Tasa Tõnis, Metsvaht Tuuli, Kalamees Riste, Vilo Jaak, Lutsar Irja
*Institute of Computer Science, University of Tartu; †Estonian Genome Center, University of Tartu; ‡Department of Microbiology, University of Tartu; and §Clinic of Anaesthesiology and Intensive Care, Tartu University Hospital, Tartu, Estonia.
Ther Drug Monit. 2017 Dec;39(6):604-613. doi: 10.1097/FTD.0000000000000456.
Our main aim has been to design a framework to improve vancomycin dosing in neonates. This required the development and verification of a computerized dose adjustment application, DosOpt, to guide the selection.
Model fitting in DosOpt uses Bayesian methods for deriving individual pharmacokinetic (PK) estimates from population priors and patient therapeutic drug monitoring measurements. These are used to simulate concentration-time curves and target-constrained dose optimization. DosOpt was verified by assessing bias and precision through several error metrics and normalized prediction distribution errors on samples simulated from the Anderson et al PK model. The performance of DosOpt was also evaluated using retrospective clinical data. Achieved probabilities of target concentration attainment were benchmarked against corresponding attainments in our clinical retrospective data set.
Simulations showed no systemic forecast biases. Normalized prediction distribution error values of the base model were distributed by standardized Gaussian (P = 0.1), showing good model suitability. A retrospective test data set included 149 treatment episodes with 1-10 vancomycin concentration measurements per patient (median 2). Individual concentrations in PK estimation improved probability of target attainment and decreased the variance of the estimation. Including 3 individual concentrations in the kinetics estimation increased the probability of Ctrough attainment within 10-15 mg/L from 16% obtained with no individual data (95% confidence interval, 11%-24%) to 43% (21%-47%).
DosOpt uses individual concentration data to estimate kinetics and find optimal doses that increase the probability of achieving desired trough concentrations. Its performance started to exceed target levels attained in retrospective clinical data sets with the inclusion of a single individual input concentration. This tool is freely available at http://www.biit.cs.ut.ee/DosOpt.
我们的主要目标是设计一个框架，以改善新生儿万古霉素的给药方案。这需要开发并验证一个计算机化的剂量调整应用程序DosOpt，以指导用药选择。
DosOpt中的模型拟合使用贝叶斯方法，从群体先验和患者治疗药物监测测量值中推导个体药代动力学（PK）估计值。这些估计值用于模拟浓度-时间曲线和目标约束剂量优化。通过几个误差指标评估偏差和精密度，并对从安德森等人的PK模型模拟的样本进行标准化预测分布误差分析，以此验证DosOpt。还使用回顾性临床数据评估了DosOpt的性能。将达到目标浓度的实际概率与我们临床回顾性数据集中的相应达成情况进行基准对比。
模拟显示无系统性预测偏差。基础模型的标准化预测分布误差值呈标准高斯分布（P = 0.1），表明模型适用性良好。一个回顾性测试数据集包括149个治疗疗程，每位患者有1至10次万古霉素浓度测量值（中位数为2次）。PK估计中的个体浓度提高了达到目标的概率，并降低了估计的方差。在动力学估计中纳入3个个体浓度，使在10 - 15 mg/L范围内达到谷浓度的概率从无个体数据时的16%（95%置信区间，11% - 24%）提高到43%（21% - 47%）。
DosOpt使用个体浓度数据来估计动力学并找到最佳剂量，从而提高达到所需谷浓度的概率。随着纳入单个个体输入浓度，其性能开始超过回顾性临床数据集中达到的目标水平。该工具可在http://www.biit.cs.ut.ee/DosOpt免费获取。
