PBPK Modeling & Simulation to Predict Transporter-Mediated Drug Secretion into Human Breast Milk

PBPK 建模

• 批准号: 10706040
• 负责人: MARY F HEBERT
• 金额: $ 65.73万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10706040
• 关键词: ABCG2 gene; Address; Breast Epithelial Cells; Breast Feeding; Breastfed infant; CYP3A4 gene; Cell Separation; Cell membrane; Characteristics; Cimetidine; Clinical; Clinical Research; Confidence Intervals; Consumption; Data; Diffusion; Dose; Drug Exposure; Drug Kinetics; Drug toxicity; Drug usage; Enzymes; Excretory function; Exposure to; Goals; Health; Human; Human Milk; In Vitro; Individual; Infant; Knowledge; Lactation; Mammary gland; Maternal Health; Mediating; Methods; Midazolam; Milk; Modeling; Mus; Oral; Pharmaceutical Preparations; Pharmacology; Physiological; Plasma; Play; Population; Postpartum Period; Pregnancy; Property; Proteins; Proteomics; Regimen; Riboflavin; Risk; Risk Assessment; Role; System; Topotecan; Toxic effect; Transfection; Translating; Vesicle; Wild Type Mouse; Woman; Xenobiotics; absorption; design; enzyme activity; falls; in silico; in vivo; infant outcome; innovation; interest; mammary; milk secretion; models and simulation; neonatal health; pharmacokinetic model; physiologically based pharmacokinetics; predictive modeling; rosuvastatin; statistics

SUMMARY Breastfeeding has multiple beneficial effects on maternal and neonatal health; however, the statistics indicate that up to 96% of lactating women in the US take one or more medications while breastfeeding. Medications consumed by lactating women may be transferred into breast milk to a significant extent, resulting in unintentional infant exposure of medications and in some cases adverse health outcomes for the infants. Quantifying drug transfer into human breast milk is important for rational risk assessment balancing the toxicity risk of drug exposure to infants and the benefits of breastfeeding. However, clinical pharmacokinetic (PK) studies in the population of lactating women are challenging and logistically not possible for every drug taken by lactating women, necessitating the use of prediction methods to address this issue. One historical approach is the prediction of drug concentrations (or drug AUC) in breast milk based on maternal plasma concentration (or AUC) and the milk-to-plasma (M/P) concentration or AUC ratio. The M/P ratio itself can be predicted using both physicochemical characteristics of drugs and physiological parameters of breast milk. While this approach may predict the M/P ratios of drugs that enter the milk predominantly by passive diffusion, no methods are currently available to accurately predict milk secretion of drugs via transport mechanisms. Nonetheless, milk secretions of many drugs, xenobiotics and endogenous substances are known to be mediated by transporters expressed in mammary epithelial cells (MECs). In this application, we propose a systems pharmacology approach to predict transporter-mediated milk secretion of drugs. Our hypothesis is that the transporter-mediated drug PK in human breast milk can be predicted using in vitro experimental data combined with Physiologically Based Pharmacokinetic (PBPK) modeling and simulation (M&S). Specifically, we propose an innovative approach which utilizes human MECs and transporter-transfected cells or plasma membrane vesicles expressing individual transporters of interest (i.e. OCT1, BCRP). Using quantitative targeted proteomics, the human MECs will allow us to determine the protein abundance of these transporters in the mammary gland. The transporter-transfected cell or plasma membrane vesicle studies will allow us to determine the in vitro intrinsic transport clearance of a drug by a single transporter. Then, the in vitro intrinsic transporter-mediated clearances will be extrapolated to in vivo in the mammary gland for PBPK M&S. PBPK model predictions will be verified using the drug PK data in human breast milk obtained from a clinical study conducted with a transporter substrate. Combined, these data will allow us to predict transporter-mediated drug PK in the milk of lactating women. These studies will address a critical gap in our understanding of drug PK in human breast milk during lactation. Since our approach can be applied to other drugs that are substrates of any transporters of interest, its significance goes well beyond the drug and transporters investigated here.

概括 母乳喂养对孕产妇和新生儿健康有多种有益的影响；但是，统计数据表明 在母乳喂养时，在美国，多达96％的哺乳妇女服用一种或多种药物。药物 通过哺乳女性消耗的妇女可能会在很大程度上转移到母乳中 无意的婴儿暴露于药物，在某些情况下为婴儿的健康结果不利。 量化药物转移到人母乳中对于平衡毒性的合理风险评估很重要 药物接触婴儿的风险和母乳喂养的益处。但是，临床药代动力学（PK）研究 在泌乳的人群中 哺乳妇女，需要使用预测方法来解决这个问题。一种历史方法是 基于母体血浆浓度（或 AUC）和牛奶与血浆（m/p）浓度或AUC比。 M/P比本身可以使用两者进行预测 母乳的药物和生理参数的物理化学特征。虽然这种方法可能 预测主要通过被动扩散以主要进入牛奶的药物的m/p比率，目前尚无方法 可以通过运输机制准确预测药物的牛奶分泌。但是，牛奶分泌 已知许多药物，异生元和内源性物质是由在 乳腺上皮细胞（MEC）。在此应用中，我们提出了一种系统药理学方法来预测 转运蛋白介导的药物分泌。我们的假设是转运蛋白介导的药物PK 可以使用体外实验数据和基于生理的基于生理的实验数据来预测人类母乳 药代动力学（PBPK）建模和仿真（M＆S）。具体来说，我们提出了一种创新的方法 它利用人类MEC和转运蛋白转染的细胞或表达的质膜囊泡 感兴趣的个别转运蛋白（即OCT1，BCRP）。使用定量靶向蛋白质组学，人类 MEC将使我们能够确定乳腺中这些转运蛋白的蛋白质丰度。这 转运蛋白转染的细胞或质膜囊泡研究将使我们能够确定体外固有 单个转运蛋白对药物的转运清除。然后，体外固有转运蛋白介导的间隙 PBPK M＆S的乳腺中的体内将被外推到体内。 PBPK模型预测将得到验证 使用从转运蛋白进行的临床研究获得的人类母乳中的药物PK数据 基材。这些数据结合在一起，将使我们能够预测泌乳牛奶中转运蛋白介导的药物PK 女性。这些研究将解决我们对人类母乳中药物PK的理解的关键差距 在哺乳期间。由于我们的方法可以应用于其他转运蛋白的其他药物 兴趣，其意义远远超出了药物，在这里进行了调查。