Department of Statistics and Applied Probability, University of California Santa Barbara, Santa Barbara, California, USA.
School of Pharmacy, Faculty of Medicine and Health, University of Sydney, New South Wales, Sydney, Australia.
Br J Clin Pharmacol. 2023 Sep;89(9):2798-2812. doi: 10.1111/bcp.15756. Epub 2023 May 23.
Pharmacokinetics have historically been assessed using drug concentration data obtained via blood draws and bench-top analysis. The cumbersome nature of these typically constrains studies to at most a dozen concentration measurements per dosing event. This, in turn, limits our statistical power in the detection of hours-scale, time-varying physiological processes. Given the recent advent of in vivo electrochemical aptamer-based (EAB) sensors, however, we can now obtain hundreds of concentration measurements per administration. Our aim in this paper was to assess the ability of these time-dense datasets to describe time-varying pharmacokinetic models with good statistical significance.
We used seconds-resolved measurements of plasma tobramycin concentrations in rats to statistically compare traditional one- and two-compartmental pharmacokinetic models to new models in which the proportional relationship between a drug's plasma concentration and its elimination rate varies in response to changing kidney function.
We found that a modified one-compartment model in which the proportionality between the plasma concentration of tobramycin and its elimination rate falls reciprocally with time either meets or is preferred over the standard two-compartment pharmacokinetic model for half of the datasets characterized. When we reduced the impact of the drug's rapid distribution phase on the model, this one-compartment, time-varying model was statistically preferred over the standard one-compartment model for 80% of our datasets.
Our results highlight both the impact that simple physiological changes (such as varying kidney function) can have on drug pharmacokinetics and the ability of high-time resolution EAB sensor measurements to identify such impacts.
药代动力学的评估历史上是通过采血和台式分析获得的药物浓度数据来进行的。这些方法的繁琐性质通常限制了每个给药事件最多进行十几项浓度测量。这反过来又限制了我们在检测小时级别的、时变的生理过程中的统计能力。然而，鉴于最近体内电化学适体（EAB）传感器的出现，我们现在可以在每次给药时获得数百项浓度测量。我们在本文中的目的是评估这些时间密集型数据集在描述具有良好统计学意义的时变药代动力学模型方面的能力。
我们使用大鼠血浆妥布霉素浓度的秒分辨率测量值，从统计学上比较了传统的单室和双室药代动力学模型与新模型，新模型中药物的血浆浓度与其消除率之间的比例关系随肾脏功能的变化而变化。
我们发现，对于所研究的一半数据集，一个经过修正的单室模型，其中妥布霉素的血浆浓度与其消除率之间的比例随时间呈倒数关系，要么符合标准的两室药代动力学模型，要么优于该模型。当我们降低药物快速分布阶段对模型的影响时，对于我们的 80%数据集，这种单室、时变模型在统计学上优于标准的单室模型。
我们的结果既强调了简单的生理变化（如肾脏功能的变化）对药物药代动力学的影响，也强调了高时间分辨率 EAB 传感器测量对识别这种影响的能力。
