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NIH grant: Integrating A Novel Human Mammary Epithelium Assay with Mother-Infant PBPK-PD Modeling for Precision Dosing of OUD Medications in Lactating Mothers (1R01DA065561-01)

source: us-gov-api (us-gov-api:nih-reporter)published 2026-08-01 00:00ingested 2026-08-03 09:17

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NIH grant 1R01DA065561-01: Integrating A Novel Human Mammary Epithelium Assay with Mother-Infant PBPK-PD Modeling for Precision Dosing of OUD Medications in Lactating Mothers
Institute: {'code': 'DA', 'abbreviation': 'NIDA', 'name': 'National Institute on Drug Abuse', 'admin_org_id': '12500072', 'admin_funding_url': ''}. PI: Yanyan  Li. Award: $689676.
Start: 2026-08-01T00:00:00.
Summary/Abstract
The increasing prevalence of opioid use disorder (OUD) among women of reproductive age underscores the
urgent need for safe and effective treatment strategies during lactation. While medication-assisted treatment
(MAT) with buprenorphine, methadone, or naltrexone may be compatible with breastfeeding, data on drug
transfer into breast milk and infant exposure across different formulations remain sparse, limiting evidence-
based clinical guidance. This project aims to develop a novel, mechanism-based, model-informed drug
development (MIDD) framework to predict maternal and infant drug exposure and evaluate infant health
impacts.
In Specific Aim 1, we will establish a human mammary epithelial bilayer system that mimics lactational
physiology to assess drug transport across the mammary barrier. In Aim 2, we will collect paired clinical blood
and breast milk samples from lactating mothers on MAT to build physiologically based pharmacokinetic (PBPK)
models predicting drug levels in mothers and infants across various formulations. In Aim 3, we will quantify
inflammatory biomarkers in breast milk, infant saliva and stools, and analyze infant fecal microbiome,
integrating these data into pharmacokinetic-pharmacodynamic (PK-PD) and machine learning (ML) models to
assess the effects of maternal drug exposure on infant health.
The integrated in vitro, clinical, and computational approaches will generate predictive tools to guide drug
dosing in lactating women, support regulatory evaluation, and reduce disparities in OUD treatment for a highly
vulnerable population. The proposed framework aligns with NIH priorities in MIDD to accelerate therapeutic
innovation and ensure drug safety in special populations.

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