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The following message was posted to: PharmPK
I am wishing to replicate the study performed by Drusano et al. in
Antimicrobial agents and Chemotherapy 2002, Vol. 46 No. 3, p. 913-916.
In this, and other studies I have read, they predominately use the
program NPEM2 of the USC*PACK to generate population PK parameters.
What would the advantage of such a program have over the program ADAPT
II, WinNonMix or NonMem. In addition, could you still use the
parameters generated in ADAPT II to run an accurate Monte Carlo
simulation. Thank you for your assistance.
Regards,
Nathan Lack
Pharm/Tox
AnorMed Inc.
T: (604) 530-1057 x.795
F: (604) 530-0976
W: www.anormed.com
AnorMED Inc.
#200-20353 64th Ave
Langley, BC
Canada V2Y 1N5
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The following message was posted to: PharmPK
Nathan,
Nathan Lack wrote:
>
> PharmPK - Discussions about Pharmacokinetics
> Pharmacodynamics and related topics
>
> The following message was posted to: PharmPK
>
> I am wishing to replicate the study performed by Drusano et al. in
> Antimicrobial agents and Chemotherapy 2002, Vol. 46 No. 3, p. 913-916.
> In this, and other studies I have read, they predominately use the
> program NPEM2 of the USC*PACK to generate population PK parameters.
> What would the advantage of such a program have over the program ADAPT
> II, WinNonMix or NonMem. In addition, could you still use the
> parameters generated in ADAPT II to run an accurate Monte Carlo
> simulation. Thank you for your assistance.
My understanding is that NPEM is an *estimation* rather than a
*simulation* program. It can be used to estimate pop PK parameters
(e.g. Clearance and its variability). WinNonMix and NONMEM are also
population estimation programs. WinNonMix cannot be used for Monte
Carlo simulation but NONMEM can. I don't know about ADAPT II for
simulation but it is not a true population estimation program. You
could use the standard two stage procedure to estimate population
parameters from individual estimates obtained with ADAPT. You can do a
stochastic (Monte Carlo) simulation using parameters from any source
you like. The "accuracy" of the simulation depends on the questions you
are trying to ask. There will always be some uncertainty in the
parameters and models no matter where they come from which may or may
not have an important impact on the objective of the simulation.
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/
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Nick,
As I recall WinNonMix is able to do Monte Carlo simulation.
JP
JP Zhang, Ph.D.
Centocor, Inc.
[Boomer can ;-) - db]
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The following message was posted to: PharmPK
Nick Holford is correct that NPEM is an estimation program
rather than a simulation program. In the Drusano work referenced it
appears that NPEM was used to estimate PK population distribution
parameters, while ADAPT II was used for the Monte Carlo simulation.
The use of NPEM has several advantages, both generically and specific to
this context:
1. NPEM (and the follow-on NPAG program, which I believe is what was
used in the Drusano paper ), use an estimation technique based on
the maximization of an exact, nonparametric likelihood function and
produce statistically consistent population estimators. Parametric
programs such as NONMEM are based on the maximization of an approximate
(using such approximations as FO, FOCE, and Laplace) parametric
likelihood
function. The approximation, at least in the case of FO and FOCE,
involves the linearization of the model function that is the
solution to the kinetics equations, and can be arbitrarily bad. The
consequences of using
such approximations are loss of statistical consistency and
degradation of statistical efficiency (assuming that statistical
efficiency is a meaningful concept in the absence of
consistency, which is debatable).
Consistency has a price in terms of the amount of computation required,
and until several years ago NPEM required a supercomputer for the best
results. However, recent improvements in the algorithm, now implemented
as NPAG, allow most computations to be performed in reasonable times on
a PC.
2. The estimators produced by NPEM/NPAG are nonparametric distributions.
No initial specification of an assumed parametric form is necessary.
However, the NPEM/NPAG estimator can then be converted into any
particular
parametric estimator desired, preserving consistency. Thus a single
NPEM/NPAG esimator can be used to explore the viability of many
different
parametric assumptions, which is useful in the Monte Carlo simulation
context.
3. The NPEM and ADAPT series of programs were both developed at USC and
share common data file formats, and to this extent may be interoperable.
Nathan - if you wish to replicate George Drusano's study using the same
analytic methodology, I suggest you get in contact with the USC folks
(www.lapk.edu/hsc/lap_apk) who can help you get started with NPEM.
Bob Leary
Bob Leary
Senior Staff Scientist
San Diego Supercomputer Center
858-534-5123
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The following message was posted to: PharmPK
JP,
"Zhang, Jianping [CNTUS]" wrote:
>
> PharmPK - Discussions about Pharmacokinetics
> Pharmacodynamics and related topics
>
> Nick,
>
> As I recall WinNonMix is able to do Monte Carlo simulation.
>
> JP
>
> JP Zhang, Ph.D.
> Centocor, Inc.
It is my recollection of WinNonMix that it had no built in mechanism
for automated replication of simulation data sets. This means that one
must simulate and estimate one data set at a time which makes it
impractical for Monte Carlo simulation experiments which typically
involve at least 100 and sometimes over 1000 replications in order to
obtain adequate precision of statistics of interest.
I have not used WinNonMix for some years for this and other reasons.
Perhaps some one from Pharsight can comment on this issue.
Nick
Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New
Zealand
email:n.holford.-at-.auckland.ac.nz tel:+64(9)373-7599x86730 fax:373-7556
http://www.health.auckland.ac.nz/pharmacology/staff/nholford/
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The following message was posted to: PharmPK
Dear All,
PKBUGs under WINBUGs also can run Monte Carlo Simulations.
http://www.med.ic.ac.uk/divisions/60/pkbugs_web/home.html
Sadray
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Dear Nathan:
You might talk this over with George Drusano. In general,
though, parametric population modeling programs like IT2B and NONMEM do
not have the same consistent behavior as the nonparametric (NP) ones.
There is no guarantee that the more subjects you study in the
population, the closer the results will be to the truth. The reason for
this is the approximations that are made in computing the likelihood in
the parametric software using the FO or FOCE approximations, for
example. Bob Leary has examined this very carefully in material he
presented at the PAGE meeting in Paris in 2002. It is on our web site
www.lapk.org, under new advances in PK/PD modeling.
In contrast, since there is nothing to integrate in the NP
approach, the likelihood is exact and the behavior is consistent.
Because of this, even if the true population distribution is Gaussian,
the means and variances obtained with the NP methods are usually of
better quality than those with parametric methods.
The literature has shown that the population parameter CV's are
often smaller with FOCE methods, and people have thought that this
means that the population parameter estimates are more precise.
However, the likelihoods are usually not reported. When we have
examined this relationship, the pop parameter CV's were indeed smaller,
but the likelihoods were less that with the NP methods, showing that
the NP methods were better able to perceive the true diversity of the
parameter distributions in the population, as they were not inhibited
by any assumptions of shape such as normal or lognormal. The likelihood
is the important thing, not the usual indices of goodness of fit, as
the parameter distributions are often genetically polymorphic. This is
not nearly as easily seen with parametric methods that do not show you
the entire population parameter distributions, but only something that
has some assumed shape, normal or lognormal, etc.
In addition, NP methods lend themselves to the design of better
(more precise) dosage regimens, using "multiple model" dosage design,
which specifically develops dosage regimens to hit target goals with
maximum precision (minimum weighted squared error). Our new clinical
software uses this approach. More information is on our web site
www.lapk.org.
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.-a-.usc.edu
Our web site= http://www.lapk.org
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The following message was posted to: PharmPK
Dear All,
We use MCSim (freely available at http://toxi.ineris.fr on the MCSim
page) to do
Monte Carlo simulations, including Markov Chain Monte Carlo.
Frederic Bois
Frederic Y. Bois,
Unite de Toxicologie Experimentale, responsable
INERIS
Parc ALATA, BP 2
60550 Verneuil en Halatte
FRANCE
tel: + 33 (0)3 44 55 65 96
fax: + 33 (0)3 44 55 66 05
email: frederic.bois.aaa.ineris.fr
web: http://www.ineris.fr, http://toxi.ineris.fr
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Nick is correct that the current version of WinNonMix does not have a
built-in mechanism for automated replication of simulation data sets.
Only one simulation can be done at a time, and a constant seed is used
in the random number generator so that consistent simulation results
will be produced with the same parameter specification. A mechanism
for automated replication of simulation data sets is being considered
for future versions.
Regards,
Linda Hughes, M.S., Software Development Group
Pharsight Corporation, 5520 Dillard Dr., Suite 210, Cary, NC 27511
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Copyright 1995-2010 David W. A. Bourne (david@boomer.org)