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产后母体生理和乳汁成分的变化:用于开发基于泌乳生理的药代动力学模型的综合数据库。

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

作者信息

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

机构信息

Predictive Technologies Division, Certara UK Limited, Sheffield, United Kingdom.

Pharmides BV, Pelt, Belgium.

出版信息

Front Pharmacol. 2025 Feb 3;16:1517069. doi: 10.3389/fphar.2025.1517069. eCollection 2025.

Abstract

INTRODUCTION

Pharmacotherapy during lactation often lacks reliable drug safety data, resulting in delayed treatment or early cessation of breastfeeding. tools, such as physiologically-based pharmacokinetic (PBPK) models, can help to bridge this knowledge gap. To increase the accuracy of these models, it is essential to account for the physiological changes that occur throughout the postpartum period.

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.

摘要

引言

哺乳期药物治疗往往缺乏可靠的药物安全性数据,导致治疗延迟或过早停止母乳喂养。基于生理的药代动力学(PBPK)模型等工具有助于弥补这一知识空白。为提高这些模型的准确性,考虑产后各阶段发生的生理变化至关重要。

方法

本研究旨在收集和分析各种生理参数纵向变化的数据,这些参数会影响哺乳期药物向母乳中的分布。在对整理后的数据进行荟萃分析后,为每个参数的可用数据拟合数学函数。通过数值和可视化诊断选择性能最佳的函数。

结果与讨论

文献检索确定了230项研究,产生了一个包含来自20801名产后妇女的36689个数据点的数据集,涵盖从分娩后即刻到产后12个月的数据。获得了足够的数据来描述产后母体血容量、乳房体积、心输出量、肾小球滤过率、血细胞比容、人血清白蛋白、α-1-酸性糖蛋白、乳汁pH值、乳汁量、乳脂肪、乳蛋白、乳汁含水量和婴儿每日乳汁摄入量的变化。尽管某些参数产后7个月后的数据有限,但为所有参数生成了数学函数。这些函数可整合到哺乳期PBPK模型中,以提高其预测能力,并为哺乳期妇女的用药疗效和安全性提供更好的信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c98/11830814/bdf39ae8d348/fphar-16-1517069-g001.jpg

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