A Systematic Review of Published Physiologically Based Pharmacokinetic Models for Drug Excretion Into Human Breastmilk: Knowledge Gaps and Opportunities to Optimize Reporting and Modeling Practices.

IF 2.8 3区 医学 Q2 PHARMACOLOGY & PHARMACY
Joshua Kiptoo, Davies Otieno, Kenta Yoshida, Neel Deferm, Aida Nakayiwa Kawuma, Francis Williams Ojara, Adeniyi Olagunju, Jackson K Mukonzo, Catriona Waitt
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Abstract

Physiologically based pharmacokinetic (PBPK) modeling is increasingly used to predict infant exposure to medications via breast milk. Existing guidance on reporting of PBPK modeling analyses is unstructured and nonbinding to researchers. We evaluated reporting practices and modeling approaches in published PBPK modeling studies. A systematic search was conducted across three online databases. Studies published through March 2025 that leveraged PBPK to evaluate milk drug excretion and infant exposure were included. We developed a 22-item structured data abstraction tool-Lactation PBPK Model Reporting Checklist (LAC-PBPK). Study findings were reported using descriptive and narrative analyses. Twenty-six published studies modeling 54 medications met the inclusion criteria. Using the data abstraction tool, only 4 (18%) of reporting domains were reported in all studies. These include the drug modeled, choice of mammary distribution model, context of PBPK modeling (model purpose), and the modeling platform. Most studies (88.4%) implemented whole-body PBPK model structure, with one-half (53%) assuming perfusion-rate-limited milk excretion. Assumptions reflect modeling limitations, primarily due to inadequate reporting in clinical lactation studies and knowledge gaps in infant and mammary physiology. PBPK models can support mechanistic assessment of drug excretion into breast milk, but confidence in model outputs depends on intended use, data availability, and transparent reporting. This highlights the need for a prioritized, fit-for-purpose lactation PBPK reporting framework. From a regulatory perspective, transparent reporting and cautious selection of approaches for estimating infant milk intake, model performance evaluation, physiological extrapolation, and parameter uncertainty analyses are essential to ensure reproducibility and confidence in PBPK-informed infant exposure assessment.

已发表的基于生理学的药物排泄到人母乳中的药代动力学模型的系统综述:知识差距和优化报告和建模实践的机会。
基于生理的药代动力学(PBPK)模型越来越多地用于预测婴儿通过母乳对药物的暴露。现有的关于PBPK建模分析报告的指导是非结构化的,对研究人员没有约束力。我们在发表的PBPK建模研究中评估了报告实践和建模方法。在三个在线数据库中进行了系统的搜索。截至2025年3月发表的利用PBPK评估乳药物排泄和婴儿暴露的研究包括在内。我们开发了一个包含22个条目的结构化数据抽象工具——哺乳PBPK模型报告清单(LAC-PBPK)。研究结果采用描述性和叙述性分析进行报告。26项已发表的研究对54种药物进行了建模,符合纳入标准。使用数据抽象工具,所有研究中只有4个(18%)报告域被报告。这些包括药物建模、乳腺分布模型的选择、PBPK建模的背景(模型目的)和建模平台。大多数研究(88.4%)采用了全身PBPK模型结构,其中一半(53%)假设灌注率有限的乳汁排泄。假设反映了模型的局限性,主要是由于临床哺乳研究报告不足以及婴儿和乳腺生理学知识的差距。PBPK模型可以支持药物排泄到母乳中的机制评估,但模型输出的可信度取决于预期用途、数据可用性和透明报告。这突出了需要一个优先的、适合目的的哺乳PBPK报告框架。从监管的角度来看,透明报告和谨慎选择估算婴儿牛奶摄入量、模型性能评估、生理外推和参数不确定性分析的方法对于确保pbpk知情婴儿暴露评估的可重复性和可信度至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.00
自引率
11.40%
发文量
146
审稿时长
8 weeks
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