Background

• Structural model - Two compartment with additional input for basal hormone synthesis in the central compartment

• Route of administration - IV infusion (1 minute)

• Dosage Regimen - 36,630 pmol

• Number of Subjects - 1

Learning Outcome

In this model, you will learn how to build a two compartment with additional input for basal hormone level. This model will help to simulate the plasma concentration profile after IV administration considering basal hormone input.

Objectives

• To analyze the intavenous datasets with parallel turnover

• To write multi-compartment model in terms of dfferential equations

Libraries

call the "necessary" libraries to get start.

using Pumas
using Plots
using CSV
using StatsPlots
using Random

Model

In this one compartment model PK31, we administer IV infusion dose to central compartment.

pk_31           = @model begin
  @param begin
    tvkin       ∈ RealDomain(lower=0)
    tvvc        ∈ RealDomain(lower=0)
    tvcl        ∈ RealDomain(lower=0)
    tvq         ∈ RealDomain(lower=0)
    tvvp        ∈ RealDomain(lower=0)
    Ω           ∈ PDiagDomain(5)
    σ²_prop     ∈ RealDomain(lower=0)

  end

  @random begin
    η           ~ MvNormal(Ω)
  end

  @pre begin
    Kin         = tvkin * exp(η[1])
    Vc          = tvvc* exp(η[2])
    Cl          = tvcl * exp(η[3])
    Q           = tvq  * exp(η[4])
    Vp          = tvvp * exp(η[5])
  end

  @dynamics begin
    Central'    =  Kin  - (Cl/Vc)*Central - (Q/Vc)*Central + (Q/Vp)*Peripheral
    Peripheral' = (Q/Vc)*Central - (Q/Vp)*Peripheral
  end

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

PumasModel
  Parameters: tvkin, tvvc, tvcl, tvq, tvvp, Ω, σ²_prop
  Random effects: η
  Covariates: 
  Dynamical variables: Central, Peripheral
  Derived: cp, dv
  Observed: cp, dv

Parameters

The parameters are as given below. tv represents the typical value for parameters.

• Kin - Basal hormonal input (pmol/hr)

• Vc - Central Volume of Distribution (L)

• Cl - Clearance (L/hr)

• Q - Intercompartmental Clearance (L/hr)

• Vp - Peripheral Volume of Distribution (L)

• Ω - Between Subject Variability,

• σ - Residual error

param = ( tvkin   = 1531.87,
          tvvc    = 8.8455,
          tvcl    = 76.5987,
          tvq     = 56.8775,
          tvvp    = 58.8033,
          Ω       = Diagonal([0.0,0.0,0.0,0.0,0.0]),
          σ²_prop = 0.015)

(tvkin = 1531.87, tvvc = 8.8455, tvcl = 76.5987, tvq = 56.8775, tvvp = 58.8
033, 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)

Dosage Regimen

A single dose of 36630 pmol is given as an IV Infusion

ev1  = DosageRegimen(36630, time=0, cmt=1, duration=0.0166)
sub1 = Subject(id=1, events=ev1)

Subject
  ID: 1
  Events: 2

Simulation

Simulate the plasma concentration profile

Random.seed!(123)
sim_sub1 = simobs(pk_31, sub1, param , obstimes=0.01:0.0001:32)

Dataframe & Plot

Convert the simulation to a dataframe and use it for plotting.

df1    = DataFrame(sim_sub1)
df1_dv = filter(x -> x.time in [0.0167,0.1167,0.167,0.25,0.583,0.833,1.083,1.583,2.083,4.083,8.083,12,23.5,24.25,26.75,32], df1)


@df df1 plot(:time, :cp, yaxis=:log,
              label="Pred - Conc",  xlabel="Time (hrs)", ylabel="Concentration (pmol/L)",
              title="Plasma Concentration vs Time", linewidth=3,
              xlims=(-0.2,35), xticks=[0,5,10,15,20,25,30,35], yticks=[10,100,1000,10000], ylims=(10,10000))
@df df1_dv scatter!(:time, :dv, label="Obs - Conc", color=[:red])

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