Re: [NMusers] Different results with ADVAN4 and ADVAN6

From: Jakob Ribbing <jakob.ribbing_at_pharmetheus.com>
Date: Mon, 12 Dec 2016 11:58:41 +0100

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.

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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 ADVAN6. I expected to see some minor differences in parameter =
estimates, OFV etc due to the change in subroutine but was surprised to =
see large differences in both parameter estimates and OFV (+180 points) =
but also a significant improvement in overall fit (graphically) while =
the data was the same. With 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 DAY =
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
>
>
>
> --
>


Received on Mon Dec 12 2016 - 05:58:41 EST

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