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Parent and metabolite model-residual error model and L2

From: wangxipei
Date: Fri, 2 Sep 2011 14:31:43 +0800 (CST)

Dear NONMEM users,


I am building a popPK model for a parent drug and its metabolite (rich data,single dose). I want to estimate the correlation between the residual errors of parent drug and its metabolite, because their measurements were from a same blood sample. (My code and part of data are shown as follows.)
Question 1: Should I use $SIGMA BLOCK (2) only, or should I use both L2 and $SIGMA BLOCK(2)?
Question 2: I am not sure about the format of L2 data item, so I show the first two individuals.
  Within one individual, the observations of parent and its metabolite at the same time point have the same L2 value,but different from other time points. Is it correct?
Question 3: Does the L2 value of the dose event affect the estimation? (I think it does not.)




----------------------------------- NONMEM code ------------------------------------------------------------------------------------------
$INPUT C ID TIME DV AMT CMT EVID L2
$DATA d.CSV IGNORE=
$SUBROUTINES ADVAN6 TOL=6
$MODEL NCOMP=7 COMP(DEFDEP, DEFDOSE) COMP(CENTRAL, DEFOBS) COMP(PERIPH) COMP(META) COMP(META2)
COMP(TRANSIT1) COMP(TRANSIT2) ; 2-COMP for parent drug, 2-comp for its metabolite, 2-transit compartments to connect them.
$PK
CL=TVCL*EXP(ETA(1))
...
 $DES
...
$ERROR
   DEL=0
   IF (F.EQ.0) DEL=1
   W=F
   IPRED=F
   IRES=DV-IPRED
   IWRES=IRES/(W+DEL)
   IF(CMT.EQ.2) Y=F + F*ERR(1)
   IF(CMT.EQ.4) Y=F + F*ERR(2) + ERR(3)
...
$SIGMA BLOCK(2)
  .2 ;parent RV
  .1 .17 ;metabolite RV
$SIGMA BLOCK(1) 0 FIX;[A]
$EST PRINT=5 MAX=9999 SIG=3 METH=1 POSTHOC MSFO=run1.MSF




--------------individual data --------------------------------------------------------------
compartment 2 is the central compartment of parent drug, and compartment 4 is the central comp. of metabolite.


IDTIMEDVAMTCMTEVIDL2
10.100110
10.331.096.201
10.660.623.202
10.660.130.402
110.329.203
110.407.403
11.50.139.204
11.50.541.404
120.073.205
120.552.405
12.50.022.206
12.50.465.406
130.076.207
130.572.407
13.50.479.408
140.480.409
160.160.4010
180.252.4011
1120.117.4012
1240.018.4013
20.100110
20.330.030.201
210.034.203
21.50.049.204
220.053.205
22.50.299.206
22.50.033.406
230.344.207
230.118.407
23.50.263.208
23.50.293.408






Many thanks in advance for any comments!
Xipei


--

Xipei Wang, Ph.D. student
Department of Pharmaceutics, School of Pharmaceutical Sciences,
Peking University Health Science Center, Beijing,
China
Email: wangxipei
Received on Fri Sep 02 2011 - 02:31:43 EDT

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