# PBPK Modeling & Simulation to Predict Transporter-Mediated Drug Secretion into Human Breast Milk

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $637,191

## Abstract

SUMMARY
Breastfeeding has multiple beneficial effects on maternal and neonatal health; however, the statistics indicate
that up to 96% of lactating women in the US take one or more medications while breastfeeding. Medications
consumed by lactating women may be transferred into breast milk to a significant extent, resulting in
unintentional infant exposure of medications and in some cases adverse health outcomes for the infants.
Quantifying drug transfer into human breast milk is important for rational risk assessment balancing the toxicity
risk of drug exposure to infants and the benefits of breastfeeding. However, clinical pharmacokinetic (PK) studies
in the population of lactating women are challenging and logistically not possible for every drug taken by
lactating women, necessitating the use of prediction methods to address this issue. One historical approach is
the prediction of drug concentrations (or drug AUC) in breast milk based on maternal plasma concentration (or
AUC) and the milk-to-plasma (M/P) concentration or AUC ratio. The M/P ratio itself can be predicted using both
physicochemical characteristics of drugs and physiological parameters of breast milk. While this approach may
predict the M/P ratios of drugs that enter the milk predominantly by passive diffusion, no methods are currently
available to accurately predict milk secretion of drugs via transport mechanisms. Nonetheless, milk secretions of
many drugs, xenobiotics and endogenous substances are known to be mediated by transporters expressed in
mammary epithelial cells (MECs). In this application, we propose a systems pharmacology approach to predict
transporter-mediated milk secretion of drugs. Our hypothesis is that the transporter-mediated drug PK in
human breast milk can be predicted using in vitro experimental data combined with Physiologically Based
Pharmacokinetic (PBPK) modeling and simulation (M&S). Specifically, we propose an innovative approach
which utilizes human MECs and transporter-transfected cells or plasma membrane vesicles expressing
individual transporters of interest (i.e. OCT1, BCRP). Using quantitative targeted proteomics, the human
MECs will allow us to determine the protein abundance of these transporters in the mammary gland. The
transporter-transfected cell or plasma membrane vesicle studies will allow us to determine the in vitro intrinsic
transport clearance of a drug by a single transporter. Then, the in vitro intrinsic transporter-mediated clearances
will be extrapolated to in vivo in the mammary gland for PBPK M&S. PBPK model predictions will be verified
using the drug PK data in human breast milk obtained from a clinical study conducted with a transporter
substrate. Combined, these data will allow us to predict transporter-mediated drug PK in the milk of lactating
women. These studies will address a critical gap in our understanding of drug PK in human breast milk
during lactation. Since our approach can be applied to other drugs that are subs...

## Key facts

- **NIH application ID:** 10914057
- **Project number:** 5R01HD112282-02
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** MARY F HEBERT
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $637,191
- **Award type:** 5
- **Project period:** 2023-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10914057

## Citation

> US National Institutes of Health, RePORTER application 10914057, PBPK Modeling & Simulation to Predict Transporter-Mediated Drug Secretion into Human Breast Milk (5R01HD112282-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10914057. Licensed CC0.

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