Evidence map›Paper›PMID 39963246›Full record

ArticleFrontiers in pharmacology2025

Postpartum changes in maternal physiology and milk composition: a comprehensive database for developing lactation physiologically-based pharmacokinetic models.

Neel Deferm, Jean Dinh, Amita Pansari, Masoud Jamei, Khaled Abduljalil

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Article
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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

5 authors.

Neel DefermPredictive Technologies Division, Certara UK Limited, Sheffield, United Kingdom.
Jean DinhPredictive Technologies Division, Certara UK Limited, Sheffield, United Kingdom.
Amita PansariPredictive Technologies Division, Certara UK Limited, Sheffield, United Kingdom.
Masoud JameiPredictive Technologies Division, Certara UK Limited, Sheffield, United Kingdom.
Khaled AbduljalilPredictive Technologies Division, Certara UK Limited, Sheffield, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Pharmacotherapy during lactation often lacks reliable drug safety data, resulting in delayed treatment or early cessation of breastfeeding. Methods: This study aimed to collect and analyze data on the longitudinal changes in various physiological parameters that can affect drug distribution into breast milk during lactation. Following meta-analysis of the collated data, mathematical functions were fitted to the available data for each parameter. The best-performing functions were selected through numerical and visual diagnostics. Results and Discussion: The literature search identified 230 studies, yielding a dataset of 36,689 data points from 20,801 postpartum women, covering data from immediately after childbirth to 12 months postpartum. Sufficient data were obtained to describe postpartum changes in maternal plasma volume, breast volume, cardiac output, glomerular filtration rate, haematocrit, human serum albumin, alpha-1-acid glycoprotein, milk pH, milk volume, milk fat, milk protein, milk water content, and daily infant milk intake. Although data beyond 7 months postpartum were limited for some parameters, mathematical functions were generated for all parameters. These functions can be integrated into lactation PBPK models to increase their predictive power and better inform medication efficacy and safety for breastfeeding women.

Indexed as

breastfeedinglactationmeta-analysismilkPBPKpostpartum

Identifiers

PMID39963246
PMCPMC11830814

What OpenQuestion holds

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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.