- On 26 Sep 2006 at 22:08:40, Lanyan Fang (fang.53.aaa.osu.edu) sent the message

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Hi, all:

I am trying to use Berkeley Madonna to do some PBPK modeling and

simulation work. I have simulated some data sets using Berkeley

Madonna. But when I use the curve fit function to fit the exact data

sets back to the original model, I got some problems. I have

simulated 7 organs and each organ has 12 time points. If I only use

one or two organs' data, the estimated parameters were the same as

the original code. But if I simultaneously fit more than two organs'

data back, the estimated parameters are far away from the original

values. Since the data set are simulated from the model, the data

sets should be refitted back to get the same parameters. This seems

to be a bug in BM. Has anyone else had a similar experience?

I also would like to know where to look for the standard errors of

the estimated parameters for curve fit in Berkeley Madonna. I would

appreciate any kind of help!

Lucy

the Ohio State University - On 28 Sep 2006 at 09:34:50, phil.lowe.aaa.novartis.com sent the message

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Dear Lucy,

Whilst the Berkeley Madonna (BM) software is an excellent tool for

modelling, especially on the simulation side (=forward modelling),

there are no built in outputs providing the standard errors of the

parameter estimates, or other useful measures of goodness of fit (for

reverse engineering). You would either have to set up the functions

to calculate them yourself, or, what I do, is use an alternative

software such as SAAM II, NONMEM or WinNonlin where fitting

statistics are provided. I find SAAM II very useful for PBPK. Others

prefer ADAPT II, ACSL/Simusolv or Matlab. Not heard of anyone using

NONMEM (anyone from PharmPK?)

With Berkeley Madonna all you can do is have a careful look at the

observed versus predicted plots. One thing I do do when using the

curvefit function is turn on the sliders for each of the parameters

being optimised. Then, as the optimiser is running, they move left

and right as different values are searched. If you watch carefully

you will notice that some parameters hardly move whilst others are

moving violently from left to right - these are ones where there is

little information in the data to support their estimation, so will

need "support" from independent experiments. The same can be seen in

e.g. NONMEM if you plot the parameter estimates versus iteration. Not

as much fun to watch though.

What may be an issue with BM is the error model. There is no built in

weighting system, so the data and the output function should be

transformed appropriately, such as to the logarithm. This may help if

the data covers many orders of magnitude. I have never done it, but

there is also an option in BM to build your own objective function

then use "Optimize" rather than "Curvefit". Perhaps someone else in

PharmPK could help here?

Also, given that in PBPK, all organs are by definition supplied by

arterial blood, the function for the arterial supply can be

predefined by an arterial concentration-time curve. This may be a

polyexponential, or an interpolation between data points. However,

once you get close to the final partition coefficients (and

permeability-surface area coefficients for a permeability-limited two-

compartment per organ model) then you should be able to carry out a

global optimisation against all the data simultaneously, including

the arterial and/or venous blood. However, if the initial estimates

are not close, the optimiser will disappear off into hyperspace - the

parameter space has many dimensions.

Best regards, Phil.

Philip Lowe PhD

Senior Fellow, Modelling & Simulation

Novartis Pharmaceuticals AG

4002 Basel

Switzerland - On 29 Sep 2006 at 10:44:46, "Ma Guangli" (guanglima.aaa.gmail.com) sent the message

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Dear Philip Lowe,

I can not agree with you any more about 'optimization' and

'curve fit'. But I think that that Lucy can not get her original

parameters is because BM stepped into local minimum. If there was no

random effect, the original parameters are global minimum to the

simulated data sets.

If a model is too complex, it is impossible to find a global

minimum (original parameters). The reason is that 'optimization' and

'curve fit' algorithms are like down hill process. It is very

difficult to find a proper position (initial values) to global minimum.

I bet that to set initial values could solve this problem. :p

close to the original parameters. The second choice is to make the

model more simple. I mean to set some of the parameters constant.

This could help to find the global minimum.

Best Regards,

Ma Guangli

[Starting from different initial values can help find the global

minimum. I have a 'mode' in boomer that starts the simplex method

from random, different points for the simplex coupled with a repeat

function. With simple models I get the same WSS for most of the runs

of say a total of 10. With more difficult models I'll commonly see

only 2-3 hit the 'global' minimum with others runs stopping with

higher WSS values. A grid search can help. I've also tried initially

holding some parameters constant to help in approaching the global

minimum with more complex models - db] - On 29 Sep 2006 at 08:10:44, "Elassaiss - Schaap, J. (Jeroen)" (jeroen.elassaiss.-at-.organon.com) sent the message

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The following message was posted to: PharmPK

Dear Philip,

About one aspect of your mail:

[..]

>software such as SAAM II, NONMEM or WinNonlin where fitting

>statistics are provided. I find SAAM II very useful for PBPK. Others

>prefer ADAPT II, ACSL/Simusolv or Matlab. Not heard of anyone using

>NONMEM (anyone from PharmPK?)

I read a discussion of PBPK modeling in the material of the advanced

course ran by L. Sheiner. Indeed I found some documentation of NONMEM's

use in PBPK:

http://www.page-meeting.org/page/page2006/P2006IV_20.pdf#search=%22PBPK%

20site%3Apage-meeting.org%22

You can find more examples with a google search:

http://www.google.nl/search?hl=nl&q=PBPK+site%3Apage-meeting.org+NONMEM&

meta=

Best regards,

Jeroen

J. Elassaiss-Schaap

Scientist PK/PD

Organon NV

PO Box 20, 5340 BH Oss, Netherlands

Phone: + 31 412 66 9320

Fax: + 31 412 66 2506

e-mail: jeroen.elassaiss.aaa.organon.com - On 29 Sep 2006 at 15:59:41, phil.lowe.at.novartis.com sent the message

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Dear Ma,

You are most likely correct, that the reason that BM failed to get

back to the original values (global minimum) is that it found a local

dip in the parameter space, or it is just heading off into the wrong

valley (my hyperspace analogy). The downhill analogy is great. Just

like skiing. Head the wrong way (wrong initials) and you do not get

back to the chalet.

Given that PBPK models have many many dimensions, the initial

estimates must be very close to get the final optimisation to all

data to work. This is why the process of creating the model normally

involves estimating the Kp and PS parameters for each organ

independently with a defined input, or forcing function, of the

arterial concentration-time profile. This should enable reasonable

initial estimates for the final assault on the global minimum. But

base camp must be well set up (sorry for the mixed metaphores of

climbing and skiing).

Best regards, Phil

[I've thought about a Bayesian approach to this problem. Some of the

parameters of a PBPK model are well documented but with some

variability, e.g. blood flow, organ weight, etc. If this information

could be included as population values and population variances, the

'program' might have a better chance of finding the global minimum by

adjusting the parameters you don't know as well more freely (by

giving them a large variance). I like the skiing analogy, in my

version I'm just walking, taking one step, blindfolded, to feel the

terrain and then jumping to the next iteration. Ok if there aren't

too many cliffs, but then that can be a problem with skiing too - db]

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