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sequential vs simultaneous

From: Samtani, Mahesh [PRDUS] <MSamtani>
Date: Wed, 10 Dec 2008 09:49:58 -0500

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: owner-nmusers
[mailto:owner-nmusers
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: owner-nmusers
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: marcg

 

 

 

 

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

 

 


Received on Wed Dec 10 2008 - 09:49:58 EST

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