RE: Autoinduction model - An increased clearance(day 1- 14)
Dear Shankar,
How rich is your dataset? In other words: do you have enough data
troughout the induction period to estimate the lagtime? You could, for
example try to fix the lagtime to a reasonable time and estimate the
inter-individual variability. Another way of estimating the
autoniduction is more physiologically based with a theoretical enzyme
compartment. For example, see:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2014348/figure/fig01/
Which drug PK are you modelling? Most likely it is a non-nucleoside
reverse transcriptase inhibitor. The cyp3a4 autoinduction with efavirenz
is debatable and less profound than autoinduction with, for example,
nevirapine.
Sincerely,
Rob ter Heine
________________________________
Quoted reply history
Van: [email protected] [mailto:[email protected]]
Namens Shankar Lanke
Verzonden: maandag 28 maart 2011 15:53
Aan: [email protected]
Onderwerp: [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
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