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

## TL;DR

This review explores methods to study how drugs pass through the placenta, emphasizing the use of in silico models and multi-model integration for safer drug use during pregnancy.

## Contribution

The paper introduces multi-model data integration as a novel strategy to improve the accuracy of placental drug transport predictions.

## Key findings

- In silico simulations using in vitro data showed higher predictive accuracy for placental drug transport.
- Multi-model integration is essential for reliable fetal drug-exposure assessment frameworks.
- Ex vivo perfusion and in silico methods were most frequently used in placental PK studies.

## Abstract

The 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.

Various 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.

A 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.

This 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.