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基于生理的药代动力学(PBPK)模型预测晚期非小细胞肺癌患者三代EGFR TKI的PET图像质量

Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict PET Image Quality of Three Generations EGFR TKI in Advanced-Stage NSCLC Patients.

作者信息

Bartelink I H, van de Stadt E A, Leeuwerik A F, Thijssen V L J L, Hupsel J R I, van den Nieuwendijk J F, Bahce I, Yaqub M, Hendrikse N H

机构信息

Department of Clinical Pharmacology and Pharmacy, Amsterdam UMC Location Vrije Universiteit Amsterdam, Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.

Department of Pulmonary Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.

出版信息

Pharmaceuticals (Basel). 2022 Jun 27;15(7):796. doi: 10.3390/ph15070796.

Abstract

INTRODUCTION

Epidermal growth factor receptor (EGFR) mutated NSCLC is best treated using an EGFR tyrosine kinase inhibitor (TKI). The presence and accessibility of EGFR overexpression and mutation in NSCLC can be determined using radiolabeled EGFR TKI PET/CT. However, recent research has shown a significant difference between image qualities (i.e., tumor-to-lung contrast) in three generation EGFR TKIs: C-erlotinib, F-afatinib and C-osimertinib. In this research we aim to develop a physiological pharmacokinetic (PBPK)-model to predict tumor-to-lung contrast and as a secondary outcome the uptake of healthy tissue of the three tracers.

METHODS

Relevant physicochemical and drug specific properties (e.g., pKa, lipophilicity, target binding) for each TKI were collected and applied in established base PBPK models. Key hallmarks of NSCLC include: immune tumor deprivation, unaltered tumor perfusion and an acidic tumor environment. Model accuracy was demonstrated by calculating the prediction error (PE) between predicted tissue-to-blood ratios (TBR) and measured PET-image-derived TBR. Sensitivity analysis was performed by excluding each key component and comparing the PE with the final mechanistical PBPK model predictions.

RESULTS

The developed PBPK models were able to predict tumor-to-lung contrast for all EGFR-TKIs within threefold of observed PET image ratios (PE tumor-to-lung ratio of -90%, +44% and -6.3% for erlotinib, afatinib and osimertinib, respectively). Furthermore, the models depicted agreeable whole-body distribution, showing high tissue distribution for osimertinib and afatinib and low tissue distribution at high blood concentrations for erlotinib (mean PE, of -10.5%, range -158%-+190%, for all tissues).

CONCLUSION

The developed PBPK models adequately predicted the image quality of afatinib and osimertinib and erlotinib. Some deviations in predicted whole-body TBR lead to new hypotheses, such as increased affinity for mutated EGFR and active influx transport (erlotinib into excreting tissues) or active efflux (afatinib from brain), which is currently unaccounted for. In the future, PBPK models may be used to predict the image quality of new tracers.

摘要

引言

表皮生长因子受体(EGFR)突变的非小细胞肺癌(NSCLC)最好使用EGFR酪氨酸激酶抑制剂(TKI)进行治疗。NSCLC中EGFR过表达和突变的存在及可及性可通过放射性标记的EGFR TKI PET/CT来确定。然而,最近的研究表明,三代EGFR TKI(即西妥昔单抗、阿法替尼和奥希替尼)在图像质量(即肿瘤与肺的对比度)上存在显著差异。在本研究中,我们旨在开发一种生理药代动力学(PBPK)模型,以预测肿瘤与肺的对比度,并作为次要结果预测三种示踪剂在健康组织中的摄取情况。

方法

收集每种TKI的相关物理化学和药物特异性特性(如pKa、亲脂性、靶点结合),并应用于已建立的基础PBPK模型中。NSCLC的关键特征包括:免疫肿瘤剥夺、肿瘤灌注不变和肿瘤酸性环境。通过计算预测的组织与血液比值(TBR)与测量的PET图像衍生TBR之间的预测误差(PE)来证明模型的准确性。通过排除每个关键成分并将PE与最终的机械PBPK模型预测进行比较来进行敏感性分析。

结果

所开发的PBPK模型能够在观察到的PET图像比值的三倍范围内预测所有EGFR-TKI的肿瘤与肺的对比度(西妥昔单抗、阿法替尼和奥希替尼的肿瘤与肺比值的PE分别为-90%、+44%和-6.3%)。此外,模型描绘的全身分布情况一致,显示奥希替尼和阿法替尼的组织分布较高,而西妥昔单抗在高血药浓度下的组织分布较低(所有组织的平均PE为-10.5%,范围为-158%至+190%)。

结论

所开发的PBPK模型充分预测了阿法替尼、奥希替尼和西妥昔单抗的图像质量。预测的全身TBR中的一些偏差导致了新的假设,例如对突变EGFR的亲和力增加以及主动流入转运(西妥昔单抗进入排泄组织)或主动流出(阿法替尼从脑内流出),而目前这些情况尚未得到解释。未来,PBPK模型可用于预测新示踪剂的图像质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0d7/9315544/395ed2dff548/pharmaceuticals-15-00796-g001a.jpg

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