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From: Michael Fossler <mfossler_at_trevenainc.com>

Date: Fri, 11 Sep 2015 01:16:25 +0000

I would argue that it is impossible. With mean data and SD's at each time-p=

oint, it is impossible to separate between and with-subject variability.

However, a model with "strong assumptions", as Leonid has aptly put it, may=

still be very useful. It should not be too difficult to come up with reaso=

nable BSV estimates, and the residual error estimates could probably be tak=

en from the assay information, once you have fit the mean curve. You should=

not expect too much from such a model, but it may be helpful in simulating=

some future studies.

Sent from my iPhone

*> On Sep 10, 2015, at 6:00 PM, Leonid Gibiansky <lgibiansky_at_quantpharm.com>=
*

wrote:

*>
*

*> It is likely impossible without strong assumptions. I would first fit the=
*

population model (fixed effects only) and then start to simulate with diff=

erent assumption trying to match observed SD or CV for peaks and troughs. Y=

ou may need to assume the structure and the magnitude of the error model an=

d the structure of the IIV model (ETAs on CL, or V, or both equal, etc.). Y=

ou may get some rough idea about the magnitude of the IIV but you may need =

strong assumptions about the residual and IIV model.

*> Leonid
*

*>
*

*>
*

*> --------------------------------------
*

*> Leonid Gibiansky, Ph.D.
*

*> President, QuantPharm LLC
*

*> web: www.quantpharm.com
*

*> e-mail: LGibiansky at quantpharm.com
*

*> tel: (301) 767 5566
*

*>
*

*>
*

*>
*

*>> On 9/10/2015 2:06 PM, Penny Zhu wrote:
*

*>> Dear Dinko
*

*>> Thank you for the suggestion. It seems this NAD approach only uses the =
*

mean data and does not estimate inter-subject variability using the standar=

d deviation data.

*>>
*

*>> My intention is to establish a population PK/PD model with appropriate e=
*

stimation of intersubject variability based on the mean and standard deviat=

ion data at each timepoint.

*>>
*

*>> A major assumption is that we have good knowledge of the base structure =
*

of the model (e.g. biexponential), and won't run the risk mistaking 2 mono =

exponential models for a biexponential model

*>>
*

*>> Your help and discussions will be very much appreciated.
*

*>>
*

*>> Penny
*

*>>
*

*>>
*

*>> -----Original Message-----
*

*>> From: Rekic, Dinko [mailto:Dinko.Rekic_at_fda.hhs.gov]
*

*>> Sent: Thursday, September 10, 2015 10:41 AM
*

*>> To: Zhu, Penny
*

*>> Subject: RE: [NMusers] Question of fitting population PK
*

*>> model using summary statistics of data instead of raw data
*

*>>
*

*>> See the link and text below.
*

*>>
*

*>> http://accp1.org/pharmacometrics/theory_popmeth.htm#npd
*

*>>
*

*>>
*

*>> Naive averaged data approach (NAD)
*

*>>
*

*>> A model without BSV and BOV is fitted to the
*

*>> mean data from all individuals.
*

*>>
*

*>> Features
*

*>>
*

*>> -Specialized software not
*

*>> necessary.
*

*>>
*

*>> Disadvantages
*

*>>
*

*>> -Does not distinguish between
*

*>> BSV and WSV.
*

*>>
*

*>> -Inappropriate means lead to
*

*>> biased parameter estimates.
*

*>>
*

*>> -May produce model distortion
*

*>> i.e., 2 mono exponential equations averaged together can
*

*>> yield a biexponential.
*

*>>
*

*>> -Covariate modeling cannot be
*

*>> performed.
*

*>>
*

*>> Kind regards
*

*>> Dinko
*

*>> _________________________________
*

*>> Dinko Rekiæ, Ph.D., MSc(Pharm)
*

*>> Pharmacometrics reviewer
*

*>> Division of Pharmacometrics
*

*>> Office of Clinical Pharmacology
*

*>> Office of Translational Science
*

*>> Center for Drug Evaluation and Research
*

*>> U.S. Food and Drug Administration
*

*>> 10903 New Hampshire Ave
*

*>> Silver Spring, MD 20993
*

*>> WO Bldg 51, Rm 3122
*

*>> Office phone: (8)240 402-3785
*

*>>
*

*>> "The contents of this message are mine personally and do not
*

*>> necessarily reflect any position of the Government or the
*

*>> Food and Drug Administration."
*

*>>
*

*>> -----Original Message-----
*

*>> From: owner-nmusers_at_globomaxnm.com
*

*>> [mailto:owner-nmusers_at_globomaxnm.com]
*

*>> On Behalf Of Penny Zhu
*

*>> Sent: Thursday, September 10, 2015 9:49 AM
*

*>> To: nmusers_at_globomaxnm.com
*

*>> Subject: [NMusers] Question of fitting population PK model
*

*>> using summary statistics of data instead of raw data
*

*>>
*

*>> Dear all
*

*>> Assuming the population PK or PD data are log-normally (or
*

*>> normally) distributed, if you have the mean and standard
*

*>> deviation of a readout at each timepoint but do not have the
*

*>> actual raw data (assuming all pateints are with the same
*

*>> dosing regimen, etc), is it possible to establish a
*

*>> well fitted population PK or PD model? How would one
*

*>> get about doing it?
*

*>>
*

*>> Your help is very much appreciated
*

*>>
*

*>> Penny
*

*>>
*

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Received on Thu Sep 10 2015 - 21:16:25 EDT

Date: Fri, 11 Sep 2015 01:16:25 +0000

I would argue that it is impossible. With mean data and SD's at each time-p=

oint, it is impossible to separate between and with-subject variability.

However, a model with "strong assumptions", as Leonid has aptly put it, may=

still be very useful. It should not be too difficult to come up with reaso=

nable BSV estimates, and the residual error estimates could probably be tak=

en from the assay information, once you have fit the mean curve. You should=

not expect too much from such a model, but it may be helpful in simulating=

some future studies.

Sent from my iPhone

wrote:

population model (fixed effects only) and then start to simulate with diff=

erent assumption trying to match observed SD or CV for peaks and troughs. Y=

ou may need to assume the structure and the magnitude of the error model an=

d the structure of the IIV model (ETAs on CL, or V, or both equal, etc.). Y=

ou may get some rough idea about the magnitude of the IIV but you may need =

strong assumptions about the residual and IIV model.

mean data and does not estimate inter-subject variability using the standar=

d deviation data.

stimation of intersubject variability based on the mean and standard deviat=

ion data at each timepoint.

of the model (e.g. biexponential), and won't run the risk mistaking 2 mono =

exponential models for a biexponential model

________________________________

Notice: This e-mail message, together with any attachments, contains inform=

ation of Trevena, Inc., 1018 West 8th Avenue, King of Prussia, PA 19406, US=

A. This information may be confidential, proprietary, copyrighted and/or le=

gally privileged.

It is intended solely for use by the individual or entity named on this mes=

sage. If you are not the intended recipient, and have received this message=

in error, please notify us immediately and delete it and any attachments f=

rom your system.

Received on Thu Sep 10 2015 - 21:16:25 EDT