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
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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informatie.
Shankar,
A couple of thoughts that may help.
(1) If the model is terminating with a note that states something along the lines of 'Infinite value of objective function', this indicates that the estimated value of TLAG for an individual is occurring at the time of a sample.
Potential solutions:
(a) Remove the eta from alag. With only sparse data between Day 1 and Day 14, the ability to discern individual differences in this parameter is probably limited. (b) If you do have enough data to see differences in individuals, ignore samples that occur very close to the estimated TLAG for an individual.
(c) Try a different distribution for IIV of this parameter
(2) Re-parameterize CLinduced vs. CLpre. There is nothing in the model or control stream that prevents CLpre > CLinduced at the typical value level or the individual level. This could occur during the search causing termination.
Re-parmeterization:
CLinduced = CLpre + ThetaN
TVCL=CLinduced - (CLinduced - CLpre)*exp(-kout(t-tlag) becomes
TVCL=CLpre + thetaN(1-exp(-kout(t-tlag))
For typical value of thetaN use a lower bound of 0
(3) The current model also assumes that all subjects have the amount of change in CLinduced vs. CLpre. So you may want to try the following which also allows the change in CL to be continuous between TIME steps.
This will allow NONMEM to integrate more smoothly around time=TLAG.
$PK
CLP = THETA(N)*EXP(ETA(A))
CLI = THETA(N+1)*EXP(ETA(B)) ;represents the increase from CLP
$DES
;T in the $DES block represents continuous time
;TIME represents the discrete time values in the input dataset
CL = CLP + CLI*(1-EXP(-KOUT*(T-TLAG))
Good Luck,
Luann Phillips
Director, PK/PD
Cognigen Corporation
(716) 633-3463 ext. 236
Shankar Lanke wrote:
> 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
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
Hi Shankar,
We published an autoinduction model a few years back (Gordi et al., Br J Clin
Pharmacol. 2005;59(2):189-98). The original paper was based on saliva samples
but the model worked well using plasma data (Asimus and Gordi, Br J Clin
Pharmacol. 2007;63(6):758-62). We also used it to describe the PK of the
compound in a PK/PD model successfully (Gordi et al., Br J Clin Pharmacol.
2005;60(6):594-604). The basic principal is similar to most other models, i.e.,
an indirect response model describes the enzyme compartment and the effect of
the drug on production rate of the enzymes. One major difference is that we
introduced a liver compartment into the system. In all other models I have
seen, plasma concentrations drive the induction effect. This means that as time
passes by, and drug concentrations in plasma decrease due to induction, you
have less and less induction. Having the liver compartment, we allow drug
concentrations (practically amounts, see the control stream below) in the liver
upon absorption to induce the enzyme. This means that subsequent doses of the
drug will have the same inducing effect, which should be closer to the reality.
I am showing a part of the control stream below. In our paper we estimated
interoccasional variability and I'll be more than happy to help you with
implementing that, if you want to. A couple of notes to make the coding easier
to follow: this is based on salivary data, where concentrations reflected the
free fraction of those in plasma (fu=14%), hence the S3 adjustment. The model
also includes a precursor compartment to capture the induction lag time.
Depending on your data, you may want to test a model without it. Finally, the
model includes a nonlinearity in the intrinsic clearance and extraction ratio
(and thereby bioavailability) of the drug, which was known for this particular
compound. You should obviously test a simpler model.
Let me know if you have any questions.
Toufigh
$SUBROUTINE ADVAN6 TRANS1 TOL=5
$MODEL NCOMP=5
COMP=(GUT DEFDOSE)
COMP=LIVER
COMP=(SALIVA DEFOBS)
COMP=ENZPOOL
COMP=PREC
$PK
....
TEN = THETA(1)
KEN = 0.693/TEN
SIND = THETA(2) ;slope for enzyme induction
CLINT = THETA(3)*EXP(ETA(1))
BFL = 0.63*WT ;l/h for 55kg
VS = THETA(4)*EXP(ETA(2))
.....
ALAG1 = THETA(5)
KA = THETA(6)
FU = 0.14
KM = THETA(7)
VL = 1
TPEN = THETA(8)
KPEN = 1/TPEN
S3 = VS/FU
BASENZ= 1
BASEPR= KEN/KPEN
F4 = BASENZ
F5 = BASEPR
$DES
BOX = CLINT*A(4)*KM/(A(2)+KM)
E = FU*BOX/(BFL+FU*BOX) ;extraction ratio
BAV = 1-E ; bioavailability
CLH = BFL*E
DADT(1) = -KA*A(1)
KS0 = BFL/VS
DADT(2) = KA*A(1)-BFL*BAV*A(2)/VL+KS0*A(3)-BFL*E*A(2)/VL
DADT(3) = BFL*BAV*A(2)/VL-KS0*A(3)
DADT(4) = KPEN*A(5)-KEN*A(4)
DADT(5) = KEN*(1+SIND*A(2))-KPEN*A(5)
Toufigh
Toufigh Gordi, PhD
President, PK/PD and Clinical Pharmacology Services
Rosa & Co. LLC: www.rosaandco.com
E-mail: [email protected]
Tel.: 408-480-7314
Fax: 408-370-9810
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Quoted reply history
On Mar 28, 2011, at 6:52 AM, Shankar Lanke wrote:
> 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
>
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
Dear Shankar,
You don't need to have information about enzyme levels or precursor to use
the model mentioned by Rob, the idea of it is only to have a more
physiological mechanism for the delay (not a lag but something that develops
with a first-order delay) and magnitude (which is dependent on the
concentration of drug rather than an on/off ). The number of parameters is
no more than the one you're using now, but avoids the change-point which
often cause numerical problems.
Best regards,
Mats
Mats Karlsson, PhD
Professor of Pharmacometrics
Dept of Pharmaceutical Biosciences
Uppsala University
Sweden
Postal address: Box 591, 751 24 Uppsala, Sweden
Phone +46 18 4714105
Fax + 46 18 4714003
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Shankar Lanke
Sent: Monday, March 28, 2011 4:51 PM
To: [email protected]
Cc: [email protected]
Subject: Re: [NMusers] Autoinduction model - An increased clearance(day 1-
14)
Dear Rob ter Heine,
I am working with Efavirenz, I working with 66 patients, 924 data points,
intense on day 1 and 14 and a trough con in between the two weeks.
I looked into the Physiological model presented by Dr. Karlsson earlier but
I did not used it since I dont have any information about ENZYME comp or
precursor.
I used the reasonable estimates based on earlier literature and aslo I tried
NPD approach.
Thank you very much Rob ter Heine, I appreciate your input.
On Mon, Mar 28, 2011 at 10:36 AM, <[email protected]> wrote:
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
_____
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
_____
De informatie in dit e-mail bericht is uitsluitend bestemd
voor de geadresseerde. Verstrekking aan en gebruik door
anderen is niet toegestaan. Door de elektronische verzending
van het bericht kunnen er geen rechten worden ontleend aan de
informatie.
_____
--
Regards,
Shankar Lanke Ph.D.
University at Buffalo
Office # 716-645-4853
Fax # 716-645-2886
Cell # 678-232-3567
Dear Shankar,
As I read it, if your autoinduction is complete after 14 days, you do
not really have the data describing the autoinduction process, except
for a single trough sample, which is probably not enough considering the
inter-occasion variability in F and probably only a marginal or absent
autoinduction. Is the clearance statistically different from day 1 on
day 14 and onward? In the 2NN study (Antivir Ther. 2005;10(1):145-55.)
only a 10% difference in efavirenz clearance was found after
autoinduction. Perhaps you should pragmatically test if clearance on day
14 is different from clearance on day 1 by estimating
Clearance day 14 and onward = (clearance day 1)*Theta.
If it does not improve your model, the autoinduction is probably not
even there. Let the data decide.
Cheers,
Rob
----------
Rob ter Heine, PhD, PharmD
Hospital pharmacy resident
Meander Medical Center, Amersfoort, The Netherlands
T: +31-33-8502335
E: [email protected]
________________________________
Quoted reply history
Van: Shankar Lanke [mailto:[email protected]]
Verzonden: maandag 28 maart 2011 16:51
Aan: Heine, R. ter (Apotheek Algemeen/Management)
CC: [email protected]
Onderwerp: Re: [NMusers] Autoinduction model - An increased
clearance(day 1- 14)
Dear Rob ter Heine,
I am working with Efavirenz, I working with 66 patients, 924 data
points, intense on day 1 and 14 and a trough con in between the two
weeks.
I looked into the Physiological model presented by Dr. Karlsson earlier
but I did not used it since I dont have any information about ENZYME
comp or precursor.
I used the reasonable estimates based on earlier literature and aslo I
tried NPD approach.
Thank you very much Rob ter Heine, I appreciate your input.
On Mon, Mar 28, 2011 at 10:36 AM, <[email protected]> wrote:
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
________________________________
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
________________________________
De informatie in dit e-mail bericht is uitsluitend bestemd
voor de geadresseerde. Verstrekking aan en gebruik door
anderen is niet toegestaan. Door de elektronische verzending
van het bericht kunnen er geen rechten worden ontleend aan de
informatie.
________________________________
--
Regards,
Shankar Lanke Ph.D.
University at Buffalo
Office # 716-645-4853
Fax # 716-645-2886
Cell # 678-232-3567
***************************DISCLAIMER****************************
De informatie in dit e-mail bericht is uitsluitend bestemd
voor de geadresseerde. Verstrekking aan en gebruik door
anderen is niet toegestaan. Door de elektronische verzending
van het bericht kunnen er geen rechten worden ontleend aan de
informatie.