RE: Why should we avoid using micro rate constants?
Dear Sumeet,
If you are assuming a distribution for your parameters (e.g. log-Normal p =
theta * exp(eta)) then it might matter if you use rate constants versus
clearances and volumes. In general, if you want to make the log-Normal
assumption you should use clearances and volumes as there is reasonable
biological prior knowledge to show these generally follow a log-Normal
distribution (do some reading on the occurrence of log-Normal distributions in
biology).
The rate constant is a ratio of two (usually) log-Normally distributed
variables (e.g. k = CL/V) and hence may not necessarily be a shape that can
itself be described as a log-Normal. Here is some R-code that highlights this:
# Simulate some realistic PK for a water soluble renally cleared drug
vd <- 40 * exp(rnorm(10000, sd = 0.5))
cl <- 6 * exp(rnorm(10000, sd = 0.5))
k <- cl / vd
# Visualise the histograms and use fitdistr function to
# fit a log-Normal
require(MASS)
# Volume:
hist(vd, freq = FALSE)
fit <- fitdistr(vd, "log-normal")$estimate
lines(dlnorm(0:max(vd), fit[1], fit[2]), lwd = 3)
# ...yes
#
# Clearance:
hist(cl, freq = FALSE)
fit <- fitdistr(cl, "log-normal")$estimate
lines(dlnorm(0:max(cl), fit[1], fit[2]), lwd = 3)
# ...yes
#
# K
hist(k, freq = FALSE)
fit <- fitdistr(k, "log-normal")$estimate
lines(dlnorm(0:max(k), fit[1], fit[2]), lwd = 3)
# ...no
People who do not like to make assumptions on distributions of parameters use a
nonparametric approach, and in this case it does not matter whether you use
rate constants or clearances and volumes. However, unless you collect rich
informative data (to get good individual parameter estimates) and lots of it
(to get a true idea of the distribution of parameters in the population) it is
usually advised to make a distributional assumption, and the log-Normal is
often sensible.
BW,
Joe
Joseph F Standing
MRC Fellow, UCL Institute of Child Health
Antimicrobial Pharmacist, Great Ormond Street Hospital
Honorary Senior Lecturer, St George's University of London
Tel: +44(0)207 905 2370
Mobile: +44(0)7970 572435
Quoted reply history
________________________________________
From: [email protected] [[email protected]] on behalf of
[email protected] [[email protected]]
Sent: 05 February 2019 06:51
To: 'Leonid Gibiansky'; 'Singla, Sumeet K'
Cc: [email protected]
Subject: RE: [NMusers] Why should we avoid using micro rate constants?
Hi All,
It could also be the statistical model. If you are estimating 4 parameters then
different parameterisations should be fairly equivalent if a BLOCK(4) structure
is used for both parameterisations. If only the diagonal option is used, then
this could be why different results/minimisations are obtained for different
parameterisations.
Kind regards,
Janet
Janet R Wade, PhD
Occams
Senior Consultant
From: [email protected] <[email protected]> On Behalf Of
Leonid Gibiansky
Sent: 04 February 2019 07:30
To: Singla, Sumeet K <[email protected]>
Cc: [email protected]
Subject: Re: [NMusers] Why should we avoid using micro rate constants?
It could be just coding error, could you show the control stream?
Thanks
Leonid
On Feb 3, 2019, at 12:44 PM, Singla, Sumeet K
<[email protected]<mailto:[email protected]>> wrote:
Hello everyone!
I have a question. I was trying to build a 2-compartment PK model for marijuana
use in occasional and chronic smokers. Initially, I was using providing rate
constants K12 and K21 in PK block and it resulted in poor fitting. Then, I
later changed to CL,V1, V2 , Q and it resulted in proper fitting. I was
perplexed as to why I couldn’t get a proper fit by providing rate constants? I
tried to look online but couldn’t find any proper explanation about when (or
not) we should use micro constants in PK block to define our model in NONMEM?
Does anyone has any useful insights into this?
Regards,
Sumeet Singla
Graduate Student
Dpt. of Pharmaceutics & Translational Therapeutics
College of Pharmacy- University of Iowa
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