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关于哺乳期研究中基于生理学的药代动力学方法的教程。

A tutorial on physiologically based pharmacokinetic approaches in lactation research.

机构信息

Certara Predictive Technologies Division, Sheffield, UK.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2024 Nov;13(11):1841-1855. doi: 10.1002/psp4.13232. Epub 2024 Sep 16.

DOI:10.1002/psp4.13232
PMID:39283747
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11578141/
Abstract

In breastfeeding mothers, managing medical conditions presents unique challenges, particularly concerning medication use and breastfeeding practices. The transfer of drugs into breast milk and subsequent exposure to nursing infants raises important considerations for drug safety and efficacy. Modeling approaches are increasingly employed to predict infant exposure levels, crucial for assessing drug safety during breastfeeding. Physiologically-based pharmacokinetic (PBPK) modeling provides a valuable tool for predicting drug exposure in lactating individuals and their infants. This tutorial offers an overview of PBPK modeling in lactation research, covering key concepts, prediction approaches, and best practices for model development and application. We delve into milk composition dynamics and its influence on drug transfer into breast milk, addressing modeling considerations, knowledge gaps, and future research directions. Practical examples and case studies illustrate PBPK modeling application in lactation studies. We demonstrate how prediction algorithms for Milk-to-Plasma (M/P) ratios within a PBPK framework can support scenarios lacking clinical lactation data or extend the utility of available lactation clinical data to support further untested clinical scenarios. This tutorial aims to assist researchers and clinicians in understanding and applying PBPK modeling to understand and support clinical scenarios in breastfeeding mothers. Advances in PBPK modeling techniques, along with ongoing research on lactation physiology and drug disposition, promise further insights into drug transfer during lactation.

摘要

在母乳喂养的母亲中,管理医疗状况会带来独特的挑战,尤其是在药物使用和母乳喂养实践方面。药物向母乳中的转移以及随后对哺乳婴儿的暴露会对药物安全性和疗效产生重要影响。建模方法越来越多地用于预测婴儿暴露水平,这对于评估母乳喂养期间的药物安全性至关重要。生理药代动力学(PBPK)建模为预测哺乳期个体及其婴儿的药物暴露提供了有价值的工具。本教程概述了 PBPK 建模在哺乳期研究中的应用,涵盖了关键概念、预测方法以及模型开发和应用的最佳实践。我们深入探讨了乳汁成分动力学及其对药物向母乳中转移的影响,解决了建模考虑因素、知识空白以及未来的研究方向。实际示例和案例研究说明了 PBPK 建模在哺乳期研究中的应用。我们展示了如何在 PBPK 框架内使用预测算法来预测乳汁-血浆(M/P)比值,以支持缺乏临床哺乳期数据的情况,或扩展可用的哺乳期临床数据的效用,以支持进一步未经测试的临床情况。本教程旨在帮助研究人员和临床医生理解和应用 PBPK 建模,以了解和支持母乳喂养母亲的临床情况。PBPK 建模技术的进步,以及对哺乳期生理学和药物处置的持续研究,有望进一步深入了解哺乳期的药物转移。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a555/11578141/ed53f9ea1178/PSP4-13-1841-g003.jpg
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Clin Pharmacokinet. 2024 May;63(5):561-588. doi: 10.1007/s40262-024-01374-3. Epub 2024 May 15.
2
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CPT Pharmacometrics Syst Pharmacol. 2024 Mar;13(3):386-395. doi: 10.1002/psp4.13090. Epub 2023 Dec 12.
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Call to action: Harmonization of pharmacovigilance regulations for post-marketing pregnancy and breastfeeding safety studies.
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