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

PBPK 建模

基本信息

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
项目类别：
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

项目摘要

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具有多种有益影响；然而，统计数据表明美国高达 96% 的哺乳期妇女在母乳喂养期间服用一种或多种药物。药物哺乳期妇女食用的食物可能会在很大程度上转移到母乳中，从而导致婴儿无意接触药物，在某些情况下会对婴儿产生不良健康后果。定量药物转移到人乳中对于平衡毒性的合理风险评估很重要婴儿接触药物的风险以及母乳喂养的好处。然而，临床药代动力学（PK）研究哺乳期妇女服用的每一种药物都具有挑战性，而且在后勤上也是不可能的哺乳期妇女，需要使用预测方法来解决这个问题。一种历史方法是根据母体血浆浓度（或 AUC）和乳汁与血浆（M/P）浓度或 AUC 比率。 M/P 比率本身可以使用两者来预测药物的理化特性和母乳的生理参数。虽然这种方法可能预测主要通过被动扩散进入乳汁的药物的 M/P 比率，目前还没有方法可通过转运机制准确预测药物的乳汁分泌。尽管如此，乳汁分泌已知许多药物、外源性物质和内源性物质是由表达的转运蛋白介导的乳腺上皮细胞（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乳汁分泌药物。我们的假设是转运蛋白介导的药物 PK 可以利用体外实验数据结合生理学来预测人类母乳药代动力学 (PBPK) 建模和模拟 (M&S)。具体来说，我们提出了一种创新方法它利用人类 MEC 和转运蛋白转染的细胞或质膜囊泡表达感兴趣的单个转运蛋白（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内在药物通过单一转运蛋白的转运清除。然后，体外内在转运蛋白介导的清除将外推至乳腺体内的 PBPK M&S。 PBPK模型预测将得到验证使用通过转运蛋白进行的临床研究获得的人母乳中的药物 PK 数据基材。综合起来，这些数据将使我们能够预测哺乳期乳汁中转运蛋白介导的药物 PK 女性。这些研究将解决我们对母乳中药物 PK 理解的一个关键差距哺乳期间。由于我们的方法可以应用于作为任何转运蛋白底物的其他药物兴趣，其意义远远超出了这里研究的药物和转运蛋白。