pkpd: Pharmacokinetic Compartment Models

In swihart/rmutil: Utilities for Nonlinear Regression and Repeated Measurements Models

Pharmacokinetic Compartment Models

Description

Mean functions for use in fitting pharmacokineticcompartment models models.

mu1.0o1c: open zero-order one-compartment model

mu1.1o1c: open first-order one-compartment model

mu1.1o2c: open first-order two-compartment model (ordered)

mu1.1o2cl: open first-order two-compartment model (ordered, absorption and transfer equal)

mu1.1o2cc: open first-order two-compartment model (circular)

Simultaneous models for parent drug and metabolite:

mu2.0o1c: zero-order one-compartment model

mu2.0o2c1: zero-order two-compartment for parent, one-compartment for metabolite, model

mu2.0o2c2: zero-order two-compartment model for both parent and metabolite

mu2.1o1c: first-order one-compartment model

mu2.0o1cfp: zero-order one-compartment first-pass model

mu2.0o2c1fp: zero-order two-compartment for parent, one-compartment for metabolite, model with first-pass

mu2.0o2c2fp: zero-order two-compartment model for both parent and metabolite with first-pass

mu2.1o1cfp: first-order one-compartment first-pass model

Usage

mu1.0o1c(p, times, dose=1, end=0.5)
mu1.1o1c(p, times, dose=1)
mu1.1o2c(p, times, dose=1)
mu1.1o2cl(p, times, dose=1)
mu1.1o2cc(p, times, dose=1)
mu2.0o1c(p, times, dose=1, ind, end=0.5)
mu2.0o2c1(p, times, dose=1, ind, end=0.5)
mu2.0o2c2(p, times, dose=1, ind, end=0.5)
mu2.1o1c(p, times, dose=1, ind)
mu2.0o1cfp(p, times, dose=1, ind, end=0.5)
mu2.0o2c1fp(p, times, dose=1, ind, end=0.5)
mu2.0o2c2fp(p, times, dose=1, ind, end=0.5)
mu2.1o1cfp(p, times, dose=1, ind)

Arguments

p	Vector of parameters. See the source file for details.
times	Vector of times.
dose	Vector of dose levels.
ind	Indicator whether parent drug or metabolite.
end	Time infusion ends.

Value

The profile of mean concentrations for the given times and doses is returned.

Author(s)

J.K. Lindsey

Examples

## Not run: 
library(repeated)
times <- rep(1:20,2)
dose <- c(rep(2,20),rep(5,20))
# set up a mean function for gar based on mu1.1o1c:
mu <- function(p) {
	ka <- exp(p[2])
	ke <- exp(p[3])
	exp(p[2]-p[1])/(ka-ke)*(exp(-ke*times)-exp(-ka*times))}
conc <- matrix(rgamma(40,2,scale=mu(log(c(1,0.3,0.2)))/2),ncol=20,byrow=TRUE)
conc[,2:20] <- conc[,2:20]+0.5*(conc[,1:19]-matrix(mu(log(c(1,0.3,0.2))),
	ncol=20,byrow=TRUE)[,1:19])
conc <- ifelse(conc>0,conc,0.01)
gar(conc, dist="gamma", times=1:20, mu=mu, preg=log(c(1,0.4,0.1)),
	pdepend=0.1, pshape=1)
# changing variance
shape <- mu
gar(conc, dist="gamma", times=1:20, mu=mu, preg=log(c(0.5,0.4,0.1)),
	pdep=0.1, shape=shape, pshape=log(c(0.5,0.4,0.1)))

## End(Not run)

swihart/rmutil documentation built on Oct. 30, 2022, 9:33 a.m.