van Wanrooy Marjolijn J P, Proost Johannes H, Rodgers Michael G G, Zijlstra Jan G, Uges Donald R A, Kosterink Jos G W, van der Werf Tjip S, Alffenaar Jan-Willem C
University of Groningen, University Medical Center Groningen, Department of Clinical Pharmacy and Pharmacology, Groningen, The Netherlands.
University of Groningen, Department of Pharmacy, Section Pharmacokinetics, Toxicology and Targeting, Groningen, The Netherlands.
Antimicrob Agents Chemother. 2015 Feb;59(2):1177-81. doi: 10.1128/AAC.03375-14. Epub 2014 Dec 8.
Efficacy of anidulafungin is driven by the area under the concentration-time curve (AUC)/MIC ratio. Determination of the anidulafungin AUC along with MIC values can therefore be useful. Since obtaining a full concentration-time curve to determine an AUC is not always feasible or appropriate, limited-sampling strategies may be useful in adequately estimating exposure. The objective of this study was to develop a model to predict the individual anidulafungin exposure in critically ill patients using limited-sampling strategies. Pharmacokinetic data were derived from 20 critically ill patients with invasive candidiasis treated with anidulafungin. These data were used to develop a two-compartment model in MW\Pharm using an iterative 2-stage Bayesian procedure. Limited-sampling strategies were subsequently investigated using two methods, a Bayesian analysis and a linear regression analysis. The best possible strategies for these two methods were evaluated by a Bland-Altman analysis for correlation of the predicted and observed AUC from 0 to 24 h (AUC0-24) values. Anidulafungin exposure can be adequately estimated with the concentration from a single sample drawn 12 h after the start of the infusion either by linear regression (R2=0.99; bias, 0.05%; root mean square error [RMSE], 3%) or using a population pharmacokinetic model (R2=0.89; bias, -0.1%; RMSE, 9%) in critically ill patients and also in less severely ill patients, as reflected by healthy volunteers. Limited sampling can be advantageous for future studies evaluating the pharmacokinetics and pharmacodynamics of anidulafungin and for therapeutic drug monitoring in selected patients. (This study has been registered at ClinicalTrials.gov under registration no. NCT01047267.).
阿尼芬净的疗效取决于浓度-时间曲线下面积（AUC）/最低抑菌浓度（MIC）比值。因此，测定阿尼芬净的AUC以及MIC值可能会有所帮助。由于获取完整的浓度-时间曲线以确定AUC并不总是可行或合适的，有限采样策略可能有助于充分估计暴露量。本研究的目的是开发一种模型，使用有限采样策略预测重症患者个体的阿尼芬净暴露量。药代动力学数据来自20例接受阿尼芬净治疗的侵袭性念珠菌病重症患者。这些数据用于在MW\Pharm中使用迭代两阶段贝叶斯程序开发一个二室模型。随后使用贝叶斯分析和线性回归分析两种方法研究有限采样策略。通过Bland-Altman分析评估这两种方法的最佳策略，以分析预测的和观察到的0至24小时AUC（AUC0-24）值之间的相关性。对于重症患者以及病情较轻的患者（如健康志愿者所反映的），在输注开始后12小时采集的单个样本浓度，通过线性回归（R2 = 0.99；偏差，0.05%；均方根误差[RMSE]，3%）或使用群体药代动力学模型（R2 = 0.89；偏差，-0.1%；RMSE，9%），均可充分估计阿尼芬净的暴露量。有限采样对于未来评估阿尼芬净药代动力学和药效学的研究以及对选定患者的治疗药物监测可能具有优势。（本研究已在ClinicalTrials.gov注册，注册号为NCT01047267。）
