Dear Kok-Yong Seng,
I already got the same problem (poor prediction of high concentration values)
in the past and I used an allometric scaling (by WT) for model stabilization.
However, my concern is on the following: how did you fix the ka value? Is it
from data from the literature? A wrong ka will lead to an over/under prediction
of the high concentration values depending on the actual Tmax of the drug.
One
solution would be to use one of the deconvolution methods (Loo Riegelmen for a
two compartment) using IV data from the literature. By doing so, you will
obtain an estimate of both F and Ka. You can also add IV data to your data set
(as
if it was an actual subject) and let NONMEM estimate F and Ka. Although IV data
wont be part of your final model, it will help finding a reasonable estimate of
Ka.
Hope that help,
--
François Gaudreault, Ph.D. Candidate
Pharmacométrie / Pharmacometrics
Charger de cours / Lecturer
Faculté de pharmacie / Faculty of Pharmacy
Université de Montréal
From: [email protected]
To: [email protected]
Subject: [NMusers] population PK modelling of very sparse data
Date: Thu, 12 Jan 2012 09:29:52 +0000
Dear all,
I would like to seek some advice from you regarding population PK modelling of
very sparse data.
I'm trying to fit a population PK model to a set of very sparse data. There
are about 700 subjects in the dataset. The intention was for each of these
subjects to self-administer daily doses for 7 days (loading phase)
followed by weekly doses for 10 weeks (maintenance phase). For each subject,
I've zero, one or two concentration measurements of the parent drug and its
major metabolite taken at least one week after the final dose. In addition,
I've information regarding
which doses, if any, were missed by the subjects (i.e. I know each subject's
adherence to the dosage regimen). BQL values are present in the data set, and
comprise about 15% of all data.
In the literature, a two-compartment model for the parent and a two-compartment
model for the metabolite (including one compartment for the depot compartment)
has been suggested. However, because of my overall data
sparseness, NONMEM was not able to produce a successful two-compartment model.
This is so even after I've fixed Ka, intercompartmental clearances for both
the parent and the metabolite, as well as the parent drug's metabolic clearance
to the metabolite (fixed
at 15.2% of the total clearance of the parent drug).
After repeated model iterations, the best performing model to date is a
one-compartment model for the parent drug and a one-compartment model for the
metabolite. Ka and the parent drug's metabolic clearance to the metabolite
were fixed. CL, V(parent drug comp), CL(metabolite) and V(metabolite comp)
were estimated. IIV was estimated for CL and CL(metabolite). I
log-transformed the data and used the M3 method to account for BQL values. RUV
is exponential error (additive in the
log scale). In addition, the model was more stable after I've incorporated
allometric scaling (by weight) to CL, V(parent drug comp), CL(metabolite) and
V(metabolite comp).
Although this is the best performing model, it is still not optimal because of
its poor prediction of high concentration values for the parent drug and
metabolite. Could you request for assistance on how to improve
this model?
Thank you and best wishes,
Kok-Yong Seng, PhD
DSO National Laboratories
Singapore
