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Poster

Modeling T-cell Exhaustion in Immunotherapy

Modeling T-cell Exhaustion in Immunotherapy

Understand how mechanistic QSP modeling reveals the role of T-cell exhaustion in relapse—and how dosing strategies can improve long-term outcomes.

T-cell engagers have demonstrated strong clinical efficacy, but maintaining durable responses remains a challenge. Over time, continuous exposure can drive T-cell exhaustion, reducing cytotoxic activity and increasing the risk of relapse. Yet the relationship between dosing, exhaustion, and long-term outcomes is complex, and difficult to quantify experimentally.

In this poster, we show how a mechanistic QSP framework can capture these dynamics—linking drug exposure, immune response, and tumor killing to better understand relapse and optimize dosing strategies.

What You’ll Learn

Download this poster to explore how QSP modeling can:

  • Quantify the impact of T-cell exhaustion on efficacy: Model how exhaustion reduces cytotoxic activity and drives relapse over time
  • Identify optimal dosing windows: Show how drug-dependent exhaustion shifts the range of effective doses and increases relapse risk
  • Simulate alternative dosing strategies: Evaluate how dose reduction or extended dosing intervals can restore response durability
  • Leverage virtual populations for patient insight: Identify variability in response and drivers of relapse across simulated patient populations

Why It Matters

In immunotherapy, more drug is not always better.
This work highlights how drug-driven biological feedback mechanisms, like T-cell exhaustion, can limit efficacy and narrow the therapeutic window.

Using a mechanistic QSP approach, teams can:

  • Predict when treatment intensity may lead to diminished returns
  • Identify dosing strategies that balance efficacy and durability
  • Enable earlier identification of patients at risk of relapse

Instead of reacting to relapse after it occurs, modeling enables a shift toward proactive, mechanism-informed treatment design.

Authors:

Jonas Denk, Ben Lang, Rachel Rose, Febe Smits, Marise R. Heerma van Voss, Imke H. Bartelink, Andrzej Kierzek, Piet H. van der Graaf, Suruchi Bakshi

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