Deng Chenhui, Plan Elodie L, Karlsson Mats O
Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden.
Pfizer (China) Research & Development Center, Shanghai, China.
J Pharmacokinet Pharmacodyn. 2016 Jun;43(3):305-14. doi: 10.1007/s10928-016-9473-1. Epub 2016 May 10.
Parameter variation in pharmacometric analysis studies can be characterized as within subject parameter variability (WSV) in pharmacometric models. WSV has previously been successfully modeled using inter-occasion variability (IOV), but also stochastic differential equations (SDEs). In this study, two approaches, dynamic inter-occasion variability (dIOV) and adapted stochastic differential equations, were proposed to investigate WSV in pharmacometric count data analysis. These approaches were applied to published count models for seizure counts and Likert pain scores. Both approaches improved the model fits significantly. In addition, stochastic simulation and estimation were used to explore further the capability of the two approaches to diagnose and improve models where existing WSV is not recognized. The results of simulations confirmed the gain in introducing WSV as dIOV and SDEs when parameters vary randomly over time. Further, the approaches were also informative as diagnostics of model misspecification, when parameters changed systematically over time but this was not recognized in the structural model. The proposed approaches in this study offer strategies to characterize WSV and are not restricted to count data.
在药代动力学分析研究中，参数变化可在药代动力学模型中表征为受试者内参数变异性（WSV）。WSV此前已成功地使用个体间变异性（IOV）以及随机微分方程（SDE）进行建模。在本研究中，提出了两种方法，即动态个体间变异性（dIOV）和适配随机微分方程，以研究药代动力学计数数据分析中的WSV。这些方法被应用于已发表的癫痫发作计数和李克特疼痛评分的计数模型。两种方法均显著改善了模型拟合。此外，还使用随机模拟和估计进一步探索这两种方法在诊断和改进未识别现有WSV的模型方面的能力。模拟结果证实，当参数随时间随机变化时，将WSV作为dIOV和SDE引入能带来益处。此外，当参数随时间系统变化但结构模型未识别到时，这些方法作为模型错误设定的诊断方法也很有用。本研究中提出的方法提供了表征WSV的策略，且不限于计数数据。
