PK12 - Intravenous and oral dosing

1 Background

To determine (oral) bioavailability, the drug is administered by both oral and intravenous routes. The administration is generally done in a crossover manner at different times, separated by a washout period. But if the drug follows a time-dependent clearance (and if the washout period is long), then it may affect the results. To avoid this situation, the doses can be administered semi-simultaneously, separated by a small time up to 1 hour.

In the current study, the test drug was administered orally, followed by a constant rate infusion (reference) for 15 minutes at 60 minutes.

  • Structural model - Two compartment model with first order absorption and elimination
  • Route of administration - Oral and IV given simultaneously
  • Dosage Regimen - 2.5 mg Oral and 0.5 mg IV infusion for 15 minutes
  • Number of Subjects - 1

PK12 Graphic Model

2 Learning Outcomes

This exercise demonstrates the bioavailability of a compound administered semi-simultaneously by oral and intravenous routes.

3 Objectives

  • To build a two-compartment model for semi-simultaneous oral and intravenous administration
  • To design a semi-simultaneous dosage regimen
  • To simulate and plot a single subject with predefined time points.

4 Libraries

Load the necessary libraries.

using PumasUtilities
using Random
using Pumas
using CairoMakie
using AlgebraOfGraphics
using CSV
using DataFramesMeta
using Dates

5 Model definition

Note the expression of the model parameters with helpful comments. The model is expressed with differential equations. Residual variability is a proportional error model.

A two compartment model with oral absorption is built for a semi-simultaneous administration of an oral dose followed by intravenous infusion.

pk_12 = @model begin
    @metadata begin
        desc = "Two Compartment Model"
        timeu = u"minute"
    end

    @param begin
        """
        Absorption Rate Constant (min⁻¹)
        """
        tvka ∈ RealDomain(lower = 0)
        """
        Clearance (L/min/kg)
        """
        tvcl ∈ RealDomain(lower = 0)
        """
        Inter-compartmental distribution (L/kg)
        """
        tvq ∈ RealDomain(lower = 0)
        """
        Volume of Central Compartment (L/kg)
        """
        tvvc ∈ RealDomain(lower = 0)
        """
        Volume of Peripheral Compartment (L/kg)
        """
        tvvp ∈ RealDomain(lower = 0)
        """
        Lag time (min)
        """
        tvlag ∈ RealDomain(lower = 0)
        """
        Bioavailability
        """
        tvF ∈ RealDomain(lower = 0)
        Ω_ka ∈ RealDomain(lower = 0.0001)
        Ω_cl ∈ RealDomain(lower = 0.0001)
        Ω_q ∈ RealDomain(lower = 0.0001)
        Ω_vc ∈ RealDomain(lower = 0.0001)
        Ω_vp ∈ RealDomain(lower = 0.0001)
        Ω_lag ∈ RealDomain(lower = 0.0001)
        Ω_F ∈ RealDomain(lower = 0.0001)
        """
        Proportional RUV
        """
        σ²_prop ∈ RealDomain(lower = 0)
    end

    @random begin
        η_ka ~ Normal(0, sqrt(Ω_ka))
        η_cl ~ Normal(0, sqrt(Ω_cl))
        η_q ~ Normal(0, sqrt(Ω_q))
        η_vc ~ Normal(0, sqrt(Ω_vc))
        η_vp ~ Normal(0, sqrt(Ω_vp))
        η_lag ~ Normal(0, sqrt(Ω_lag))
        η_F ~ Normal(0, sqrt(Ω_F))
    end

    @pre begin
        CL = tvcl * exp(η_cl)
        Q = tvq * exp(η_q)
        Vc = tvvc * exp(η_vc)
        Vp = tvvp * exp(η_vp)
        Ka = tvka * exp(η_ka)
    end

    @dosecontrol begin
        lags = (Depot = tvlag * exp(η_lag),)
        bioav = (Depot = tvF * exp(η_F),)
    end

    @dynamics Depots1Central1Periph1

    @derived begin
        cp = @. 1000 * (Central / Vc)
        """
        Observed Concentrations (μg/L)
        """
        dv ~ @. Normal(cp, sqrt(cp^2 * σ²_prop))
    end
end
PumasModel
  Parameters: tvka, tvcl, tvq, tvvc, tvvp, tvlag, tvF, Ω_ka, Ω_cl, Ω_q, Ω_vc, Ω_vp, Ω_lag, Ω_F, σ²_prop
  Random effects: η_ka, η_cl, η_q, η_vc, η_vp, η_lag, η_F
  Covariates: 
  Dynamical variables: Depot, Central, Peripheral
  Derived: cp, dv
  Observed: cp, dv

6 Initial Estimates of Model Parameters

The model parameters for simulation are the following. Note that tv represents the typical value for parameters.

  • Ka - Absorption Rate Constant (min⁻¹)
  • Cl - Clearance (L/min/kg)
  • Q - Inter-compartmental distribution (L/kg)
  • Vc - Volume of Central Compartment (L/kg)
  • Vp - Volume of Peripheral Compartment (L/kg)
  • lag - Lag time (min)
  • F - Bioavailability
  • σ - Residual Error
param = (
    tvka = 0.103,
    tvcl = 0.015,
    tvq = 0.021,
    tvvc = 0.121,
    tvvp = 0.276,
    tvlag = 4.68,
    tvF = 0.046,
    Ω_ka = 0.01,
    Ω_cl = 0.01,
    Ω_q = 0.01,
    Ω_vc = 0.01,
    Ω_vp = 0.01,
    Ω_lag = 0.01,
    Ω_F = 0.01,
    σ²_prop = 0.04,
)

7 Dosage Regimen

Dosage Regimen - 2.5 mg/kg orally followed by a 15 minute intravenous infusion of 0.5 mg/kg starting 60 minutes after oral dosing, administered to a single subject (sub1).

ev_oral = DosageRegimen(2.5, time = 0, cmt = 1)
1×10 DataFrame
Row time cmt amt evid ii addl rate duration ss route
Float64 Int64 Float64 Int8 Float64 Int64 Float64 Float64 Int8 NCA.Route
1 0.0 1 2.5 1 0.0 0 0.0 0.0 0 NullRoute 
ev_inf = DosageRegimen(0.5, time = 60, cmt = 2, duration = 15)
1×10 DataFrame
Row time cmt amt evid ii addl rate duration ss route
Float64 Int64 Float64 Int8 Float64 Int64 Float64 Float64 Int8 NCA.Route
1 60.0 2 0.5 1 0.0 0 0.0333333 15.0 0 NullRoute 
ev1 = DosageRegimen(ev_oral, ev_inf)
2×10 DataFrame
Row time cmt amt evid ii addl rate duration ss route
Float64 Int64 Float64 Int8 Float64 Int64 Float64 Float64 Int8 NCA.Route
1 0.0 1 2.5 1 0.0 0 0.0 0.0 0 NullRoute
2 60.0 2 0.5 1 0.0 0 0.0333333 15.0 0 NullRoute

8 Single-individual that receives the defined dose

sub1 = Subject(id = 1, events = ev1, observations = (cp = nothing,))
Subject
  ID: 1
  Events: 3
  Observations: cp: (n=0)

9 Single-Subject Simulation

Simulate plasma concentrations with specific observation time points

Initialize the random number generator with a seed for reproducibility of the simulation.

Random.seed!(123)

Define the timepoints at which concentration values will be simulated.

sim_s1 = simobs(
    pk_12,
    sub1,
    param,
    obstimes = [6, 10, 15, 20, 30, 45, 60, 63, 66, 75, 80, 90, 107, 119, 134, 150],
)
SimulatedObservations
  Simulated variables: cp, dv
  Time: [6, 10, 15, 20, 30, 45, 60, 63, 66, 75, 80, 90, 107, 119, 134, 150]

10 Visualize Results

@chain DataFrame(sim_s1) begin
    dropmissing(:cp)
    data(_) *
    mapping(:time => "Time (minutes)", :cp => "Concentration (μg/L)") *
    visual(Lines; linewidth = 4)
    draw(;
        figure = (; fontsize = 22),
        axis = (;
            yscale = log10,
            yticks = map(i -> 10^i, 1:0.5:3),
            ytickformat = x -> string.(round.(x; digits = 1)),
            xticks = 0:20:160,
        ),
    )
end

11 Perform a Population Simulation

We perform a population simulation with 40 participants.

This code demonstrates how to write the simulated concentrations to a comma separated file (.csv).

par = (
    tvka = 0.103,
    tvcl = 0.015,
    tvq = 0.021,
    tvvc = 0.121,
    tvvp = 0.276,
    tvlag = 4.68,
    tvF = 0.046,
    Ω_ka = 0.0625,
    Ω_cl = 0.0016,
    Ω_q = 0.0169,
    Ω_vc = 0.0064,
    Ω_vp = 0.0121,
    Ω_lag = 0.0144,
    Ω_F = 0.0144,
    σ²_prop = 0.04,
)

ev1 = DosageRegimen([2.5, 0.5], time = [0, 60], cmt = [1, 2], duration = [0, 15])
pop = map(i -> Subject(id = i, events = ev1), 1:40)

Random.seed!(1234)
pop_sim = simobs(pk_12, pop, par, obstimes = 0:1:150)
pkdata_12_sim = DataFrame(pop_sim)
#CSV.write("pk_12_sim.csv", pkdata_12_sim)

12 Conclusion

This tutorial showed how to build a two-compartment model for semi-simultaneous oral and intravenous administration and perform a single subject and population simulation.