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
population PK modelling of very sparse data
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
Quoted reply history
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
Hi Kok-Yong Seng,
If there is a prior PopPK model that was published, with parameter estimates
and uncertainty, you should consider using the $PRIOR functionality in NONMEM.
Such sparse data will not have strong contribution to all of the parameters,
but from your email it seems that you feel that the published two compartment
model may be more accurate than your limited one compartment model. The NM7
help files have several examples of using $PRIOR.
Good Luck,
Dan Tatosian
Quoted reply history
________________________________
From: [email protected] [mailto:[email protected]] On
Behalf Of Seng Kok Yong
Sent: Thursday, January 12, 2012 7:32 AM
To: [email protected]; [email protected]
Subject: RE: [NMusers] population PK modelling of very sparse data
Dear Rob and all,
Enclosed please find my .ctl file and a segment of my dataset for your
reference.
To answer Francois questions, ka was obtained from a previous popPK paper on
the same drug. I also tried fitting the model with different Ka values (all
fixed) but the results still show poor predictions at high concentration values.
As far as I know, the drug is only administered orally and there might not be
any IV data available.
Thank you for your kind attention and advice!
Best wishes,
Kok-Yong Seng
________________________________
From: [email protected] [[email protected]]
Sent: Thursday, January 12, 2012 6:52 PM
To: Seng Kok Yong
Subject: RE: [NMusers] population PK modelling of very sparse data
Hi Kok-Yong Seng,
perhaps you could share some control stream and data on the list?
Sincerely,
Rob ter Heine
________________________________
Van: [email protected] [mailto:[email protected]] Namens
Seng Kok Yong
Verzonden: donderdag 12 januari 2012 10:30
Aan: [email protected]
Onderwerp: [NMusers] population PK modelling of very sparse data
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
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