Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict PET Image Quality of Three Generations EGFR TKI in Advanced-Stage NSCLC Patients
Abstract
:1. Introduction
2. Results
2.1. Components of the Mechanistical PBPK Model
2.2. PBPK Model Validation Using PET Data
2.3. Sensitivity Analysis
3. Discussion
Future Perspective
4. Materials and Methods
4.1. Overview
4.2. Scan Data
4.3. PBPK Model: Base Model Selection
4.4. Base Models: Physicochemical Drug Distribution
4.5. Extension of the Physicochemical Base Models with EGFR Target Binding
4.6. Extension of the Physicochemical-EGFR Models with Lysosomal Sequestration (Mechanistical PBPK Model)
4.7. Including Hallmarks of NSCLC
4.8. Simulation of Tumor-to-Lung Contrast and Tissue Distribution
4.9. Software and Statistics
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Erlotinib | Afatinib | Osimertinib | |
---|---|---|---|
Lung | |||
EGFR binding | 0.21% | 16.73% | 0.14% |
Lysosomal trapping | n.a. | 49.19% | 59.16% |
NL/NP | 1.12% | 0.01% | 0.00% |
Albumin | 73.01% | n.a. | n.a. |
AP- | n.a. | 32.63% | 39.74% |
IW | 14.54% | 1.08% | 0.72% |
EW | 0.36% | 0.36% | 0.23% |
Tumor | |||
EGFR binding | 1.89% | 72.17% | 1.85% |
Lysosomal trapping | n.a. | 11.99% | 42.79% |
NL/NP | 1.05% | 0.00% | 0.00% |
Albumin | 72.32% | n.a. | n.a. |
AP- | n.a. | 14.63% | 52.91% |
IW | 13.71% | 0.48% | 0.96% |
EW | 11.02% | 0.73% | 1.49% |
Erlotinib | Afatinib | Osimertinib | |||||||
---|---|---|---|---|---|---|---|---|---|
Predicted | Observed | Prediction Error (%) | Predicted | Observed | Prediction Error (%) | Predicted | Observed | Prediction Error (%) | |
Brain | 0.13 | n.a. | n.a. | 2.85 | 0.08 (0.03) | 189.7 | 1.52 | 0.79 (0.5) | 62.7 |
Lung | 0.28 | 0.51 (0.2) | −58.8 | 6.89 | 2.54 (1.2) | 92.4 | 3.11 | 7.01 (1.6) | −77.1 |
Spleen | 0.17 | 1.46 (0.4) | −157.8 | 48.72 | 13.23 (2.3) | 114.6 | 25.33 | 18.09 (7.7) | 33.3 |
Kidney | 0.21 | 1.69 (0.6) | −155.6 | 26.20 | 6.93 (1.8) | 116.3 | 10.48 | 5.61 (2.0) | 60.6 |
Bone | 0.14 | 1.23 (0.2) | −158.3 | 2.72 | 4.81 (2.0) | −55.3 | 1.48 | 4.24 (0.7) | −96.6 |
Tumor | 0.30 | 1.42 (0.5) | −131.1 | 15.36 | 3.60 (2.4) | 124.1 | 2.33 | 5.60 (2.0) | −82.4 |
Tissue-Specific Input Parameters | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Fnl | Fnp | Few | Fiw | Flys 2 | Tissue Concentration of AP- (mg/g) 2 | Albumin Tissue to Plasma Ratio 3 | EGFR (nM) | |||||||||||||
Blood cells | 1.7 × 10−3 | 0.0029 | n.a. | 0.60 | n.a. | 0.50 | n.a. | n.a. | ||||||||||||
Bone | 0.017 | 0.0017 | 0.1 | 0.35 | n.d. | 0.67 | 0.10 | n.a. | ||||||||||||
Brain | 0.039 | 0.0015 | 0.16 | 0.61 | 0.014 | 0.40 | 0.048 | n.a. | ||||||||||||
Kidney | 0.039 1 | 0.012 1 | 0.27 | 0.47 | 0.017 | 2.44 1 | 0.13 | 177 | ||||||||||||
Lung 3 | 0.0088 1 | 0.0030 1 | 0.34 | 0.43 | 0.015 | 0.57 1 | 0.21 | 31.1 | ||||||||||||
Tumor | 0.01 | 299 | ||||||||||||||||||
Spleen | 0.021 1 | 0.017 1 | 0.21 | 0.53 | 0.053 | 3.18 | 0.097 | 54.6 | ||||||||||||
Plasma 4 | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | ||||||||||||
Lung-specific parameters 2 | ||||||||||||||||||||
Fnl | Fnp | Few | Fiw | pHew | Flys | pH lysosome | Fcell type | |||||||||||||
-Alveolar macrophages | 0.00881 | 0.00301 | 0.34 | 0.45 | 7.4 | 0.078 | 4.75 | 0.041 | ||||||||||||
-Type II cells | 0.03 | 5.1 | 0.083 | |||||||||||||||||
-Residual cells | 0.01 | 5.1 | 0.88 | |||||||||||||||||
Tumor-specific input parameters | ||||||||||||||||||||
Residual cells | 0.008 | 0.0030 | 0.34 | 0.45 | 6.7 | 0.01 | 5.1 | 1 | ||||||||||||
Compound-specific parameters | ||||||||||||||||||||
Erlotinib | Afatinib | Osimertinib | References | |||||||||||||||||
Log P | 3.3 | 3.6 | 3.2 | Colclough et al. (2021) [31] | ||||||||||||||||
pKa | 5.5 | 8.2 | 9.0 | Colclough et al. (2021) [31] | ||||||||||||||||
B:P ratio 5 | 0.95 | 1.27 | 0.79 | Van de Stadt et al. (2021) [21] | ||||||||||||||||
Kd EGFR (nM) | 2164 | 2 | 155 | Joly-Tonetti et al. (2021) [44] | ||||||||||||||||
Funbound 6 | 0.088 | 0.095 | 0.017 | Colclough et al. (2021) [31] |
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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. Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict PET Image Quality of Three Generations EGFR TKI in Advanced-Stage NSCLC Patients. Pharmaceuticals 2022, 15, 796. https://doi.org/10.3390/ph15070796
Bartelink IH, van de Stadt EA, Leeuwerik AF, Thijssen VLJL, Hupsel JRI, van den Nieuwendijk JF, Bahce I, Yaqub M, Hendrikse NH. 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. https://doi.org/10.3390/ph15070796
Chicago/Turabian StyleBartelink, I. H., E. A. van de Stadt, A. F. Leeuwerik, V. L. J. L. Thijssen, J. R. I. Hupsel, J. F. van den Nieuwendijk, I. Bahce, M. Yaqub, and N. H. Hendrikse. 2022. "Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict PET Image Quality of Three Generations EGFR TKI in Advanced-Stage NSCLC Patients" Pharmaceuticals 15, no. 7: 796. https://doi.org/10.3390/ph15070796