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Application of physiologically based pharmacokinetic modeling to predicting drug disposition in pregnant populations.

机译：基于生理学的药代动力学模型在预测孕妇人群中药物处置方面的应用。

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摘要

Various known physiological and biological changes can affect the pharmacokinetics of drugs in pregnant patients, and these changes can compromise efficacy, safety and toxicity of these drugs. Due to ethical and practical considerations, it is not possible to directly evaluate these changes in the pharmacokinetic profile of drugs during pregnancy. Alternative approaches that predict drug disposition during pregnancy that are practical and easy to apply are highly desirable. To address this need, this dissertation research applied physiologically based pharmacokinetic (PBPK) modeling and simulation to predict and describe systemic drug exposure during pregnancy. The models incorporated known time-dependent changes in the physiology, ontogeny, and biology during pregnancy to describe alterations in drug disposition during pregnancy. Systemic exposure predictions of several probe compounds as pregnancy matures were conducted.;All modeling and simulation exercises were performed using GastroPlus TM Version 8.0.002 (Simulations Plus, Inc., Lancaster, CA, USA). PBPK models were used to predict or describe systemic exposure in pregnant women as pregnancy progresses (i.e., across trimesters), based on physiologic changes during pregnancy that can impact ADME pathways. A simple and readily implementable multi-tissueorgan whole-body PBPK model was utilized. The structural model incorporated baseline physiologic data from healthy non-pregnant women. Subsequently, changes in key pregnancy-related physiological parameters (e.g., total body weight, total body water, cardiac output, plasma volume, red blood cell volume, glomerular filtration rate, creatinine clearance, and uterine blood flow) as well as change in cytochrome P450 activity were introduced for each trimester of pregnancy, based on literature data. The models were then applied to a series of reference drugs that differed with respect to the primary clearance pathways: metformin (renal excretion), oseltamivir carboxylate (renal excretion), caffeine (CYPIA2 metabolism), nevirapine (CYP3A4 + CYP2B6 metabolism), lopinavir (CYP3A4 metabolism) and tacrolimus (CYP3A4 metabolism).;For each probe compound, plasma concentration-time profiles were simulated for the medications across each population (non-pregnant women, lst , 2nd, and 3rd trimester pregnant women). The predicted systemic exposure metrics (Cmax, AUC) were compared to published clinical data. Parameter sensitivity analyses (PSA) were performed to identify the critical parameters that most influenced model parameters (Cmax, and AUC -0t). Population estimates (90% confidence intervals) were estimated for the pharmacokinetic parameters were generated through model simulations involving 2500 subjects using a virtual population approach. The PBPK model-simulated pharmacokinetic profiles for test medications were in agreement with observed clinical data for the changes in exposure (AUC and Cmax) during pregnancy. The fold error (EE) was calculated based on the ratio of clinical observed and model predicted PK parameter values. The fold error for the base PBPK model was less than 0.15 (15%) for all the reference drugs. PSA validate the key parameters, which were responsible for the observed changes in systemic exposure during pregnancy. For population estimates, the pharmacokinetic parameters were comparable to clinical observed values for all the probe drugs. The ratio of observed and predicted values ranged from 0.85 to 1.15, indicating, that the PBPK modeling approach was useful in predicting drug pharmacokinetics for drugs that are extensively metabolized as well as really excreted medications in pregnant women during each trimester.;In summary, this dissertation research demonstrated that PBPK modeling represents a potentially useful tool to help establish dosing guidelines that ensure safe and effective drug therapy for pregnant patients. During drug development, PBPK modeling can inform the design of clinical studies and aid in anticipation of potential exposure changes in pregnant women for compounds a priori.
机译：各种已知的生理和生物学变化都会影响孕妇药物的药代动力学，这些变化会损害这些药物的功效，安全性和毒性。出于道德和实践考虑，不可能在怀孕期间直接评估药物药代动力学方面的这些变化。非常需要实用且易于应用的可预测怀孕期间药物处置的替代方法。为了满足这一需求，本论文的研究应用了基于生理学的药代动力学（PBPK）建模和模拟来预测和描述妊娠期间全身性药物暴露。这些模型结合了怀孕期间生理，个体发育和生物学的已知时间依赖性变化，以描述怀孕期间药物处置的变化。进行了几种探针化合物随着怀孕成熟的系统暴露预测。所有建模和模拟练习均使用GastroPlus TM版本8.0.002（Simulations Plus，Inc.，兰开斯特，加利福尼亚州，美国）进行。 PBPK模型基于怀孕期间会影响ADME途径的生理变化，用于预测或描述孕妇随着妊娠进展（即妊娠中期）的全身暴露。利用了一个简单且易于实现的多组织器官全身PBPK模型。结构模型纳入了健康非孕妇妇女的基础生理数据。随后，改变与怀孕相关的关键生理参数（例如，总体重，总水量，心输出量，血浆量，红细胞量，肾小球滤过率，肌酐清除率和子宫血流量）以及细胞色素的变化根据文献数据，在妊娠的每个三个月都引入了P450活性。然后将模型应用于主要清除途径不同的一系列参考药物：二甲双胍（肾排泄），奥司他韦羧酸盐（肾排泄），咖啡因（CYPIA2代谢），奈韦拉平（CYP3A4 + CYP2B6代谢），洛匹那韦（ CYP3A4代谢）和他克莫司（CYP3A4代谢）。对于每种探针化合物，模拟了每个人群（非孕妇，第一，第二和第三孕期孕妇）的药物血浆浓度-时间曲线。将预测的全身暴露指标（Cmax，AUC）与已发表的临床数据进行比较。进行了参数敏感性分析（PSA），以识别影响最大的模型参数（Cmax和AUC -0t）的关键参数。使用虚拟人口方法，通过涉及2500名受试者的模型模拟，生成了药代动力学参数的人口估计值（90％置信区间）。 PBPK模型模拟的测试药物的药代动力学特征与观察到的妊娠期间临床暴露（AUC和Cmax）变化的临床数据一致。根据临床观察值与模型预测的PK参数值之比计算出倍数误差（EE）。对于所有参考药物，基本PBPK模型的折叠误差均小于0.15（15％）。 PSA验证了关键参数，这些关键参数负责观察怀孕期间全身暴露的变化。对于人群估计，药代动力学参数与所有探针药物的临床观察值相当。观察值与预测值之比在0.85至1.15之间，表明PBPK模型化方法可用于预测每个月妊娠期间孕妇体内大量代谢的药物以及真正排泄的药物的药物药代动力学。论文研究表明，PBPK模型代表了一种潜在的有用工具，可帮助建立剂量指南，以确保对孕妇进行安全有效的药物治疗。在药物开发过程中，PBPK建模可以为临床研究提供信息，并有助于预测孕妇先验化合物的潜在暴露变化。

著录项

  • 作者

    Gollen, Rakesh.;

  • 作者单位

    Long Island University, The Brooklyn Center.;

  • 授予单位 Long Island University, The Brooklyn Center.;
  • 学科 Medicine.;Pharmaceutical sciences.;Pharmacology.;Statistics.;Applied mathematics.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 187 p.
  • 总页数 187
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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