Dyck J B, Maze M, Haack C, Azarnoff D L, Vuorilehto L, Shafer S L
Department of Anesthesiology, Veterans Administration Medical Center, San Diego, California 92161-9125.
Anesthesiology. 1993 May;78(5):821-8. doi: 10.1097/00000542-199305000-00003.
This investigation extended the pharmacokinetic analysis of our previous study, of intravenous dexmedetomidine in 10 healthy male volunteers, and prospectively tested the resulting compartmental pharmacokinetics in an additional six subjects using a computer-controlled infusion pump (CCIP) to target four different plasma concentrations of dexmedetomidine for 30 min at each concentration.
A three-compartment mamillary pharmacokinetic model best described the intravenous dexmedetomidine concentration versus time profile following the 5 min intravenous infusion of 2 micrograms/kg in our previous study. Nonlinear regression was performed using both two-stage and pooled data techniques to determine the population pharmacokinetics. The pooled technique allowed covariates, such as weight, age, and height of the subjects, to be incorporated into the nonlinear regression to test the hypothesis that these additional covariates would reduce the residual error between the measured concentrations and the predicted values.
The addition of age, weight, lean body mass, and body surface area as covariates of the pharmacokinetic parameters did not improve the predictive value of the model. However, the model was improved when subject height was a covariate of the volume in the central compartment. The residual error in the pharmacokinetic model was markedly lower with the pooled versus the two-stage approach. The following pharmacokinetic values were obtained from the pooled analysis of the zero-order dexmedetomidine infusion: V1 = 8.05, V2 = 12.4, V3 = 175 (L), Cl1 = (0.0101height [cm]) -1.33, Cl2 = 2.05, and Cl3 = 2.0 (L/min). Prospective evaluation of the pooled pharmacokinetic parameters using a computer-controlled infusion in six healthy volunteers showed the precision (average [(absolute error)/measured concentration]) of the CCIP to be 31.5% and the bias (average [error/measured concentration]) to be -22.4%. A pooled regression of the combined CCIP and zero-order data confirmed that the covariate, height (cm), was related in linear fashion to Cl1. A striking nonlinearity of dexmedetomidine pharmacokinetics related to concentration was observed during the CCIP infusion. The final pharmacokinetic values for the entire data set were: V1 = 7.99, V2 = 13.8, V3 = 187 (L), Cl1 = (0.00791height [cm]) -0.927, Cl2 = 2.26, and Cl3 = 1.99 (L/min).
Pharmacokinetics of dexmedetomidine are best described by a three-compartment model. Addition of age, weight, lean body mass, and body surface area do not improve the predictive value of the model. Additional improvement in CCIP accuracy for dexmedetomidine infusions would require magnification modification of the model based on the targeted concentration.
本研究扩展了我们之前对10名健康男性志愿者静脉注射右美托咪定的药代动力学分析，并前瞻性地在另外6名受试者中使用计算机控制输注泵（CCIP）以靶向四种不同的右美托咪定血浆浓度，每种浓度维持30分钟，来测试由此产生的房室药代动力学。
三室乳突状药代动力学模型最能描述我们之前研究中2微克/千克静脉输注5分钟后静脉注射右美托咪定的浓度与时间曲线。使用两阶段和合并数据技术进行非线性回归以确定群体药代动力学。合并技术允许将受试者的体重、年龄和身高协变量纳入非线性回归，以检验这些额外协变量会减少测量浓度与预测值之间残差误差的假设。
将年龄、体重、瘦体重和体表面积作为药代动力学参数的协变量并未提高模型的预测价值。然而，当受试者身高作为中央室容积的协变量时，模型得到了改善。与两阶段方法相比，药代动力学模型中的残差误差在合并方法中明显更低。从右美托咪定零级输注的合并分析中获得以下药代动力学值：V1 = 8.05，V2 = 12.4，V3 = 175（L），Cl1 =（0.0101×身高[cm]）-1.33，Cl2 = 2.05，Cl3 = 2.0（L/分钟）。在6名健康志愿者中使用计算机控制输注对合并药代动力学参数进行前瞻性评估显示，CCIP的精密度（平均[（绝对误差）/测量浓度]）为31.5%，偏差（平均[误差/测量浓度]）为-22.4%。CCIP和零级数据合并回归证实，协变量身高（cm）与Cl1呈线性关系。在CCIP输注期间观察到右美托咪定药代动力学与浓度相关的显著非线性。整个数据集的最终药代动力学值为：V1 = 7.99，V2 = 13.8，V3 = 187（L），Cl1 =（0.00791×身高[cm]）-0.927，Cl2 = 2.26，Cl3 = 1.99（L/分钟）。
右美托咪定的药代动力学最好用三室模型来描述。添加年龄、体重、瘦体重和体表面积并不能提高模型的预测价值。要进一步提高右美托咪定输注的CCIP准确性，需要根据目标浓度对模型进行放大修改。
