Model-Informed Selection of the Recommended Phase 2 Dosage for Anti-TIGIT Immunotherapy Leveraging co-Expressed PD-1...

Refining dose projections requires a deep understanding of drug-target relationships at the site of action, which is often challenging to achieve. Here we present a case study of how one can refine dose projections for a TIGIT-targeted immunotherapy by leveraging information from the well-studied PD-1 pathway since the co-expression of PD-1 and TIGIT on immune cells provides a unique opportunity to extrapolate data from one target to inform the dosing strategy for the other. We develop a fit-for-purpose mathematical model that captures the experimentally observed relationship between the concentration of a mouse PD-1 antagonist in the plasma and PD-1 target engagement within the tumor microenvironment (TME). We then assess the applicability of this PD-1 model to elucidate the relationship between drug concentration and target engagement for tiragolumab, an anti-TIGIT antibody, across various doses. This analysis aims to refine our understanding of the dose-response relationship for targeting TIGIT, a critical step in optimizing therapeutic efficacy, without conducting additional experiments. The approach is then extended to project efficacious doses for M6223, another anti-TIGIT antibody, using the established PD-1 model, by leveraging the M6223 clinical PK and PD data, as well as virtual population analysis. This work provides a case study of a possible framework for refining dose projections via quantitative estimation of drug-target relationship at the site of action by leveraging established drug-target relationships. Through extrapolating information from a well-characterized pathway, we offer a method to inform dose optimization strategies with limited data using model-informed drug development.