I-023 Imke Bartelink

Enabling Tyrosine Kinase Inhibitor Precision Medicine through PET-Guided Physiologically-Based Pharmacokinetic Modeling

Suzanne van der Gaag, Maqsood Yaqub, Robbin Grijseels, Daan W. van Valkengoed, Evelien N. de Lange, Ruben van den Broek, Victor L.J.L. Thijssen, Adrianus J. de Langen, Mathilde C.M. Kouwenhoven, Idris Bahc, Bart A. Westerman, N. Harry Hendrikse, Imke H. Bartelink

Amsterdam UMC Location Vrije Universiteit Amsterdam & Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands;

Objectives: Osimertinib, a tyrosine kinase inhibitor (TKI), is used in treating non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutations. Treatment efficacy may suffer due to heterogeneous drug distribution. This heterogeneity can be assessed through microdosed radiolabeled drugs and positron emission tomography (PET). Precision dosing using microdosed TKI-PET encounters challenges, due to pharmacokinetic (PK) differences between micro- and therapeutic doses. This study aims to predict osimertinib’s tissue concentration-time profiles for both microdose and therapeutic dose scenarios using a whole-body physiologically-based PK (PBPK) model, accommodating non-linear PK processes.

Methods: Two PBPK-models were combined [1,2]. The first included absorption, absorption, distribution, Cytochrome P450 metabolism, and elimination (ADME) [1] and the second tailored to NSCLC, including immune tumor deprivation, unaltered tumor perfusion, acidic tumor environment, reversible and saturable EGFR binding [2]. After optimization, this novel PBPK-model was evaluated using [11C]-osimertinib microdose concentrations quantified in venous, arterial, lung, lung tumor, brain, bone, spleen, kidney and liver [3], and plasma concentrations after an 80 mg oral therapeutic dose [4]. Accuracy was assessed by comparing predicted and observed concentrations, aiming for less than a three-fold difference.

Results: The new PBPK-model adequately predicted the ADME of osimertinib in blood and all tissues for both microdose and therapeutic dose scenarios. Predicted and mean observed microdose concentrations in tumor tissue after 5 and 60 minutes were 0.85 / 0.69 ± 0.29 nM and 0.21 / 0.61 ± 0.32 nM, respectively and in lung tissue 0.95/ 0.87 ± 0.32 nM and 0.28 / 0.61 ± 0.29 nM, respectively. Sixty minutes post-dose, predicted and observed microdose concentrations were highest in the spleen and liver (1.24 / 2.89 nM and 3.5 / 2.97 nM, respectively), while lowest in the blood and brain (0.1 / 0.01, 0.08 /0.14 nM, respectively). Following the administration of an oral 80 mg dose of osimertinib a predicted time to reach the maximum concentration (Tmax) of 9.8 hours, aligned with the observed Tmax of 7 hours, and predicted Cmax of 232 nM corresponded with the clinically observed Cmax of 168 nM [4]. Target occupancy at therapeutic dose levels of 80 mg QD exceeded 10% in the tumor and only 5% in skin at therapeutic dose levels. Even at 240mg QD dosing (far above maximum tolerated dose) tumor- target saturation was not reached. Up to 200% higher tumor-target attainment was observed at 80 mg in T790M mutations, compared to other activating mutations.

Conclusions: By including target binding and hallmarks NSCLC into PBPK-modeling, tissue PK predictions improved, offering insights into distribution mechanisms and suggesting drug penetration variability among tumor mutations. T790M mutations and higher daily doses increased EGFR binding without saturating non-target tissues. Upon evaluating drug penetrance variability, the PET-based PBPK-model holds promise for enhancing TKI treatment efficacy and minimizing side effects in precision medicine. Furthermore this EGFR-TKI specific model can be used to evaluate the viability of novel EGFR-TKIs.

References:

  1. Bartelink IH, van de Stadt EA, Leeuwerik AF, Thijssen VLJL, Hupsel JRI, van den Nieuwendijk JF, et al. Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict PET Image Quality of Three Generations EGFR TKI in Advanced-Stage NSCLC Patients. Pharmaceuticals 2022;15(7):796.
  2. Reddy VP, Walker M, Sharma P, Ballard P, Vishwanathan K. Development, verification, and prediction of osimertinib drug-drug interactions using PBPK modeling approach to inform drug label. CPT Pharmacometrics Syst Pharmacol 2018;9(5):321–30.
  3. van de Stadt EA, Yaqub M, Schuit RC, Bartelink IH, Leeuwerik AF, Schwarte LA, et al. Relationship between Biodistribution and Tracer Kinetics of 11C-Erlotinib, 18F-Afatinib and 11C-Osimertinib and Image Quality Evaluation Using Pharmacokinetic/Pharmacodynamic Analysis in Advanced Stage Non-Small Cell Lung Cancer Patients. Diagnostics 2022;12(4):883.
  4. Vishwanathan K, So K, Thomas K, Bramley A, Englisg S, Collier J. Absolute Bioavailability of Osimertinib in Healthy Adults. Clin Pharmacol Drug Dev 2019;8(2):198–207

Reference: PAGE 32 (2024) Abstr 11113 [www.page-meeting.org/?abstract=11113]

Poster: Drug/Disease Modelling - Oncology

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