目的:哌拉西林/他唑巴坦广泛用于治疗晚发性新生儿败血症,但该人群的安全性和药代动力学数据有限.此外,新生儿的有机不成熟导致哌拉西林的药代动力学变化。这会影响抗生素治疗的临床疗效并增加产生耐药性的可能性。本研究旨在评估已报告的哌拉西林群体药代动力学模型在早产和足月墨西哥新生儿重症监护患者模型信息精确给药策略中的应用的预测性能。
方法:确定了已发布的哌拉西林群体药代动力学模型,其中包括研究人群中的新生儿。从参考模型中,结构化模型,群体药代动力学参数,提取个体间和残留变异性数据,并在药代动力学软件(NONMEM®7.4版)中复制.对于临床研究,设计了抽样时间表,2-3份250µL的血液样本取自符合纳入标准的新生儿.哌拉西林血浆浓度通过液相色谱/串联质谱法测定。收集临床治疗资料,和哌拉西林血浆浓度使用参考药代动力学模型进行先验或贝叶斯方法估计。在偏倚和精度方面使用统计方法来评估使用不同方法观察到的和估计的新生儿哌拉西林血浆浓度之间的差异,并确定最适合新生儿数据的药代动力学模型。
结果:共收集了25例新生儿患者的70份血浆样本,其中15例为早产儿。总体中位数值(范围)出生后年龄,胎龄,体重,采样当天的血清肌酐为12(3-26)天,34.2(26-41.1)周,1.78(0.08-3.90)Kg,0.47(0.20-0.90)mg/dL,分别。确定了3种哌拉西林在2个月以下婴儿中的群体药代动力学模型,并评估其在新生儿数据中的预测性能。使用先验方法,没有药代动力学模型适合我们的人群。Cohen-Wolkowiez等人发表的模型。2014年,贝叶斯方法显示了我们新生儿数据中评估的药代动力学模型的最佳性能.该程序需要两个血液样本(给药前和给药后),and,当应用时,它预测了66.6%的观测值,相对中位数绝对预测误差小于30%。
结论:Cohen-Wolkowiez等人建立的群体药代动力学模型。2014年在预测早产和足月墨西哥新生儿重症监护患者血浆哌拉西林浓度方面表现优异.贝叶斯方法,包括两种不同的哌拉西林血浆浓度,在偏倚和精度方面是临床可接受的。其用于模型知情的精确给药的应用可以是优化我们人群中哌拉西林剂量的一种选择。
OBJECTIVE: Piperacillin/tazobactam is extensively used off-label to treat late-onset neonatal sepsis, but safety and pharmacokinetic data in this population are limited. Additionally, the organic immaturity of the newborns contributes to a high
piperacillin pharmacokinetic variability. This affects the clinical efficacy of the antibiotic treatment and increases the probability of developing drug resistance. This study aimed to evaluate the predictive performance of reported
piperacillin population pharmacokinetic models for their application in a model-informed precision dosing strategy in preterm and term Mexican neonatal intensive care patients.
METHODS: Published population pharmacokinetic models for
piperacillin which included neonates in their study population were identified. From the reference models, structured models, population pharmacokinetic parameters, and interindividual and residual variability data were extracted to be replicated in pharmacokinetic software (NONMEM® version 7.4). For the clinical study, a sampling schedule was designed, and 2-3 blood samples of 250 µL were taken from neonates who met the inclusion criteria.
Piperacillin plasma concentrations were determined by liquid chromatography/tandem mass spectrometry. The clinical treatment data were collected, and piperacillin plasma concentrations were estimated using reference pharmacokinetic models for an a priori or Bayesian approach. Statistical methods were used in terms of bias and precision to evaluate the differences between observed and estimated neonatal piperacillin plasma concentrations with the different approaches and to identify the pharmacokinetic model that best fits the neonatal data.
RESULTS: A total of 70 plasma samples were collected from 25 neonatal patients, of which 15 were preterm neonates. The overall median value (range) postnatal age, gestational age, body weight, and serum creatinine at the sampling collecting day were 12 (3-26) days, 34.2 (26-41.1) weeks, 1.78 (0.08-3.90) Kg, 0.47 (0.20-0.90) mg/dL, respectively. Three population pharmacokinetic models for piperacillin in infants up to 2 months were identified, and their predictive performance in neonatal data was evaluated. No pharmacokinetic model was suitable for our population using an a priori approach. The model published by Cohen-Wolkowiez et al. in 2014 with a Bayesian approach showed the best performance of the pharmacokinetic models evaluated in our neonatal data. The procedure requires two blood samples (predose and postdose), and, when applied, it predicted 66.6% of the observations with a relative median absolute predicted error of less than 30%.
CONCLUSIONS: The population pharmacokinetic model developed by Cohen-Wolkowiez et al. in 2014 demonstrated superior performance in predicting the plasma concentration of piperacillin in preterm and term Mexican neonatal intensive care patients. The Bayesian approach, including two different piperacillin plasma concentrations, was clinically acceptable regarding bias and precision. Its application for model-informed precision dosing can be an option to optimize the piperacillin dosage in our population.