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 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.

Mechanistic PK/PD modeling to address early-stage biotherapeutic dosing feasibility questions.

Early assessment of dosing requirements should be an integral part of developability assessments for a discovery program. If a very high dose is required to achieve the desired pharmacological effect, it may not be clinically feasible or commercially desirable to develop the biotherapeutic for the selected target unless extra measures are taken to develop a high concentration formulation or maximize yield during manufacturing. A quantitative understanding of the impact of target selection, biotherapeutic format, and optimal drug properties on potential dosing requirements to achieve efficacy can affect many early decisions. Early prediction of dosing requirements for biotherapeutics, as opposed to small molecules, is possible due to a strong influence of target biology on pharmacokinetics and dosing. Mechanistic pharmacokinetic/pharmacodynamic (PK/PD) models leverage knowledge and competitor data available at an early stage of drug development, including biophysics of the target(s) and disease physiology, to rationally inform drug design criteria. Here we review how mathematical mechanistic PK/PD modeling can and has been applied to guide early drug development decisions.

Early Feasibility Assessment: A Method for Accurately Predicting Biotherapeutic Dosing to Inform Early Drug Discovery Decisions.

The application of model-informed drug discovery and development (MID3) approaches in the early stages of drug discovery can help determine feasibility of drugging a target, prioritize between targets, or define optimal drug properties for a target product profile (TPP). However, applying MID3 in early discovery can be challenging due to the lack of pharmacokinetic (PK) and pharmacodynamic (PD) data at this stage. Early Feasibility Assessment (EFA) is the application of mechanistic PKPD models, built from first principles, and parameterized by data that is readily available early in drug discovery to make effective dose predictions. This manuscript demonstrates the ability of EFA to make accurate predictions of clinical effective doses for nine approved biotherapeutics and outlines the potential of extending this approach to novel therapeutics to impact early drug discovery decisions.