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Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict PET Image Quality of Three Generations EGFR TKI in

I H Bartelink1, E A van de Stadt2, A F Leeuwerik1

  • 1Department of Clinical Pharmacology and Pharmacy, Amsterdam UMC Location Vrije Universiteit Amsterdam, Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.

Pharmaceuticals (Basel, Switzerland)
|July 27, 2022
PubMed
Abstract

Insights

Physiologically based pharmacokinetic (PBPK) models accurately predicted tumor-to-lung contrast for three generations of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) used in non-small cell lung cancer (NSCLC) imaging. These models can guide the development of new PET imaging tracers for EGFR-mutated NSCLC.

Area of Science:

  • Radiochemistry and Nuclear Medicine
  • Pharmacokinetics and Pharmacodynamics
  • Oncology and Cancer Research

Background:

  • Non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutations is effectively treated with EGFR tyrosine kinase inhibitors (TKIs).
  • Radiolabeled EGFR TKIs and PET/CT imaging can assess EGFR overexpression and mutation status in NSCLC.
  • Significant differences in image quality, specifically tumor-to-lung contrast, exist among first, second, and third-generation EGFR TKIs (e.g., 11C-erlotinib, 18F-afatinib, 11C-osimertinib).

Purpose of the Study:

  • To develop a physiologically based pharmacokinetic (PBPK) model to predict tumor-to-lung contrast for three generations of EGFR TKIs.
  • To predict the uptake of tracers in healthy tissues as a secondary outcome.
  • To evaluate the potential of PBPK models in optimizing PET imaging tracer development for NSCLC.

Main Methods:

  • Collected physicochemical and drug-specific properties (pKa, lipophilicity, target binding) for each TKI.
  • Integrated these properties into established PBPK models, incorporating NSCLC hallmarks like immune deprivation, perfusion, and acidic tumor environment.
  • Validated model accuracy by comparing predicted tissue-to-blood ratios (TBR) with measured PET-image-derived TBR and performed sensitivity analyses.

Main Results:

  • The developed PBPK models successfully predicted tumor-to-lung contrast for all tested EGFR-TKIs within a threefold error margin (PE: -90% for erlotinib, +44% for afatinib, -6.3% for osimertinib).
  • Models demonstrated agreeable whole-body distribution predictions, indicating high tissue distribution for osimertinib and afatinib, and low distribution for erlotinib at high blood concentrations (mean PE: -10.5%).

Conclusions:

  • The PBPK models adequately predicted the image quality for afatinib and osimertinib, and erlotinib.
  • Deviations in predicted whole-body TBR suggest potential new hypotheses regarding EGFR affinity and active transport mechanisms (influx/efflux) not currently included in the models.
  • PBPK modeling holds promise for predicting the image quality of novel PET imaging tracers for EGFR-mutated NSCLC in the future.
Keywords:
EGFR TKINSCLCPBPK modelingPET/CT

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