Learn how a minimal PBPK model translates FcRn binding into predictive PK and IgG dynamics across species to support biologics development.
Understanding how FcRn binders impact IgG recycling and clearance is critical, but translating those effects across species remains a major challenge. Differences in receptor biology, trafficking, and binding affinities can significantly impact pharmacokinetics, making it difficult to predict human outcomes from preclinical data.
In this poster, we show how a minimal PBPK model can bridge that gap, linking in vitro binding data to in vivo PK/PD predictions and enabling more confident translation across species.
What you’ll learn
Download this poster to explore how PBPK modeling can:
- Translate FcRn binding into PK/PD predictions: Predict IgG levels and drug pharmacokinetics directly from measured binding affinities
- Enable cross-species scaling with mechanistic insight: Apply body-weight scaling and receptor biology to predict outcomes in mice, NHP, and humans
- Incorporate albumin binding for half-life extension strategies: Evaluate how dual FcRn/albumin interactions influence drug exposure
- Support biologics optimization earlier in development: Use mechanistic modeling to guide design decisions before clinical data is available/li>
Why it matters
For biologics targeting FcRn, small differences in binding and trafficking can lead to large differences in clinical exposure.
This work demonstrates how a mechanistic PBPK framework can:
- Reduce uncertainty in cross-species translation
- Provide insight into drivers of variability (e.g., uptake rates, IgG baseline levels)
- Enable rational design of half-life extension strategies
Instead of relying solely on empirical scaling, teams can move toward mechanism-based prediction of PK and PD.
Authors:
James Wade, Katharina Koep, Tim Paulusse, Calin Dragoi, Allison Christiaansen, Achim Doerner, Anis Krache, Enrico Guarnera, Tom Snowden, Rachel Rose, Piet van der Graaf
