Eugene Andy R
Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental, Therapeutics, Gonda 19, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Int J Clin Pharmacol Toxicol. 2017;6(1):242-249. Epub 2017 Jan 3.
The primary aim of this article is to test the hypothesis that nonparametric pharmacometric modeling will accurately identify CYP2B6 genotype subgroups based on data from a study that reported results based on parametric pharmacokinetics (PK).
Propofol concentration-time data were originally reported in the Kansaku et al. 2011 publication. Nonparametric Nonlinear Mixed Effects Modeling (NLME) was conducted using the PMETRICS R package while population pharmacokinetic model parameters were estimated using a FORTRAN compiler. Finally, model-based dosing simulations were conducted in the MATLAB Simbiology.
A total of 51 patients were included in the final PK analysis. A two-compartment gamma multiplicative error model adequately described the propofol concentration-time data. The precision of the goodness-of-fit plots resulted in an R of 0.927 and an R of 0.992 for the population prediction and individual predictions, respectively. Neither the UGT1A9 nor the CYP2B6 G516T gene variants resulted in statistically significant PK parameter differences while the CYP2B6 A785G gene variants resulted in statistically significant differences for the elimination rate. Model-based dosing-simulations comparing patients with the CYP2B6 AA & AG genotypes to both GG genotypes and patients from a multicenter trial suggest a 50% decrease in propofol infusion dose, to 25mg/kg/min, be made to result in approximately equivalent drug exposures.
Based on the pharmacometric modeling and simulation, if no dosage adjustments are made for the elderly CYP2B6 AA and AG genotypes, a 250% higher propofol blood exposure will be evident within 1-hour from the start of the infusion. Thus, based on the pharmacokinetic model, genotyping elderly patients for the CYP2B6 AA and AG gene variants will decrease the total propofol blood exposure during anesthesia and sedation when an infusion dose adjustment is made to 25mg/kg/min.
本文的主要目的是检验以下假设：基于一项报告了参数化药代动力学（PK）结果的研究数据，非参数药代动力学建模能够准确识别CYP2B6基因型亚组。
丙泊酚浓度-时间数据最初发表于Kansaku等人2011年的出版物中。使用PMETRICS R软件包进行非参数非线性混合效应建模（NLME），同时使用FORTRAN编译器估计群体药代动力学模型参数。最后，在MATLAB Simbiology中进行基于模型的给药模拟。
最终的PK分析共纳入51例患者。二室γ乘法误差模型充分描述了丙泊酚浓度-时间数据。拟合优度图的精度分别使群体预测和个体预测的R值为0.927和0.992。UGT1A9和CYP2B6 G516T基因变体均未导致具有统计学意义的PK参数差异，而CYP2B6 A785G基因变体导致消除率具有统计学意义的差异。将CYP2B6 AA和AG基因型患者与GG基因型患者以及多中心试验患者进行基于模型的给药模拟表明，丙泊酚输注剂量降低50%至25mg/kg/min，可导致药物暴露大致相当。
基于药代动力学建模和模拟，如果不对老年CYP2B6 AA和AG基因型患者进行剂量调整，从输注开始1小时内丙泊酚血药暴露将明显高出250%。因此，基于药代动力学模型，对老年患者进行CYP2B6 AA和AG基因变体基因分型，当输注剂量调整为25mg/kg/min时，可降低麻醉和镇静期间丙泊酚总的血药暴露。
