- On 24 Sep 2004 at 16:59:20, L T (choco4evermore.-a-.yahoo.ca) sent the message

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Dear all:

I am a masters student working on a project attempting to do population

pharmacokinetics on some PK data we have on antiretrovirals in HIV

patients, and I am totally new to population PK. We have the WinNonMix

Professional 2.0.1 program (We have both the WinNonlin and WinNonmix),

and I am having trouble understanding the use of WinNonMix to model our

data. I have gone through the user gudies and references but they

don't help at all since they only provide instructions as to where to

click and how to enter values, but no explanations at all regarding

fixed effects and random effects (for example). I would appreciate any

help/hints/pointers in using and understanding this program.

We basially have a bunch of concentration vs. time PK data, and for

example:

1) we have to enter the mixed effect equation(s) for V/F, K10 and K01.

From the concentration vs. time PK profile data we have, how do we

know what kind of random effect function we should use? (e.g. log,

exponential, linear etc.), and what kind of variance function (e.g.

power, linear etc.) to input for error structure specification? Do we

have to obtain these functions through other ways using our conc vs.

time data, or just guess?

2.) I know this is a program-specific question...but we are asked to

input initial estimate for the paramters (V/F, K10, K01), yet the

program does not specify the units for these parameters (L? mL? h-1?),

and I couldn't find instructions anywhere in the 'help' section. Has

anyone used this program and can give me some insight? My 'modelling'

obviously doesn't work despite numerous 'guessing' in the units, since

all my 'predicted' values for the parameters came out as zero.

I would appreciate any help and suggestions on reading materials and

references regarding this topic. Will I need specialized training

(e.g. workshops, classes) for me to know how to do population

modelling?

Thanks!

Lillian Ting

MSc. candidate, Pharmaceutical Sciences

University of British Columbia - On 24 Sep 2004 at 15:59:14, Roger Jelliffe (jelliffe.at.usc.edu) sent the message

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Dear Ms. Ting:

You might ask whether or not your software has the guarantee

that

the more subjects you study, the closer the results get to the true

ones.

No currently available software for parametric modeling has this

guarantee.

Also, that about the precision of parameter estimates?

If you go to our web site, www.lapk.org, and click on new

advances

in population modeling, you will see that Dr. Robert Leary has shown

that

methods which use the FO or FOCE approximation to compute the likelihood

function, such as the ones you describe, do not have the guarantee of

consistent behavior, nor do they have the best statistical efficiency

(precision of parameter estimation). However, nonparametric methods

such

as the NPML of Alain Mallet, and the NPEM, NPAG, and NPOD by Schumitzky

and

by Leary, from our lab, easily compute the likelihood function exactly,

and

therefore do in fact have the proven property of consistency, and much

better statistical efficiency than methods that use approximate

likelihoods.

Because of this, if you are interested in population PK/PD

modeling, you might consider using proven consistent and precise

methods.

In addition, the NP models lend themselves best to developing dosage

regimens that are maximally precise, as they permit the calculation of

the

expected precision with which any regimen will hit a desired therapeutic

target, using multiple model (MM) dosage design. Information on how to

get

the software is also available.

Very best regards,

Roger Jelliffe

Roger W. Jelliffe, M.D. Professor of Medicine,

Division of Geriatric Medicine,

Laboratory of Applied Pharmacokinetics,

USC Keck School of Medicine

2250 Alcazar St, Los Angeles CA 90033, USA

Phone (323)442-1300, fax (323)442-1302, email= jelliffe.aaa.usc.edu

Our web site= http://www.lapk.org - On 25 Sep 2004 at 17:38:08, Nick Holford (n.holford.-a-.auckland.ac.nz) sent the message

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Lillian,

It seems like you need some basic training in the terminology and

methods of population modelling. This should then help you figure out

how to use WinNonMix that are available.

The random effects for parameters and residual error are often chosen

to arise from a log normal distribution. This assumption is usually

quite robust. You may need an additional additive random effect on the

concentration residual error. If this advice does not mean anything to

you then you really do need some basic training. It shouldn't take long

if you can meet up with someone familiar with this area.

With regard to units -- its pretty much up to you and not the program.

If doses are in mg and concentrations in mg/L and time in hours then

volumes will be L, clearances in L/h, rate constants h-1 (K10 etc).

My personal advice is not to bother with WinNonMix. It might seem

easier to use than other programs initially because the interface is

like WinNonLin but I have found that it quickly becomes hard or

impossible to do things you might want to do that are much easier with

other programs (e.g. NONMEM).

Nick

--

Nick Holford, Dept Pharmacology & Clinical Pharmacology

University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New

Zealand

email:n.holford.aaa.auckland.ac.nz tel:+64(9)373-7599x86730 fax:373-7556

http://www.health.auckland.ac.nz/pharmacology/staff/nholford/ - On 29 Sep 2004 at 13:44:07, "Jadhav, Pravin *" (JadhavP.aaa.cder.fda.gov) sent the message

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Hi Lillian,

Take a look at these articles to start with-

1. Sheiner LB and Grasela TH: An introduction to mixed effect modeling:

Concepts definitions and justification J Pharmacokin Biopharm

19[3]:11S-24S,

1991

2. Grasela TH and Sheiner LB: Pharmacostatistical modeling for

observational

data J Pharmacokin Biopharm 19[3]:25S-36S, 1991

Then more detailed theory can be learned from statistical literature

&/or

books. My personal preferences are (examples provided deal with PK/PD

models):

1. Model building for Nonlinear Mixed-effects models

http://www.pharmastatsci.com/download/model%20building.pdf

2. Mixed effects modeling in S and S-Plus by Pinheiro and Bates 2000

ISBN

0-387-98957-9

3. fitting PK models with NLME

http://www.insightful.com/events/2002uc/speakers/pinheiro.pdf

And I agree with Nick. There are softwares around like NONMEM that are

very

flexible and powerful.

Thanks,

Pravin

Pravin Jadhav

Graduate student

Department of Pharmaceutics

MCV/VCU - On 29 Sep 2004 at 15:48:16, Roger Jelliffe (jelliffe.aaa.usc.edu) sent the message

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Dear Lillian:

About population modeling. There is much more and better stuff

around than just the present NONMEM. Ask whether NONMEM has guaranteed

mathematical consistency. It it really true that the more patients you

study in a population, the closer the parameter estimates get to the

truth?

No guarantee. What about the efficiency (precision) of the parameter

estimates? Much better with methods that use exact likelihoods.

Nonparametric population (NP) approaches use exact likelihoods, which

are

usually not reported in studies using NONMEM or similar approaches which

use approximations (FO, FOCE) to compute the likelihood. NP methods are

consistent, and have much better efficiency (more precise parameter

estimates).

Many populations have genetically determined subpopulations

such

as fast and slow metabolizers. These will not be detected by parametric

methods without further help (covariates, etc.). NP methods estimate the

entire parameter distributions, not simply means and covariances.

However,

since NP methods are consistent, so are their means and covariances.

Better

estimates than with NONMEM of other methods that use FO or FOCE

approximations of the likelihood.

Further, what will you do with the results you get? Do you just

want to report them, or do you want to develop dosage regimens to

achieve

selected therapeutic target goals? With parametric methods, there is no

way

to estimate in advance the precision with which you will be able to hit

a

therapeutic target. There is only 1 model, which is based on the

estimated

central tendencies of the parameter distributions. The full shape of the

distribution cannot be considered unless it is assumed to be symmetrical

about the central tendency.

With NP pop models, though, you have many support points

reflecting the entire most likely distribution of the parameter values

in

the population. You now have many support points to make predictions

from.

Each of these predictions can be weighted by its probability. Because of

this, any candidate dosage regimen can be evaluated by its ability to

hit

the target, by computing the weighted squared error of the regimen's

failure to hit the target. Now, one can find the specific regimen which

minimizes that weighted squared error. This is the maximally precise,

multiple model (MM) dosage regimen.

We now have software to make such NP population models (small

and

large, linear and nonlinear), and to examine relationships between

parameters and covariates. We also have clinical software for MM dosage

regimens for patients having 2 compartment linear models. They can track

the behavior of the drug well, even in acutely ill, unstable patients,

and

can develop maximally precise dosage regimens for them. They are in beta

phase release from our web site www.lapk.org/beta. Other demonstration

software can be obtained from www.lapk.org, as well as information on

obtaining the USC*PACK software. You can also go to teaching topics on

the

web site, and get much more information about all this, as well as

references.

Very best regards,

Roger Jelliffe

Roger W. Jelliffe, M.D. Professor of Medicine,

Division of Geriatric Medicine,

Laboratory of Applied Pharmacokinetics,

USC Keck School of Medicine

2250 Alcazar St, Los Angeles CA 90033, USA

Phone (323)442-1300, fax (323)442-1302, email= jelliffe.aaa.usc.edu

Our web site= http://www.lapk.org

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