Drug-stimulated biomarker turnover
How does repeated exposure accelerate biomarker removal? Explore fixed regimens and bounded dose-amplitude optimization in normalized benchmark units. No clinical dose, patient prediction, global optimum, or paper-figure agreement is claimed.
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Optimize dose amplitudes
Administration times and model parameters stay fixed. Adjust starting amplitudes within the declared bounds, then compare the optimized response with the target.
Fixed-time benchmark amplitude optimization; not a clinical regimen or a paper-optimum reproduction.
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Biology
How does repeated exposure accelerate biomarker removal?
Assumptions & evidence
Assumptions & limitations
- Explore fixed regimens and bounded dose-amplitude optimization in normalized benchmark units. No clinical dose, patient prediction, global optimum, or paper-figure agreement is claimed.
Parameter origins
- https://github.com/Craig-Lab/DiffDose/tree/f6c39363bdd127282a555121cbe072c49dd5f276/IDR.py (make_params, initial_state, forward RHS)
Validation & references
One or more implementation checks are available; this is not a claim of full model reproduction.
No reference paper is attached to this model.
Model details
6 parameters · 2 states · 2 equations · 3 derived outputs
Classification & API access
- Therapeutic
- generic biomarker-removal stimulus
- Modality
- Other biologic or therapeutic modality
- Target
- generic biomarker turnover pathway
- Disease / scope
- Mechanism-agnostic biomarker turnover research
| Parameter | Default | Unit | Description |
|---|---|---|---|
Half-maximal effect concentrationEC50 | 0.81 | dimensionless | Half-maximal effect concentration |
Maximum effectEmax | 8.8 | dimensionless | Maximum removal stimulation |
Apparent distribution volumeV | 3 | dimensionless | Benchmark volume ratio |
Elimination rate constantkel | 0.49 | 1/day | Drug elimination rate |
Biomarker production ratekin | 0.92 | 1/day | Biomarker production rate |
Turnover loss ratekout | 0.02 | 1/day | Baseline biomarker removal rate |
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