Dear Elisabet,
In addition to Sebastian's answer, the indirect response model he mentions
is a model for the turn-over of the (induced) enzyme. Thus the half-life of
the enzyme you may be able to get info in literature on a reasonable value
(probably 1-3 days). This type of model was used in:
A mechanism-based pharmacokinetic-enzyme model for cyclophosphamide
autoinduction in breast cancer patients.
Hassan et al,
Br J Clin Pharmacol. 1999 Nov;48(5):669-77. 2.
The associated model file looked like this:
$PROBLEM Cyklofosfamid induktion
$INPUT NUMB ID TIME DV NEWA AMT RATE CMT FLAG DURA
$DATA /users/mats/Cancer/Musse/Data/cp11.dta IGNORE=#
$SUBROUTINES ADVAN9 TOL=4
$MODEL COMP=CENTRAL
COMP=PERI
COMP=4OH
COMP=ENZ
$PK
CLUI = THETA(1)*EXP(ETA(1))
CLI = THETA(2)*EXP(ETA(2))
V1 = THETA(3)*EXP(ETA(3))
Q = THETA(4)*EXP(ETA(4))
V2 = THETA(5)*EXP(ETA(5))
CLOH = THETA(6)*EXP(ETA(6))
VOH = THETA(7)*EXP(ETA(7))
EMAX = THETA(8)*EXP(ETA(8))
EC50 = THETA(9)*EXP(ETA(9))
KENZ = THETA(10)*EXP(ETA(10))
S1 = V1
S3 = VOH
K10 = CLUI /V1
K12 = Q /V1
K13 = CLI /V1
K21 = Q /V2
K30 = CLOH /VOH
$DES
CP = A(1)/V1
DADT(1)=-A(1)*(K10+K12+K13*A(4)) + K21*A(2)
DADT(2)= A(1)* K12 - K21*A(2)
DADT(3)= A(1)*A(4)* K13 - K30*A(3)
DADT(4)= KENZ*(1+EMAX*CP/(CP+EC50)-A(4))
$THETA
(0,.26) ;CLUI
(0,2.2) ;CLI
(0,9.75) ;V1
(0,12.6) ;Q
(0,21.5) ;V2
(0,387) ;CLOH
(7,253) ;VOH
(0,351) ;EMAX
(5540 FIX) ;EC50
(0,.0365) ;KENZ
(0,1.38) ;ADD ERROR
(0.04,.0642) ;PROP ERROR
(0,.026) ;ADD ERROR
(0.04,.07) ;PROP ERROR
$OMEGA 0.01 .2673 .267 .41 .219 .0274 2.48 .194 0 FIX .11
$ERROR
W = 1
IF(F.GT.0) W= SQRT(THETA(11)**2+THETA(12)**2*F**2)
IF(F.GT.0.AND.CMT.EQ.3) W= SQRT(THETA(13)**2+THETA(14)**2*F**2)
IPRED = F
IRES = DV-IPRED
IWRES = IRES / W
Y = IPRED+EPS(1)*W
$SIGMA 1 FIX ;RESIDUAL ERROR
$ESTIMATION MAXEVALS=9990 POSTHOC PRINT=1 MSFO=msfb31 NOABORT
$TABLE ID TIME IPRED IWRES ONEHEADER NOPRINT FILE=sdtab31
$TABLE ID FLAG TIME IPRED IWRES ONEHEADER NOPRINT FILE=mutab31
$TABLE ID CLUI CLI V1 Q V2 CLOH VOH EMAX KENZ ETA1 ETA2
ETA3 ETA4 ETA5 ETA6 ETA7 ETA8 ETA10
ONEHEADER NOPRINT FILE=patab31
Mats Karlsson, PhD
Professor of Pharmacometrics
Dept of Pharmaceutical Biosciences
Faculty of Pharmacy
Uppsala University
Box 591
75124 Uppsala
Phone: +46 18 4714105
Fax + 46 18 4714003
-----Original Message-----
From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On
Behalf Of Sebastian Ueckert
Sent: 23 February 2012 22:43
To: Elisabet Størset
Cc: nmusers@globomaxnm.com
Subject: Re: [NMusers] Coding delayed covariate effects
Dear Elisabet,
The most complete way to handle your problem would be to treat the
covariates as observations and build a separate model for them. Your data
set would change from wide to long format. So instead of having
ID TIME DV CYTC
1 0 X1 Y1
1 1 X2 Y2
you would have
ID TIME DV TYPE
1 0 X1 1
1 0 Y1 2
1 1 X2 1
1 1 Y2 2
where TYPE is a flag specifying the type of measurement.
In your model file you use the TYPE flag to return the prediction
corresponding to the current row i.e.,
IF(TYPE.EQ.1) Y=..... ;model prediction for concentration
IF(TYPE.EQ.2) Y=.... ; model prediction for covariate
This would increase the modeling effort considerably but has the advantage
that you can now use any model to link your covariates and clearance. One
option for a delay between change in covariate and clearance would be an
indirect response model. Another advantage of this approach, is that you
acknowledge the uncertainty in the covariate (since it is just another type
of measurement).
Concerning your second question, you could have a look at prediction
corrected VPCs as described by Bergstrand et al.:
Bergstrand, Martin, Andrew C Hooker, Johan E Wallin, and Mats O Karlsson.
Prediction-corrected Visual Predictive Checks for Diagnosing Nonlinear
Mixed-effects Models.
The AAPS Journal 13, no. 2 (June 2011): 143151.
I hope that helps!
Best regards,
Sebastian
Sebastian Ueckert, MSc, PhD student
-----------------------------------------------
Pharmacometrics Research Group,
Department of Pharmaceutical Biosciences, Uppsala University
-----------------------------------------------
P.O. Box 591
SE-751 24 Uppsala
Sweden
-----------------------------------------------
sebastian.ueck...@farmbio.uu.se
-----------------------------------------------
Work: +46-(0)18-471 4437
On Thu, Feb 23, 2012 at 2:50 PM, Elisabet Størset
<elisabet.stor...@gmail.com> wrote:
> Dear nonmemusers
>
>
> In my PK modeling project, a drug (elimination: 100 % hepatic
> metabolism) is given orally twice daily over a period of 10 weeks. 100
> subjects contribute daily trough concentrations. During this period,
> covariates vary within subjects. Two covariates that are difficult to
> handle are changes in corticosteroid regime and changes in cytokine
> concentrations, which both are expected to alter metabolism (CYP
> enzymes) at a transcriptional level. This means that a covariate
> effect on CL is expected to be delayed and/or last for some time (unknown
for how long).
>
>
> My first strategy is to code this in the normal way:
>
> TVCL=THETA(1) + THETA(2) *(cytokine concentration) or (corticosteroid
> dose)
>
>
> However, when coding the covariate as continuous with this method, it
> will not take into account that an effect does still have an impact on
> the parameter after the covariate value has returned to normal in the data
set.
>
>
> Example 1 - Low dose steroids are typically administered daily. Then,
> high dose methylprednisolone is given I.V. for 3 days, before going
> back to the low dose. The inductive effect on CYP enzymes from the
> high dose could be delayed and then last for 1-2 weeks even though the
> high dose is not maintained.
>
> Example 2 - Cytokines marks an inflammation, which could have
> temporary impact on CL/F. Cytokine concentration returns to normal,
> but the impact on the parameter could be lasting for some days.
>
>
> Does anyone have experience of a strategy to code these kinds of
> covariate relationships in NONMEM? Could NONMEM estimate for how long
> time CL/F might be affected, or is this too much to ask for? :)
>
>
>
> Additional question: Is there any way to make the visual predictive
> check an appropriate validation method when only trough concentrations
> are known over a long period of time, when doses are frequently
> changing within and between subjects during the period?
>
>
> Thank you all in advance for your kind help :)
>
>
> Elisabet, pharmacy student, University of Bergen
