Pharmaceutical Sciences Department, Faculty of Chemistry, Universidad de la República, Uruguay, General Flores, 2124 Montevideo, Uruguay.
Biomed Res Int. 2021 Apr 8;2021:3108749. doi: 10.1155/2021/3108749. eCollection 2021.
To develop a population pharmacokinetic model for Uruguayan patients under treatment with cyclosporine (CsA) that can be applied to TDM. . A total of 53 patients under treatment with CsA were included. 37 patients with at least one pharmacokinetic profile described with four samples were considered for model building, while the remaining 16 were considered for the assessments of predictive performances. Pharmacokinetic parameter estimation was performed using a nonlinear mixed effect modelling implemented in the Monolix® software (version 2019R1, Lixoft, France); meanwhile, simulations were performed in R v.3.6.0 with the mlxR package.
A two-compartment model with a first-order disposition model including lag time was used as a structural model. The final model was internally validated using prediction corrected visual predictive check (pcVPC) and other graphical diagnostics. A total of 621 CsA steady-state concentrations were analyzed for model development. Population estimates for the absorption constant (ka) and lag time were 0.523 h and 0.512 h, respectively; apparent clearance (CL/F) was 30.3 L/h (relative standard error [RSE] ± 8.25%) with an interindividual variability of 39.8% and interoccasion variability of 38.0%; meanwhile, apparent clearance of distribution (Q/F) was 17.0 L/h (RSE ± 12.1%) with and interindividual variability of 53.2%. The covariate analysis identified creatinine clearance (ClCrea) as an individual factor influencing the Cl of CsA. The predictive capacity of the population model was demonstrated to be effective since predictions made for new patients were accurate for C1 and C2 (MPPEs below 50%). Bayesian forecasting improved significantly in the second and third occasions.
A population pharmacokinetic model was developed to reasonably estimate the individual cyclosporine clearance for patients. Hence, it can be utilized to individualize CsA doses for prompt and adequate achievement of target blood concentrations of CsA.
为接受环孢素(CsA)治疗的乌拉圭患者开发一种群体药代动力学模型,以便应用于治疗药物监测。共纳入 53 例接受 CsA 治疗的患者。对至少有 1 个包含 4 个样本的药代动力学谱描述的 37 例患者进行模型构建,而其余 16 例患者用于评估预测性能。采用非线性混合效应模型在 Monolix®软件(版本 2019R1,法国 Lixoft)中进行药代动力学参数估算;同时,在 R v.3.6.0 中使用 mlxR 包进行模拟。
采用包括滞后时间的一阶处置模型的二室模型作为结构模型。使用预测校正的可视化预测检查(pcVPC)和其他图形诊断对最终模型进行内部验证。共分析了 621 例 CsA 稳态浓度用于模型开发。群体估计的吸收常数(ka)和滞后时间分别为 0.523 h 和 0.512 h;表观清除率(CL/F)为 30.3 L/h(相对标准误差 [RSE]±8.25%),个体间变异性为 39.8%,个体间变异性为 38.0%;同时,分布表观清除率(Q/F)为 17.0 L/h(RSE±12.1%),个体间变异性为 53.2%。协变量分析确定肌酐清除率(ClCrea)为影响 CsA 清除的个体因素。群体模型的预测能力得到了有效证明,因为对新患者的预测在 C1 和 C2 方面是准确的(平均预测误差 [MPPE]低于 50%)。贝叶斯预测在第二和第三次显著提高。
为接受环孢素治疗的患者开发了一种群体药代动力学模型,以合理估计个体环孢素清除率。因此,它可以用于个体化 CsA 剂量,以快速、充分地达到 CsA 的目标血药浓度。