Comparison of dose selection based on target engagement versus inhibition of receptor–ligand interaction for checkpoint inhibitors
Immune checkpoint inhibitors block the interaction between a receptor on one cell and its ligand on another cell, thus preventing the transduction of an immunosuppressive signal. While inhibition of the receptor–ligand interaction is key to the pharmacological activity of these drugs, it can be tech...
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Veröffentlicht in: | CPT: Pharmacometrics & Systems Pharmacology 2024-06, Vol.13 (6), p.919-925 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Immune checkpoint inhibitors block the interaction between a receptor on one cell and its ligand on another cell, thus preventing the transduction of an immunosuppressive signal. While inhibition of the receptor–ligand interaction is key to the pharmacological activity of these drugs, it can be technically challenging to measure these intercellular interactions directly. Instead, target engagement (or receptor occupancy) is commonly measured, but may not always be an accurate predictor of receptor–ligand inhibition, and can be misleading when used to inform clinical dose projections for this class of drugs. In this study, a mathematical model explicitly representing the intercellular receptor–ligand interaction is used to compare dose prediction based on target engagement or receptor–ligand inhibition for two checkpoint inhibitors, atezolizumab and magrolimab. For atezolizumab, there is little difference between target engagement and receptor–ligand inhibition, but for magrolimab, the model predicts that receptor–ligand inhibition is significantly less than target engagement. The key variables explaining the difference between these two drugs are the relative concentrations of the target receptors and their ligands. Drug‐target affinity and receptor–ligand affinity can also have divergent effects on target engagement and inhibition. These results suggest that it is important to consider ligand–receptor inhibition in addition to target engagement and demonstrate the impact of using modeling for efficacious dose estimation. |
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ISSN: | 2163-8306 2163-8306 |
DOI: | 10.1002/psp4.13152 |