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
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Behalf Of Ken Kowalski
Sent: Tuesday, December 09, 2008 2:27 PM
To: 'Gastonguay, Marc'; 'Gibiansky Leonid'; 'Xiao, Alan'; 'Hussein, Ziad';
'nmusers nmusers'
Subject: RE: [NMusers] FO vs FOCE, sequential vs simultaneous
The method that Marc describes is labeled the PPP&D method in the Zhang et al
paper below. With this approach you set up the model just as if you were going
to do a simultaneous fit (that is the dataset contains DVs for both the PK and
PD (or metabolite)) but all of the population PK parameters (thetas, omegas and
sigmas) are fixed at the estimates from a separate model fit to the PK (or
parent) data alone (i.e., the first sequential step). As Marc suggests, if you
use FOCE in the second sequential step the model will be driven by the
individual conditional random effects obtained from the first step since the PK
data is included along with the PD data (or metabolite) in the data file. I
have had a lot of success using this approach and it can certainly cut down on
run-time as compared to the simultaneous model fit.
Regards,
Ken
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Gastonguay, Marc
Sent: Tuesday, December 09, 2008 1:56 PM
To: Gibiansky Leonid; Xiao, Alan; Hussein, Ziad; nmusers nmusers
Subject: Re: [NMusers] FO vs FOCE, sequential vs simultaneous
There's an additional, related point to consider with respect to estimation
method, in selecting a simultaneous vs sequential approach....
In the case where simultaneous modeling under conditional estimation is not
feasible (run-time, convergence, etc), it is preferable to use a sequential
approach. In the first step, model PK (or parent) using conditional estimation
or FO/POSTHOC, and run the second sequential step (e.g. PD or metabolite)
conditioned on the individual estimates obtained in the first step. By doing
so, the second step (PD or metabolite) model will be driven by individual
conditional random effect estimates obtained the first step. This is preferable
to running a simultaneous model under FO, where only the population typical
values would be used to drive the second stage endpoint (PD or metabolite)
model.
For more on this point, see:
Zhang L, Beal SL, Sheiner LB. J Pharmacokinet Pharmacodyn. 2003
Dec;30(6):387-404. Simultaneous vs. sequential analysis for population PK/PD
data I: best-case performance.
Regards,
Marc
Marc R. Gastonguay, Ph.D.
President & CEO, Metrum Research Group LLC [ www.metrumrg.com]
Scientific Director, Metrum Institute [ www.metruminstitute.org]
Direct: 860-670-0744 Main: 860-735-7043
Email: [EMAIL PROTECTED]
On Dec 9, 2008, at 12:33 PM, Leonid Gibiansky wrote:
Hi Alan,
Here:
http://quantpharm.com/pdf_files/PAGE_2008_Poster_1268_web.pdf
I used all datasets that I had, and I was not able to find any problem where FO
was superior to FOCE.
Not-converged FOCE is better, in my opinion, than converged FO (although you
can always check using diagnostic plots).
If you cannot use FOCE due to time restrictions, it is better to use FO than
just abandon modeling. Still, I would try to run the final model with FOCEI.
Concerning sequential vs simultaneous: there are several points to consider,
and this is usually relates to the PK-PD case. For PK-PD, the main question is
the comparison of PK and PD variabilities. Usually, PK variability is smaller,
and PK data are more reliable. Then, sequential modeling can be more warranted.
If PK and PD variabilities are similar (both residual and inter-subject) you
can use joint fit. I usually do PK first, then PK-PD, and then try to fit
combined model at the very last stage.
For parent-metabolite case, both sets of data are equally reliable (or not
reliable), and variability is usually similar. Then the question boils down to
time and convenience. Again, I usually do parent fist, then fix parameters and
do metabolite, and then, if possible, do simultaneous fit. This often saves
time: parent model is more simple, it can be done in standard ANDANs for 1-2
compartment models that are much quicker. You can experiment freely with random
effect, covariates, residual error, etc. Joint model often needs to be solved
using ADVAN5, 7 or even $DES which are more CPU-consuming. You want to do
minimum number of runs here. Thus, you want to start with good parent model,
and study metabolite part only. The final joint run fits all parts together.
Thanks
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566
Xiao, Alan wrote:
Dear All,
I know this is an old topic, too, but would like to see the statistics. When
you have a dataset with about 10% of dense Phase II data (predose, 2, 4, 8, and
12 hrs post dose on day 1 and at steady state, twice-daily dose regimen) and
about 90% of very sparse Phase III data (1-2 samples/patient), which method do
you prefer: FO or FOCE? or FO for model development but FOCE for model
refinement/finalization? If FOCE is not practical because of long run-time or
numerical difficulties in converge, do you stop here or would you use FO?
Thanks,
Alan
