Background

• Structural model - One compartment linear elimination with first order absorption

• Route of administration - Oral and IV given simulataneously

• Dosage Regimen - 100 mg IV and 100 mg Oral

• Number of Subjects - 1

Objective

In this tutorial, you will learn to build one compartment model and to simulate the model for different subjects and dosage regimens.

Libraries

call the "necessary" libraries to get started

using Pumas
using Plots
using CSV
using StatsPlots
using Random

Model

In this one compartment model, we administer dose both oral and iv at two different occasions.

pk_06        = @model begin
  @param begin
    tvcl     ∈ RealDomain(lower=0)
    tvvc     ∈ RealDomain(lower=0)
    tvfe     ∈ RealDomain(lower=0)
    tvka     ∈ RealDomain(lower=0)
    tvlag    ∈ RealDomain(lower=0)
    tvF      ∈ RealDomain(lower=0)
    Ω        ∈ PDiagDomain(6)
    σ²_prop  ∈ RealDomain(lower=0)
    σ_add    ∈ RealDomain(lower=0)
  end

  @random begin
    η        ~ MvNormal(Ω)
  end

  @pre begin
    Cl       = tvcl * exp(η[1])
    Vc       = tvvc * exp(η[2])
    Ka       = tvka * exp(η[3])
    fe       = tvfe * exp(η[4])
    lags     = (Depot = tvlag * exp(η[5]),)
    bioav    = (Depot = tvF * exp(η[6]),)
  end

  @dynamics begin
    Depot'   = -Ka*Depot
    Central' =  Ka*Depot - (Cl/Vc)*Central
    Urine'   =  fe*(Cl/Vc)*Central
  end

  @derived begin
    cp       = @. (Central/Vc)
    dv       ~ @. Normal(cp, sqrt(cp^2*σ²_prop))
    ae       = @. Urine
    dv_ae    ~ @. Normal(ae, σ_add)
  end
end

PumasModel
  Parameters: tvcl, tvvc, tvfe, tvka, tvlag, tvF, Ω, σ²_prop, σ_add
  Random effects: η
  Covariates: 
  Dynamical variables: Depot, Central, Urine
  Derived: cp, dv, ae, dv_ae
  Observed: cp, dv, ae, dv_ae

Parameters

Parameters provided for simulation. tv represents the typical value for parameters.

• Cl - Clearance (L/hr)

• Vc - Volume of Central Compartment (L)

• Fe - Fraction of drug excreted in Urine

• lag - lag time (hr)

• Ka - Absorption rate constant (hr⁻¹)

• F - Bioavailability

• Ω - Between Subject Variability

• σ - Residual error

param = ( tvcl    = 6.02527,
          tvvc    = 290.292,
          tvfe    = 0.0698294,
          tvka    = 0.420432,
          tvlag   = 0.311831,
          tvF     = 1.13591,
          Ω       = Diagonal([0.0,0.0,0.0,0.0,0.0,0.0]),
          σ²_prop = 0.015,
          σ_add   = 3)

(tvcl = 6.02527, tvvc = 290.292, tvfe = 0.0698294, tvka = 0.420432, tvlag =
 0.311831, tvF = 1.13591, Ω = [0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0; … ; 0.
0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0], σ²_prop = 0.015, σ_add = 3)

Dosage Regimen

For IV

A single subject receives an IV-bolus dose of 12.5 mg or 12500μg

ev1  = DosageRegimen(12500, time=0, cmt=2)
sub1 = Subject(id=1, events=ev1)

Subject
  ID: 1
  Events: 1

For Oral

A single subject receives an oral dose of 25 mg or 25000μg

ev2  = DosageRegimen(25000, time=0, cmt=1)
sub2 = Subject(id=2,events=ev2)

Subject
  ID: 2
  Events: 1

Simulation

Here, we will learn how to simulataneously simulate plasma conc and urine amount after IV and Oral administration.

For IV

Lets simulate for plasma concentration and amount excreted unchanged in urine.

Random.seed!(123)
sim_sub1_iv = simobs(pk_06, sub1, param, obstimes=0.1:0.1:168)
df_sim_iv   = DataFrame(sim_sub1_iv)

For Oral

Lets simulate the plasma concentration and amount excreted unchanged in urine.

Random.seed!(123)
sim_sub2_oral = simobs(pk_06, sub2, param, obstimes=0.1:0.1:168)
df_sim_oral   = DataFrame(sim_sub2_oral)
filter!(x -> x.time .> 0.2, df_sim_oral)

Dataframe & Plot

Combined Plot of IV data and Oral data

df_iv_plasma   = filter(x -> x.time in [0.3, 0.6, 1, 2, 3, 4, 6, 8, 24, 48, 72, 96, 168], df_sim_iv)
df_iv_urine    = filter(x -> x.time in [24,48], df_sim_iv)
df_oral_plasma = filter(x -> x.time in [0.3, 0.6, 1, 2, 3, 4, 6, 8, 24, 48, 72, 96, 168], df_sim_oral)
df_oral_urine  = filter(x -> x.time in [24,48], df_sim_oral)


@df df_sim_iv plot(:time,:cp, yaxis=:log,
                    title="Plasma Drug Conc. & Amt Excreted in Urine",label="Pred - Conc IV",
                    xlabel="Time (hr)", ylabel="Conc. (ug/L) & Amount (ug)", linewidth=3,
                    xticks=[0,20,40,60,80,100,120,140,160,180], ylims=(1,2000))
@df df_iv_plasma scatter!(:time, :dv, label="Obs - Conc IV")
@df df_iv_urine plot!(:time, :ae, label="Pred - Amt IV", linewidth=3)
@df df_iv_urine scatter!(:time, :dv_ae, label="Obs - Amt IV")
@df df_sim_oral plot!(:time,:cp, yaxis=:log, label="Pred - Conc Oral", linewidth=3)
@df df_oral_plasma scatter!(:time, :dv, label="Obs - Conc Oral")
@df df_oral_urine plot!(:time, :ae, label="Pred - Amt Oral", linewidth=3)
@df df_oral_urine scatter!(:time, :dv_ae, label="Obs - Amt Oral", linewidth=3)

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