Damoiseaux David, Li Wenlong, Beijnen Jos H, Schinkel Alfred H, Huitema Alwin D R, Dorlo Thomas P C
Department of Pharmacy & Pharmacology, The Netherlands Cancer Institute, Postbox 90203, 1006 BE Amsterdam, the Netherlands.
Division of Pharmacology, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
J Pharm Sci. 2022 Feb;111(2):495-504. doi: 10.1016/j.xphs.2021.09.029. Epub 2021 Sep 23.
The effect of transporters and enzymes on drug pharmacokinetics is increasingly evaluated using genetically modified animals that have these proteins either knocked-out or their human orthologues transgenically expressed. Analysis of pharmacokinetic data obtained in such experiments is typically performed using non-compartmental analysis (NCA), which has limitations such as not being able to identify the PK parameter that is affected by the genetic modification of the enzymes or transporters and the requirement of intense and homogeneous sampling of all subjects. Here we used a compartmental population pharmacokinetic modeling approach using PK data from a series of genetically modified mouse experiments with lorlatinib to extend the results and conclusions from previously reported NCA analyses. A compartmental population pharmacokinetic model was built and physiologically plausible covariates were evaluated for the different mouse strains. With the model, similar effects of the strains on the area under the concentration-time curve (AUC) from 0 to 8 hours were found as for the NCA. Additionally, the differences in AUC between the strains were explained by specific effects on clearance and bioavailability for the strain with human expressing CYP3A4. Finally, effects of multidrug efflux transporters ATP-binding cassette (ABC) sub-family B member 1 (ABCB1) and G member 2 (ABCG2) on brain efflux were quantified. Use of compartmental population PK modeling yielded additional insight into the role of drug-metabolizing enzymes and drug transporters in mouse experiments compared to the NCA. Furthermore, these models allowed analysis of heterogeneous pooled datasets and the sparse organ concentration data in contrast to classical NCA analyses.
转运体和酶对药物药代动力学的影响越来越多地通过基因改造动物来评估，这些动物的这些蛋白质要么被敲除，要么其人类同源物被转基因表达。在此类实验中获得的药代动力学数据的分析通常使用非房室分析（NCA），这种方法存在局限性，例如无法识别受酶或转运体基因改造影响的药代动力学参数，以及需要对所有受试者进行密集且均匀的采样。在这里，我们使用房室群体药代动力学建模方法，利用来自一系列用洛拉替尼进行的基因改造小鼠实验的药代动力学数据，来扩展先前报道的NCA分析的结果和结论。构建了一个房室群体药代动力学模型，并针对不同的小鼠品系评估了具有生理合理性的协变量。利用该模型，发现品系对0至8小时浓度 - 时间曲线（AUC）的影响与NCA的结果相似。此外，对于表达人CYP3A4的品系，AUC之间的差异通过对清除率和生物利用度的特定影响来解释。最后，定量了多药外排转运体ATP结合盒（ABC）亚家族B成员1（ABCB1）和G成员2（ABCG2）对脑外排的影响。与NCA相比，使用房室群体药代动力学建模对小鼠实验中药物代谢酶和药物转运体的作用有了更多的了解。此外，与经典的NCA分析相比，这些模型允许分析异质汇总数据集和稀疏的器官浓度数据。
