Exercise PK21 - Nonlinear Kinetics - Heteroinduction

2020-11-12

Background

  • Structural model - One compartment Oral administration consists of time dependent change in the elimanation rate constant.

  • Route of administration - Oral

  • Dosage Regimen - Nortriptyline (NT) 10 mg (or 10000 μg) Oral, three times daily for 29 days (i.e. 696 hours), after 216 hours treatment with an enzyme inducer i.e. Pentobarbital (PB) for a period of 300 hours i.e up till 516 hours of treatment with NT.

  • Number of Subjects - 1

pk21

Learning Outcome

By the application of the present model, we will learn how to simulate model for heteroinduction having first order input/ output model to repeated oral dose data on treatment with an enzyme inducer for a limited duration and how to develop a better fit for the available data.

Objectives

In this exercise you will learn how to

  • Simulate an Oral One Compartment with an enzyme induction consists of time dependent change in the elimination rate constant.

Certain assumptions to be considered:

  • The fractional turnover rate i.e. Kout of the enzyme has a longer half-life than the drug or the inducer

  • The duration from one level of enzyme activity to other will be influenced by Kout of the enzyme

  • The Kout is not regulated by the PB. The interpretation V includes bioavailability (i.e., it is really V/F).

  • Write a differential equation for a one-compartment model with oral absorption including time dependent change in elimination rate constant.

Libraries

call the "necessary" libraries to get start.

using Pumas
using Plots
using CSV
using StatsPlots
using Random

Model

In this one compartment model, we administer dose in Depot compartment at 'time= 0' that is given every '8 hours' for '87 additional doses'. A second drug which is an enzyme inducer (Pentobarbital) is added at 216 hrs for 300 hrs up to 516 hours of treatment with NT.

Note:- We do not have concentrations of Pentobarbital and hence it is not included in the model.

pk_21        = @model begin
  @param begin
    tvka      RealDomain(lower=0)
    tvclss    RealDomain(lower=0)
    tvlag     RealDomain(lower=0)
    tvclpre   RealDomain(lower=0)
    tvkout    RealDomain(lower=0)
    tvv       RealDomain(lower=0)
    Ω         PDiagDomain(3)
    σ²_prop   RealDomain(lower=0)
  end

  @random begin
    η        ~ MvNormal(Ω)
  end

  @covariates TBP TBP2

  @pre begin
    Ka       = tvka * exp(η[1])
    Clpre    = tvclpre * exp(η[3])  # Preinduction Clearance
    Clss     = tvclss * exp(η[2])   # Postinduction Clearance
    lags     = (Depot=tvlag,)
    Vc       = tvv
    Kout     = tvkout
    Kpre     = Clpre/Vc
    Kss      = Clss/Vc
    Kperi    = Kss-(Kss-Kpre)*exp(-Kout*(t-TBP))
    A        = Kss - (Kss-Kpre)*exp(-Kout*(TBP2-TBP))
    Kpost    = Kpre - (Kpre-A)*exp(-Kout*(t-TBP2))
    K10      = (t<TBP) * Kpre + (t>=TBP && t<TBP2) * Kperi + (t>=TBP2) * Kpost
  end

  @dynamics begin
    Depot'   = -Ka*Depot
    Central' =  Ka*Depot - K10*Central
  end

  @derived begin
    cp       = @. (1000/263.384)*Central/Vc
    dv       ~ @. Normal(cp, sqrt(cp^2*σ²_prop))
  end
end
PumasModel
  Parameters: tvka, tvclss, tvlag, tvclpre, tvkout, tvv, Ω, σ²_prop
  Random effects: η
  Covariates: TBP, TBP2
  Dynamical variables: Depot, Central
  Derived: cp, dv
  Observed: cp, dv

Parameters

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

  • Ka - Absorption Rate Constant (1/hr)

  • CLss - Intrinsic Clearance post-treatment (L/hr),

  • tlag - Lag-time (hrs),

  • CLpre - Intrinsic Clearance pre-treatment (L/hr),

  • Kout - Fractional turnover rate (1/hr),

  • V - Volume of distribution (L),

  • Ω - Between Subject Variability,

  • σ - Residual error.

param = ( tvka    = 1.8406,
          tvclss  = 114.344,
          tvlag   = 0.814121,
          tvclpre = 46.296,
          tvkout  = 0.00547243,
          tvv     = 1679.4,
          Ω       = Diagonal([0.0,0.0,0.0]),
          σ²_prop = 0.015)
(tvka = 1.8406, tvclss = 114.344, tvlag = 0.814121, tvclpre = 46.296, tvkou
t = 0.00547243, tvv = 1679.4, Ω = [0.0 0.0 0.0; 0.0 0.0 0.0; 0.0 0.0 0.0], 
σ²_prop = 0.015)

Dosage Regimen

In this section the Dosage regimen is mentioned for:

  • Oral dosing of 10 mg or 10000 μg at time=0 that is given every 8 hours for 87 additional doses for a single subject.

ev1  = DosageRegimen(10000, cmt=1, time=0, ii=8, addl=87)
sub1 = Subject(id=1, events=ev1, covariates=(TBP=216,TBP2=516))
Subject
  ID: 1
  Events: 88
  Covariates: TBP, TBP2

Simulation

Let's simulate for plasma concentration with the specific observation time points after Oral administration of NT before, during and after treatment with PB.

Random.seed!(123)
sim_sub1 = simobs(pk_21, sub1, param, obstimes=0:1:800)
df1      = DataFrame(sim_sub1)

Dataframe & Plot

Use the dataframe for plotting

df1_dv = filter(x -> x.time in [0,168,171,172,175,216,360,361,363,365,368,384,432,504,505,507,509,552,600,696,697,699,701,704], df1)

@df df1 plot(:time, :cp,
              title= "Plasma Concentration vs Time", label="Pred - Conc",
              xlabel="Time (hr)", ylabel="Concentration (nM)",
              linewidth=3,  xlims=(100,800), ylims=(0,120),
              xticks=[0,100,200,300,400,500,600,700,800], yticks=[0,20,40,60,80,100,120])
@df df1_dv scatter!(:time, :cp, label="Obs - Conc")