Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Department of Clinical Pharmacy and Toxicology, CARIM School for Cardiovascular Diseases, Maastricht University Medical Center+, Maastricht, The Netherlands.
Eur J Drug Metab Pharmacokinet. 2024 Jul;49(4):517-526. doi: 10.1007/s13318-024-00904-5. Epub 2024 Jun 15.
Several population pharmacokinetic (popPK) studies have been reported that can guide the prediction of osimertinib plasma concentrations in individual patients. It is currently unclear which popPK model offers the best predictive performance and which popPK models are most suitable for nonadherence management and model-informed precision dosing. Therefore, the objective of this study was to externally validate all osimertinib popPK models available in the current literature.
Published popPK models for osimertinib were constructed using NONMEM version 7.4.4. The predictive quality of the identified models was assessed with goodness-of-fit (GoF) plots, conditional weighted residuals (CWRES) plots and a prediction-corrected visual predictive check (pcVPC) for osimertinib and its active metabolite AZ5104. A subset from the Dutch OSIBOOST trial, where 11 patients with low osimertinib exposure were included, was used as evaluation cohort.
The population GoF plots for all four models poorly followed the line of identity. For the individual GoF plots, all models performed comparable and were closely distributed among the line of identity. CWRES of the four models were skewed. The pcVPCs of all four models showed a similar trend, where all observed concentrations fell in the simulated shaded areas, but in the lower region of the simulated areas.
All four popPK models can be used to individually predict osimertinib concentrations in patients with low osimertinib exposure. For population predictions, all four popPK models performed poorly in patients with low osimertinib exposure. A novel popPK model with good predictive performance should be developed for patients with low osimertinib exposure. Ideally, the cause for the relatively low osimertinib exposure in our evaluation cohort should be known.
NCT03858491.
已有多项群体药代动力学（popPK）研究报告，可以指导对个体患者奥希替尼血浆浓度的预测。目前尚不清楚哪种 popPK 模型具有最佳的预测性能，以及哪种 popPK 模型最适合不依从管理和模型指导的精准剂量给药。因此，本研究的目的是对外验证当前文献中所有奥希替尼 popPK 模型。
使用 NONMEM 版本 7.4.4 构建已发表的奥希替尼 popPK 模型。通过拟合度（GoF）图、条件权重残差（CWRES）图和奥希替尼及其活性代谢物 AZ5104 的预测校正可视化核查（pcVPC）评估所识别模型的预测质量。使用荷兰 OSIBOOST 试验的一个子集作为评估队列，该子集中纳入了 11 例奥希替尼暴露水平较低的患者。
所有四个模型的群体 GoF 图都与身份线相差较大。对于个体 GoF 图，所有模型的表现都相当，且与身份线紧密分布。四个模型的 CWRES 均存在偏度。所有四个模型的 pcVPC 显示出相似的趋势，所有观察到的浓度都落在模拟的阴影区域内，但在模拟区域的较低区域。
所有四个 popPK 模型都可用于预测奥希替尼暴露水平较低的患者的个体奥希替尼浓度。对于群体预测，所有四个 popPK 模型在奥希替尼暴露水平较低的患者中表现不佳。对于奥希替尼暴露水平较低的患者，应开发具有良好预测性能的新型 popPK 模型。理想情况下，应了解我们评估队列中奥希替尼暴露相对较低的原因。
NCT03858491。
