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Re: [NMusers] Different results with ADVAN4 and ADVAN6

From: Sven Mensing <sv.mensing_at_gmail.com>
Date: Mon, 12 Dec 2016 14:01:53 +0100

Hi,

I prefer to code my peripheral compartment using

PERI = K23*A(2) - K32*A(3)
DADT(1) = -KA*A(1)
DADT(2) = KA*A(1) - K*A(2) - PERI
DADT(3) = PERI

helps avoinding errors and I tend to believe it saves some runtime :)

Kind regards

Sven


2016-12-12 13:02 GMT+01:00 Silber Baumann, Hanna <
hanna.silber_baumann_at_roche.com>:

> Jakob, Niels,
> Thank you for finding the typo. That was the problem. I had 2 people
> checking the code for me in addition to myself. Clearly sometimes, fresh
> eyes is what is needed.
>
> Have a nice day all of you.
>
> -Hanna
>
> On Mon, Dec 12, 2016 at 11:58 AM, Jakob Ribbing <
> jakob.ribbing_at_pharmetheus.com> wrote:
>
>> Hi Hanna,
>>
>> I did not check the whole model code, but could it be a typo in the rate
>> for re-distribution that produces the difference?
>>
>> DADT(3) = K23*A(2) - *K23**A(3)
>>
>> Kind regards
>>
>> Jakob
>>
>> Jakob Ribbing, Ph.D.
>>
>> Senior Consultant, Pharmetheus AB
>>
>>
>> Cell/Mobile: +46 (0)70 514 33 77 <+46%2070%20514%2033%2077>
>>
>> Jakob.Ribbing_at_Pharmetheus.com
>>
>> www.pharmetheus.com
>>
>>
>> Phone, Office: +46 (0)18 513 328
>>
>> Uppsala Science Park, Dag Hammarskjölds väg 52B
>>
>> SE-752 37 Uppsala, Sweden
>>
>>
>> *This communication is confidential and is only intended for the use of
>> the individual or entity to which it is directed. It may contain
>> information that is privileged and exempt from disclosure under applicab=
le
>> law. If you are not the intended recipient please notify us immediately.
>> Please do not copy it or disclose its contents to any other person.*
>>
>>
>>
>>
>> On 12 Dec 2016, at 10:13, Silber Baumann, Hanna <
>> hanna.silber_baumann_at_roche.com> wrote:
>>
>> Dear nmusers,
>> I have a data set which contains single and multiple ascending dose data=
.
>> The model development was initially performed on the single dose data.
>> I initially developed a model using ADVAN4 TRANS 2 (2 compartment linear
>> model with oral administration) which I later reparameterized into ADVAN=
6.
>> I expected to see some minor differences in parameter estimates, OFV etc
>> due to the change in subroutine but was surprised to see large differenc=
es
>> in both parameter estimates and OFV (+180 points) but also a significant
>> improvement in overall fit (graphically) while the data was the same. Wi=
th
>> the ADVAN4 the model fit was particularly poor to parts of the multiple
>> dose data, with the ADVAN6 the overall fit to all data was much improved=
. I
>> was using NONMEM7.3 for the analysis.
>>
>> I guess the ADVAN4 model gets stuck in a local minima, but using the
>> final estimates from the ADVAN6 model does not help. I would be grateful
>> for an explanation of the reasons why this happens.
>>
>> I have included the two models below.
>> Kind regards,
>> Hanna Silber
>>
>> $PROBLEM PK with ADVAN4
>>
>> $INPUT C ID TAD TIME AMT DV EVID CMT PTIM LDV DOSE BW BMI CLCR SEX AGE
>> STUDY DAY BLQ
>>
>> $DATA nmpk05DEC16.csv IGNORE=_at_
>>
>> $SUBROUTINES ADVAN4 TRANS4
>>
>> $PK
>> CL = THETA(1) * EXP(ETA(1))
>> V2 = THETA(2) * EXP(ETA(2))
>> KA = THETA(3) * EXP(ETA(3))
>> ALAG1 = THETA(6) * EXP(ETA(4))
>> Q = THETA(7) * EXP(ETA(5))
>> V3 = THETA(8) * EXP(ETA(6))
>>
>> S2 = V2/1000
>>
>> $ERROR
>> IPRED = F
>> W = SQRT(THETA(4)**2*IPRED**2 + THETA(5)**2)
>> Y = IPRED + W*EPS(1)
>> IRES = DV-IPRED
>> IWRES = IRES/W
>>
>> $THETA
>> (0,12.7) ;1 CL
>> (0,275) ;2 V2
>> (0,3.06) ;3 KA
>> (0, 0.12) ;4 Prop.RE (sd)
>> (0, 0.0153) ;5 Add.RE (sd)
>> (0,0.474) ;6 ALAG1
>> (0,26.3) ;7 Q
>> (0,133) ;8 V3
>>
>> $OMEGA BLOCK(2) 0.0747 ;1 IIV CL
>> 0.0723 0.0942 ;2 IIV V2
>> $OMEGA
>> 1.76 ;3 IIV KA
>> 0.00166 ;4 IIV ALAG
>> 0.036 ;5 IIV Q
>> 0.0407 ;6 IIV V3
>>
>> $SIGMA
>> 1 FIX ;
>>
>> $EST METHOD=1 INTER MAXEVAL=9999 NOABORT SIG=3 PRINT=1 POSTHOC
>> $COV
>> ######################################################
>>
>> $PROBLEM PK with ADVAN6
>>
>> $INPUT C ID TAD TIME AMT DV EVID CMT PTIM LDV DOSE BW BMI CLCR SEX AGE
>> STUDY DAY BLQ
>>
>> $DATA nmpk05DEC16.csv IGNORE=_at_
>>
>> $SUBROUTINES ADVAN6 TOL=5
>>
>> $MODEL
>> COMP = (ABS) ;1
>> COMP = (CENT) ;2
>> COMP = (PER) ;3
>>
>> $PK
>> CL = THETA(1) * EXP(ETA(1))
>> V2 = THETA(2) * EXP(ETA(2))
>> KA = THETA(3) * EXP(ETA(3))
>> ALAG1 = THETA(6) * EXP(ETA(4))
>> Q = THETA(7) * EXP(ETA(5))
>> V3 = THETA(8) * EXP(ETA(6))
>>
>> K=CL/V2
>> K23 = Q/V2
>> K32 = Q/V3
>>
>> A_0(1) = 0
>> A_0(2) = 0
>> A_0(3) = 0
>>
>> $DES
>> DADT(1) = -KA*A(1)
>> DADT(2) = KA*A(1) - K*A(2) - K23*A(2) + K32*A(3)
>> DADT(3) = K23*A(2) - K23*A(3)
>>
>> $ERROR
>> CONC = A(2)*1000/V2
>> IPRED = CONC
>> IF(CONC.EQ.0) IPRED = 1
>>
>> W = SQRT(THETA(4)**2*IPRED**2 + THETA(5)**2)
>> Y = IPRED + W*EPS(1)
>> IRES = DV-IPRED
>> IWRES = IRES/W
>>
>> $THETA
>> (0,12.1) ;1 CL
>> (0,275) ;2 V2
>> (0,3.06) ;3 KA
>> (0, 0.12) ;4 Prop.RE (sd)
>> (0, 0.0153) ;5 Add.RE (sd)
>> (0,0.474) ;6 ALAG1
>> (0,26.3) ;7 Q
>> (0,133) ;8 V3
>>
>> $OMEGA BLOCK(2) 0.0747 ;1 IIV CL
>> 0.0723 0.0942 ;2 IIV V2
>> $OMEGA
>> 1.76 ;3 IIV KA
>> 0.00166 ;4 IIV ALAG
>> 0.036 ;5 IIV Q
>> 0.0407 ;6 IIV V3
>>
>> $SIGMA
>> 1 FIX ;
>>
>> $EST METHOD=1 INTER MAXEVAL=9999 NOABORT SIG=3 PRINT=1 POSTHOC
>> $COV
>>
>> ###############################
>> Data set example:
>> C ID TAD TIME AMT DV EVID CMT PTIM LDV DOSE BW BMI CLCR SEX AGE STUDY DA=
Y
>> BLQ
>> 0 11001 0 0 5 0 1 1 0 0 5 54.8 20.63 74.32657 0 44 1 1 0
>> 0 11001 0.5 0.5 0 1.94 0 2 0.5 0.662688 5 54.8 20.63 74.32657 0 44 1 1 0
>> 0 11001 1 1 0 14.6 0 2 1 2.681022 5 54.8 20.63 74.32657 0 44 1 1 0
>> 0 11001 1.5 1.5 0 22.4 0 2 1.5 3.109061 5 54.8 20.63 74.32657 0 44 1 1 0
>> 0 11001 2 2 0 18.1 0 2 2 2.895912 5 54.8 20.63 74.32657 0 44 1 1 0
>> 0 11001 2.5 2.5 0 15.4 0 2 2.5 2.734368 5 54.8 20.63 74.32657 0 44 1 1 0
>> 0 11001 3 3 0 16.3 0 2 3 2.791165 5 54.8 20.63 74.32657 0 44 1 1 0
>> 0 11001 4 4 0 15.5 0 2 4 2.74084 5 54.8 20.63 74.32657 0 44 1 1 0
>> 0 11001 6 6 0 11.9 0 2 6 2.476538 5 54.8 20.63 74.32657 0 44 1 1 0
>> 0 11001 8 8 0 11.5 0 2 8 2.442347 5 54.8 20.63 74.32657 0 44 1 1 0
>> 0 11001 12 12 0 7.71 0 2 12 2.042518 5 54.8 20.63 74.32657 0 44 1 1 0
>> 0 11001 16.017 16.017 0 8.71 0 2 16 2.164472 5 54.8 20.63 74.32657 0 44 =
1
>> 2 0
>> 0 11001 24 24 0 5.55 0 2 24 1.713798 5 54.8 20.63 74.32657 0 44 1 2 0
>> 0 11001 48 48 0 3.5 0 2 48 1.252763 5 54.8 20.63 74.32657 0 44 1 3 0
>> 0 11001 72 72 0 1.86 0 2 72 0.620576 5 54.8 20.63 74.32657 0 44 1 4 0
>> 0 11001 120.883 120.883 0 0.597 0 2 120 -0.51584 5 54.8 20.63 74.32657 0
>> 44 1 6 0
>> 0 11001 144.9 144.9 0 0.356 0 2 144 -1.03282 5 54.8 20.63 74.32657 0 44 =
1
>> 7 0
>> 0 11001 168.883 168.883 0 0.177 0 2 168 -1.73161 5 54.8 20.63 74.32657 0
>> 44 1 8 0
>>
>>
>>
>> --
>>
>>
>>
>
>
> --
>
>
> *Hanna Silber Baumann, PhD*
>
> Pharmacometrician
>
> Principal Scientist
> Clinical Pharmacometrics, Clinical Pharmacology
>
> Roche Pharma Research and Early Development
>
>
> Roche Innovation Center Basel
>
>
> F. Hoffmann-La Roche Ltd
> Grenzacherstrasse 124
> 4070 Basel
>
> Switzerland
>
> Phone +41 61 687 76 81 <+41%2061%20687%2076%2081>
>
>
> Confidentiality Note: This message is intended only for the use of the
> named recipient(s) and may contain confidential and/or proprietary
> information. If you are not the intended recipient, please contact the
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> _________________________________________________________________________
>
>

Received on Mon Dec 12 2016 - 08:01:53 EST

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