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Re: Variability on infusion duration

From: Ayyappa Chaturvedula <ayyappach>
Date: Thu, 6 Aug 2020 23:20:25 -0500

Hi Patricia,
If the stopping of pump is an artifact and you are interested in getting par=
ent-metabolite parameters without bias, I would approach in a progressive ma=
nner:
1. I would model parent IV data alone with an eta on Duration and then fix d=
uration parameter with EBE (for doses that have this problem).
2. I would combine parent IV and oral data with fixed EBE of duration to se=
e 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 a=
ll parameters with duration of EBEs continued to be fixed.
4. Your current model may also be tried with fixed duration EBEs for VPC. Yo=
u may get similar model from steps 1-3 but given complex model going on , I w=
ould 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 <pklei05
>
> 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 v=
ariability on infusion duration.
>
> To answer you question, Ayyappa, I intend to simulate population from my m=
odel and I see that including variability on infusion duration would not rea=
sonable.
> Using an individual modeling approach to estimate duration and fix in popu=
lation model is an interesting suggestion, but unfortunately I think observa=
tions next to and after end of infusion were too sparse.
> My dataset also includes concentration measurements after daily oral intak=
e and 2-hour infusion of the drug. An active metabolite of the drug is also c=
aptured in my model. Both compounds could be best described with a three com=
partment model. Visual predictive checks demonstrate that the parent drug me=
asured after 2-hour infusion is well described by the model (after oral admi=
nistration, 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 dura=
tion of the long-term infusion, but I am also thankful for any other suggest=
ion to improve the model fit. RE is modelled as additive error in the log sp=
ace.
>
> 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/V=
2)*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 <ayyappach
>> Hi Patricia,
>> What is the purpose of your modeling exercise? I am not sure your scenari=
o could be assigned to any particular distribution. If you intend to simulat=
e population from the model, then your assumptions would not be reasonable. I=
f you have rich data, you may try individual modeling approach to estimate d=
uration and fix in population model. Regards,
>> Ayyappa
>>>> On Aug 5, 2020, at 1:04 PM, Bill Denney <wdenney
rote:
>>> Similar to Leonid's solution, you can try using an exponential distribut=
ion:
>>> D1 = DUR*(1-EXP(-EXP(ETA(1))))
>>> The exponential within an exponential gives left skew and ensures that D=
1 ≤
>>> DUR.
>>> For subjects who you know had an incomplete infusion duration, I would a=
dd
>>> 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-nmusers
alf
>>> Of Leonid Gibiansky
>>> Sent: Wednesday, August 5, 2020 12:51 PM
>>> To: Patricia Kleiner <pklei05
>>> 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>
Received on Fri Aug 07 2020 - 00:20:25 EDT

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