RE: Indirect response model
Dear all,
I agree, implementing the actual PK model (i.e. IPP) or using linear
interpolation to describe the individual PK profiles as suggested by Juergen
and David is necessary in this example.
However, for understanding when input concentrations can be used directly as
a reasonable approximation I have the following question: If the input
concentrations were to be used, as originally presented by Nishit, wouldn't
nonmem use the input concentrations according to last observation carried
BACKWARD, rather than forward? In that case, for ID 1101 (the third
individual in the dataset):
0 < TIME <= 840 -> COP=0.13884
840 < TIME <= 842 -> COP=0.12987
842 < TIME <= 844 -> COP=0.12147
844 < TIME <= 1848 -> COP=227.79
...
I haven't checked myself that nonmem uses LOCB rather than LOCF, but have
been told so by Mats Karlsson which usually makes checking superfluous:>) I
think this could be important in other situations as well: The LOCB-rule
could induce false covariate relations if the drug affects a potential
covariate. For example, disease level/score/stage may falsely appear to
affect the drug clearance if investigated within nonmem. To properly
quantify such a covariate relation a simultaneous fit of the PK-PD model may
be necessary, treating the covariate (biomarker) as an integrated part of
the model. Getting back to this thread:
Nishit, if using nonmem version 6, the dummy dose into compartment 1 is not
needed for initializing the baseline. Dummy-dosing records can be removed
from the datafile and this line in the model file:
F1 = KFOR/KCL
can be replace by
A_0(1) = KFOR/KCL
Lastly, either fit log-transformed DV:s using an additive error model OR add
"INTERACTION" on the $ESTIMATION, to account for the interaction between
ETA1 and EPS1 in the current error model. I would prefer the first
alternative for several reasons.
Kind regards,
Jakob
$INPUT ID TIME DV AMT=DOSE COP MDV
ID 1101 in the dataset:
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
Quoted reply history
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
Behalf Of David H Salinger
Sent: den 16 maj 2007 18:14
To: Jurgen Bulitta
Cc: Modi, Nishit [ALZUS]; [EMAIL PROTECTED]; [email protected]
Subject: Re: [NMusers] Indirect response model
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