associatin/dissociation model
From: musor000@optonline.net
Subject: [NMusers] associatin/dissociation model
Date: Wed, 05 Oct 2005 22:16:54 -0400
Hello Team,
I try to fit an association/dissociation model. It does not converge. I tried
different models of errors and different starting values. Here are questions:
1. Is it better to use moles or grams? I use grams. Obviously, model will
look slighly different if we use moles.
2. If there is huge difference between Kon and Koff, can it cause problems?
Thanks!
Pavel
$PROBLEM Population SC dosing.
$INPUT ID TIME DOSE=AMT CP=DV MDV EVID CMT
;CMT: 1-INJ SITE, 2-T, 3-V, 4-TV
;EVID: 0-OBSERVATION, 1-DOSE, 4-DOSE & RESET.
$DATA PKCADI01.DA IGNORE #
$SUBROUTINES ADVAN8 TOL=8
;TOL - THE NUMBER OF ACCURATE DIGITS
$MODEL COMP=(INJ INITIALOFF)
COMP=(T)
COMP=(V)
COMP=(TV)
COMP=(AUC)
$EST MAXEVAL=9999 SIG=3 NOABORT PRINT=30
METHOD=1 POSTHOC INTERACTION ;REPEAT
$PK
VD =THETA(1)*DEXP(ETA(1))
KA =THETA(2)
KON =THETA(3)
KOFF =THETA(4)
RIN =THETA(5)
CLV =THETA(6)*DEXP(ETA(2))
CLT=CLV
;CLT =THETA(7)*DEXP(ETA(3))
CLTV=CLT
KD=KOFF/KON
KET =CLT /VD
KETV =CLTV/VD
KEV =CLV /VD
;HL =0.693147/KE
QT=0.912
QV=1-QT
F3=RIN/KEV
S4=VD
S2=VD ;The amount A in the observation compartment
;at the time of observation, divided by the
;value of a parameter S, is used as the prediction.
$DES
DADT(1) = - KA*A(1) ; Inj Site
DADT(2) = - KET *A(2) - QT*KON*A(2)*A(3) + QT*KOFF*A(4) + KA*A(1) ; T
DADT(3) = RIN - KEV *A(3) - QV*KON*A(2)*A(3) + QV*KOFF*A(4) ; V
DADT(4) = - KETV*A(4) + KON*A(2)*A(3) - KOFF*A(4) ; TV
DADT(5) = A(2) ; AUC
AUC=A(5)
IF (EVID.EQ.4) AUC=0
A1=A(1)
A2=A(2)
A3=A(3)
A4=A(4)
$THETA
(1,80,300) ;VD - VOLUME OF DISTRIBUTION
(0.1,3,30) ;KA - ABSORBTION COEFFICIENT
(0.1,10,1000) ;KON - ASSOCIATION RATE CONSTANT
(0.1,1,1000) ;KOFF - DISSOCIATION RATE CONSTANT
(0.002,0.04,0.1) ;RIN - PRODUCTION RATE OF V
(0.05,5,30) ;CLV - CLEARANCE
;(0.05,0.3,5) ;CLT - CLEARANCE
;(0.05,0.3,5) ;CLTV - CLEARANCE
$ERROR
CONC=F
Y=CONC+ERR(1)
;Y=CONC*EXP(ERR(1)) + ERR(2) ;CONCENTRATION ERROR
;IF (CMT.EQ.2) Y=CONC*EXP(ERR(1)) + ERR(2) ;CONCENTRATION ERROR
;IF (CMT.EQ.4) Y=CONC*EXP(ERR(1)) + ERR(3) ;CONCENTRATION ERROR
IPRE=CONC ;individual-specific prediction
IRES=DV-IPRE ;individual-specific residual
IWRE=IRES/CONC ;individual-specific weighted residual
$OMEGA DIAGONAL(3) 50 0.6 0.3 ;VARIANCE OF THETA
$SIGMA 50 ;0.5 ;VARIANCE OF EPS