Identifying the Optimal Sampling Strategy for the Bayesian Estimation of Vancomycin AUC0-24 in Adult Hematologic Cancer Patients.

IF 4.6 2区 医学 Q1 PHARMACOLOGY & PHARMACY
Clinical Pharmacokinetics Pub Date : 2025-02-01 Epub Date: 2025-01-19 DOI:10.1007/s40262-025-01478-4
Alexandre Duong, Jessica Le Blanc, Denis Projean, Amélie Marsot
{"title":"Identifying the Optimal Sampling Strategy for the Bayesian Estimation of Vancomycin AUC<sub>0-24</sub> in Adult Hematologic Cancer Patients.","authors":"Alexandre Duong, Jessica Le Blanc, Denis Projean, Amélie Marsot","doi":"10.1007/s40262-025-01478-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and objective: </strong>The latest consensus recommends using the ratio between the area under the curve over 24 h (AUC<sub>0-24</sub>) and minimal inhibitory concentration (MIC) as the therapeutic target for vancomycin in clinical practice, with a Bayesian approach and population pharmacokinetic (popPK) model being particularly recommended. While using both post-dose peak concentration (C<sub>peak</sub>) and pre-dose concentration (C<sub>trough</sub>) is more accurate than C<sub>trough</sub> alone, the optimal sampling strategy for estimating AUC<sub>0-24</sub> is still unclear. The objective of this study was to determine the best sampling time(s) to estimate AUC<sub>0-24</sub> using the Bayesian approach in these specific adult hematologic cancer patients.</p><p><strong>Methods: </strong>A virtual population (n = 7000) was simulated based on the distribution of the significant covariates (ideal body weight and estimated glomerular filtration rate) from the population used to develop the previous pharmacokinetic model. The dosing regimens from the Le Blanc et al. nomogram were used to generate, with NONMEM<sup>®</sup> (v.7.5), simulated pharmacokinetic (PK) profiles of one loading dose followed by three maintenance doses (steady state). Strategies involving two samples taken during earlier maintenance doses and one sample taken at steady state were tested using the Bayesian approach to predict PK parameters. These strategies were then evaluated for their ability to predict AUC<sub>0-24</sub> at steady state (AUC<sub>0-24,ss</sub>) RESULTS: For single-sample strategies, a sample taken anytime from 4 h post-dose can estimate AUC<sub>0-24,ss</sub> with precision similar to C<sub>trough</sub> (R<sup>2</sup> ≈ 0.75), regardless of renal function (R<sup>2</sup> ≈ 0.73-0.77). For two-sample strategies, taking samples at least midway through the dosing interval provides the highest precision for estimating AUC<sub>0-24,ss</sub> during the first two maintenance doses (R<sup>2</sup> ≈ 0.75-0.77). In both strategies, using C<sub>peak</sub> did not yield as precise results as sampling midway through the dosing interval or at C<sub>trough</sub>.</p><p><strong>Conclusion: </strong>This study is the first to test multiple limited sampling strategies using a dosing nomogram stratified by renal function. The results show that vancomycin sampling can extend beyond traditional C<sub>peak</sub> and C<sub>trough</sub> without compromising the accuracy of maximum a posteriori Bayesian estimation of AUC<sub>0-24,ss</sub>, thereby providing an opportunity to investigate these limited sampling strategies combined with model-informed precision dosing in a clinical setting.</p>","PeriodicalId":10405,"journal":{"name":"Clinical Pharmacokinetics","volume":" ","pages":"297-305"},"PeriodicalIF":4.6000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Pharmacokinetics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s40262-025-01478-4","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/19 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0

Abstract

Background and objective: The latest consensus recommends using the ratio between the area under the curve over 24 h (AUC0-24) and minimal inhibitory concentration (MIC) as the therapeutic target for vancomycin in clinical practice, with a Bayesian approach and population pharmacokinetic (popPK) model being particularly recommended. While using both post-dose peak concentration (Cpeak) and pre-dose concentration (Ctrough) is more accurate than Ctrough alone, the optimal sampling strategy for estimating AUC0-24 is still unclear. The objective of this study was to determine the best sampling time(s) to estimate AUC0-24 using the Bayesian approach in these specific adult hematologic cancer patients.

Methods: A virtual population (n = 7000) was simulated based on the distribution of the significant covariates (ideal body weight and estimated glomerular filtration rate) from the population used to develop the previous pharmacokinetic model. The dosing regimens from the Le Blanc et al. nomogram were used to generate, with NONMEM® (v.7.5), simulated pharmacokinetic (PK) profiles of one loading dose followed by three maintenance doses (steady state). Strategies involving two samples taken during earlier maintenance doses and one sample taken at steady state were tested using the Bayesian approach to predict PK parameters. These strategies were then evaluated for their ability to predict AUC0-24 at steady state (AUC0-24,ss) RESULTS: For single-sample strategies, a sample taken anytime from 4 h post-dose can estimate AUC0-24,ss with precision similar to Ctrough (R2 ≈ 0.75), regardless of renal function (R2 ≈ 0.73-0.77). For two-sample strategies, taking samples at least midway through the dosing interval provides the highest precision for estimating AUC0-24,ss during the first two maintenance doses (R2 ≈ 0.75-0.77). In both strategies, using Cpeak did not yield as precise results as sampling midway through the dosing interval or at Ctrough.

Conclusion: This study is the first to test multiple limited sampling strategies using a dosing nomogram stratified by renal function. The results show that vancomycin sampling can extend beyond traditional Cpeak and Ctrough without compromising the accuracy of maximum a posteriori Bayesian estimation of AUC0-24,ss, thereby providing an opportunity to investigate these limited sampling strategies combined with model-informed precision dosing in a clinical setting.

确定成人血液病患者万古霉素AUC0-24贝叶斯估计的最佳抽样策略。
背景与目的:最新的共识是在临床实践中使用24h曲线下面积(AUC0-24)与最小抑制浓度(MIC)之比作为万古霉素的治疗靶点,并特别推荐贝叶斯方法和群体药代动力学(popPK)模型。虽然同时使用剂量后峰浓度(Cpeak)和剂量前浓度(Ctrough)比单独使用Ctrough更准确,但估计AUC0-24的最佳采样策略仍不清楚。本研究的目的是确定在这些特定的成人血液学癌症患者中使用贝叶斯方法估计AUC0-24的最佳采样时间。方法:根据用于开发先前药代动力学模型的人群的重要协变量(理想体重和估计肾小球滤过率)的分布,模拟虚拟人群(n = 7000)。使用Le Blanc等人的nomogram给药方案,使用NONMEM®(v.7.5)生成一个负荷剂量和三个维持剂量(稳态)的模拟药代动力学(PK)谱。采用贝叶斯方法预测PK参数,对早期维持剂量期间采集的两个样本和稳态时采集的一个样本进行了策略测试。结果:对于单样本策略,无论肾功能如何(R2≈0.73-0.77),在给药后4小时内的任何时间采集的样本都可以以与Ctrough相似的精度(R2≈0.75)估计AUC0-24,ss。对于双样本策略,至少在给药间隔的中间取样,为估计前两次维持给药期间的auc0 -24,ss提供了最高的精度(R2≈0.75-0.77)。在这两种策略中,使用Cpeak的结果都不如在给药间隔中或在给药间隔中取样精确。结论:本研究是第一个使用按肾功能分层的给药图测试多种有限抽样策略的研究。结果表明,万古霉素采样可以超越传统的Cpeak和cough,而不会影响最大后验贝叶斯估计auc0 -24,ss的准确性,从而为在临床环境中研究这些有限的采样策略与模型信息精确给药提供了机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
8.80
自引率
4.40%
发文量
86
审稿时长
6-12 weeks
期刊介绍: Clinical Pharmacokinetics promotes the continuing development of clinical pharmacokinetics and pharmacodynamics for the improvement of drug therapy, and for furthering postgraduate education in clinical pharmacology and therapeutics. Pharmacokinetics, the study of drug disposition in the body, is an integral part of drug development and rational use. Knowledge and application of pharmacokinetic principles leads to accelerated drug development, cost effective drug use and a reduced frequency of adverse effects and drug interactions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信