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Updated: Jun 25, 2025
Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo
Published on: February 6, 2015
Comparison of dose selection based on target engagement versus inhibition of receptor-ligand interaction for
Sarah A Head1, Carter Johnson1, Saheli Sarkar1
1Certara, Concord, Massachusetts, USA.
Abstract:
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.
Insights
Measuring target engagement may mislead dosing for immune checkpoint inhibitors. A mathematical model shows receptor-ligand inhibition is a more accurate predictor, especially for drugs like magrolimab, highlighting the need for advanced modeling in dose estimation.
Area of Science:
- Immunology
- Pharmacology
- Mathematical Biology
Background:
- Immune checkpoint inhibitors (ICIs) block immunosuppressive signals by preventing receptor-ligand interactions.
- Direct measurement of receptor-ligand inhibition is challenging; target engagement (receptor occupancy) is often used as a surrogate.
- Target engagement may not accurately predict receptor-ligand inhibition, potentially leading to misleading clinical dose projections.
Purpose of the Study:
- To compare dose prediction accuracy between target engagement and receptor-ligand inhibition for ICIs using a mathematical model.
- To investigate the impact of drug-specific variables on the relationship between target engagement and receptor-ligand inhibition.
Main Methods:
- Development and application of a mathematical model explicitly representing intercellular receptor-ligand interactions.
- Simulation of dose prediction for two ICIs, atezolizumab and magrolimab, under target engagement and receptor-ligand inhibition metrics.
- Analysis of key variables influencing the divergence between target engagement and receptor-ligand inhibition, including receptor/ligand concentrations and affinities.
Main Results:
- For atezolizumab, target engagement and receptor-ligand inhibition showed minimal difference in model predictions.
- For magrolimab, receptor-ligand inhibition was predicted to be significantly lower than target engagement.
- Relative concentrations of target receptors and their ligands were identified as key factors driving the observed differences between drugs.
Conclusions:
- Relying solely on target engagement can be misleading for ICI dose estimation, particularly for drugs like magrolimab.
- Incorporating receptor-ligand inhibition, alongside target engagement, is crucial for accurate and efficacious dose determination.
- Mathematical modeling provides a valuable tool for understanding ICI pharmacodynamics and optimizing clinical dosing strategies.
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