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Models Reference

Python simulation functions for all PK and PD models supported by NeoPKPD.

Model Categories

  • Pharmacokinetic Models

    Compartmental PK simulation functions

    PK Functions

  • Pharmacodynamic Models

    Effect model simulation functions

    PD Functions

Pharmacokinetic Functions

Function Model Parameters
simulate_pk_iv_bolus One-comp IV cl, v
simulate_pk_oral_first_order One-comp oral ka, cl, v
simulate_pk_twocomp_iv_bolus Two-comp IV cl, v1, q, v2
simulate_pk_twocomp_oral Two-comp oral ka, cl, v1, q, v2
simulate_pk_threecomp_iv_bolus Three-comp IV cl, v1, q2, v2, q3, v3
simulate_pk_transit_absorption Transit n_transit, ktr, ka, cl, v
simulate_pk_michaelis_menten MM elimination vmax, km, v

Pharmacodynamic Functions

Function Model PD Parameters
simulate_pkpd_direct_emax Direct Emax e0, emax, ec50
simulate_pkpd_sigmoid_emax Sigmoid Emax e0, emax, ec50, gamma
simulate_pkpd_biophase_equilibration Effect compartment ke0, e0, emax, ec50
simulate_pkpd_indirect_response Indirect response kin, kout, ic50, imax

Common Parameters

All simulation functions share these parameters:

Parameter Type Required Description
doses list[dict] Yes List of dose events
t0 float Yes Simulation start time
t1 float Yes Simulation end time
saveat list[float] Yes Output time points

Dose Event Format

doses = [
    {"time": 0.0, "amount": 100.0},              # Bolus
    {"time": 12.0, "amount": 100.0},             # Bolus
    {"time": 0.0, "amount": 100.0, "duration": 1.0}  # Infusion
]

Quick Examples

One-Compartment IV Bolus

import neopkpd

neopkpd.init_julia()

result = neopkpd.simulate_pk_iv_bolus(
    cl=5.0,
    v=50.0,
    doses=[{"time": 0.0, "amount": 100.0}],
    t0=0.0,
    t1=24.0,
    saveat=[float(t) for t in range(25)]
)

print("Concentrations:", result["observations"]["conc"][:5])

Two-Compartment with Distribution

result = neopkpd.simulate_pk_twocomp_iv_bolus(
    cl=10.0,
    v1=20.0,
    q=15.0,
    v2=50.0,
    doses=[{"time": 0.0, "amount": 500.0}],
    t0=0.0,
    t1=48.0,
    saveat=[t * 0.5 for t in range(97)]
)

Direct Emax PD

result = neopkpd.simulate_pkpd_direct_emax(
    cl=5.0, v=50.0,
    doses=[{"time": 0.0, "amount": 100.0}],
    e0=0.0,
    emax=100.0,
    ec50=2.0,
    t0=0.0, t1=24.0,
    saveat=[float(t) for t in range(25)]
)

print("Effect:", result["observations"]["effect"][:5])

Return Structure

All functions return a dictionary:

{
    "t": [0.0, 1.0, ...],              # Time points
    "states": {
        "A_central": [100.0, 90.5, ...]  # State variables
    },
    "observations": {
        "conc": [2.0, 1.81, ...],        # Concentrations
        "effect": [...]                   # Effects (PKPD only)
    },
    "metadata": {
        "model": "OneCompIVBolus",
        "version": "0.1.0"
    }
}

Next Steps