使用最少采样和BestDose软件在儿童和青少年中准确实现白消安的目标暴露量
Accurately Achieving Target Busulfan Exposure in Children and Adolescents With Very Limited Sampling and the BestDose Software.
作者信息
Neely Michael, Philippe Michael, Rushing Teresa, Fu Xiaowei, van Guilder Michael, Bayard David, Schumitzky Alan, Bleyzac Nathalie, Goutelle Sylvain
机构信息
*Laboratory of Applied Pharmacokinetics and Bioinformatics, Division of Pediatric Infectious Diseases, University of Southern California Children's Hospital Los Angeles; †Institute of Pediatric Hematology and Oncology; ‡Pharmacy department, Institute of Pediatric Hematology and Oncology, Hospices Civils de Lyon; §Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558, Université Lyon 1, Villeurbanne, France; ¶Pharmacy Department; and ‖Pathology and Laboratory Medicine, University of Southern California Children's Hospital Los Angeles.
出版信息
Ther Drug Monit. 2016 Jun;38(3):332-42. doi: 10.1097/FTD.0000000000000276.
BACKGROUND
Busulfan dose adjustment is routinely guided by plasma concentration monitoring using 4-9 blood samples per dose adjustment, but a pharmacometric Bayesian approach could reduce this sample burden.
METHODS
The authors developed a nonparametric population model with Pmetrics. They used it to simulate optimal initial busulfan dosages, and in a blinded manner, they compared dosage adjustments using the model in the BestDose software to dosage adjustments calculated by noncompartmental estimation of area under the time-concentration curve at a national reference laboratory in a cohort of patients not included in model building.
RESULTS
Mean (range) age of the 53 model-building subjects was 7.8 years (0.2-19.0 years) and weight was 26.5 kg (5.6-78.0 kg), similar to nearly 120 validation subjects. There were 16.7 samples (6-26 samples) per subject to build the model. The BestDose cohort was also diverse: 10.2 years (0.25-18 years) and 46.4 kg (5.2-110.9 kg). Mean bias and imprecision of the 1-compartment model-predicted busulfan concentrations were 0.42% and 9.2%, and were similar in the validation cohorts. Initial dosages to achieve average concentrations of 600-900 ng/mL were 1.1 mg/kg (≤12 kg, 67% in the target range) and 1.0 mg/kg (>12 kg, 76% in the target range). Using all 9 concentrations after dose 1 in the Bayesian estimation of dose requirements, the mean (95% confidence interval) bias of BestDose calculations for the third dose was 0.2% (-2.4% to 2.9%, P = 0.85), compared with the standard noncompartmental method based on 9 concentrations. With 1 optimally timed concentration 15 minutes after the infusion (calculated with the authors' novel MMopt algorithm) bias was -9.2% (-16.7% to -1.5%, P = 0.02). With 2 concentrations at 15 minutes and 4 hours bias was only 1.9% (-0.3% to 4.2%, P = 0.08).
CONCLUSIONS
BestDose accurately calculates busulfan intravenous dosage requirements to achieve target plasma exposures in children up to 18 years of age and 110 kg using only 2 blood samples per adjustment compared with 6-9 samples for standard noncompartmental dose calculations.
背景
白消安剂量调整通常通过血浆浓度监测来指导，每次剂量调整需采集4 - 9份血样，但药动学贝叶斯方法可减少血样采集负担。
方法
作者使用Pmetrics开发了一个非参数群体模型。他们用该模型模拟白消安的最佳初始剂量，并以盲法将BestDose软件中使用该模型进行的剂量调整与在一个未纳入模型构建的患者队列中的国家参考实验室通过非房室药时曲线下面积估计法计算的剂量调整进行比较。
结果
53名模型构建受试者的平均（范围）年龄为7.8岁（0.2 - 19.0岁），体重为26.5千克（5.6 - 78.0千克），与近120名验证受试者相似。构建模型时每名受试者有16.7份血样（6 - 26份血样）。BestDose队列也具有多样性：年龄为10.2岁（0.25 - 18岁），体重为46.4千克（5.2 - 110.9千克）。单室模型预测的白消安浓度的平均偏差和不精密度分别为0.42%和9.2%，在验证队列中相似。达到平均浓度600 - 900纳克/毫升的初始剂量在体重≤12千克时为1.1毫克/千克（67%在目标范围内），体重>12千克时为1.0毫克/千克（76%在目标范围内）。在剂量需求的贝叶斯估计中使用第1剂后的所有9个浓度时，BestDose对第3剂计算的平均（95%置信区间）偏差为0.2%（-2.4%至2.9%，P = 0.85），而基于9个浓度的标准非房室方法偏差为0.2%（-2.4%至2.9%，P = 0.85）。在输注后15分钟采集1个最佳时间点的浓度（使用作者新的MMopt算法计算）时偏差为 -9.2%（-16.7%至 -1.5%，P = 0.02）。在15分钟和4小时采集2个浓度时偏差仅为1.9%（-0.3%至4.2%，P = 0.08）。
结论
与标准非房室剂量计算需6 - 9份血样相比，BestDose仅通过每次调整采集2份血样就能准确计算18岁及以下、体重达110千克儿童达到目标血浆暴露量所需的白消安静脉剂量。
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