Mahesh,
I agree that if you are happy with the model structure then it can be
useful to do a simultaneous fit of parent and metabolite. However, a
sequential process may be useful to evaluate the model structure
assumptions.
Nick
Samtani, Mahesh [PRDUS] wrote:
Dear Dr. Holford,
All I was suggesting was that once you have decided on which assumption you
like the most and start modeling the metabolite data then shouldn't
simultaneous modeling of the parent + metabolite be more preferable? The model
will take long run time any ways because it will be a $DES model. By doing
simultaneous modeling of the parent + metabolite you will probably improve the
parameters of the parent drug because the metabolite information will bolster
the parameters of the parent drug. Similarly common parameters between the
parent and metabolite will benefit as well because of the simultaneous
estimation.
Of course if the metabolite data is really dirty and you are really afraid that
the metabolite data will contaminate the parent drug's parameters then that
situation seems to be a reasonable place to try sequential modeling.
Kindly advise,
Mahesh
-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] Behalf Of Nick Holford
Sent: Wednesday, December 10, 2008 3:12 PM
To: nmusers@globomaxnm.com
Subject: Re: [NMusers] Simultaneous vs sequential for modeling parent
AND metabolites in pop PK
James, Mahesh,
The "F=1 fallacy" has caused me no end of stress over the years, and
next time I may have to contend with the (almost irrefutable) argument
"but Nick Holford said it was true".
As I never said this was a fallacy I dont think you need be stressed by
it. Everytime you use a model to describe oral PK data alone you must
make an assumption about the dose that is absorbed. By default NONMEM
will make the assumption that Foral=1 but of course when you interpret
the parameters you should remember the assumption.
Similarly you may assume Fm=1 and estimate metabolite parameters of
CLm/Fm and Vm/Fm where Fm is the fraction of parent converted to
metabolite. For simply descriptive purposes the Fm=1 assumption is fine
but more might be learned by making a different assumption (as Mahesh
points out). Fm might be guessable from in vitro descriptions of
metabolism or one may assume a Vm (metabolite volume) perhaps the same
as the parent. The latter can aid in proposing plausible values for Fm
but the descriptive value of the model is unchanged whichever assumption
is used.
I dont understand why Mahesh adds to each of the assumptions "[and
preferably do simultaneous parent/metabolite modeling]". Perhaps he
could give some reasons for this because I dont see it has anything to
do with the choice of which assumption to make.
IMHO the important thing is to recognize that an assumption must be
made. I have seen people use NONMEM to estimate Fm and CLm and Vm as if
they could identify these 3 parameters independently. NONMEM will do
what it is told and make estimates of the parameters even though one of
them cannot be identified. This is the important lesson to learn for
those who have not had experience of parent metabolite modelling with
NONMEM.
I want to stick to my original assertion that PKPD and parent-metabolite
models are similar when applying a modelling strategy of either
sequential or simultaneous fitting of data.
We should remember the Box aphorism "All models are wrong but some
models are useful". Part of the process is modelling is to evaluate the
model to see how wrong it is. The sequential then simultaneous approach
can be helpful for model evaluation as described by Liping Zhang in her
second paper on PKPD models.
I see no reason why this should not apply to parent-metabolite models.
However, usually parent-metabolite models are simpler because commonly
one has first-order kinetics and essentially immediate conversion of
parent to metabolite. The implicit assumptions of complete, rapid and
first-order conversion may be fine but it still sensible to look at the
data to check that these assumptions are compatible with the results
from a sequential model.
Nick
James G Wright wrote:
Nick Holford wrote:
Non-proportionality (aka non-linearity) of metabolite formation from
parent is not really an issue. With the right design (i.e. suitable
doses of parent) then this can be discovered from just giving the
parent.
The key assumptions of metabolite models are about how the metabolite is
formed - linearly or nonlinearly, from plasma or during first-pass,
immediately or not. Making sure your assumptions are true is really the
issue for me with any analysis.
But to really know how much of the parent is eventually
transformed to the metabolite needs additional information i.e. direct
administration of the metabolite.
Of course, if fraction formed is the parameter that you need to
estimate.
Fitting a metabolite PK model is not dependent on assuming 100%
metabolite formation from parent, as you originally stated, unless you
mislabel the parameters as actual CL, instead of CL/F. Not knowing F
doesn't affect the application and predictive utility of the model any
more than not knowing F for the parent after oral administration.
The widespread presentation of "F=1" as an "unverifiable assumption"
causes model end-users to suspect that the assumption could in some
sense be wrong ("Aah, but how do you know F=1?"), leading to the
perception that the metabolite model is misleading and unreliable.
I think it would be helpful if you could explicitly retract your
assertion regarding the assumption "that all drug goes to metabolite".
The "F=1 fallacy" has caused me no end of stress over the years, and
next time I may have to contend with the (almost irrefutable) argument
"but Nick Holford said it was true".
Best regards, James
Mahesh wrote:
Dear NMusers,
I think in trying to generalize the case between sequential PK/PD vs.
sequential parent/metabolite we maybe forgetting some PK concepts.
1) In the case of parent/metabolite modeling the metabolite data often
carries important information about the parent drug.
Eg.a. If there is formation limited kinetics going on then the
terminal slopes of the parent and metabolite will both be reflective
of the parent's kel
Eg.b. If there is severe flip-flop kinetics going on then the
terminal slopes of the parent and metabolite will both be reflective
of the parent drug's ka
2) There are common parameters (e.g.. k-metabolite) between the parent
and metabolite that may be estimated in a more meaningful manner using
simultaneous modeling.
Given these considerations, my guess is that simultaneous modeling of
the parent and metabolite maybe more scientifically useful (use all
the information to get the best parameter estimates).
On a related note; it is generally well known that if you administer
only the parent and measure parent & metabolite then the volume of
metabolite is not identifiable. In this case there are 3 options:
a) Fix the metabolite volume to that of the parent [and preferably do
simultaneous parent/metabolite modeling]
b) Use prior knowledge to assign a fixed fraction of the parent to
get converted to metabolite [and preferably do simultaneous
parent/metabolite modeling]
c) If you have no idea about the Vm or fm then use a sequential
empirical (transit/delay) compartmental modeling recently described by
Don Mager in a DMD paper [2004 Aug;32(8):786-93]
Is there any consensus on which of these 3 approaches to use.
Best regards,
Mahesh
--
Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
[EMAIL PROTECTED] tel:+64(9)923-6730 fax:+64(9)373-7090
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
