Dear All,
I am working on Efavirenz (NNRTI dosed at 600 mg/day oral) population
pharmacokinetic data analysis (70 patients, 800 samples). Intense sampling
data on day 1 and 14. Individual day 1 and 14 analysis shows an increased
absorption in few patients plus increased clearance (auto induction) in
some of these patients and other patients (who revealed no change in
absorption). It is shown in earlier clinical studies that fed state results
in increased bioavailability. I have no covariate information which may
support this (available info Wt, Sex, total protein and TBR). Moreover the
absorption profiles are very haphazard with troughs and peaks in the
absorption phase, similar to what Bulitta et.al 2009, Pg 3462-3471
showed(Antimicrobial agents and chemotherapy). I have tried this
Michaelis-Menton model in absorption with and without lag time ( run
fails).
NONMEM runs for one C.M. with no variability in absorption and clearance
as an OBF value of 1498, 2CM 1390 (Vd is low 68 liters cv% 7 and Vd perip
CV% 64 ). 2 CM with autoinduction model (for all patients)is OBV 1284 (run
fails).
Efavirenz preclinical trials shows that dose variability results in non
linearity in absorption (Balani et.al 1998, vol 27, No.1), no clinical
trials supporting this info and dose is 600 mg/day in my data. Auto
induction was shown in clinical trials (zhu et.al). I have identified the
patients who have clearance and absorption variability and created two
columns in data sheet.Runs including the variability in absorption
and clearance for some patients completely fails with Vd as low as 30 and
perip as high as 400 liters.
My questions to NM community are
1. How to handle this data if we dont have any covariate information for
absorption and clearance variability.
2. Any references to model the data without crashing the runs
Your suggestions are appreciated.
Thank you very much in advance.
Regards,
Modeling variability in absorption and clearance (Auto induction)
Dear Shankar Lanke,
you can try modeling variable absorption with a chain of transit
compartments and estimating the mean absorption and to account for
interoccasion variability in relative oral bioavailability (e.g.
F1=1*EXP(ETA(1)).
Other things that you should account for with efavirenz in my opnion are
1.The liquid formulation of efavirenz has a lower bioavailability.
2. slow metabolizers may exist due to CYP2B6 polymorphism. If you do not
have genotype data available, you could try mixture modeling of multiple
subpopulations.
3. weight is a known covariate on clearance and volume of distribution
4. CYP3A4 autoinduction of efavirenz is debatable.
Cheers,
Rob ter Heine
------
R. ter Heine, PhD, PharmD
Hospital pharmacist i.t.
Meander Medical Center, Amersfoort, The Netherlands
T: +31-33-8502335
E: [email protected]
________________________________
Quoted reply history
Van: [email protected] [mailto:[email protected]]
Namens Shankar Lanke
Verzonden: maandag 14 mei 2012 17:33
Aan: nmusers
Onderwerp: [NMusers] Modeling variability in absorption and clearance
(Auto induction)
Dear All,
I am working on Efavirenz (NNRTI dosed at 600 mg/day oral) population
pharmacokinetic data analysis (70 patients, 800 samples). Intense
sampling data on day 1 and 14. Individual day 1 and 14 analysis shows an
increased absorption in few patients plus increased clearance (auto
induction) in some of these patients and other patients (who revealed no
change in absorption). It is shown in earlier clinical studies that fed
state results in increased bioavailability. I have no covariate
information which may support this (available info Wt, Sex, total
protein and TBR). Moreover the absorption profiles are very haphazard
with troughs and peaks in the absorption phase, similar to what Bulitta
et.al 2009, Pg 3462-3471 showed(Antimicrobial agents and
chemotherapy). I have tried this Michaelis-Menton model in absorption
with and without lag time ( run fails).
NONMEM runs for one C.M. with no variability in absorption and
clearance as an OBF value of 1498, 2CM 1390 (Vd is low 68 liters cv% 7
and Vd perip CV% 64 ). 2 CM with autoinduction model (for all
patients)is OBV 1284 (run fails).
Efavirenz preclinical trials shows that dose variability results in non
linearity in absorption (Balani et.al 1998, vol 27, No.1), no clinical
trials supporting this info and dose is 600 mg/day in my data. Auto
induction was shown in clinical trials (zhu et.al). I have identified
the patients who have clearance and absorption variability and created
two columns in data sheet.Runs including the variability in absorption
and clearance for some patients completely fails with Vd as low as 30
and perip as high as 400 liters.
My questions to NM community are
1. How to handle this data if we dont have any covariate information for
absorption and clearance variability.
2. Any references to model the data without crashing the runs
Your suggestions are appreciated.
Thank you very much in advance.
Regards,
***************************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.
Dear Amit,
Thank you very much for the abstract file and ctl file. Ette chapter is
very helpful too. A mixture model improved the OBF value, I will work on
the transit compartment model for absorption.
Thank you very much once again.
Regards
Quoted reply history
On Tue, May 15, 2012 at 4:27 AM, Amit Taneja <[email protected]>wrote:
> ** ** ** ** **
>
> Shankar,****
>
> ** **
>
> Here is a poster presented at PAGE 2011 wherein time dependent increase in
> Ka is parametrised using an Emax function. In principle, time dependent
> increase in clearance (metabolic enzyme induction) may be parametrised in
> this way.****
>
> ** **
>
> Here is another parametrisation for clearance autoinduction, using an
> exponential function discussed on the nm user’s list not too long ago. ***
> *
>
> ** **
>
> To be truly mechanistic, and describe the relationship between the parent
> compound, an active metabolite and enzyme kinetics you may want to consider
> the parametrisation discussed in the following paper (also recently
> discussed on this forum)****
>
> ** **
>
> A mechanism-based pharmacokinetic-enzyme model for cyclophosphamide
> autoinduction in breast cancer patients.****
>
> Hassan et al,****
>
> Br J Clin Pharmacol. 1999 Nov;48(5):669-77. 2.****
>
> ** **
>
> ** **
>
> Hope this helps****
>
> ** **
>
> Amit Taneja****
>
> Phd fellow****
>
> Pharmacology,****
>
> ****Leiden** **Amsterdam** **Center**** for Drug Research****
>
> **Leiden**, the ****Netherlands********
>
> ** **
>
> On 3/4/2012 5:33 PM, Wang, Xiaofeng wrote: ****
>
> Dear nmusers,
>
> I have a drug with clearance autoinduction. I have sparse data(three
> observations on day 1, two between day 7 and 14, and one on day 28). I am
> trying to run a time-dependent clearance model. I tried FO and FOCEI, but I
> always got unreasonable estimate for the initial clearance (CLI) which is
> about 0.15 L/h (from knowledge of previous studies, the reasonable initial
> clearance should be around 20 L/h and maximum induction occurs around day
> 7). Could someone give me some advice about my model and my data? Thank you.
>
> Xiaofeng
>
> The CTL file:
> $PROB
> $INPUT C ID TIME AMT DV MDV EVID ADDL II CMT
> $DATA C:/ IGNORE=C
> $SUBROUTINES ADVAN6 TOL=6
> $MODEL NCOMPARTMENTS=3
> COMP=(DEPOT,DEFDOSE)
> COMP=(CENTRAL,DEFOBS)
> COMP=(PERIP)
> $PK
> CLI=THETA(1)*EXP(ETA(1))
> CLSS=THETA(2)*EXP(ETA(2))
> KIN=THETA(3)*EXP(ETA(3))
> V2= THETA(4)*EXP(ETA(4))
> Q= THETA(5)*EXP(ETA(5))
> V3= THETA(6)*EXP(ETA(6))
> KA= THETA(7)*EXP(ETA(7))
> S2=V2
> K23=Q/V2
> K32=Q/V3
>
> $DES
> CL=CLSS-(CLSS-CLI)*EXP(-KIN*T)
> K20=CL/V2
> DADT(1)=-KA*A(1)
> DADT(2)=KA*A(1)+K32*A(3)-K23*A(2)-K20*A(2)
> DADT(3)=K23*A(2)-K32*A(3)
> $ERROR
> IPRE=LOG(1)
> IF(F.GT.0) IPRE=LOG(F)
> Y = IPRE+EPS(1)
> $EST METHOD=0 POSTHOC PRINT=10 MAX=9999 SIG=2 NOABORT MSFO=050.MSF
> $THETA
> (0, 20);[CLI]
> (0, 65);[CLSS]
> (0, 0.02) ;[KIN]
> (0, 45);[V2]
> (0, 5);[Q]
> (0, 58);[V3]
> (0, 0.2);[KA]
>
> $OMEGA .25 .25 .25 .25 .25 .25 .25
> $SIGMA .2
> $COV PRINT=E
> $TABLE ID TIME DV CLI CLSS KIN V2 Q V3 KA IPRE CWRES ONEHEADER NOPRINT
> FILE=050.TAB
> $TABLE ID TIME CLI CLSS KIN V2 Q V3 KA FIRSTONLY NOAPPEND NOPRINT
> FILE=050.PAR
> $TABLE ID ETA1 ETA2 ETA3 ETA4 ETA5 ETA6 ETA7 FIRSTONLY NOAPPEND NOPRINT
> FILE=050.ETA
> $TABLE ID TIME CLI CLSS KIN V2 Q V3 KA FIRSTONLY NOAPPEND NOPRINT
> FILE=PATAB050
>
> ****
>
> ** **
>
> ** **
> ------------------------------
>
> *From:* [email protected] [mailto:[email protected]]
> *On Behalf Of *Shankar Lanke
> *Sent:* Monday, May 14, 2012 6:44 PM
> *To:* [email protected]
> *Cc:* [email protected]
> *Subject:* Re: [NMusers] Modeling variability in absorption and clearance
> (Auto induction)****
>
> ** **
>
> Dear Heine,****
>
> ** **
>
> Thank you very much for your response on Efavirenz data (tablets), I have
> tried transit compartmental model and what I noticed is, it accounts for
> the delay in the absorption but not troughs within the absorption phase. (I
> am not sure if it is the sampling error in 26 patients or anything else). I
> dont have a genotype data, including wt as a covariate in clearance and Vd
> isnt helping much (OBF ~). Based on your experience have you ever come
> across a situation where continuous dosing may result in increased in
> absorption rate constant. Individual analysis results shown increased Ka in
> 60% of patients. ****
>
> ** **
>
> Thank you once again.****
>
> ** **
>
> Regards,****
>
> ** **
>
> On Mon, May 14, 2012 at 12:06 PM, <[email protected]> wrote:****
>
> Dear Shankar Lanke,****
>
> ****
>
> you can try modeling variable absorption with a chain of transit
> compartments and estimating the mean absorption and to account for
> interoccasion variability in relative oral bioavailability (e.g.
> F1=1*EXP(ETA(1)). ****
>
> ****
>
> Other things that you should account for with efavirenz in my opnion are**
> **
>
> ****
>
> 1.The liquid formulation of efavirenz has a lower bioavailability. ****
>
> 2. slow metabolizers may exist due to CYP2B6 polymorphism. If you do not
> have genotype data available, you could try mixture modeling of multiple
> subpopulations.****
>
> 3. weight is a known covariate on clearance and volume of distribution****
>
> 4. CYP3A4 autoinduction of efavirenz is debatable. ****
>
> ****
>
> Cheers,****
>
> Rob ter Heine****
>
> ****
>
> ------****
>
> R. ter Heine, PhD, PharmD****
>
> Hospital pharmacist i.t.****
>
> **Meander** **Medical** **Center**, **Amersfoort**, The ****Netherlands***
> *****
>
> T: +31-33-8502335****
>
> E: [email protected]****
>
> ** **
> ------------------------------
>
> *Van:* [email protected] [mailto:[email protected]]
> *Namens *Shankar Lanke
> *Verzonden:* maandag 14 mei 2012 17:33
> *Aan:* nmusers
> *Onderwerp:* [NMusers] Modeling variability in absorption and clearance
> (Auto induction)****
>
> Dear All,****
>
> ****
>
> I am working on Efavirenz (NNRTI dosed at 600 mg/day oral) population
> pharmacokinetic data analysis (70 patients, 800 samples). Intense sampling
> data on day 1 and 14. Individual day 1 and 14 analysis shows an increased
> absorption in few patients plus increased clearance (auto induction) in
> some of these patients and other patients (who revealed no change in
> absorption). It is shown in earlier clinical studies that fed state results
> in increased bioavailability. I have no covariate information which may
> support this (available info Wt, Sex, total protein and TBR). Moreover the
> absorption profiles are very haphazard with troughs and peaks in the
> absorption phase, similar to what Bulitta et.al 2009, Pg 3462-3471
> showed(Antimicrobial agents and chemotherapy). I have tried this
> Michaelis-Menton model in absorption with and without lag time ( run
> fails). ****
>
> ****
>
> NONMEM runs for one C.M. with no variability in absorption and clearance
> as an OBF value of 1498, 2CM 1390 (Vd is low 68 liters cv% 7 and Vd perip
> CV% 64 ). 2 CM with autoinduction model (for all patients)is OBV 1284 (run
> fails). ****
>
> ****
>
> Efavirenz preclinical trials shows that dose variability results in non
> linearity in absorption (Balani et.al 1998, vol 27, No.1), no clinical
> trials supporting this info and dose is 600 mg/day in my data. Auto
> induction was shown in clinical trials (zhu et.al). I have identified
> the patients who have clearance and absorption variability and created two
> columns in data sheet.Runs including the variability in absorption
> and clearance for some patients completely fails with Vd as low as 30 and
> perip as high as 400 liters.****
>
> ****
>
> My questions to NM community are****
>
> 1. How to handle this data if we dont have any covariate information for
> absorption and clearance variability.****
>
> 2. Any references to model the data without crashing the runs****
>
> ****
>
> Your suggestions are appreciated.****
>
> ****
>
> Thank you very much in advance.****
>
> ****
>
> Regards,****
>
> ** **
>
> ** **
> ------------------------------
>
> 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.
> Assistant Professor
> Department of Pharmaceutical Sciences
> ****College** of **Pharmacy****
> The ****University** of **Findlay****
> (C) 678-232-3567
> (O) 419-434-5448
> Fax# 419-434-4390****
>
--
Regards,
Shankar Lanke Ph.D.
Assistant Professor
Department of Pharmaceutical Sciences
College of Pharmacy
The University of Findlay
(C) 678-232-3567
(O) 419-434-5448
Fax# 419-434-4390