- Contemporary messages sorted: [ by date ] [ by thread ] [ by subject ] [ by author ] [ by messages with attachments ]

From: Penny Zhu <penny.zhu_at_novartis.com>

Date: Fri, 11 Sep 2015 10:38:36 -0700

Dear Dr Gibiansky

Thank you very much for the suggestion. I largely agree with you that it s=

eems to be an trial and error thing to make the variability match the model=

prediction if we have a strong assumption about the model structure.

I was also wondering whether it is possible to simulate individual patient =

data at each timepoint based on the mean, steandard deviation, and an using=

an assumption that within patients (especially in adjacent timepionts) the=

Pk concentrations are more correlated compared to between patients. Then =

use these simulated data to fit the population PK model.

Best regards.

Penny Zhu

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

*> From: Leonid Gibiansky [mailto:lgibiansky_at_quantpharm.com]
*

*>
*

*> Sent: Thursday, September 10, 2015 5:10 PM
*

*> To: Zhu, Penny; nmusers_at_globomaxnm.com
*

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

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

*>
*

*> 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#np=
*

d

*> >
*

*> >
*

*> > 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 Rekić, 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 Fri Sep 11 2015 - 13:38:36 EDT

Date: Fri, 11 Sep 2015 10:38:36 -0700

Dear Dr Gibiansky

Thank you very much for the suggestion. I largely agree with you that it s=

eems to be an trial and error thing to make the variability match the model=

prediction if we have a strong assumption about the model structure.

I was also wondering whether it is possible to simulate individual patient =

data at each timepoint based on the mean, steandard deviation, and an using=

an assumption that within patients (especially in adjacent timepionts) the=

Pk concentrations are more correlated compared to between patients. Then =

use these simulated data to fit the population PK model.

Best regards.

Penny Zhu

d

Received on Fri Sep 11 2015 - 13:38:36 EDT

*
This archive was generated by hypermail 2.3.0
: Fri Sep 27 2019 - 16:46:01 EDT
*