Exercise PK47 - Plasma Protein Binding Modeling

2020-11-12

Background

  • Structrual Model - Plasma Protein Binding Model

  • Number of Subjects - 4

  • Number of Compounds - 2

Learning Outcome

  • To get an understanding of the determinants of the unbound concentration, free fraction and total concentration.

  • How the modeling of plasma protein data is done

  • To understand the relationship how the binding protein concentration effects the unbound drug concentration

Objectives

  • To analyze In vitro plasma protein data of two compounds

  • To analyze binding data at two different binding concentration

  • To show and understand the relationship between Cu and fu

  • To understand the properties of binding site that can be modeled

Libraries

call the necessary 'libraries' to get started

using Pumas
using Plots
using CSV
using StatsPlots
using Random

Model

In this plasma protein binding model, we have included free drug concentration (dose) and total protein concentration (Pt) as a covariate and with the help of a simple non-linear fit we will estimate the parameters.

pk_47     = @model begin
  @param begin
    tvka   RealDomain(lower=0)
    tvn    RealDomain(lower=0)
    Ω      PDiagDomain(2)
    σ_add  RealDomain(lower=0)
  end

  @random begin
    η     ~ MvNormal(Ω)
  end

  @covariates dose Pt

  @pre begin
    Ka    = tvka * exp(η[1])
    n     = tvn * exp(η[2])
    _dose = dose
    _Pt   = Pt
  end

  @vars begin
    fu    = (1-(1/(1+(_dose/(n*_Pt))+(1/(Ka*n*_Pt)))))*100
  end

  @derived begin
    dv    = @. Normal(fu, σ_add)
  end
end
PumasModel
  Parameters: tvka, tvn, Ω, σ_add
  Random effects: η
  Covariates: dose, Pt
  Dynamical variables: 
  Derived: dv, fu
  Observed: dv, fu

Parameters

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

  • tvka - Affinity constant between drug and protein

  • tvn - Number of binding sites per molecule

## Compound 1
param1 = (tvka  = 6.09,
          tvn   = 2.833,
          Ω     = Diagonal([0.0,0.0,0.0]),
          σ_add = 2.0398)

## Compound 2
param2 = (tvka  = 10.2353,
          tvn   = 1.937,
          Ω     = Diagonal([0.0,0.0,0.0]),
          σ_add = 2.2774)

Dosage Regimen

Since there is no dosage regimen we will create a dataframe with no events and read the file using read_pumas which will be used for simulation.

## Compound 1
df_sub1  = DataFrame(id=1, time=0:1:9999, dv=missing, dose=0.01:0.01:100, Pt=0.3)
df_sub2  = DataFrame(id=2, time=0:1:9999, dv=missing, dose=0.01:0.01:100, Pt=50)
df1      = vcat(df_sub1, df_sub2)
pop1     = read_pumas(df1, observations=[:dv], covariates=[:dose, :Pt], event_data=false)

## Compound 2
df_sub3  = DataFrame(id=3, time=0:1:9999, dv=missing, dose=0.01:0.01:100, Pt=0.1)
df_sub4  = DataFrame(id=4, time=0:1:9999, dv=missing, dose=0.01:0.01:100, Pt=10)
df2      = vcat(df_sub3, df_sub4)
pop2     = read_pumas(df2, observations=[:dv], covariates=[:dose, :Pt], event_data=false)

Simulation

We will now simulate the experimental dat for Compound 1 at two different levels of protein concentration(Pt) 0.3 and 50.

##Compound 1
Random.seed!(123)
sim_1    = simobs(pk_47, pop1, param1)
df1_cmp1 = DataFrame(sim_1)

## Compound 2
Random.seed!(123)
sim_2    = simobs(pk_47, pop2, param2)
df2_cmp2 = DataFrame(sim_2)

Dataframe and Plotting

Compound 1

id1 = filter(x -> x.id == "1", df1_cmp1)
id2 = filter(x -> x.id == "2", df1_cmp1)
df1_dv_cmp1 = filter(x -> x.dose in [0.01,0.05,0.1,0.5,1,5,10,50,100], df1_cmp1)

@df id1 plot(:dose , :fu, xaxis=:log, linewidth=2,
              title = "Compound 1 - Free fraction vs Unbound Concentration", label="Pred - 0.3mg Protein Conc",
              xlabel="Unbound concentration (..)", ylabel="Free Fraction (%)", legend=:topleft,
              ylims=(-2,120), yticks=[0,20,40,60,80,100,120])
@df id2 plot!(:dose , :fu, xaxis=:log, label="Pred - 50mg Protein Conc", linewidth=2)
@df df1_dv_cmp1 scatter!(:dose, :dv, label="Obs")

Compound 2

id3 = filter(x -> x.id == "3", df2_cmp2)
id4 = filter(x -> x.id == "4", df2_cmp2)
df2_dv_cmp2 = filter(x -> x.dose in [0.01,0.05,0.1,0.5,1,5,10,50,100], df2_cmp2)

@df id3 plot(:dose , :fu, xaxis=:log, linewidth=2,
              title = "Compound 2 - Free fraction vs Unbound Concentration", label="Pred - 0.3mg Protein Conc", legend=:topleft,
              xlabel="Unbound concentration (..)", ylabel="Free Fraction (%)",
              ylims=(-2,120), yticks=[0,20,40,60,80,100,120])
@df id4 plot!(:dose , :fu, xaxis=:log, label="Pred - 50mg Protein Conc", linewidth=2)
@df df2_dv_cmp2 scatter!(:dose, :dv, label="Obs")