Felton T W, Roberts J A, Lodise T P, Van Guilder M, Boselli E, Neely M N, Hope W W
The University of Manchester, Manchester Academic Health Science Centre, NIHR Clinical Research Facility in Respiratory Medicine, University Hospital of South Manchester NHS Foundation Trust, Manchester, United Kingdom.
Burns Trauma and Critical Care Research Centre, The University of Queensland, Brisbane and Royal Brisbane and Women's Hospital Brisbane, Queensland, Australia.
Antimicrob Agents Chemother. 2014 Jul;58(7):4094-102. doi: 10.1128/AAC.02664-14. Epub 2014 May 5.
Piperacillin-tazobactam is frequently used for empirical and targeted therapy of infections in critically ill patients. Considerable pharmacokinetic (PK) variability is observed in critically ill patients. By estimating an individual's PK, dosage optimization Bayesian estimation techniques can be used to calculate the appropriate piperacillin regimen to achieve desired drug exposure targets. The aim of this study was to establish a population PK model for piperacillin in critically ill patients and then analyze the performance of the model in the dose optimization software program BestDose. Linear, with estimated creatinine clearance and weight as covariates, Michaelis-Menten (MM) and parallel linear/MM structural models were fitted to the data from 146 critically ill patients with nosocomial infection. Piperacillin concentrations measured in the first dosing interval, from each of 8 additional individuals, combined with the population model were embedded into the dose optimization software. The impact of the number of observations was assessed. Precision was assessed by (i) the predicted piperacillin dosage and by (ii) linear regression of the observed-versus-predicted piperacillin concentrations from the second 24 h of treatment. We found that a linear clearance model with creatinine clearance and weight as covariates for drug clearance and volume of distribution, respectively, best described the observed data. When there were at least two observed piperacillin concentrations, the dose optimization software predicted a mean piperacillin dosage of 4.02 g in the 8 patients administered piperacillin doses of 4.00 g. Linear regression of the observed-versus-predicted piperacillin concentrations for 8 individuals after 24 h of piperacillin dosing demonstrated an r(2) of >0.89. In conclusion, for most critically ill patients, individualized piperacillin regimens delivering a target serum piperacillin concentration is achievable. Further validation of the dosage optimization software in a clinical trial is required.
哌拉西林 - 他唑巴坦常用于重症患者感染的经验性和靶向治疗。在重症患者中观察到相当大的药代动力学(PK)变异性。通过估计个体的PK,剂量优化贝叶斯估计技术可用于计算合适的哌拉西林治疗方案,以实现所需的药物暴露目标。本研究的目的是建立重症患者哌拉西林的群体PK模型,然后在剂量优化软件程序BestDose中分析该模型的性能。将线性模型(以估计的肌酐清除率和体重作为协变量)、米氏(MM)模型和平行线性/MM结构模型拟合到146例医院感染重症患者的数据中。将另外8名个体在第一个给药间隔内测得的哌拉西林浓度与群体模型相结合,嵌入到剂量优化软件中。评估了观察次数的影响。通过(i)预测的哌拉西林剂量和(ii)治疗后第二个24小时观察到的与预测的哌拉西林浓度的线性回归来评估精密度。我们发现,分别以肌酐清除率和体重作为药物清除率和分布容积的协变量的线性清除模型最能描述观察到的数据。当至少有两个观察到的哌拉西林浓度时,剂量优化软件预测8例接受4.00 g哌拉西林剂量的患者的平均哌拉西林剂量为4.02 g。哌拉西林给药24小时后8名个体观察到的与预测的哌拉西林浓度的线性回归显示r²>0.89。总之,对于大多数重症患者,可以实现提供目标血清哌拉西林浓度的个体化哌拉西林治疗方案。需要在临床试验中对剂量优化软件进行进一步验证。