Structural model - Two compartment linear elimination with zero order absorption

Route of administration - Three consecutive constant rate IV infusion

Dosage Regimen - 1st dose:- 26.9 mcg/kg over 15min, 2nd dose:- 139 mcg/kg from 15min to 8hr, 3rd dose:- 138.95 mcg/kg between 8hr to 24hr

Number of Subjects - 1

In this model, "Two compartment model- Experimental design" issues helps in understanding how to fit a model to the observed data and further to assess the impact of the best experimental design for estimation.

In this model you will learn how to build a two compartment model and simulate for a single subject.

call the "necessary" libraries to get start.

using Pumas using Plots using CSV using StatsPlots using Random

In this two compartment model we administer three consecutive IV infusion for a single subject and we assess the disposition of drug and fitting the model to the observed data.

pk_39 = @model begin @param begin tvcl ∈ RealDomain(lower=0) tvvc ∈ RealDomain(lower=0) tvvp ∈ RealDomain(lower=0) tvq ∈ RealDomain(lower=0) Ω ∈ PDiagDomain(4) σ²_prop ∈ RealDomain(lower=0) end @random begin η ~ MvNormal(Ω) end @pre begin CL = tvcl * exp(η[1]) Vc = tvvc * exp(η[2]) Vp = tvvp * exp(η[3]) Q = tvq * exp(η[4]) end @dynamics begin Central' = (Q/Vp)*Peripheral - (Q/Vc)*Central -(CL/Vc)*Central Peripheral' = -(Q/Vp)*Peripheral + (Q/Vc)*Central end @derived begin cp = @. Central/Vc dv ~ @. Normal(cp, sqrt(cp^2*σ²_prop)) end end

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

Parameters provided for simulation. `tv`

represents the typical value for parameters.

Cl - Clearance (L/kg/hr)

Vc - Volume of Central Compartment (L/kg)

Vp - Volume of Peripheral Compartment (L/kg)

Q - Intercompartmental clearance (L/kg/hr)

Ω - Between Subject Variability

σ - Residual error

param = (tvcl = 0.417793, tvvc = 0.320672, tvvp = 2.12265, tvq = 0.903188, Ω = Diagonal([0.0,0.0,0.0,0.0]), σ²_prop = 0.005)

(tvcl = 0.417793, tvvc = 0.320672, tvvp = 2.12265, tvq = 0.903188, 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 = 0.005)

Single subject receiving three consecutive IV infusion

1st dose:

**26.9 mcg/kg over 15min**2nd dose:

**139 mcg/kg from 15min to 8hr**3rd dose:

**138.95 mcg/kg between 8hr to 24hr**

ev1 = DosageRegimen(26.9, time=0, cmt=1, duration=0.25) ev2 = DosageRegimen(139, time=0.25, cmt=1, duration=7.85) ev3 = DosageRegimen(138.95, time=8, cmt=1, duration=16) evs = DosageRegimen(ev1,ev2,ev3) sub1 = Subject(id=1, events=evs)

Subject ID: 1 Events: 6

Lets simulate for plasma concentration with the specific observation time points after IV infusion.

Random.seed!(123) sim_sub1 = simobs(pk_39, sub1, param, obstimes=0:0.01:60) df1 = DataFrame(sim_sub1)

Save the simulated data into a dataframe to plots

df1_dv = filter(x -> x.time in [0.25,0.5,1,2,3,6,8,9,10,12,18,21,24,24.5,25,26,28,30,32,34,36,42,48,60], df1) @df df1 plot(:time, :cp, title="Two compartment model - Experimental design issues",label="Pred - Conc", xlabel="Time (hr)",ylabel="Concentration (ug/l)", linewidth=3, xticks=[0,10,20,30,40,50,60], xlims=(-0.2,60), yticks=[0,10,20,30,40,50,60], ylims=(0,60)) @df df1_dv scatter!(:time,:dv, label="Obs - Conc", color=[:red])

Experimental design in this exercise deals with reducing the 24 observation dataset to 14 observation and 5 observation and fitting the two compartment model to these datasets to estimate final parameters.