Explore two connected presentations from MetrumRG highlighting how quantitative modeling can support first-in-human dose prediction, cross-modality benchmarking, and translational strategy for T-cell engagers.
POSTER
PI-110 – Impact of Parameter Nonidentifiability on Clinical Dose Projections Using a QSP Model of T Cell Engagers
Interrogates key assumptions in published T-cell engager dose prediction models.
Compares alternative modeling approaches using available preclinical and clinical data.
Highlights what matters most for improving first-in-human dose selection.
Presenters: Kirsten Utsey, Senior Scientist I, Quantitative Systems Pharmacology, Metrum Research Group, with contributions from MetrumRG team members
Beyond Survival: Transforming Autoimmune Therapy Through Model-Informed Development of CAR-T and TCEs
Explores how lymphoma-built CAR-T and T-cell engager models can be translated into autoimmune settings.
Shows how simulations can support cross-modality benchmarking when head-to-head trials are not feasible.
Demonstrates practical use cases for decision-making around dose, schedule, and development strategy.
Presenters: Daniel Kirouac, Vice President, Quantitative Systems Pharmacology, Metrum Research Group, with contributions from Cole Zmurchok and the MetrumRG team
Where/When: Friday, March 6, 2026 | 9:30–10:30 AM MST | Aurora Ballroom D
In today's high-pressure biopharma environment, prioritizing experiments and clinical trial designs that yield the most valuable information relative to the cost and time invested is essential for success. Quantitative Systems Pharmacology (QSP) modeling allows drug developers to simulate thousands of experiments, clinical trials, and alternate hypotheses in silico before committing to costly experiments. The results of these simulations are then used to address uncertainties and guide decision making throughout research and development.
What is Quantitative Systems Pharmacology?
QSP is a powerful interdisciplinary approach for deriving insights and guiding decisions in drug discovery and development. QSP models integrate knowledge by combining mechanistic understanding of biology and pharmacology with a variety of data sources. The resulting mathematical models yield actionable insights in the face of uncertainty.
Key Applications of QSP
Target Discovery: Identification and Validation
Exploration of Novel Mechanisms of Action (MOA)
Lead Selection
In Silico Asset Design → Learn about our ADC In Silico Design Engine
Translational Research Support
Dose and Regimen Selection
Informing Trial Designs
QSP-Informed Drug Development
When empirical clinical trial evidence is limited, QSP approaches are particularly useful to guide decision making. QSP models bridge gaps in data, make projections, and expose mechanistic insights. The application of QSP modeling spans the drug development life-cycle, from target discovery and lead selection through clinical development and post-marketing considerations.
Case Studies: Explore the Impact of QSP in Action
Optimizing ADC and TCE Combination Therapy
Discover how a clinical-stage biotech company partnered withMetrumRGto apply QSP modeling in support of an upcoming ADC-TCE combination therapy clinical trial.
Early Termination Decision for a Nonviable Treatment
See how MetrumRG helped a global pharma company use QSP modeling to assess a novel osteoporosis treatment, leading to early termination and $25M in savings.
Learn how MetrumRG used agent-based modeling (ABM) to help a global biotech predict long-term transgene expression and identify key durability factors for a hemophilia B gene therapy.
Proven Expertise: decades of experience and an extensive track record of applying QSP approaches to solve research and development problems
Innovative Science and Technology: rigorous cutting-edge science, novel methods and tools, modular QSP platform building blocks
Strategic and Collaborative Approach: thoughtful understanding of the problem, strategic and customized QSP solutions, collaborative execution, transparent deliverables
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