Dear Nishit,
Jurgen is correct, you are using concentration (COP) as a forcing function.
But, in ID 1101, for example, the COP has a value of 0 from TIME=0 to 840 and
then values around 0.12 until TIME 1848. Unless this is what you had in mind,
I would suggest two steps: 1. Include more time points for COP. These need
not coincide with TIMEs were you have DV values. 2. Create a linear
interpolation of COP to be used in the $DES block.
One way to do this linear interpolation is to add two columns to your data
file: PTME (previous time) and PCOP (previous concentration). Then, compute
SLOPE = (COP-PCOP)/(TIME-PTME)
and use a linear interpolation of conc:
PCOP +SLOPE*(T-PTME)
in place of COP in computing your COEF parameter (must be in $DES block).
I hope this helps,
David Salinger
RFPK, Univ. of Washington
On Tue, 15 May 2007, Jurgen Bulitta wrote:
Dear Nishit,
If I understand correctly, you are using concentration (COP) of
your drug as a time dependent covariate which is then used as
forcing function for your PD model.
As concentrations change over time, you probably need the
CALLFL = 0 option ($PK CALLFL=0) to read in the concentration
at every new time. I would write out COP in $TABLE in order to
check, if COP changes over time as it should.
This should work much better, but it will still give you a piecewise
constant concentration profile. This may cause numerical problems.
Instead, I would include the differential equations for your PK
model. This should give you better numerical stability and more
correct concentration predictions. You could start with reading in
the individual PK parameters (IPP approach, see reference below)
and then go to more complex PKPD analyses.
You might try the MATRIX=S statement in $COV, if you like to get
the covariance step to work.
Hope some of this works.
Best regards
Juergen
Reference:
Zhang, L., S. L. Beal, and L. B. Sheiner. 2003. Simultaneous vs.
sequential analysis for population PK/PD data I: best-case performance.
J Pharmacokinet Pharmacodyn 30:387-404.
-----------------------------------------------
Juergen Bulitta, PhD, Post-doctoral Fellow
Pharmacometrics, University at Buffalo, NY, USA
Phone: +1 716 645 2855 ext. 281, [EMAIL PROTECTED]
-----------------------------------------------
-----Ursprüngliche Nachricht-----
Von: "Modi, Nishit [ALZUS]" <[EMAIL PROTECTED]>
Gesendet: 15.05.07 18:31:57
An: [EMAIL PROTECTED]
CC: [email protected]
Betreff: [NMusers] Indirect response model
I am conducting a sequential pharmacokinetic-pharmacodynamic model. The
pharmacokinetic fits look good and I was using an indirect response model. The
PD model is that the drug inhibits clearance of the analyte (PD response), thus
one expects that the response increases with increasing drug (Model II). There
is a baseline measured (=Kfor/Kcl) and a dummy dose=1 unit given. It seems
despite trying various permutations of the model, eta1 seems to be very small
and no covariance step is conducted. The model and data for the first 3
subjects are reproducted below. Any assistance would be appreciated. Note
that since conc (COP) are read in, the model only requires a single
differential equation. Any insight would be appreciated.
Nishit
$PROBLEM PD - ADVAN6
$DATA C:\PDDATA.CSV
$INPUT ID TIME DV AMT=DOSE COP MDV
; data are subject ID, Time, DV=PD response, Amt (dummy dose of 1 inserted),
COP=plasma conc which drive PD model, MDV
$SUBROUTINES ADVAN6 TOL=6
$MODEL
COMP=(EFFECT, DEFDOSE, DEFOBS)
$PK
KFOR = THETA(1)
KCL = THETA(2)*EXP(ETA(1))
IC50 = THETA(3)
IMAX = THETA(4)
F1 = KFOR/KCL
COEF = IMAX*COP/(IC50+COP)
$DES
DADT(1) = KFOR-KCL*(1-COEF)*A(1)
$ERROR
W = F
Y = F*EXP(ERR(1))
IPRED = F
IRES = DV-IPRED
IF (W.LE.0.) W=1
IWRES = IRES/W
$THETA (0,0.3)
$THETA (0, 0.003)
$THETA (0,10)
$THETA (0, 0.3, 1)
$OMEGA 0.01
$SIGMA 0.5
$ESTIMATION METHOD=1 MAXEVAL=5000 PRINT=20
$COVR
$TABLE ID TIME PRED IPRED IRES KFOR KCL IC50 IMAX
NOPRINT ONEHEADER
FILE=C:\PD.TAB
1001 0 . 1 0 1
1001 0 98.3 . 0 0
1001 168 90.6 . 122.44 0
1001 840 92.8 . 183.69 0
1002 0 . 1 0 1
1002 0 105.1 . 0 0
1002 840 88.5 . 61.253 0
1002 842 106.7 . 106.8 0
1002 844 122.1 . 116.4 0
1002 1848 129.1 . 121.46 0
1002 1850 160.4 . 212.63 0
1002 1852 157.1 . 231.89 0
1101 0 . 1 0 1
1101 0 68.1 . 0 0
1101 840 88.1 . 0.13884 0
1101 842 105.5 . 0.12987 0
1101 844 108.8 . 0.12147 0
1101 1848 113.3 . 227.79 0
1101 1850 62.6 . 379.54 0
1101 1852 138.7 . 412.18 0
