From: Leonid Gibiansky <*LGibiansky*>

Date: Mon, 02 Feb 2009 09:45:47 -0500

Hi Dong-Seok Yim,

If equilibration rates (K23, K32) are much larger than the elimination

rates, then parent and metabolite will be at equilibrium (K23 A2 = K23

A3), and you will not be able to estimate K23, K32 separately, only K23

to K32 ratio. If this is true, you can see it on the plot of parent vs.

metabolite concentrations (by subject): the plot should show strong

correlation.

If you have enough data in the range where the parent and drug

concentrations are not proportional, you should be able to estimate all

the parameters.

One visual test that may help is to plot scatter-plot matrix of random

effects (ETAs vs. ETAs) and look for strong correlations. Also, you may

check correlation of parameter estimates.

$COV PRINT=E

should give you the eigenvalues of the correlation matrix. If the ratio

of the largest to the smallest is above 1000, parameters may not be

trusted. Correlation matrix itself may help to identify correlated

parameters, so look on the correlation between K23 and K32, in particular.

A helpful way to visualize parameter correlations is to do a bootstrap,

and then look at the scatter plot matrix of the parameters vs.

parameters plots. For example, you may be interested in K32 vs K23 (for

all bootstrap samples) plot. If the run time is short, use 1000 samples.

If it is long, 100 should give you a general idea of what is going on.

Also, the code that you show us should not compile since VS and VA are

not defined. I guess, it is just a typo, but you are missing VS=V2,

VA=V3 somewhere.

Thanks

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

Dong-Seok Yim wrote:

*>
*

*> Hi, colleagues,
*

*>
*

*> I am currently trying to model plasma concentrations of parent and
*

*> metabolite with interconversion. (nmtran code is in the bottom).
*

*> The data came from a densely sampled PK study in healthy subjects. I put
*

*> the metabolite Vd identical to the parent Vd as recommended. Then, I
*

*> managed to obtain some estimates that make the individual plots look nice.
*

*>
*

*> However, I doubt whether we can get reliable estimates for K23 or K32
*

*> and (hence, even K20 and K30 ) without urine concentration data and
*

*> without any priori knowledge on the metabolic ratios etc.
*

*>
*

*> I suspect that the estimates of K23, K32, K20, K30 and V2 are all
*

*> interconnected as the Ka and Ke are in the flip-flop phenomenon - Am I
*

*> wrong ?
*

*>
*

*> Searching for some references for the metabolite PK modeling using
*

*> NONMEM, I found an article reporting detailed pop PK parameters of
*

*> CPT-11 and its metaolites in patients (Rujia Xie, Ron H.J. Mathijssen,
*

*> Alex Sparreboom, Jaap Verweij, and Mats O. Karlsson. Journal of
*

*> Clinical Oncology, Vol 20, No 15 (August 1), 2002: pp 3293-3301)
*

*> In the paper, interconversion rate constants of interconverted
*

*> forms (CPT-11 lactone and CPT-11 carboxylate / SN-38 lactone and SN-38
*

*> carboxylate ) were tabulated without using any urine data. -
*

*>
*

*> If any of the authors comment on the method to obtain reliable values
*

*> using plasma concenentrations only, I would appreciate.
*

*>
*

*> Any comments from nmusers other than above authors are also much welcomed !
*

*>
*

*> Thanks !
*

*>
*

*> Dong-Seok Yim
*

*>
*

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

*>
*

*> $MODEL NCOMP=3
*

*> COMP=(DEPOT,DEFDOSE)
*

*> COMP=(PARENT)
*

*> COMP=(METABOLITE)
*

*>
*

*> $PK
*

*> KA = THETA(1)*EXP(ETA(1))
*

*> K20 = THETA(2)*EXP(ETA(2))
*

*> V2 = THETA(3)*EXP(ETA(3)) ; V2 = parent Vd
*

*> K30 = THETA(4)*EXP(ETA(4))
*

*> V3 = V2 ; V3 = metabolite Vd
*

*> K23 = THETA(5)*EXP(ETA(5))
*

*> K32 = THETA(6)*EXP(ETA(6))
*

*> CL20 = K20*VS
*

*> CL30 = K30*VA
*

*> S2=VS
*

*> S3=VA
*

*>
*

*> $DES
*

*> DADT(1)=-KA*A(1)
*

*> DADT(2)=KA*A(1)-K23*A(2)+K32*A(3)-K20*A(2)
*

*> DADT(3)=K23*A(2)-K32*A(3)-K30*A(3)
*

*>
*

*> Dong-Seok Yim M.D., Ph.D.
*

*>
*

*> Associate Professor
*

*> Department of Pharmacology
*

*> College of Medicine
*

*> The Catholic University of Korea
*

*> 505 Banpo-Dong, Seocho-Gu, Seoul 137-701, Korea
*

*>
*

*> Tel +82-2-590-1201
*

*> Fax +82-2-536-2485
*

*> yimds *

Received on Mon Feb 02 2009 - 09:45:47 EST

Date: Mon, 02 Feb 2009 09:45:47 -0500

Hi Dong-Seok Yim,

If equilibration rates (K23, K32) are much larger than the elimination

rates, then parent and metabolite will be at equilibrium (K23 A2 = K23

A3), and you will not be able to estimate K23, K32 separately, only K23

to K32 ratio. If this is true, you can see it on the plot of parent vs.

metabolite concentrations (by subject): the plot should show strong

correlation.

If you have enough data in the range where the parent and drug

concentrations are not proportional, you should be able to estimate all

the parameters.

One visual test that may help is to plot scatter-plot matrix of random

effects (ETAs vs. ETAs) and look for strong correlations. Also, you may

check correlation of parameter estimates.

$COV PRINT=E

should give you the eigenvalues of the correlation matrix. If the ratio

of the largest to the smallest is above 1000, parameters may not be

trusted. Correlation matrix itself may help to identify correlated

parameters, so look on the correlation between K23 and K32, in particular.

A helpful way to visualize parameter correlations is to do a bootstrap,

and then look at the scatter plot matrix of the parameters vs.

parameters plots. For example, you may be interested in K32 vs K23 (for

all bootstrap samples) plot. If the run time is short, use 1000 samples.

If it is long, 100 should give you a general idea of what is going on.

Also, the code that you show us should not compile since VS and VA are

not defined. I guess, it is just a typo, but you are missing VS=V2,

VA=V3 somewhere.

Thanks

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

Dong-Seok Yim wrote:

Received on Mon Feb 02 2009 - 09:45:47 EST