Evidence map›Paper›PMID 40853187›Full record

ArticleJournal of clinical pharmacology2025

Application of Lactation Physiologically Based Pharmacokinetic Modeling to Predict Milk Exposure of Passively Diffused Drugs.

Ruhul Kayesh, Michelle Pressly, Daphne Guinn, Zhoumeng Lin, Stephan Schmidt, Kiara Fairman, Carrie Ceresa, Elimika Pfuma Fletcher

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ruhul KayeshOffice of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
Michelle PresslyOffice of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.ORCID 0000-0003-3667-9003
Daphne GuinnOffice of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
Zhoumeng LinDepartment of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.ORCID 0000-0002-8731-8366
Stephan SchmidtDepartment of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, University of Florida, Orlando, FL, USA.ORCID 0000-0002-4998-1167
Kiara FairmanDivision of Biochemical Toxicology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA.
Carrie CeresaDivision of Pediatrics and Maternal Health, Office of Rare Diseases, Pediatrics, Urologic and Reproductive Medicine, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
Elimika Pfuma FletcherOffice of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.

Funding

FDA Perinatal Health Center of Excellence programIntramural FDA HHS FD999999Oak Ridge Institute for Science and EducationResearch Participation ProgramUS Food and Drug Administration
6 · The paper itself

Abstract

Physiologically based pharmacokinetic (PBPK) models have gained interest as a tool for predicting drug transfer to human milk and assessing the exposure levels in infants. Our group previously developed an integrated lactation PBPK model framework to understand the transfer of drugs into milk and the resulting exposure in infants. As a part of the framework, the current paper focuses on performance of lactation PBPK models to predict maternal plasma and milk concentrations for drugs that are not substrates of P-glycoprotein (P-gp) or breast cancer resistance protein (BCRP) transporters-atenolol, escitalopram, and alprazolam. PBPK models for healthy adult subjects were developed for atenolol and escitalopram in Simcyp v23 and verified against clinical data. For alprazolam, the Simcyp library model was used. The built-in lactation compartment in Simcyp was utilized for the three drugs. Atenolol and escitalopram adult healthy PBPK models showed agreement of plasma concentration-time profiles with respective clinical data. The lactation PBPK models predicted milk concentration profiles with reasonable agreement for all three drugs as well. The predicted milk concentration-time profile, milk pharmacokinetic parameters and milk-to-plasma (M/P) ratio of atenolol, alprazolam, and escitalopram were within 2-fold of the reported values, suggesting agreement between simulation and clinical data. The current work shows the potential of lactation PBPK models to predict drug exposure in human milk for drugs that are not P-gp or BCRP substrates. The approach will be further evaluated for drugs that are substrates for these active transporters known to be present in mammary tissues.

Indexed as

AlprazolamAtenololCitalopramLactationMilk, HumanModels, BiologicalAdultFemaleHumansYoung AdultAlprazolamAtenololCitalopramalprazolamatenololescitalopramlactationmodelPBPK

Identifiers

PMID40853187
PMCPMC13003612

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.