Immuno-oncology drug development promises durable responses — but response heterogeneity, complex mechanisms of action, immune-mediated toxicity, and the difficulty of designing combinations make development decisions hard to get right with data alone.
QSP models for immuno-oncology drug development connect mechanism, dose, schedule, biomarker behavior, and clinical outcome in a single framework — making them well suited to questions that are otherwise hard to answer empirically. This eBook walks through 14 real-world quantitative systems pharmacology case studies spanning monoclonal antibodies, bispecific antibodies, T cell engagers, engineered cytokines, oncolytic viruses, CAR-T therapies, and mRNA-based vaccines. Each case shows how immuno-oncology modeling supported a specific development decision — from first-in-human dose selection to combination strategy to biomarker interpretation.
Download this eBook to learn how QSP modeling for immuno-oncology supported:
- A ~10x higher first-in-human starting dose for a T cell engager compared to traditional MABEL approaches
- A 50-100x higher starting dose for a tri-specific T cell engager — later validated in the clinic and licensed by AbbVie
- Immuno-oncology dose optimization for a PD-L1x4-1BB bispecific balancing two competing mechanisms
- RP2D selection under cytokine release syndrome (CRS) constraints, with positive regulatory feedback
Get the eBook to see how these quantitative systems pharmacology case studies turn immuno-oncology’s biological complexity into clearer clinical strategy.
