System construction for population pharmacokinetic and pharmacodynamic modeling using electronic health records: toward precision medicine

项目摘要 -- 为了改善患者的预后，必须确定可以减少治疗变异性的因素。 并被量化。对于药物治疗，药物动力学(PK)和 药效学(PD)是可变性的主要来源。人口PK/PD研究已被证明 对确定这些因素非常有用，但没有得到充分利用。这种方法的一个主要障碍是 难以从大量患者那里获得数据，这需要准确的时间信息和 适当的剂量和药物血浆浓度或反应测量的精确值 为PK/PD研究格式化。电子健康记录(EHR)是一个潜在的极好的来源 这样的数据，但用于PK/PD建模的数据提取和预处理的标准化方法是 缺乏。我们将致力于开发EHR数据的抽象、验证和预处理方法，以 为PK/PD研究构建数据。在目标1中，我们将开发用于数据预处理和构建的程序 PK/PD数据集，并使用测试数据集验证所开发的程序。在目标2中，我们将评估 通过执行临界点分析，使用EHR的数据构建系统的健壮性 故意在测试数据集中引入错误。这一方法得到了美国食品和药物管理局的强烈推荐 药品监督管理局确定分析对处理缺失数据的方法的敏感性。在《目标3》中， 我们将开发一个基于贝叶斯PK/PD的他克莫司的初步剂量优化算法 预测模型，该模型将作为未来研究的临床支持决策工具的基础。A更多 高效和标准化的数据建设系统将通过以下方式促进基于人口的PK/PD研究 提供PK/PD数据流水线。这项工作的最终产品将是一个可推广和验证的 使用电子健康记录数据分析药物暴露和反应的数据管道，将得到广泛应用 适用于一系列药物的许多人群研究。我们经过验证的系统将扩展 向更广泛的研究社区提供机会，以执行基于人口的PK/PD研究并促进 找到影响PK/PD曲线的因素的机会。这项研究将被用来提高精度 医学涵盖了广泛的治疗方法。

Project Summary   In order to improve patient outcomes, factors that can reduce variability in treatment must be identified and quantified. For drug therapies, individual differences in pharmacokinetics (PK) and pharmacodynamics (PD) are major sources of variability. Population PK/PD studies have been proven to be very useful to identify these factors, but are underutilized. A major barrier to this approach is the difficulty of obtaining data from a large number of patients, which requires precise time information and accurate values for dosing and drug plasma concentration or response measurements appropriately formatted for PK/PD studies. Electronic health records (EHRs) are potentially an excellent source for such data, but standardized methods for data extraction and preprocessing for PK/PD modeling are lacking. We will work to develop methods for abstraction, validation and preprocessing of EHR data to construct data for PK/PD studies. In Aim 1, we will develop programs for data preprocessing and building PK/PD datasets, and validate the developed programs using the test datasets. In Aim 2, we will evaluate the robustness of data construction system using EHRs by performing tipping point analyses by intentionally introducing errors into test datasets. This approach is highly recommended by the Food and Drug Administration to determine the sensitivity of analysis to methods of handling missing data. In Aim 3, we will develop a preliminary dose optimization algorithm for tacrolimus based on a Bayesian PK/PD prediction model, which will serve as a basis for a clinical support decision tool for future study. A more efficient and standardized system for data construction will promote population based PK/PD studies by providing a PK/PD data pipeline. The ultimate product of this work will be a generalizable and validated data pipeline for analysis of drug exposures and responses using EHR data, which will be widely applicable to many population studies for an array of medications. Our validated system will extend opportunities to a wider research community to perform population based PK/PD studies and facilitate the chance of finding factors affecting PK/PD profiles. This research will be utilized to advance precision medicine across a wide array of therapies.