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

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

am currently running the model with your suggested expressions to describe v=

ariability on infusion duration.

odel and I see that including variability on infusion duration would not rea=

sonable.

lation model is an interesting suggestion, but unfortunately I think observa=

tions next to and after end of infusion were too sparse.

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).

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.

2)*A(2))) - (FMET*((CL1/V2)*A(2)))

*A(6) - K57*A(5) + K75*A(7)

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,

rote:

ion:

1 ≤

dd

alf

Received on Fri Aug 07 2020 - 00:20:25 EDT