Efalizumab PASI response dynamics
A reduced pharmacodynamic turnover model representing the time course of PASI response toward an efalizumab-associated steady state.
- Therapeutic
- Efalizumab, Efalizumab / hu1124 / Raptiva
- Target
- ITGAL, CD11a, LFA-1 alpha
- Disease
- Psoriasis
Biological model
A reduced pharmacodynamic turnover model representing the time course of PASI response toward an efalizumab-associated steady state.
Intervention
Efalizumab, Efalizumab / hu1124 / Raptiva
Biological focus
ITGAL, CD11a, LFA-1 alpha
Context
Psoriasis
Model details
A reduced pharmacodynamic turnover model representing the time course of PASI response toward an efalizumab-associated steady state.
Modeled states
- PASI scoredimensionless
Modeled dynamic state for pasi score.
- elapsed hh
Modeled dynamic state for elapsed h.
Key readouts
- PASI scoredimensionless
Model-derived readout for pasi score.
- Current PASI responseunknown
Model-derived readout for current pasi response.
- PASI change rateunknown
Model-derived readout for pasi change rate.
- Tp hunknown
Model-derived readout for tp h.
- Yss current PASIunknown
Model-derived readout for yss current pasi.
Explore this model
Choose a starting point to view its result. Adjust key model inputs when you want to explore a different outcome.
Gottlieb escalating-dose PASI progression
How do the response time scale and treatment-associated steady state determine PASI dynamics? Explore this biological starting configuration through PASI score, Current PASI response, PASI change rate. This is a mechanistic product exploration, not a paper-result reproduction.
Questions to explore
- How do the response time scale and treatment-associated steady state determine PASI dynamics?
Starting result
This result reflects the starting settings. Run your changes to update it.
Model inputs
Five scientist-facing controls at most.
Compare a parameter
How does one model input change the response?
How do the response time scale and treatment-associated steady state determine PASI dynamics?
Tp h
Model parameter controlling tp h.
Exploratory range around the default value.
5 evenly spaced values in hour. Other model inputs and the simulation window stay fixed.
Interpret with care
- Interpret trajectories as deterministic model behavior, not as a patient-specific prediction or dosing recommendation.
- Paper-result and exact-anchor checks remain in the private validation lane and are not part of this public scenario.
Review the server-confirmed fixed price before starting the comparison.
Comparative response
End-of-window response across the selected parameter values.
Choose an exploratory range around the default, review the fixed price, and run the comparison.
Scientific reference
Supporting publication
Prediction of the Pharmacokinetics, Pharmacodynamics, and Efficacy of a Monoclonal Antibody, Using a Physiologically Based Pharmacokinetic FcRn ModelFrontiers in Immunology
