Neoadjuvant nivolumab tumor–immune dynamics
A reduced QSP model connecting nivolumab exposure and PD-1 checkpoint inhibition to effector and regulatory T-cell dynamics, tumor-cell burden, and tumor regression.
- Therapeutic
- Nivolumab
- Target
- PD-1 / PDCD1
- Disease
- Non-small cell lung cancer
Biological model
A reduced QSP model connecting nivolumab exposure and PD-1 checkpoint inhibition to effector and regulatory T-cell dynamics, tumor-cell burden, and tumor regression.
Model details
A reduced QSP model connecting nivolumab exposure and PD-1 checkpoint inhibition to effector and regulatory T-cell dynamics, tumor-cell burden, and tumor regression.
Modeled states
- checkpoint complex moleculemolecule
Modeled dynamic state for checkpoint complex molecule.
- nivolumab central mgmg
Modeled dynamic state for nivolumab central mg.
- teff cellscell
Modeled dynamic state for teff cells.
- treg cellscell
Modeled dynamic state for treg cells.
- tumor cellscell
Modeled dynamic state for tumor cells.
- tumor diameter percentpercent
Modeled dynamic state for tumor diameter percent.
- tumor regression percentpercent
Modeled dynamic state for tumor regression percent.
Key readouts
- PD-1 blockadeunknown
Model-derived readout for pd-1 blockade.
- Effector T cellscell
Model-derived readout for effector t cells.
- Regulatory T cellscell
Model-derived readout for regulatory t cells.
- Tumor-cell burdencell
Model-derived readout for tumor-cell burden.
- Tumor diameter relative to baselineunknown
Model-derived readout for tumor diameter relative to baseline.
Explore this model
Choose a starting point to view its result. Adjust key model inputs when you want to explore a different outcome.
Regimen-averaged neoadjuvant nivolumab input
Explore how a regimen-averaged nivolumab input rate changes PD-1 blockade, effector and regulatory T-cell balance, and tumor regression. The packaged equations use a continuous average input parameter; discrete infusion events are not executed by this reduced model. This is a mechanistic product exploration, not a paper-result reproduction.
Questions to explore
- How does neoadjuvant PD-1 blockade propagate through T-cell balance to tumor regression?
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 does neoadjuvant PD-1 blockade propagate through T-cell balance to tumor regression?
k elim nivo day
Model parameter controlling k elim nivo day.
Exploratory range around the default value.
5 evenly spaced values in 1/day. 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
A Computational Model of Neoadjuvant PD-1 Inhibition in Non-Small Cell Lung CancerThe AAPS Journal
