(Warning 48) DES-DEFINED ITEMS ARE COMPUTED ONLY WHEN EVENT TIME INCREASES
Hi all,
I'm having troubles with the following warning message as the prediction for
first records weren't right.
#########################
(Warning 48) DES-DEFINED ITEMS ARE COMPUTED ONLY WHEN EVENT TIME INCREASES. E.
G., DISPLAYED VALUES ASSOCIATED WITH THE FIRST EVENT RECORD OF AN INDIVIDUAL
RECORD ARE COMPUTED WITH (THE LAST ADVANCE TO) AN EVENT CREATING MVMODEL ROUTINE
##########################
I tried to fit a PKPD model, given PK is a bit complicated, I did in two
steps, predicted the PK concentration, then add the concentration column to the
dataset for the PD part. Here is the example of the dataset:
ID
TIME
AMT
RATE
EVID
DV
LNDV
MDV
CMT
CP
1
0
0
0
0
88
4.477337
0
2
0
1
192.1667
0.52149
0.297994
1
0
0
1
1
0
1
720.1667
0.4448
0.264238
1
0
0
1
1
0.53923
1
1176
0
0
0
16.5
2.80336
0
2
0.77665
Here is the model (kept the dosing records in the datasets but ignore it by
setting diff. eqn =0):
$DES
DADT(1)=0
DRUG=1-IMAX*CP**GAM/(IC50**GAM+CP**GAM)
DADT(2)=KIN*DRUG*EXP(BETA*T) - KOUT*A(2)
CP2 = CP
CA = A(2)
But, when I look at the individual prediction, I don't understand why the
concentration CP I used to compute inhibition was nonzero in the first record,
i.e. CP2, it's different from what is in the dataset. As a result of nonzero
concentration, my second data point, still at
zero concentration, was a way off (see CA at TIME = 192.17).
ID
TIME
EVID
AMT
CMT
CA
CP
CP2
KIN
MRT
KOUT
IC50
IMAX
GAM
DRUG
BETA
DV
1
0.0000
0.0000
0.0000
2.0000
177.220000
0.0000
0.8556
109.1200
0.2211
0.1306
0.1120
1.0000
1.0000
0.1470
0.0000
88.0000
1
192.1700
1.0000
0.5215
1.0000
838.270000
0.0000
0.0000
109.1200
0.2211
0.1306
0.1120
1.0000
1.0000
1.0000
0.0000
0.0000
1
720.1700
1.0000
0.4448
1.0000
145.570000
0.5392
0.5392
109.1200
0.2211
0.1306
0.1120
1.0000
1.0000
0.1719
0.0000
0.0000
Can anybody point out if there is a problem in my dataset or script or an issue
with NONMEM? If latter, would the workaround by forcing DRUG = 1 at TIME = 0
work? Many thanks.
Yen Lin