RE: Autoinduction model - An increased clearance(day 1- 14)
Dear Shankar,
I would guess that it is the term (t-tlag) which is negative in your code as
long as t<tlag. Add code specifying that CL=pre-induced CL as long as
t.LE.tlag.
Although it physiologically makes sense to have a lagtime for the induction,
the estimation of this parameter is dependent on the information in the data
you are using. Try to fit a model without the lag time to see if it is
significant. In your code you are also adding the random effect only on CL
which says that the between patient variability is the same at pre-induction
state as at induced state. It could be worth exploring if the model supports
separate IIV in pre-induced CL compared to induced CL. The time to steady
state of induction which is determined by kout in your model could also be
different between individuals.
There are also example of more semi-physiological models for autoinduction
that could be worth exploring
*Hassan et al. Br J Clin Pharmacol. 1999 Nov;48(5):669-77. A mechanism-based
pharmacokinetic-enzyme model for cyclophosphamide autoinduction in breast
cancer patients.
In this enzyme turnover model you set the enzyme amount to 1 at baseline and
estimate the change from baseline. There is no need to have information
about the enzyme levels.
You are also using only one transit absorption compartment, but it could be
worth exploring a more complex transit model where you estimate the number
of transit compartments unless you already have explored this (Savic et al J
Pharmacokinet Pharmacodyn. 2007 Oct;34(5):711-26)
Best regards,
Ulrika
Ulrika Simonsson, PhD
Assoc Prof of Pharmacometrics
Uppsala Pharmacometrics
Department of Pharmaceutical Biosciences
Uppsala University
BMC, Box 591, 751 24 Uppsala
Sweden
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Shankar Lanke
Sent: den 28 mars 2011 15:53
To: [email protected]
Subject: [NMusers] Autoinduction model - An increased clearance(day 1- 14)
Dear All,
I am working on a Pop PK data where the patients are treated with HIV drug.
An autoinduction is involved with prolonged administration of the drug. An
increased CL is expected from day 1 to day 14.
We have intense data on day 1 and day 14 with sparse data between. Since a
lag period is involved for the induction I used the equation CL = CLinduced
-(CLinduced - CLpre)*exp(-kout*(t-Tlag)) described by Johan Gabrielsson as
more appropriate.
Also when I included a lag period for absorption in my earlier model my fits
are better and OBF decreased by 200.
However the final model with or without lag time for absorption + auto
induction model is either terminated or covariance step is being aborted.
I changed the initial estimates several times but still no luck. Though the
Auto induction model aborts the fits are better than the lag time model
however the estimates for Vd are 4 fold less than the expected.
I appreciate your input and suggestions. Here is my code.
$SUBROUTINES ADVAN13 TRANS1 TOL=5 ;(I used ADVAN6 too)
$MODEL
NPAR=9 NCOMP=4
COMP=(DEPOT,DEFDOSE)
COMP=(LAG)
COMP=(OBSV,DEFOBS)
COMP=(PERIP)
$PK
CLP=THETA(1)
CLI=THETA(6)
KOUT=THETA(7)
TLAG=THETA(8)*EXP(ETA(6))
TVCL=CLI-(CLI-CLP)*EXP(-KOUT*(TIME-TLAG))
CL=TVCL*EXP(ETA(1))
TVV2=THETA(2)
V2=TVV2*EXP(ETA(2))
TVQ=THETA(3)
Q=TVQ*EXP(ETA(3))
TVV3=THETA(4)
V3=TVV3*EXP(ETA(4))
TVKA=THETA(5)
KA=TVKA*EXP(ETA(5))
TVALAG1=THETA(9)
ALAG1=TVALAG1*EXP(ETA(7))
S3=V2
$DES
K=CL/V2
K23=Q/V2
K32=Q/V3
DADT(1)=-KA*A(1)
DADT(2)=KA*A(1)-A(2)/ALAG1
DADT(3)=A(2)/ALAG1-K23*A(3)-K*A(3)+K32*A(4)
DADT(4)=K23*A(3)-K32*A(4)
$ERROR
DEL=0
IF (F.LE.0.0001) DEL=1
IPRE=F
W1= 1
W2= F
IRES= DV-IPRE
IWRE=IRES/(W1+W2)
Y = F + W1*ERR(1) + W2*ERR(2)
DV2=ABS(V2-TVV2)
$EST METHOD=1 INTERACTION PRINT=5 MAX=9999 SIG=3 MSFO=JLM.MSF
$THETA
(0, 6);[CLP]
(0, 90);[V2]
(0, 19);[Q]
(0, 200);[V3]
(0, 0.16);[KA]
(0, 8);[CLI]
(0, 0.001);[KOUT]
(0, 250);[TLAG]
(0, 0.3);[ALAG1]
$OMEGA
0.23 ;[CL] omega(1,1)
0.18;[V2] omega(2,2)
0 FIXED ;[Q] omega(3,3)
0.42;[V3] omega(4,4)
0.19;[KA] omega(5,5)
0.09;[TLAG for Ka]
0.1;[ALAG1 for CLI]
$SIGMA
0.06 ;[P] sigma(1,1)
0.09 ;[A] sigma(2,2)
$COV MATRIX=S
Regards,
Shankar Lanke Ph.D.
University at Buffalo
Office # 716-645-4853
Fax # 716-645-2886
Cell # 678-232-3567