From: Emmanuel Chigutsa <*Emmanuel.Chigutsa*>

Date: Wed, 11 Nov 2009 10:52:29 +0200

Hi Jia

Two things:

1. Do you have any reason to think that the CL (or other parameters) are =

dose dependent e.g. saturable kinetics? You will need a column in the =

dataset (DS, for dose) that specifies the AMT in each record for an =

individual. In your parameter output table, you can include DS so that =

you can see whether the parameter values are correlated with the DS as =

well. If so, then you should put the dose in your model as a fixed =

effect, rather than a random effect. You can code for an effect of dose =

on parameters (PAR) as follows:

TVPAR=THETA(1)*DSEFF ; Dose effect

DSEFF=THETA(2)*DS ; This assumes a linear effect of the dose on the =

typical value of the parameter

2. IOV is a separate issue you can investigate regardless of your dose. =

I would try the above first and then add IOV to the model. You can code =

your IOV as follows:

OCC1=0

OCC2=0

IF (TIME.LT.100) OCC1=1

IF(TIME.GE.100) OCC2=1

IIV=ETA(1)

IOV=ETA(2)*OCC1 + ETA(3)*OCC2

TVPAR=THETA(1)*EXP(IIV+IOV)

$OMEGA

0.1 ; IIV

0.1 ; IOV_OCC1

SAME ; IOV_OCC2 constrained to be the same as that for OCC1

ETA1 above will be your IIV whilst ETA2 and ETA3 will be your IOV.The =

parameters that should be estimated with IOV are the ones that you think =

can change between 2 different occasions (which may be most of your =

parameters). You need to investigate them one at a time.

Further reading on IOV can be found in the paper below:

Karlsson and Sheiner. The importance of modeling interoccasion variability =

in population pharmacokinetic analyses. Journal of pharmacokinetics and =

biopharmaceutics. 1993. vol 21, No. 6.

Regards,

Emmanuel

Emmanuel Chigutsa (BPharm. Hons)

Research Fellow, Pharmacometrics Group

Division of Clinical Pharmacology, University of Cape Town

K-45 Old Main Building, Groote Schuur Hospital

Anzio Road, Observatory, 7925

Cape Town, South Africa

Telephone: +27 214066758

Fax: +27 214066759

Mobile: +27 782826538

Email: emmanuel.chigutsa

*>>> Jia Ji <jackie.j.ji *

Dear All,

I am a new NONMEM user and now trying to model clnical PK data with a =

two-compartment model. More than half of patients in our trial had =

escalated dose in the second cycle. So I should have inter-occasion =

variability. But I got a couple of questions here.

First, what parameters should be estimated with IOV? I have seen some =

models with IOV on CL, but not on others (maybe because I have seen too =

few models). Now I have IOV on CL, V1 and Q and it run successfully. But =

when I run with either two of them, I got minimization problem. But, am I =

having too many IOVs in the model?

Second, I was wondering how to model IOV. Now I have the code as

; Define IOV, DESC = DOSE ESCALATION

DESC=0

IF (TIME.GE.100) DESC=1

ETCL = ETA(1) + DESC*ETA(5)

ETV1 = ETA(2) + DESC*ETA(6)

ETQ = ETA(3) + DESC*ETA(7)

ETV2 = ETA(4)

So IOV is modeled as additive relationship to IIV. But what about =

multiplying relationship? Like

; Define IOV, DESC = DOSE ESCALATION

DESC=0

IF (TIME.GE.100) DESC=1

ETCL = ETA(1)*(1+DESC*ETA(5))

ETV1 = ETA(2)*(1+DESC*ETA(6))

ETQ = ETA(3)*(1+DESC*ETA(7))

ETV2 = ETA(4)

When I run with this multiplying relationship, I got increased OFV and =

minimization terminated due to rounding error. But I didn't understand why =

it is not working.

Thank you so much for your patience and time!

Jia

___________________________________________________________________________=

___________________

UNIVERSITY OF CAPE TOWN

This e-mail is subject to the UCT ICT policies and e-mail disclaimer =

published on our website at http://www.uct.ac.za/about/policies/emaildiscla=

imer/ or obtainable from +27 21 650 4500. This e-mail is intended only for =

the person(s) to whom it is addressed. If the e-mail has reached you in =

error, please notify the author. If you are not the intended recipient of =

the e-mail you may not use, disclose, copy, redirect or print the content. =

If this e-mail is not related to the business of UCT it is sent by the =

sender in the sender's individual capacity.

___________________________________________________________________________=

__________________________

Received on Wed Nov 11 2009 - 03:52:29 EST

Date: Wed, 11 Nov 2009 10:52:29 +0200

Hi Jia

Two things:

1. Do you have any reason to think that the CL (or other parameters) are =

dose dependent e.g. saturable kinetics? You will need a column in the =

dataset (DS, for dose) that specifies the AMT in each record for an =

individual. In your parameter output table, you can include DS so that =

you can see whether the parameter values are correlated with the DS as =

well. If so, then you should put the dose in your model as a fixed =

effect, rather than a random effect. You can code for an effect of dose =

on parameters (PAR) as follows:

TVPAR=THETA(1)*DSEFF ; Dose effect

DSEFF=THETA(2)*DS ; This assumes a linear effect of the dose on the =

typical value of the parameter

2. IOV is a separate issue you can investigate regardless of your dose. =

I would try the above first and then add IOV to the model. You can code =

your IOV as follows:

OCC1=0

OCC2=0

IF (TIME.LT.100) OCC1=1

IF(TIME.GE.100) OCC2=1

IIV=ETA(1)

IOV=ETA(2)*OCC1 + ETA(3)*OCC2

TVPAR=THETA(1)*EXP(IIV+IOV)

$OMEGA

0.1 ; IIV

0.1 ; IOV_OCC1

SAME ; IOV_OCC2 constrained to be the same as that for OCC1

ETA1 above will be your IIV whilst ETA2 and ETA3 will be your IOV.The =

parameters that should be estimated with IOV are the ones that you think =

can change between 2 different occasions (which may be most of your =

parameters). You need to investigate them one at a time.

Further reading on IOV can be found in the paper below:

Karlsson and Sheiner. The importance of modeling interoccasion variability =

in population pharmacokinetic analyses. Journal of pharmacokinetics and =

biopharmaceutics. 1993. vol 21, No. 6.

Regards,

Emmanuel

Emmanuel Chigutsa (BPharm. Hons)

Research Fellow, Pharmacometrics Group

Division of Clinical Pharmacology, University of Cape Town

K-45 Old Main Building, Groote Schuur Hospital

Anzio Road, Observatory, 7925

Cape Town, South Africa

Telephone: +27 214066758

Fax: +27 214066759

Mobile: +27 782826538

Email: emmanuel.chigutsa

Dear All,

I am a new NONMEM user and now trying to model clnical PK data with a =

two-compartment model. More than half of patients in our trial had =

escalated dose in the second cycle. So I should have inter-occasion =

variability. But I got a couple of questions here.

First, what parameters should be estimated with IOV? I have seen some =

models with IOV on CL, but not on others (maybe because I have seen too =

few models). Now I have IOV on CL, V1 and Q and it run successfully. But =

when I run with either two of them, I got minimization problem. But, am I =

having too many IOVs in the model?

Second, I was wondering how to model IOV. Now I have the code as

; Define IOV, DESC = DOSE ESCALATION

DESC=0

IF (TIME.GE.100) DESC=1

ETCL = ETA(1) + DESC*ETA(5)

ETV1 = ETA(2) + DESC*ETA(6)

ETQ = ETA(3) + DESC*ETA(7)

ETV2 = ETA(4)

So IOV is modeled as additive relationship to IIV. But what about =

multiplying relationship? Like

; Define IOV, DESC = DOSE ESCALATION

DESC=0

IF (TIME.GE.100) DESC=1

ETCL = ETA(1)*(1+DESC*ETA(5))

ETV1 = ETA(2)*(1+DESC*ETA(6))

ETQ = ETA(3)*(1+DESC*ETA(7))

ETV2 = ETA(4)

When I run with this multiplying relationship, I got increased OFV and =

minimization terminated due to rounding error. But I didn't understand why =

it is not working.

Thank you so much for your patience and time!

Jia

___________________________________________________________________________=

___________________

UNIVERSITY OF CAPE TOWN

This e-mail is subject to the UCT ICT policies and e-mail disclaimer =

published on our website at http://www.uct.ac.za/about/policies/emaildiscla=

imer/ or obtainable from +27 21 650 4500. This e-mail is intended only for =

the person(s) to whom it is addressed. If the e-mail has reached you in =

error, please notify the author. If you are not the intended recipient of =

the e-mail you may not use, disclose, copy, redirect or print the content. =

If this e-mail is not related to the business of UCT it is sent by the =

sender in the sender's individual capacity.

___________________________________________________________________________=

__________________________

Received on Wed Nov 11 2009 - 03:52:29 EST