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From: Leonid Gibiansky <lgibiansky_at_quantpharm.com>

Date: Thu, 10 Sep 2015 17:09:37 -0400

It is likely impossible without strong assumptions. I would first fit

the population model (fixed effects only) and then start to simulate

with different assumption trying to match observed SD or CV for peaks

and troughs. You may need to assume the structure and the magnitude of

the error model and the structure of the IIV model (ETAs on CL, or V, or

both equal, etc.). You 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 standard deviation data.
*

*>
*

*> My intention is to establish a population PK/PD model with appropriate estimation of intersubject variability based on the mean and standard deviation 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 RekiÄ‡, 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
*

*>
*

Received on Thu Sep 10 2015 - 17:09:37 EDT

Date: Thu, 10 Sep 2015 17:09:37 -0400

It is likely impossible without strong assumptions. I would first fit

the population model (fixed effects only) and then start to simulate

with different assumption trying to match observed SD or CV for peaks

and troughs. You may need to assume the structure and the magnitude of

the error model and the structure of the IIV model (ETAs on CL, or V, or

both equal, etc.). You 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:

Received on Thu Sep 10 2015 - 17:09:37 EDT

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