Background

Following info:

• Structural model - Two compartment model with parallel linear and Non-linear elimination

• Route of administration - IV bolus (Single dose)

• Dosage Regimen - 0.1mg/kg, 0.3mg/kg, 1mg/kg, 3mg/kg, and 10 mg/kg

• Number of Subjects - 5

Learning Outcomes

To understand the antibody kinetics with linear and nonlinear elimination after IV bolus dose in man.

Objectives

• To build a two compartment model with parallel linear and non-linear elimination to understand the antibody kinetics.

• To simulate dataset for 5 subjects after single dose IV bolus administration

Libraries

Call the "necessary" libraries to get started.

using Pumas
using Plots
using CSV
using StatsPlots
using DataFrames
using Random

Model

Two compartment model with parallel linear and Non- linear elimination

PK26            = @model begin
  @param begin
    tvvmax      ∈ RealDomain(lower=0)
    tvkm        ∈ RealDomain(lower=0)
    tvvp        ∈ RealDomain(lower=0)
    tvvc        ∈ RealDomain(lower=0)
    tvq         ∈ RealDomain(lower=0)
    tvcll       ∈ RealDomain(lower=0)
    Ω           ∈ PDiagDomain(6)
    σ           ∈ RealDomain(lower=0)
  end

  @random begin
    η           ~ MvNormal(Ω)
  end

  @pre begin
    Vmax        = tvvmax*exp(η[1])
    Km          = tvkm*exp(η[2])
    Vp          = tvvp*exp(η[3])
    Vc          = tvvc*exp(η[4])
    Q           = tvq*exp(η[5])
    CLl         = tvcll*exp(η[6]) # Linear clearance
   # CLmm       = Vmax/(Km+C)     Non-linear clearance
  end

  @dynamics begin
    Central'    = -(Vmax/(Km+(Central/Vc)))*(Central/Vc) - CLl*(Central/Vc)-(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*σ))
  end
end

PumasModel
  Parameters: tvvmax, tvkm, tvvp, tvvc, tvq, tvcll, Ω, σ
  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.

• Vmax - Maximum rate of Metabolism(mg/hr/kg)

• Km - Michaelis constant (mg/L/kg)

• Vp - Volume of Peripheral Compartment(L/kg)

• Vc - Volume of Central Compartment(L/kg)

• Q - Inter-compartmental Clearance(L/hr/kg)

• CLl - Linear Clearance(L/hr/kg)

• Ω - Between Subject Variability

• σ - Residual error

param = ( tvvmax  = 0.0338,
          tvkm    = 0.0760,
          tvvp    = 0.0293,
          tvvc    = 0.0729,
          tvq     = 0.0070,
          tvcll   = 0.0069,
          Ω       = Diagonal([0.0,0.0,0.0,0.0,0.0,0.0]),
          σ       = 0.04)

(tvvmax = 0.0338, tvkm = 0.076, tvvp = 0.0293, tvvc = 0.0729, tvq = 0.007, 
tvcll = 0.0069, 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], σ = 0.04)

Dosage Regimen

5 subjects received an IV bolus dose of 0.1, 0.3, 1, 3 and 10 mg/kg respectively at time=0

DR1 = DosageRegimen(0.1, time=0)
DR2 = DosageRegimen(0.3, time=0)
DR3 = DosageRegimen(1, time=0)
DR4 = DosageRegimen(3, time=0)
DR5 = DosageRegimen(10, time=0)
s1  = Subject(id=1, events=DR1)
s2  = Subject(id=2, events=DR2)
s3  = Subject(id=3, events=DR3)
s4  = Subject(id=4, events=DR4)
s5  = Subject(id=5, events=DR5)
pop = Population([s1,s2,s3,s4,s5])

Population
  Subjects: 5
  Covariates:

Simulation

To simulate plasma concentration data for 5 subjects with specific obstimes.

Random.seed!(123)
sim1 = simobs(PK26,s1,param,obstimes =   0.1:0.01: 1.5)
Random.seed!(123)
sim2 = simobs(PK26,s2,param,obstimes =   0.1:0.01:7)
Random.seed!(123)
sim3 = simobs(PK26,s3,param,obstimes =   0.1:0.1:21)
Random.seed!(123)
sim4 = simobs(PK26,s4,param,obstimes =   0.1:0.1:30)
Random.seed!(123)
sim5 = simobs(PK26,s5,param,obstimes =   0.1: 0.1:43)
sim  = [sim1,sim2,sim3,sim4,sim5]

Dataframe and Plots

df1    = DataFrame(sim1)
df1_dv = filter(x -> x.time in [0.07, 0.15, 0.33, 0.43, 0.96, 1.2],df1)
df2    = DataFrame(sim2)
df2_dv = filter(x -> x.time in [0.23, 0.33, 0.51, 1.18, 1.75, 2.2, 3.06, 3.5, 4, 4.5, 5, 5.5, 6.89],df2)
df3    = DataFrame(sim3)
df3_dv = filter(x -> x.time in [0.2, 0.4, 1.2, 3.2, 4.5, 6, 7.1, 9, 10, 11, 12, 13, 14.3,21],df3)
df4    = DataFrame(sim4)
df4_dv = filter(x -> x.time in [0.4, 0.5, 0.6, 1.1, 3.2, 5, 7.1, 9, 10, 12, 14.2, 15, 16, 17, 19, 20.9, 22, 24, 25, 26, 27, 27.5, 28.1],df4)
df5    = DataFrame(sim5)
df5_dv = filter(x -> x.time in [0.2, 0.5, 1.2, 2, 3.2, 5, 7.1, 9, 12, 14.1, 16, 18, 20, 21.1, 22, 24, 25, 26.5, 28.1, 30, 32, 34, 36, 38, 40, 42.1],df5)

DF  = [df1_dv,df2_dv,df3_dv,df4_dv,df5_dv]
DF = vcat(df1_dv,df2_dv,df3_dv,df4_dv,df5_dv)

@df df1 plot(:time, :cp,
              title = "plasma concentration vs Time", label ="S1 pred",
              yaxis =:log, xlabel = "Time(days)", ylabel = "Concentration(mg/L)",
              linewidth =3,legend = :outertopright)
@df df2 plot!(:time, :cp,label= "S2 pred" )
@df df3 plot!(:time, :cp,label= "S3 pred")
@df df4 plot!(:time, :cp,label= "S4 pred")
@df df5 plot!(:time, :cp,label= "S5 pred")
@df df1_dv scatter!(:time, :dv, label= "S1 obs")
@df df2_dv scatter!(:time, :dv, label= "S2 obs")
@df df3_dv scatter!(:time, :dv, label= "S3 obs")
@df df4_dv scatter!(:time, :dv, label= "S4 obs")
@df df5_dv scatter!(:time, :dv, label= "S5 obs")

---