Jung Woojin, Ryu Hyo-Jeong, Chae Jung-Woo, Yun Hwi-Yeol
College of Pharmacy, Chungnam National University, Daejeon 34134, Republic of Korea.
Department of Bio-AI Convergence, Chungnam National University, Daejeon 34134, Republic of Korea.
Pharmaceutics. 2023 Jan 16;15(1):304. doi: 10.3390/pharmaceutics15010304.
Compartment modeling is a widely accepted technique in the field of pharmacokinetic analysis. However, conventional compartment modeling is performed under a homogeneity assumption that is not a naturally occurring condition. Since the assumption lacks physiological considerations, the respective modeling approach has been questioned, as novel drugs are increasingly characterized by physiological or physical features. Alternative approaches have focused on fractal kinetics, but evaluations of their application are lacking. Thus, in this study, a simulation was performed to identify desirable fractal-kinetics applications in conventional modeling. Visible changes in the profiles were then investigated. Five cases of finalized population models were collected for implementation. For model diagnosis, the objective function value (OFV), Akaike's information criterion (AIC), and corrected Akaike's information criterion (AICc) were used as performance metrics, and the goodness of fit (GOF), visual predictive check (VPC), and normalized prediction distribution error (NPDE) were used as visual diagnostics. In most cases, model performance was enhanced by the fractal rate, as shown in a simulation study. The necessary parameters of the fractal rate in the model varied and were successfully estimated between 0 and 1. GOF, VPC, and NPDE diagnostics show that models with the fractal rate described the data well and were robust. In the simulation study, the fractal absorption process was, therefore, chosen for testing. In the estimation study, the rate application yielded improved performance and good prediction-observation agreement in early sampling points, and did not cause a large shift in the original estimation results. Thus, the fractal rate yielded explainable parameters by setting only the heterogeneity exponent, which reflects true physiological behavior well. This approach can be expected to provide useful insights in pharmacological decision making.
房室模型是药代动力学分析领域中一种广泛接受的技术。然而，传统的房室模型是在一个并非自然存在的均一性假设下进行的。由于该假设缺乏生理学考量，随着新型药物越来越多地具有生理学或物理特征，相应的建模方法受到了质疑。替代方法聚焦于分形动力学，但对其应用的评估却很缺乏。因此，在本研究中，进行了一项模拟以确定传统建模中理想的分形动力学应用。然后研究了曲线中的可见变化。收集了五个最终确定的群体模型用于实施。对于模型诊断，目标函数值（OFV）、赤池信息准则（AIC）和校正赤池信息准则（AICc）被用作性能指标，而拟合优度（GOF）、可视化预测检验（VPC）和标准化预测分布误差（NPDE）被用作可视化诊断方法。在大多数情况下，如模拟研究所示，分形速率提高了模型性能。模型中，分形速率的必要参数各不相同，且成功估计值在0到1之间。GOF、VPC和NPDE诊断表明，具有分形速率的模型能很好地描述数据且具有稳健性。因此，在模拟研究中选择了分形吸收过程进行测试。在估计研究中，速率应用在早期采样点提高了性能并达成了良好的预测 - 观测一致性，且未导致原始估计结果出现大幅偏移。这样，通过仅设置反映真实生理行为的异质性指数，分形速率就能产生可解释的参数。这种方法有望在药理决策中提供有用的见解。
