From: GREAT ORMOND STREET HOSPITAL FOR CHILDREN NHS
FOUNDATION TRUST <*joseph.standing *>

Date: Tue, 5 Feb 2019 15:05:43 +0000

Dear Sumeet,

Actually please ignore my last remark, and thanks to Jonathan French for po=

inting out to me that, the ratio of two log-Normals is indeed log-Normal. =

I should have deferred to statistical theory rather than fuzzy memory of No=

rmal/Normal being Cauchy so must somehow extend to log-Normals and trying t=

o make silly plots that did not properly display the density due to my code=

/parameter choice:

https://stats.stackexchange.com/questions/21735/what-are-the-mean-and-varia=

nce-of-the-ratio-of-two-lognormal-variables/21740

Corrected R-code to properly plot the density when the mean is closer to ze=

ro:

# 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(seq(0,max(k),length=200),dlnorm(seq(0,max(k),length=200), fit[1],=

fit[2]), lwd = 3)

# ...YES!

Lucky no-one who taught me statistics follows NMUsers!

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

________________________________________

From: owner-nmusers

of STANDING, Joseph (GREAT ORMOND STREET HOSPITAL FOR CHILDREN NHS FOUNDAT=

ION TRUST) [joseph.standing

Sent: 05 February 2019 10:53

To: janet.wade

Cc: nmusers

Subject: RE: [NMusers] Why should we avoid using micro rate constants?

--- This message was sent from an email address external to NHSmail but giv=

es the appearance of being from an NHSmail (

t should verify the sender and content before acting upon information conta=

ined within. ---

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 as=

sumption you should use clearances and volumes as there is reasonable biolo=

gical prior knowledge to show these generally follow a log-Normal distribut=

ion (do some reading on the occurrence of log-Normal distributions in biolo=

gy).

The rate constant is a ratio of two (usually) log-Normally distributed vari=

ables (e.g. k = CL/V) and hence may not necessarily be a shape that can i=

tself be described as a log-Normal. Here is some R-code that highlights th=

is:

# 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 u=

se a nonparametric approach, and in this case it does not matter whether yo=

u use rate constants or clearances and volumes. However, unless you collec=

t rich informative data (to get good individual parameter estimates) and lo=

ts of it (to get a true idea of the distribution of parameters in the popul=

ation) it is usually advised to make a distributional assumption, and the l=

og-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

________________________________________

From: owner-nmusers

of janet.wade

Sent: 05 February 2019 06:51

To: 'Leonid Gibiansky'; 'Singla, Sumeet K'

Cc: nmusers

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 i=

s 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

Of Leonid Gibiansky

Sent: 04 February 2019 07:30

To: Singla, Sumeet K <sumeet-singla

Cc: nmusers

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

to:sumeet-singla

Hello everyone!

I have a question. I was trying to build a 2-compartment PK model for marij=

uana use in occasional and chronic smokers. Initially, I was using providin=

g 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 explanatio=

n about when (or not) we should use micro constants in PK block to define o=

ur 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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and may be unlawful. Thank you for your co-operation.

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This message may contain confidential information. If you are not the inten=

ded 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 ta=

ke 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 sta=

ff in England and Scotland. NHSmail is approved for exchanging patient data=

and other sensitive information with NHSmail and other accredited email se=

rvices.

For more information and to find out how you can switch, https://portal.nhs=

.net/help/joiningnhsmail

Received on Tue Feb 05 2019 - 10:05:43 EST

Date: Tue, 5 Feb 2019 15:05:43 +0000

Dear Sumeet,

Actually please ignore my last remark, and thanks to Jonathan French for po=

inting out to me that, the ratio of two log-Normals is indeed log-Normal. =

I should have deferred to statistical theory rather than fuzzy memory of No=

rmal/Normal being Cauchy so must somehow extend to log-Normals and trying t=

o make silly plots that did not properly display the density due to my code=

/parameter choice:

https://stats.stackexchange.com/questions/21735/what-are-the-mean-and-varia=

nce-of-the-ratio-of-two-lognormal-variables/21740

Corrected R-code to properly plot the density when the mean is closer to ze=

ro:

# 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(seq(0,max(k),length=200),dlnorm(seq(0,max(k),length=200), fit[1],=

fit[2]), lwd = 3)

# ...YES!

Lucky no-one who taught me statistics follows NMUsers!

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

________________________________________

From: owner-nmusers

of STANDING, Joseph (GREAT ORMOND STREET HOSPITAL FOR CHILDREN NHS FOUNDAT=

ION TRUST) [joseph.standing

Sent: 05 February 2019 10:53

To: janet.wade

Cc: nmusers

Subject: RE: [NMusers] Why should we avoid using micro rate constants?

--- This message was sent from an email address external to NHSmail but giv=

es the appearance of being from an NHSmail (

t should verify the sender and content before acting upon information conta=

ined within. ---

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 as=

sumption you should use clearances and volumes as there is reasonable biolo=

gical prior knowledge to show these generally follow a log-Normal distribut=

ion (do some reading on the occurrence of log-Normal distributions in biolo=

gy).

The rate constant is a ratio of two (usually) log-Normally distributed vari=

ables (e.g. k = CL/V) and hence may not necessarily be a shape that can i=

tself be described as a log-Normal. Here is some R-code that highlights th=

is:

# 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 u=

se a nonparametric approach, and in this case it does not matter whether yo=

u use rate constants or clearances and volumes. However, unless you collec=

t rich informative data (to get good individual parameter estimates) and lo=

ts of it (to get a true idea of the distribution of parameters in the popul=

ation) it is usually advised to make a distributional assumption, and the l=

og-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

________________________________________

From: owner-nmusers

of janet.wade

Sent: 05 February 2019 06:51

To: 'Leonid Gibiansky'; 'Singla, Sumeet K'

Cc: nmusers

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 i=

s 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

Of Leonid Gibiansky

Sent: 04 February 2019 07:30

To: Singla, Sumeet K <sumeet-singla

Cc: nmusers

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

to:sumeet-singla

Hello everyone!

I have a question. I was trying to build a 2-compartment PK model for marij=

uana use in occasional and chronic smokers. Initially, I was using providin=

g 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 explanatio=

n about when (or not) we should use micro constants in PK block to define o=

ur 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 inten=

ded 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 ta=

ke 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 sta=

ff in England and Scotland. NHSmail is approved for exchanging patient data=

and other sensitive information with NHSmail and other accredited email se=

rvices.

For more information and to find out how you can switch, https://portal.nhs=

.net/help/joiningnhsmail

***************************************************************************=

*****************************************

This message may contain confidential information. If you are not the inten=

ded 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 ta=

ke 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 sta=

ff in England and Scotland. NHSmail is approved for exchanging patient data=

and other sensitive information with NHSmail and other accredited email se=

rvices.

For more information and to find out how you can switch, https://portal.nhs=

.net/help/joiningnhsmail

Received on Tue Feb 05 2019 - 10:05:43 EST