Alihodzic Dzenefa, Broeker Astrid, Baehr Michael, Kluge Stefan, Langebrake Claudia, Wicha Sebastian Georg
Department of Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany.
Front Pharmacol. 2020 Mar 3;11:172. doi: 10.3389/fphar.2020.00172. eCollection 2020.
Routine clinical TDM data is often used to develop population pharmacokinetic (PK) models, which are applied in turn for model-informed precision dosing. The impact of uncertainty in documented sampling and infusion times in population PK modeling and model-informed precision dosing have not yet been systematically evaluated. The aim of this study was to investigate uncertain documentation of (i) sampling times and (ii) infusion rate exemplified with two anti-infectives.
A stochastic simulation and estimation study was performed in NONMEM using previously published population PK models of meropenem and caspofungin. Uncertainties, i.e. deviation between accurate and planned sampling and infusion times (standard deviation (SD) ± 5 min to ± 30 min) were added randomly in R before carrying out the simulation step. The estimation step was then performed with the accurate or planned times (replacing real time points by scheduled study values). Relative bias (rBias) and root mean squared error (rRMSE) were calculated to determine accuracy and precision of the primary and secondary PK parameters on the population and individual level. The accurate and the misspecified (using planned sampling times) model were used for Bayesian forecasting of meropenem to assess the impact on PK/PD target calculations relevant to dosing decisions.
On the population level, the estimates of the proportional residual error (prop.-err.) and the interindividual variability (IIV) on the central volume of distribution (V1) were most affected by erroneous records in the sampling and infusion time (e.g. rBias of prop.-err.: 75.5% vs. 183% (meropenem) and 10.1% vs. 109% (caspofungin) for ± 5 vs. ± 30 min, respectively). On the individual level, the rBias of the planned scenario for the typical values V1, Q and V2 increased with increasing uncertainty in time, while CL, AUC and elimination half-life were least affected. Meropenem as a short half-life drug (1 h) was more affected than caspofungin ( 9-11 h). The misspecified model provided biased PK/PD target information (e.g. falsely overestimated time above MIC (T > MIC) when true T > MIC was <0.4 and thus patients at risk of undertreatment), while the accurate model gave precise estimates of the indices across all simulated patients.
Even 5-minute-uncertainties caused bias and significant imprecision of primary population and individual PK parameters. Thus, our results underline the importance of accurate documentation of time.
常规临床治疗药物监测(TDM)数据常被用于建立群体药代动力学(PK)模型,这些模型进而应用于模型指导的精准给药。群体PK建模和模型指导的精准给药中记录的采样和输注时间的不确定性影响尚未得到系统评估。本研究的目的是通过两种抗感染药物举例,调查(i)采样时间和(ii)输注速率的不确定记录情况。
在NONMEM中进行了一项随机模拟和估计研究,使用先前发表的美罗培南和卡泊芬净的群体PK模型。在进行模拟步骤之前,在R中随机添加不确定性,即准确和计划的采样及输注时间之间的偏差(标准差(SD)±5分钟至±30分钟)。然后使用准确或计划时间进行估计步骤(用预定研究值替换实际时间点)。计算相对偏差(rBias)和均方根误差(rRMSE),以确定群体和个体水平上主要和次要PK参数的准确性和精密度。使用准确模型和错误指定模型(使用计划采样时间)对美罗培南进行贝叶斯预测,以评估对与给药决策相关的PK/PD目标计算的影响。
在群体水平上,采样和输注时间的错误记录对分布容积(V1)的比例残差误差(prop.-err.)和个体间变异性(IIV)的估计影响最大(例如,对于±5分钟和±30分钟,prop.-err.的rBias:美罗培南分别为75.5%对183%,卡泊芬净分别为10.1%对109%)。在个体水平上,典型值V1、Q和V2的计划场景的rBias随着时间不确定性的增加而增加,而清除率(CL)、曲线下面积(AUC)和消除半衰期受影响最小。美罗培南作为半衰期短的药物(约1小时)比卡泊芬净(约9 - 11小时)受影响更大。错误指定的模型提供有偏差的PK/PD目标信息(例如,当真实的高于最低抑菌浓度时间(T > MIC)<0.4时,错误高估T > MIC,从而使患者有治疗不足的风险),而准确模型对所有模拟患者的指标给出了精确估计。
即使5分钟的不确定性也会导致群体和个体主要PK参数出现偏差和显著不精确。因此,我们的结果强调了准确记录时间的重要性。