Centre for Applied Pharmacokinetic Research (CAPKR), The University of Manchester, Manchester, UK.
Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland.
CPT Pharmacometrics Syst Pharmacol. 2023 Mar;12(3):333-345. doi: 10.1002/psp4.12911. Epub 2023 Feb 8.
Whole-body physiologically-based pharmacokinetic (PBPK) models have many applications in drug research and development. It is often necessary to inform these models with animal or clinical data, updating model parameters, and making the model more predictive for future applications. This provides an opportunity and a challenge given the large number of parameters of such models. The aim of this work was to propose new mechanistic model structures with reduced complexity allowing for parameter optimization. These models were evaluated for their ability to estimate realistic values for unbound tissue to plasma partition coefficients (Kpu) and simulate observed pharmacokinetic (PK) data. Two approaches are presented: using either established kinetic lumping methods based on tissue time constants (drug-dependent) or a novel clustering analysis to identify tissues sharing common Kpu values or Kpu scalars based on similarities of tissue composition (drug-independent). PBPK models derived from these approaches were assessed using PK data of diazepam in rats and humans. Although the clustering analysis produced minor differences in tissue grouping depending on the method used, two larger groups of tissues emerged. One including the kidneys, liver, spleen, gut, heart, and lungs, and another including bone, brain, muscle, and pancreas whereas adipose and skin were generally considered distinct. Overall, a subdivision into four tissue groups appeared most physiologically relevant in terms of tissue composition. Several models were found to have similar abilities to describe diazepam i.v. data as empirical models. Comparability of estimated Kpus to experimental Kpu values for diazepam was one criterion for selecting the appropriate PK model structure.
全身体生理药代动力学（PBPK）模型在药物研发中有许多应用。通常需要根据动物或临床数据来提供这些模型，更新模型参数，使其更具预测性，以用于未来的应用。由于这些模型的参数众多，这既提供了机会，也带来了挑战。本工作的目的是提出具有简化结构的新机制模型，以允许参数优化。评估这些模型估算真实的游离组织与血浆分配系数（Kpu）的能力，并模拟观察到的药代动力学（PK）数据。提出了两种方法：使用基于组织时变常数的既定动力学集总方法（药物依赖性），或使用基于组织成分相似性（药物独立性）来识别具有共同 Kpu 值或 Kpu 标度的组织的新型聚类分析。使用大鼠和人类地西泮的 PK 数据评估来自这些方法的 PBPK 模型。尽管聚类分析根据所使用的方法产生了组织分组的微小差异，但出现了两个较大的组织组。一组包括肾脏、肝脏、脾脏、肠道、心脏和肺，另一组包括骨骼、大脑、肌肉和胰腺，而脂肪和皮肤通常被认为是不同的。总体而言，就组织成分而言，将组织分为四组似乎最具生理相关性。一些模型被发现具有相似的能力来描述地西泮静脉注射数据，就像经验模型一样。地西泮估计的 Kpu 值与实验 Kpu 值的可比性是选择合适 PK 模型结构的标准之一。
