Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Br J Clin Pharmacol. 2022 Jun;88(6):2863-2874. doi: 10.1111/bcp.15218. Epub 2022 Jan 27.
Use of electronic health record (EHR) data to estimate population pharmacokinetic (PK) profiles necessitates several assumptions. We sought to investigate sensitivity to some of these assumptions about dose timing and absorption rates.
A population PK study with 363 subjects was performed using real-world data extracted from EHRs to estimate the tacrolimus population PK profile. Data were extracted and built using our automated system, EHR2PKPD, suitable for quickly constructing large PK datasets from the EHR. Population PK studies for oral medications performed using EHR data often assume a regular dosing schedule as prescribed without incorporating exact dosing time. We assessed the sensitivity of the PK parameter estimates to assumptions about dose timing using last-dose times extracted by our own natural language processing system, medExtractR. We also investigated the sensitivity of estimates to absorption rate constants that are often fixed at a published value in tacrolimus population PK analyses. We conducted simulation studies to investigate how drug PK profiles and experimental designs such as concentration measurements design affect sensitivity to incorrect assumptions about dose timing and absorption rates.
There was no appreciable difference in parameter estimates with assumed versus extracted last-dose time, and our sensitivity analysis revealed little difference between parameters estimated across a range of assumed absorption rate constants.
Our findings suggest that drugs with a slower elimination rate (or a longer half-life) are less sensitive to dose timing errors and that experimental designs which only allow for trough blood concentrations are usually insensitive to deviation in absorption rate.
使用电子健康记录 (EHR) 数据来估计群体药代动力学 (PK) 曲线需要进行多项假设。我们旨在研究这些关于剂量时间和吸收速率的假设的敏感性。
使用从 EHR 中提取的真实世界数据进行了一项包含 363 名受试者的群体 PK 研究，以估算他克莫司的群体 PK 曲线。使用我们的自动系统 EHR2PKPD 提取和构建数据，该系统适用于从 EHR 快速构建大型 PK 数据集。使用 EHR 数据进行的口服药物群体 PK 研究通常假设按照规定的常规剂量方案给药，而不考虑确切的给药时间。我们使用我们自己的自然语言处理系统 medExtractR 提取的最后一剂时间来评估 PK 参数估计对剂量时间假设的敏感性。我们还研究了吸收速率常数估计值对假设的敏感性，这些常数在他克莫司群体 PK 分析中经常固定为公布值。我们进行了模拟研究，以研究药物 PK 曲线和实验设计（如浓度测量设计）如何影响对剂量时间和吸收速率错误假设的敏感性。
假设的最后一剂时间与提取的最后一剂时间之间的参数估计没有明显差异，我们的敏感性分析表明，在一系列假设的吸收速率常数下估计的参数之间几乎没有差异。
我们的研究结果表明，消除率较慢（或半衰期较长）的药物对剂量时间错误的敏感性较低，并且仅允许测量谷浓度的实验设计通常对吸收速率的偏差不敏感。
