Hi Ayappa,
thanks for your helpful and interesting suggestion! Would you include 2- and
48-h i.v. data from the beginning?
All subjects in my dataset received multiple cycles of treatment, I will
also try to include IOV on infusion duration to obtain EBEs for duration of
each administration.
Again, thanks a lot!
Regards,
Patricia
On Thu, 6 Aug 2020 23:20:25 -0500
Ayyappa Chaturvedula <ayyapp...@gmail.com> wrote:
Hi Patricia,
If the stopping of pump is an artifact and you are
interested in getting parent-metabolite parameters
without bias, I would approach in a progressive manner:
1. I would model parent IV data alone with an eta on
Duration and then fix duration parameter with EBE (for
doses that have this problem).
2. I would combine parent IV and oral data with fixed
EBE of duration to see if other parameters are
comparable to explain combined data. You may need to
have oral bioavailability here.
3. I would extend the model to include metabolite
compartments and estimate all parameters with duration of
EBEs continued to be fixed.
4. Your current model may also be tried with fixed
duration EBEs for VPC. You may get similar model from
steps 1-3 but given complex model going on , I would
check in different ways to be confident.
I also welcome comments/suggestions from the experts on
this approach.
Regards,
Ayyappa
On Aug 6, 2020, at 1:43 AM, Patricia Kleiner
<pkle...@uni-bonn.de> wrote:
Dear all,
first of all, thanks a lot for your fast and helpful
replies and efforts. I am currently running the model
with your suggested expressions to describe variability
on infusion duration.
To answer you question, Ayyappa, I intend to simulate
population from my model and I see that including
variability on infusion duration would not reasonable.
Using an individual modeling approach to estimate
duration and fix in population model is an interesting
suggestion, but unfortunately I think observations next
to and after end of infusion were too sparse.
My dataset also includes concentration measurements
after daily oral intake and 2-hour infusion of the drug.
An active metabolite of the drug is also captured in my
model. Both compounds could be best described with a
three compartment model. Visual predictive checks
demonstrate that the parent drug measured after 2-hour
infusion is well described by the model (after oral
administration, no parent drug above lloq was observed in
plasma), but after 48-hour long-term infusion,
variability is highly inflated (please see attached PNG
file).
This is why I was thinking about to implement
variability on infusion duration of the long-term
infusion, but I am also thankful for any other suggestion
to improve the model fit. RE is modelled as additive
error in the log space.
Thanks and best regards,
Patricia
$SUBROUTINES ADVAN6 TOL=5
$MODEL
NCOMP=7
COMP=(DEPOT,DEFDOSE)
COMP(CENTPRNT)
COMP (PERPRNT1)
COMP (PERPRNT2)
COMP (CENTMETB)
COMP (PERMETB1)
COMP (PERMETB2)
$PK
;; PK Parameters
TVKA=THETA(1)
KA=TVKA*EXP(ETA(6))
TVV2=THETA(2)
V2=TVV2*EXP(ETA(3))
TVCL1=THETA(3)
CL1=TVCL1*EXP(ETA(1))
TVQ3=THETA(4)
Q3=TVQ3
TVV3=THETA(5)
V3=TVV3
TVQ4=THETA(6)
Q4=TVQ4
TVV4=THETA(7)
V4=TVV4
FMET=0.6
F1=THETA(20)
IF(STDY.EQ.2) F1=(0.8*FMET)
TVV5=THETA(8)
V5=TVV5*EXP(ETA(4))
TVQ6=THETA(9)
Q6=TVQ6
TVV6=THETA(10)
V6=TVV6*EXP(ETA(5))
TVQ7=THETA(11)
Q7=TVQ7
TVV7=THETA(12)
V7=TVV7*EXP(ETA(7))
TVCL2=THETA(13)
CL2=TVCL2*EXP(ETA(2))
TVALAG1=THETA(14)
ALAG1=TVALAG1
;;scaling parameter
S2=V2/1000
S5=V5/1000
;;microconstants
K23=Q3/V2
K32=Q3/V3
K24=Q4/V2
K42=Q4/V4
K56=Q6/V5
K65=Q6/V6
K57=Q7/V5
K75=Q7/V7
K50=CL2/V5
$DES
C2=A(2)/S2
C5=A(5)/S5
DADT(1) = - KA*A(1)
DADT(2) = - K23*A(2) + K32*A(3) - K24*A(2) + K42*A(4)
-((1-FMET)*((CL1/V2)*A(2))) - (FMET*((CL1/V2)*A(2)))
DADT(3) = K23*A(2) - K32*A(3)
DADT(4) = K24*A(2) - K42*A(4)
DADT(5) = KA*A(1) + (FMET*((CL1/V2)*A(2))) - K50*A(5)
- K56*A(5) + K65*A(6) - K57*A(5) + K75*A(7)
DADT(6) = K56*A(5) - K65*A(6)
DADT(7) = K57*A(5) - K75*A(7)
$ERROR
IPRED=-5
IF(F.GT.0) THEN
IPRED=LOG(F)
ENDIF
IF(STRAT1.EQ.1) THEN ; PRNT after 2 hour infusion
W=SQRT(THETA(15)**2)
Y = (IPRED + W*EPS(1))
ENDIF
IF(STRAT1.EQ.2) THEN ; METB after 2 hour infusion
W=SQRT(THETA(16)**2)
Y = (IPRED + W*EPS(2))
ENDIF
IF(STRAT1.EQ.3) THEN; PRNT after 48 hour infusion
W=SQRT(THETA(17)**2)
Y = (IPRED + W*EPS(3))
ENDIF
IF(STRAT1.EQ.4) THEN; METB after 48 hour infusion
W=SQRT(THETA(18)**2)
Y = (IPRED + W*EPS(4))
ENDIF
IF(STRAT1.EQ.5) THEN; METB after oral administration
W=SQRT(THETA(19)**2)
Y = (IPRED + W*EPS(5))
ENDIF
IRES = DV-IPRED
DEL=0
IF(W.EQ.0) DEL=0.0001
IWRES = (IRES/(W+DEL))
On Wed, 5 Aug 2020 14:55:02 -0500
Ayyappa Chaturvedula <ayyapp...@gmail.com> wrote:
Hi Patricia,
What is the purpose of your modeling exercise? I am not
sure your scenario could be assigned to any particular
distribution. If you intend to simulate population from
the model, then your assumptions would not be reasonable.
If you have rich data, you may try individual modeling
approach to estimate duration and fix in population
model. Regards,
Ayyappa
On Aug 5, 2020, at 1:04 PM, Bill Denney
<wden...@humanpredictions.com> wrote:
Similar to Leonid's solution, you can try using an
exponential distribution:
D1 = DUR*(1-EXP(-EXP(ETA(1))))
The exponential within an exponential gives left skew
and ensures that D1 ≤
DUR.
For subjects who you know had an incomplete infusion
duration, I would add
an indicator variable (1 if incomplete, 0 if full
duration) so that the
subjects with complete duration have the known complete
duration.
D1 = DUR*(1 - Incomplete*EXP(-EXP(ETA(1))))
Thanks,
Bill
-----Original Message-----
From: owner-nmus...@globomaxnm.com
<owner-nmus...@globomaxnm.com> On Behalf
Of Leonid Gibiansky
Sent: Wednesday, August 5, 2020 12:51 PM
To: Patricia Kleiner <pkle...@uni-bonn.de>;
nmusers@globomaxnm.com
Subject: Re: [NMusers] Variability on infusion duration
may be
D1=DUR*EXP(ETA(1))
IF(D1.GT.DocumentedInfusionDuration)
D1=DocumentedInfusionDuration
On 8/5/2020 12:18 PM, Patricia Kleiner wrote:
Dear all,
I am developing a PK model for a drug administered as a
long-term
infusion of 48 hours using an elastomeric pump. End of
infusion was
documented, but sometimes the elastomeric pump was
already empty at
this time. Therefore variability of the concentration
measurements
observed at this time is quite high.
To address this issue, I try to include variability on
infusion
duration assigning the RATE data item in my dataset to
-2 and model
duration in the PK routine. Since the "true" infusion
duration can
only be shorter than the documented one, implementing
IIV with a
log-normal distribution
(D1=DUR*EXP(ETA(1)) cannot describe the situation.
I tried the following expression, where DUR ist the
documented
infusion
duration:
D1=DUR-THETA(1)*EXP(ETA(1))
It works but does not really describe the situation
either, since I
expect the deviations from my infusion duration to be
left skewed. I
was wondering if there are any other possibilities to
incorporate
variability in a more suitable way? All suggestions will
be highly
appreciated!
Thank you very much in advance!
Patricia
<VPC_48h_infusion.PNG>
