Exercise PK44_1 - Estimation of inhibitory constant Ki

2020-11-12

Background

  • Structural model - Estimation of inhibitory rate constant in competitive enzyme inhibition model

  • Number of subjects - 1

  • Number of componds - 1

Learning Outcome

  • To get an understanding about inhibitory models

  • To understand the relationship between rate of metabolite formation and concentration

Objectives

  • To analyze enzyme data by means of simulteneous nonlinear regression

Libraries

call the necessary 'libraries' to get started

using Pumas
using Plots
using CSV
using StatsPlots
using Random

Model - Competitive Inhibitory Model

pk_44_cim        = @model begin
  @param begin
    tvvmax        RealDomain(lower=0)
    tvkm          RealDomain(lower=0)
    tvki          RealDomain(lower=0)
    Ω             PDiagDomain(2)
    σ_add         RealDomain(lower=0)
  end

  @random begin
    η            ~ MvNormal(Ω)
  end

  @covariates conc I

  @pre begin
    Vmax         = tvvmax * exp(η[1])
    Km           = tvkm * exp(η[2])
    Ki           = tvki
    _conc        = conc
    _I           = I
  end

  @derived begin
    ## Competitive Inhibiton Model
    rate_cim     = @. Vmax * _conc / ((Km * (1 + (_I/Ki)) + _conc))
    dv_rate_cim  ~ @. Normal(rate_cim, σ_add)
  end
end
PumasModel
  Parameters: tvvmax, tvkm, tvki, Ω, σ_add
  Random effects: η
  Covariates: conc, I
  Dynamical variables: 
  Derived: rate_cim, dv_rate_cim
  Observed: rate_cim, dv_rate_cim

Parameters

Parameters provided for simulation are as below.tv represents the typical value for parameters.

  • Vmax - Maximum metabolic rate (μM*gm_protein/min)

  • Km - Michaelis-Mentons constant (μmol/L)

  • Ki - Inhibitory constant (μmol/L)

  • I - Inhibitor concentration/Exposure

param = ( tvvmax = 99.8039,
          tvkm   = 11.3192,
          tvki   = 5.05605,
          Ω      = Diagonal([0.0,0.0]),
          σ_add  = 1.256)
(tvvmax = 99.8039, tvkm = 11.3192, tvki = 5.05605, Ω = [0.0 0.0; 0.0 0.0], 
σ_add = 1.256)

Creating a Dataset

In this dataset we will be having time, concentration data and Exposure(I) which will be used to estimate the rate of metabolite concentration

df_sub1 = map(i -> DataFrame(id=i,time = 1:1:1000, dv_rate_cim = missing, conc = 1:1:1000, I=0), 1:6)
df      = vcat(DataFrame.(df_sub1)...)
df[(df[:id] .== 2), :I] = 10
df[(df[:id] .== 3), :I] = 25
df[(df[:id] .== 4), :I] = 50
df[(df[:id] .== 5), :I] = 75
df[(df[:id] .== 6), :I] = 100
df_sub1 = df
sub1    = read_pumas(df_sub1, observations=[:dv_rate_cim], covariates=[:conc, :I], event_data=false)
Population
  Subjects: 6
  Covariates: conc, I
  Observables: dv_rate_cim

Simulation

We will now simulate the rate of metabolite formation

Random.seed!(123)
sim_sub1 = simobs(pk_44_cim, sub1, param)
df1      = DataFrame(sim_sub1)

Dataframe and Plot

Use the DataFrame for plotting

Competitive Inhibitory Model
df1_dv = filter(x -> x.conc in [1,5,10,15,26,104,251,502,1000], df1)

@df df1 plot(:conc, :rate_cim, group=:I,
              title = "Rate of Metabolite formation vs Concentration",
              xlabel = "Concentration (uM)", ylabel = "Metabolic rate (nmol/min/mg protein)",
              linewidth=3, legend=:bottomright, labeltitle="Exposure",
              xticks=[0,100,200,300,400,500,600,700,800,900,1000], xlims=(-5,1010),
              yticks=[0,10,20,30,40,50,60,70,80,90,100], ylims=(0,100))
@df df1_dv scatter!(:conc, :dv_rate_cim, label=false)
@df df1 plot(:conc, :rate_cim, group=:I, xaxis=:log,
              title = "Rate of Metabolite formation vs Concentration", labeltitle="Exposure",
              xlabel = "Concentration (uM)", ylabel = "Metabolic rate (nmol/min/mg protein)",
              linewidth=3, legend=:topleft, xticks=[1,10,100,1000], xlims=(0.95,1005),
              yticks=[0,10,20,30,40,50,60,70,80,90,100], ylims=(0,100))
@df df1_dv scatter!(:conc, :dv_rate_cim, label=false)