See how a mechanistic QSP platform captures disease progression and treatment response to enable better decisions in hematologic drug development.
Modeling complex hematological diseases like MDS and AML requires more than fitting clinical data. It demands a mechanistic understanding of how disease and treatment interact across multiple cell lineages.
What You’ll Learn
Download this poster to explore how mechanistic QSP modeling can:
- Reproduce disease biology across states
Capture key differences between healthy, MDS, and AML hematopoiesis, including blast expansion and cytopenias - Predict treatment response with mechanistic fidelity
Simulate azacitidine effects, including selective killing of abnormal cells and re-differentiation dynamics - Support combination strategy evaluation
Provide a flexible platform for exploring new drug mechanisms and treatment combinations - Bridge preclinical and clinical insights
Integrate clinical and in vitro data to inform translational decision-making
Why It Matters
Traditional approaches often struggle to connect biological complexity with clinical outcomes.
This work shows how a mechanistic QSP framework can:
- Link cellular-level dynamics to patient-level outcomes
- Provide insight into why therapies succeed, or fail
- Enable more confident decisions in early and late-stage development
For teams working in hematologic malignancies, this means moving from descriptive models to predictive, decision-enabling platforms.
Authors:
Viji Chelliah, Christopher J Morris, Rong Fan, Chris Walker, Girish Gudi, Piet H van der Graaf
