Pharmacogenomic Cohort Dosing Simulator (3D)

Cohort CYP450 genotype

Normal metabolizer cohort — baseline CYP450 clearance, mean activity score 1.0.

Population & regimen

Inter-individual variability 30%

Dose per intake 150 mg

Dosing interval τ 12 h

Simulation speed 3 h/s

Cohort outcome (n = 121)

In window

–

Toxic

–

Sub-therapeutic

–

Elapsed

–

Below effective floor In therapeutic window Above toxic ceiling

How it works

Every one of the 121 columns is a virtual patient sharing the selected CYP450 genotype but not an identical clearance — real populations show log-normal inter-individual variability even within one activity-score bucket. Each patient i gets its own clearance CL_i = CL_normal·activity(genotype)·e^σ·z_i, z_i ~ N(0,1), then runs the same one-compartment dosing math as a single patient, all given the identical dose and interval:

CLᵢ = CL_normal · activity(genotype) · e^(σ·zᵢ)      σ = ln(1+CV²)
kₑᵢ = CLᵢ / Vd
Cᵢ(t) = Σₙ D·kₐ /(Vd·(kₐ−kₑᵢ)) · [e^(−kₑᵢ(t−tₙ)) − e^(−kₐ(t−tₙ))]

Genotype sets the cohort's mean clearance; variability (CV) sets how far individual patients scatter around that mean even though they share a phenotype bucket.
Column height and color track each patient's live blood concentration: blue below the effective floor, green inside the therapeutic window, red above the toxic ceiling.
The same dose and interval that keeps most of a normal-metabolizer cohort green can leave a poor-metabolizer cohort turning red, or an ultrarapid cohort staying blue — the population view makes the guideline-level dosing question visible: what fraction of real patients does one fixed regimen actually cover?

Real-world relevance: CPIC pharmacogenomic dosing guidelines are written from exactly this kind of population simulation — because genotyping every patient before every prescription isn't always practical, guidelines pick a regimen (or a genotype-specific adjustment) that keeps the largest share of a real, variable population inside the window.

Genotype NM · mean CL – L/h · CV 30%

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