AUTHOR=Li Zhimin , Wu Yue , Zeng Siyu , Wang Fei , Zhang Jiao , Li Shiran , Yang Yong , Yang Yujie 

TITLE=Strategy advancements in placental pharmacokinetics: from in vitro experiments to in silico prediction

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1694886

DOI=10.3389/fphar.2025.1694886

ISSN=1663-9812

ABSTRACT=BackgroundThe placental barrier is a critical interface that regulates drug transport between maternal and fetal circulation and is an important component in assessing fetal drug-exposure risk. Since pregnant women are often excluded from clinical trials, pharmacokinetic (PK) analysis data on placental drug transport remain limited. Currently, in vitro experiments and in silico simulation strategies are the primary and effective means for understanding drug transport across the placenta.MethodVarious in vitro experimental methods, including cell monolayer models, ex vivo placental perfusion, and organ-on-a-chip platforms, along with model-based computational simulations, were systematically reviewed. The advantages, limitations, and potential future applications of these methods were evaluated.ResultA total of seven studies using cell models, 28 employing ex vivo perfusion, six utilizing placenta-on-a-chip technology, and 39 focusing on in silico simulations, were identified, involving 8, 34, 5, and 42 drugs, respectively. Antiviral agents, antibiotics, and opioids were the most frequently investigated drug types. Overall, in silico simulations informed by in vitro data as baseline parameters and constraints demonstrated higher predictive accuracy. Integrating multi-model data was shown to be a reliable strategy for improving the precision of placental PK studies.ConclusionThis review highlights the current strategies in placental PK research and supports safer drug use during pregnancy. Multi-model data integration is essential for developing reliable and quantitative fetal drug-exposure assessment frameworks, thus addressing data gaps caused by the exclusion of pregnant women from clinical trials.