Predicting Escitalopram Exposure to Breastfeeding Infants: Integrating Analytical and In Silico Techniques.

Abstract

Escitalopram is used for post-partum depression; however, there are limited pharmacokinetic data of escitalopram in milk and plasma of infants breastfed by women taking the drug. The objective of this study was to apply physiologically-based pharmacokinetic (PBPK) modelling to predict infant drug exposure (plasma area under the curve from time zero to infinity [AUC∞]) based on drug monitoring data of escitalopram in breast milk. Using a newly developed liquid chromatography-tandem mass spectrometry (LC-MS/MS) method, we quantified escitalopram concentrations in milk samples of 18 breastfeeding women with escitalopram therapy at steady state, collected at three to five time points. The escitalopram concentrations in breast milk were used with infant feeding parameters from the literature to simulate infant daily dose. We used PK-Sim® to develop an adult PBPK model for escitalopram and extrapolated it to a population of 1600 infants up to 12months of age. An integration of the simulated infant daily dose and the virtual infants with variable physiological-pharmacological parameters was used to predict drug exposure (plasma AUC∞) distribution in the population of infants breastfed by women receiving escitalopram 20mg/day. Escitalopram concentrations in milk were 50±17ng/mL (mean ± standard deviation). The simulated infant plasma AUC∞ following escitalopram exposure through breast milk was low, with a median of 1.7% (range 0.5-5.9%) of the corresponding maternal plasma AUC∞, indicating no substantial exposure. Infant exposure levels to escitalopram in breast milk are low. A PBPK modeling approach can be used to translate data on drug monitoring in milk into a population distribution of infant plasma levels for drug safety assessment.