Indirect response PD model
From: "Tsai, Max" max.tsai@spcorp.com
Subject: [NMusers] Indirect response PD model
Date: Fri, January 28, 2005 4:38 pm
Hello. I am rather new at modeling with NONMEM. As a learning
opportunity, I'm trying to verify the results of a model that I
ran successfully using WinNonMix. I have a set of PD data (shown
below). The PK data has already been modeled and the PK parameters
are fixed in the control stream (shown below). The PD model is an
indirect response model with inhibition of production. When I
run the model, it goes through many, many iterations before it
fails. I have tried many different things such as changing
initial estimates and altering error structures, but I come
across the same errors: either there are rounding errors or
r matrix is algorithmically singular and non-positive definite.
I'm sure that the model can fit the data unless the algorithms
for the two programs are that different (has anyone ever seen
one program converge, but not the other?) If anyone could shed
some light on this issue, it would be much appreciated.
-Max
$PROB PK-PD DATA
$INPUT ID AMT TIME DV CMT
$DATA CXCRPD.csv
$SUBROUTINE ADVAN6 TOL=3
$MODEL NCOMP = 4
COMP = (GUT)
COMP = (CENTRAL)
COMP = (PERIPH)
COMP = (EFFECT)
$PK
;PK MODEL
V = 216
KA = 2.35
K20 = 0.55
K23 = 0.17
K32 = 0.074
S2 = V/1000
;PD MODEL
TVKIN = THETA(1)
TVKOUT = THETA(2)
TVIC50 = THETA(3)
KIN = TVKIN * EXP(ETA(1))
KOUT = TVKOUT * EXP(ETA(2))
IC50 = TVIC50 * EXP(ETA(3))
$DES
DADT(1) = -KA * A(1)
DADT(2) = (KA * A(1)) - ((K20 + K23) * A(2)) + (K32 * A(3))
DADT(3) = K23 * A(2) - K32 * A(3)
CP = A(2)/S2
COEF = 1 - (CP / (IC50 + CP))
DADT(4) = KIN * COEF - KOUT * A(4)
$THETA (0, 15); KIN
(0, 0.15); KOUT
(0, 5); IC50
$OMEGA 0.1; KIN
0.1; KOUT
0.1; IC50
$ERROR
Y = F + EPS(1)
$SIGMA 0.1
$EST MAXEVAL=5000 PRINT=1
$COV
$SCAT (RES WRES) VS TIME
Partial data set
ID AMT TIME DV CMT
101 10 0 . 1
101 . 0 6.4 4
101 . 2 5.7 4
101 . 4 4.9 4
101 . 6 3.8 4
101 . 8 3.5 4
101 . 12 2.2 4
101 . 24 2.4 4
102 10 0 . 1
102 . 0 3.566667 4
102 . 2 3 4
102 . 4 2.4 4
102 . 6 1.6 4
102 . 8 1.8 4
102 . 12 2.4 4
102 . 24 2.9 4
104 10 0 . 1
104 . 0 2.666667 4
104 . 2 2.2 4
104 . 4 1.6 4
104 . 6 1.4 4
104 . 8 1.4 4
104 . 12 2 4
104 . 24 2.3 4
Max Tsai, Ph.D.
Senior Scientist (DM/PK)
Schering-Plough Corporation
2015 Galloping Hill Road
K-15-2-2650
Kenilworth, NJ 07033-0530
(: (908) 740-3911
: (908) 740-2916
*: max.tsai@spcorp.com