Background

To determine (oral) bioavailability, the drug is administered by both oral and intravenous route. The adminstration generaly is done in crossover manner at diffrent times separated by a washout period. But if the drug follows a time-dependant 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 upto 1 hour.

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

• Structural model - Two compartment model with first order absorption and elimination

• Route of administration - Oral and IV given simulataneously

• Dosage Regimen - 2.5 mg Oral and 0.5mg IV infusion of 15 mins

• Number of Subjects - 1

Learning Outcomes

This exercise will help to determine bioavailability of a compound administered semi-simultaneously by oral and intravenous route

Objectives

• To bulid a two compartment model for semi-simultaneous oral and intravenous administeration

• To use final parameter estimates

• To design semi-simultaneous dosage regimen

• To simulate and plot a single subject with predefined time points.

Libraries

call the "necessary" libraries to get started.

using Pumas
using Plots
using CSV
using StatsPlots
using Random

Model

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

pk_12           = @model begin
  @param   begin
    tvka        ∈ RealDomain(lower=0)
    tvcl        ∈ RealDomain(lower=0)
    tvq         ∈ RealDomain(lower=0)
    tvvc        ∈ RealDomain(lower=0)
    tvvp        ∈ RealDomain(lower=0)
    tvlag       ∈ RealDomain(lower=0)
    tvF         ∈ RealDomain(lower=0)
    Ω           ∈ PDiagDomain(7)
    σ²_prop     ∈ RealDomain(lower=0)
  end

  @random begin
    η           ~ MvNormal(Ω)
  end

  @pre begin
    Cl          = tvcl * exp(η[1])
    Q           = tvq * exp(η[2])
    Vc          = tvvc * exp(η[3])
    Vp          = tvvp * exp(η[4])
    Ka          = tvka * exp(η[5])
    lags        = (Depot = tvlag * exp(η[6]),)
    bioav       = (Depot = tvF * exp(η[7]),)
  end

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

  @derived begin
    cp          = @. 1000*(Central/Vc)
    dv          ~ @. Normal(cp, sqrt(cp^2*σ²_prop))
  end
end

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

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       = Diagonal([0.0,0.0,0.0,0.0,0.0,0.0,0.0]),
          σ²_prop = 0.04)

(tvka = 0.103, tvcl = 0.015, tvq = 0.021, tvvc = 0.121, tvvp = 0.276, tvlag
 = 4.68, tvF = 0.046, 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 = 0.04)

Dosage Regimen

Dosage Regimen = 2.5 mg/kg oraly followed by 15 min intravenous infusion of 0.5 mg/kg starting from 60 mins after oral dosing administered to a single subject (sub1)

ev_oral = DosageRegimen(2.5, time=0, cmt=1)
ev_inf  = DosageRegimen(0.5, time=60, cmt=2, duration=15)
ev1     = DosageRegimen(ev_oral,ev_inf)
sub1    = Subject(id=1, events=ev1)

Subject
  ID: 1
  Events: 3

Simulation

To simulate plasma concentration with the specific observation time points for a given dosage regimen 'DR'

Random.seed!(123)
sim_s1  = simobs(pk_12, sub1, param, obstimes = [6,10,15,20,30,45,60,63,66,75,80,90,107,119,134,150]);

Dataframe & Plot

df1 = DataFrame(sim_s1)
dropmissing!(df1, :cp)
@df df1 plot(:time, :cp, yaxis=:log,
             label= "Pred - cp", xlabel="Time (min)", ylabel="Concentration (ug/L)",
             title= "Time Vs Plasma concentration", color=[:blue], linewidth=4,
             xticks=[0,20,40,60,80,100,120,140,160], xlims=(0,160), ylims=(10,2000))
@df df1 scatter!(:time, :dv, yaxis=:log,
                 label="Obs - dv", color=[:red], markershape=[:circle])

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