Re: Autoinduction model - An increased clearance(day 1- 14)
Dear Shankar,
In addition to what's been already said, you have a couple of problems
in your code. ALAG1 is a reserved name in NONMEM. You use it as
1/Ktransit for your transit absorption compartment. But NONMEM also
uses it to delay a dose entering compartment 1 by ALAG1 time.
The second problem is how you formulate dependence of CL on TIME. Your
CL changes at the observation and dosing times only, not continuously.
You essentially substitute you function with piece-wise constant
function that change value at each observation and dosing time. So, the
results will depend on how often and where the observation times are.
Non-continuous parameters are also a sourse of numerical problems. To
make CL a continuous function of time, you need to have the function
inside $DES and use T rather than TIME.
Regards,
Katya
Ekaterina Gibiansky, Ph.D.
CEO&CSO, QuantPharm LLC
Web: www.quantpharm.com
Email: [email protected]
Quoted reply history
On 3/28/2011 11:15 AM, Ulrika Simonsson wrote:
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
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