in females compared to males. These findings highlight the complexity of modeling EFV, in general, but specifically in pregnant populations, and the importance of validating such models for accurate clinical application. The study highlights the importance of tailoring dosing regimens for pregnant individuals to ensure both safety and efficacy, particularly when using combination therapies with UGT substrate drugs. Although drug-drug interactions between LTG and EFV appear minimal, further research is needed to improve predictive models and enhance their accuracy. |
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ISSN: | 1999-4923 1999-4923 |
DOI: | 10.3390/pharmaceutics16091163 |