Hello everyone!
I have a question. I was trying to build a 2-compartment PK model for marijuana
use in occasional and chronic smokers. Initially, I was using providing rate
constants K12 and K21 in PK block and it resulted in poor fitting. Then, I
later changed to CL,V1, V2 , Q and it resulted in proper fitting. I was
perplexed as to why I couldn't get a proper fit by providing rate constants? I
tried to look online but couldn't find any proper explanation about when (or
not) we should use micro constants in PK block to define our model in NONMEM?
Does anyone has any useful insights into this?
Regards,
Sumeet Singla
Graduate Student
Dpt. of Pharmaceutics & Translational Therapeutics
College of Pharmacy- University of Iowa
Why should we avoid using micro rate constants?
You may have found a local minima with the microconstant parameterization.
There generally isn’t any rhyme or reason for why one chooses macro or micro
constants. People will argue that the macro model with Q and Vp is more
interpretable than k12 and k21. That, and scaling reasons, are why one might
choose one parameterization over another.
Pete Bonate
Quoted reply history
On Feb 3, 2019, at 2:54 PM, Singla, Sumeet K
<[email protected]<mailto:[email protected]>> wrote:
Hello everyone!
I have a question. I was trying to build a 2-compartment PK model for marijuana
use in occasional and chronic smokers. Initially, I was using providing rate
constants K12 and K21 in PK block and it resulted in poor fitting. Then, I
later changed to CL,V1, V2 , Q and it resulted in proper fitting. I was
perplexed as to why I couldn’t get a proper fit by providing rate constants? I
tried to look online but couldn’t find any proper explanation about when (or
not) we should use micro constants in PK block to define our model in NONMEM?
Does anyone has any useful insights into this?
Regards,
Sumeet Singla
Graduate Student
Dpt. of Pharmaceutics & Translational Therapeutics
College of Pharmacy- University of Iowa
Sumeet
As mentioned it may have found a local minimum with the micro constants. You
could try with different starting values and/or limits.
You might also convert the CL/V constants to k12/k21/kel/v1 and see how they
compare, use them as initial estimates. I seem to remember the equations for
conversion between parameter types in one of the NONMEM manuals.
David Bourne
Quoted reply history
> On Feb 3, 2019, at 1:44 PM, Singla, Sumeet K <[email protected]> wrote:
>
> Hello everyone!
>
> I have a question. I was trying to build a 2-compartment PK model for
> marijuana use in occasional and chronic smokers. Initially, I was using
> providing rate constants K12 and K21 in PK block and it resulted in poor
> fitting. Then, I later changed to CL,V1, V2 , Q and it resulted in proper
> fitting. I was perplexed as to why I couldn’t get a proper fit by providing
> rate constants? I tried to look online but couldn’t find any proper
> explanation about when (or not) we should use micro constants in PK block to
> define our model in NONMEM? Does anyone has any useful insights into this?
>
> Regards,
> Sumeet Singla
> Graduate Student
> Dpt. of Pharmaceutics & Translational Therapeutics
> College of Pharmacy- University of Iowa
It could be just coding error, could you show the control stream?
Thanks
Leonid
Quoted reply history
> On Feb 3, 2019, at 12:44 PM, Singla, Sumeet K <[email protected]> wrote:
>
> Hello everyone!
>
> I have a question. I was trying to build a 2-compartment PK model for
> marijuana use in occasional and chronic smokers. Initially, I was using
> providing rate constants K12 and K21 in PK block and it resulted in poor
> fitting. Then, I later changed to CL,V1, V2 , Q and it resulted in proper
> fitting. I was perplexed as to why I couldn’t get a proper fit by providing
> rate constants? I tried to look online but couldn’t find any proper
> explanation about when (or not) we should use micro constants in PK block to
> define our model in NONMEM? Does anyone has any useful insights into this?
>
> Regards,
> Sumeet Singla
> Graduate Student
> Dpt. of Pharmaceutics & Translational Therapeutics
> College of Pharmacy- University of Iowa
>
Hi All,
It could also be the statistical model. If you are estimating 4 parameters then different parameterisations should be fairly equivalent if a BLOCK(4) structure is used for both parameterisations. If only the diagonal option is used, then this could be why different results/minimisations are obtained for different parameterisations.
Kind regards,
Janet
Janet R Wade, PhD
Occams
Senior Consultant
Quoted reply history
From: owner-nmusers_at_globomaxnm.com <owner-nmusers_at_globomaxnm.com> On Behalf Of Leonid Gibiansky
Sent: 04 February 2019 07:30
To: Singla, Sumeet K <sumeet-singla_at_uiowa.edu>
Cc: nmusers_at_globomaxnm.com
Subject: Re: [NMusers] Why should we avoid using micro rate constants?
It could be just coding error, could you show the control stream?
Thanks
Leonid
On Feb 3, 2019, at 12:44 PM, Singla, Sumeet K <sumeet-singla_at_uiowa.edu <mailto:sumeet-singla_at_uiowa.edu> > wrote:
Hello everyone!
I have a question. I was trying to build a 2-compartment PK model for marijuana use in occasional and chronic smokers. Initially, I was using providing rate constants K12 and K21 in PK block and it resulted in poor fitting. Then, I later changed to CL,V1, V2 , Q and it resulted in proper fitting. I was perplexed as to why I couldn’t get a proper fit by providing rate constants? I tried to look online but couldn’t find any proper explanation about when (or not) we should use micro constants in PK block to define our model in NONMEM? Does anyone has any useful insights into this?
Regards,
Sumeet Singla
Graduate Student
Dpt. of Pharmaceutics & Translational Therapeutics
College of Pharmacy- University of Iowa
Hi All,
It could also be the statistical model. If you are estimating 4 parameters then
different parameterisations should be fairly equivalent if a BLOCK(4) structure
is used for both parameterisations. If only the diagonal option is used, then
this could be why different results/minimisations are obtained for different
parameterisations.
Kind regards,
Janet
Janet R Wade, PhD
Occams
Senior Consultant
Quoted reply history
From: [email protected] <[email protected]> On Behalf Of
Leonid Gibiansky
Sent: 04 February 2019 07:30
To: Singla, Sumeet K <[email protected]>
Cc: [email protected]
Subject: Re: [NMusers] Why should we avoid using micro rate constants?
It could be just coding error, could you show the control stream?
Thanks
Leonid
On Feb 3, 2019, at 12:44 PM, Singla, Sumeet K <[email protected]
<mailto:[email protected]> > wrote:
Hello everyone!
I have a question. I was trying to build a 2-compartment PK model for marijuana
use in occasional and chronic smokers. Initially, I was using providing rate
constants K12 and K21 in PK block and it resulted in poor fitting. Then, I
later changed to CL,V1, V2 , Q and it resulted in proper fitting. I was
perplexed as to why I couldn’t get a proper fit by providing rate constants? I
tried to look online but couldn’t find any proper explanation about when (or
not) we should use micro constants in PK block to define our model in NONMEM?
Does anyone has any useful insights into this?
Regards,
Sumeet Singla
Graduate Student
Dpt. of Pharmaceutics & Translational Therapeutics
College of Pharmacy- University of Iowa
Dear Sumeet,
If you are assuming a distribution for your parameters (e.g. log-Normal p = theta * exp(eta)) then it might matter if you use rate constants versus clearances and volumes. In general, if you want to make the log-Normal assumption you should use clearances and volumes as there is reasonable biological prior knowledge to show these generally follow a log-Normal distribution (do some reading on the occurrence of log-Normal distributions in biology).
The rate constant is a ratio of two (usually) log-Normally distributed variables (e.g. k = CL/V) and hence may not necessarily be a shape that can itself be described as a log-Normal. Here is some R-code that highlights this:
# Simulate some realistic PK for a water soluble renally cleared drug
vd <- 40 * exp(rnorm(10000, sd = 0.5))
cl <- 6 * exp(rnorm(10000, sd = 0.5))
k <- cl / vd
# Visualise the histograms and use fitdistr function to
# fit a log-Normal
require(MASS)
# Volume:
hist(vd, freq = FALSE)
fit <- fitdistr(vd, "log-normal")$estimate
lines(dlnorm(0:max(vd), fit[1], fit[2]), lwd = 3)
# ...yes
#
# Clearance:
hist(cl, freq = FALSE)
fit <- fitdistr(cl, "log-normal")$estimate
lines(dlnorm(0:max(cl), fit[1], fit[2]), lwd = 3)
# ...yes
#
# K
hist(k, freq = FALSE)
fit <- fitdistr(k, "log-normal")$estimate
lines(dlnorm(0:max(k), fit[1], fit[2]), lwd = 3)
# ...no
People who do not like to make assumptions on distributions of parameters use a nonparametric approach, and in this case it does not matter whether you use rate constants or clearances and volumes. However, unless you collect rich informative data (to get good individual parameter estimates) and lots of it (to get a true idea of the distribution of parameters in the population) it is usually advised to make a distributional assumption, and the log-Normal is often sensible.
BW,
Joe
Joseph F Standing
MRC Fellow, UCL Institute of Child Health
Antimicrobial Pharmacist, Great Ormond Street Hospital
Honorary Senior Lecturer, St George's University of London
Tel: +44(0)207 905 2370
Mobile: +44(0)7970 572435
Quoted reply history
________________________________________
From: owner-nmusers_at_globomaxnm.com [owner-nmusers_at_globomaxnm.com] on behalf of janet.wade_at_telia.com [janet.wade_at_telia.com]
Sent: 05 February 2019 06:51
To: 'Leonid Gibiansky'; 'Singla, Sumeet K'
Cc: nmusers_at_globomaxnm.com
Subject: RE: [NMusers] Why should we avoid using micro rate constants?
Hi All,
It could also be the statistical model. If you are estimating 4 parameters then different parameterisations should be fairly equivalent if a BLOCK(4) structure is used for both parameterisations. If only the diagonal option is used, then this could be why different results/minimisations are obtained for different parameterisations.
Kind regards,
Janet
Janet R Wade, PhD
Occams
Senior Consultant
From: owner-nmusers_at_globomaxnm.com <owner-nmusers_at_globomaxnm.com> On Behalf Of Leonid Gibiansky
Sent: 04 February 2019 07:30
To: Singla, Sumeet K <sumeet-singla_at_uiowa.edu>
Cc: nmusers_at_globomaxnm.com
Subject: Re: [NMusers] Why should we avoid using micro rate constants?
It could be just coding error, could you show the control stream?
Thanks
Leonid
On Feb 3, 2019, at 12:44 PM, Singla, Sumeet K <sumeet-singla_at_uiowa.edu<mailto:sumeet-singla_at_uiowa.edu>> wrote:
Hello everyone!
I have a question. I was trying to build a 2-compartment PK model for marijuana use in occasional and chronic smokers. Initially, I was using providing rate constants K12 and K21 in PK block and it resulted in poor fitting. Then, I later changed to CL,V1, V2 , Q and it resulted in proper fitting. I was perplexed as to why I couldnt get a proper fit by providing rate constants? I tried to look online but couldnt find any proper explanation about when (or not) we should use micro constants in PK block to define our model in NONMEM? Does anyone has any useful insights into this?
Regards,
Sumeet Singla
Graduate Student
Dpt. of Pharmaceutics & Translational Therapeutics
College of Pharmacy- University of Iowa
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Dear Sumeet,
If you are assuming a distribution for your parameters (e.g. log-Normal p =
theta * exp(eta)) then it might matter if you use rate constants versus
clearances and volumes. In general, if you want to make the log-Normal
assumption you should use clearances and volumes as there is reasonable
biological prior knowledge to show these generally follow a log-Normal
distribution (do some reading on the occurrence of log-Normal distributions in
biology).
The rate constant is a ratio of two (usually) log-Normally distributed
variables (e.g. k = CL/V) and hence may not necessarily be a shape that can
itself be described as a log-Normal. Here is some R-code that highlights this:
# Simulate some realistic PK for a water soluble renally cleared drug
vd <- 40 * exp(rnorm(10000, sd = 0.5))
cl <- 6 * exp(rnorm(10000, sd = 0.5))
k <- cl / vd
# Visualise the histograms and use fitdistr function to
# fit a log-Normal
require(MASS)
# Volume:
hist(vd, freq = FALSE)
fit <- fitdistr(vd, "log-normal")$estimate
lines(dlnorm(0:max(vd), fit[1], fit[2]), lwd = 3)
# ...yes
#
# Clearance:
hist(cl, freq = FALSE)
fit <- fitdistr(cl, "log-normal")$estimate
lines(dlnorm(0:max(cl), fit[1], fit[2]), lwd = 3)
# ...yes
#
# K
hist(k, freq = FALSE)
fit <- fitdistr(k, "log-normal")$estimate
lines(dlnorm(0:max(k), fit[1], fit[2]), lwd = 3)
# ...no
People who do not like to make assumptions on distributions of parameters use a
nonparametric approach, and in this case it does not matter whether you use
rate constants or clearances and volumes. However, unless you collect rich
informative data (to get good individual parameter estimates) and lots of it
(to get a true idea of the distribution of parameters in the population) it is
usually advised to make a distributional assumption, and the log-Normal is
often sensible.
BW,
Joe
Joseph F Standing
MRC Fellow, UCL Institute of Child Health
Antimicrobial Pharmacist, Great Ormond Street Hospital
Honorary Senior Lecturer, St George's University of London
Tel: +44(0)207 905 2370
Mobile: +44(0)7970 572435
Quoted reply history
________________________________________
From: [email protected] [[email protected]] on behalf of
[email protected] [[email protected]]
Sent: 05 February 2019 06:51
To: 'Leonid Gibiansky'; 'Singla, Sumeet K'
Cc: [email protected]
Subject: RE: [NMusers] Why should we avoid using micro rate constants?
Hi All,
It could also be the statistical model. If you are estimating 4 parameters then
different parameterisations should be fairly equivalent if a BLOCK(4) structure
is used for both parameterisations. If only the diagonal option is used, then
this could be why different results/minimisations are obtained for different
parameterisations.
Kind regards,
Janet
Janet R Wade, PhD
Occams
Senior Consultant
From: [email protected] <[email protected]> On Behalf Of
Leonid Gibiansky
Sent: 04 February 2019 07:30
To: Singla, Sumeet K <[email protected]>
Cc: [email protected]
Subject: Re: [NMusers] Why should we avoid using micro rate constants?
It could be just coding error, could you show the control stream?
Thanks
Leonid
On Feb 3, 2019, at 12:44 PM, Singla, Sumeet K
<[email protected]<mailto:[email protected]>> wrote:
Hello everyone!
I have a question. I was trying to build a 2-compartment PK model for marijuana
use in occasional and chronic smokers. Initially, I was using providing rate
constants K12 and K21 in PK block and it resulted in poor fitting. Then, I
later changed to CL,V1, V2 , Q and it resulted in proper fitting. I was
perplexed as to why I couldn’t get a proper fit by providing rate constants? I
tried to look online but couldn’t find any proper explanation about when (or
not) we should use micro constants in PK block to define our model in NONMEM?
Does anyone has any useful insights into this?
Regards,
Sumeet Singla
Graduate Student
Dpt. of Pharmaceutics & Translational Therapeutics
College of Pharmacy- University of Iowa
********************************************************************************************************************
This message may contain confidential information. If you are not the intended
recipient please inform the
sender that you have received the message in error before deleting it.
Please do not disclose, copy or distribute information in this e-mail or take
any action in relation to its contents. To do so is strictly prohibited and may
be unlawful. Thank you for your co-operation.
NHSmail is the secure email and directory service available for all NHS staff
in England and Scotland. NHSmail is approved for exchanging patient data and
other sensitive information with NHSmail and other accredited email services.
For more information and to find out how you can switch,
https://portal.nhs.net/help/joiningnhsmail
The input data are composed of amount as dosing and concentration in
plasma. Concentration is expressed as amount divided by volume of
distribution. The rate constant is movement of either amount or
concentration pending on modelers intention. Whence, in the modeling
fitting, dosing amount needs to be converted into concentration by
introduction of volume of distribution.
With introduction of the volume of distribution (K21),
K12= Q/V1, and
K21=Q/V2
Therefore by introduction of Volume (V1), system can define all other
parameters.
Simply, by model fitting using micro-constant only means we are treating
amount and concentration as same unit. Hence, it should be avoided. You
can still fit model with micro-constant but requires defining volume and
conversion of amount into concentration to do proper modeling,
Regards,
SaeHeum Song,
Independent Consultant,
Quoted reply history
On Tue, Feb 5, 2019 at 2:05 AM <[email protected]> wrote:
> Hi All,
>
>
>
> It could also be the statistical model. If you are estimating 4 parameters
> then different parameterisations should be fairly equivalent if a BLOCK(4)
> structure is used for both parameterisations. If only the diagonal option
> is used, then this could be why different results/minimisations are
> obtained for different parameterisations.
>
>
>
> Kind regards,
>
> Janet
>
>
>
>
>
> *Janet R Wade, PhD*
>
> *Occams*
> Senior Consultant
>
>
>
>
>
>
>
> *From:* [email protected] <[email protected]> *On
> Behalf Of *Leonid Gibiansky
> *Sent:* 04 February 2019 07:30
> *To:* Singla, Sumeet K <[email protected]>
> *Cc:* [email protected]
> *Subject:* Re: [NMusers] Why should we avoid using micro rate constants?
>
>
>
> It could be just coding error, could you show the control stream?
>
> Thanks
>
> Leonid
>
>
> On Feb 3, 2019, at 12:44 PM, Singla, Sumeet K <[email protected]>
> wrote:
>
> Hello everyone!
>
>
>
> I have a question. I was trying to build a 2-compartment PK model for
> marijuana use in occasional and chronic smokers. Initially, I was using
> providing rate constants K12 and K21 in PK block and it resulted in poor
> fitting. Then, I later changed to CL,V1, V2 , Q and it resulted in proper
> fitting. I was perplexed as to why I couldn’t get a proper fit by providing
> rate constants? I tried to look online but couldn’t find any proper
> explanation about when (or not) we should use micro constants in PK block
> to define our model in NONMEM? Does anyone has any useful insights into
> this?
>
>
>
> Regards,
>
> *Sumeet Singla*
>
> Graduate Student
>
> Dpt. of Pharmaceutics & Translational Therapeutics
>
> College of Pharmacy- University of Iowa
>
>
>
>
Thank you so much!!
These are all wonderful insights and replies and I am definitely going to
ponder over it today.
Regards,
Sumeet Singla
Quoted reply history
On Feb 5, 2019, at 7:52 AM, Saeheum Song
<[email protected]<mailto:[email protected]>> wrote:
The input data are composed of amount as dosing and concentration in plasma.
Concentration is expressed as amount divided by volume of distribution. The
rate constant is movement of either amount or concentration pending on modelers
intention. Whence, in the modeling fitting, dosing amount needs to be converted
into concentration by introduction of volume of distribution.
With introduction of the volume of distribution (K21),
K12= Q/V1, and
K21=Q/V2
Therefore by introduction of Volume (V1), system can define all other
parameters.
Simply, by model fitting using micro-constant only means we are treating amount
and concentration as same unit. Hence, it should be avoided. You can still
fit model with micro-constant but requires defining volume and conversion of
amount into concentration to do proper modeling,
Regards,
SaeHeum Song,
Independent Consultant,
On Tue, Feb 5, 2019 at 2:05 AM
<[email protected]<mailto:[email protected]>> wrote:
Hi All,
It could also be the statistical model. If you are estimating 4 parameters then
different parameterisations should be fairly equivalent if a BLOCK(4) structure
is used for both parameterisations. If only the diagonal option is used, then
this could be why different results/minimisations are obtained for different
parameterisations.
Kind regards,
Janet
Janet R Wade, PhD
Occams
Senior Consultant
From: [email protected]<mailto:[email protected]>
<[email protected]<mailto:[email protected]>> On Behalf
Of Leonid Gibiansky
Sent: 04 February 2019 07:30
To: Singla, Sumeet K <[email protected]<mailto:[email protected]>>
Cc: [email protected]<mailto:[email protected]>
Subject: Re: [NMusers] Why should we avoid using micro rate constants?
It could be just coding error, could you show the control stream?
Thanks
Leonid
On Feb 3, 2019, at 12:44 PM, Singla, Sumeet K
<[email protected]<mailto:[email protected]>> wrote:
Hello everyone!
I have a question. I was trying to build a 2-compartment PK model for marijuana
use in occasional and chronic smokers. Initially, I was using providing rate
constants K12 and K21 in PK block and it resulted in poor fitting. Then, I
later changed to CL,V1, V2 , Q and it resulted in proper fitting. I was
perplexed as to why I couldn’t get a proper fit by providing rate constants? I
tried to look online but couldn’t find any proper explanation about when (or
not) we should use micro constants in PK block to define our model in NONMEM?
Does anyone has any useful insights into this?
Regards,
Sumeet Singla
Graduate Student
Dpt. of Pharmaceutics & Translational Therapeutics
College of Pharmacy- University of Iowa