Discover how mechanistic QSP modeling integrates clinical and real-world data to predict patient response and optimize CAR-T therapy strategies.
CAR-T therapies have delivered remarkable outcomes in multiple myeloma, but for many patients, responses are not durable.
Relapse remains common, driven by complex interactions between tumor biology, antigen expression, and CAR-T cell dynamics. At the same time, emerging strategies like multi-antigen targeting introduce new opportunities, and new uncertainty.
In this poster, we show how a mechanistic QSP platform can integrate clinical and real-world data to better understand response variability, identify patient-specific drivers, and evaluate next-generation CAR-T strategies.
What You’ll Learn
Download this poster to explore how QSP modeling can:
- Identify patient-specific determinants of response: Quantify the impact of tumor burden, antigen expression, and CAR-T killing rates on outcomes
- Integrate clinical trial and real-world data: Combine published datasets with real-world patient data to validate predictions and improve model relevance
- Support virtual population analysis: Simulate variability across patients to understand response heterogeneity and relapse risk
- Evaluate multi-antigen targeting strategies: Compare sequential and combination approaches targeting BCMA and GPRC5D
Why It Matters
One of the biggest challenges in CAR-T development is understanding why patients respond differently—and how to improve outcomes.
This work demonstrates how a mechanistic QSP framework can:
- Move beyond average response to patient-level insight
- Identify key biological drivers of efficacy and resistance
- Support rational design of combination and sequencing strategies
Rather than relying on trial-and-error, teams can use modeling to predict which patients benefit, and how to improve durability of response.
Authors:
Vicky Kostiou*; Eric Jurgens*; Viji Chelliah; Piet H. van der Graaf; Andrzej M. Kierzek; Ross S. Firestone; Kevin Miller; Bruno Almeida Costa; Sridevi Rajeeve; Alexander M. Lesokhin; Neha Korde; Carlyn R. Tan; Hamza Hashmi; Hani Hassoun; Kylee Maclachlan; Urvi A. Shah; Malin Hultcrantz; Issam Hamadeh; Sergio A. Giralt; David J. Chung; Heather J. Landau; Michael Scordo; Gunjan Shah; Saad Z. Usmani; Sham Mailankody
*equal contribution
