RE: [NMusers] Different results with ADVAN4 and ADVAN6

From: <E.Olofsen_at_lumc.nl>
Date: Mon, 12 Dec 2016 10:37:11 +0000

Dear Hanna,

You could perhaps try $SUBROUTINES ADVAN13 TOL=9 to check if this is rela=
ted to the accuracy of the solutions of the differential equations. ADVAN13=
 runs faster than ADVAN6 and/or allows a higher tolerance setting.

Best regards,

Erik

________________________________
From: owner-nmusers_at_globomaxnm.com [owner-nmusers_at_globomaxnm.com] on behalf=
 of Silber Baumann, Hanna [hanna.silber_baumann_at_roche.com]
Sent: Monday, December 12, 2016 10:13 AM
To: nmusers_at_globomaxnm.com
Subject: [NMusers] Different results with ADVAN4 and ADVAN6

Dear nmusers,
I have a data set which contains single and multiple ascending dose data. T=
he model development was initially performed on the single dose data.
I initially developed a model using ADVAN4 TRANS 2 (2 compartment linear mo=
del 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 b=
oth parameter estimates and OFV (+180 points) but also a significant improv=
ement in overall fit (graphically) while the data was the same. With the AD=
VAN4 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 usin=
g 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 e=
xplanation 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 0.6=
62688 5 54.8 20.63 74.32657 0 44 1 =
  1 0
0 11001 1 1 0 14.6 0 2 1 2.6=
81022 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 3.1=
09061 5 54.8 20.63 74.32657 0 44 1 =
  1 0
0 11001 2 2 0 18.1 0 2 2 2.8=
95912 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 2.7=
34368 5 54.8 20.63 74.32657 0 44 1 =
  1 0
0 11001 3 3 0 16.3 0 2 3 2.7=
91165 5 54.8 20.63 74.32657 0 44 1 =
  1 0
0 11001 4 4 0 15.5 0 2 4 2.7=
4084 5 54.8 20.63 74.32657 0 44 1 1 =
  0
0 11001 6 6 0 11.9 0 2 6 2.4=
76538 5 54.8 20.63 74.32657 0 44 1 =
  1 0
0 11001 8 8 0 11.5 0 2 8 2.4=
42347 5 54.8 20.63 74.32657 0 44 1 =
  1 0
0 11001 12 12 0 7.71 0 2 12 2.0=
42518 5 54.8 20.63 74.32657 0 44 1 =
  1 0
0 11001 16.017 16.017 0 8.71 0 2 16 2.1=
64472 5 54.8 20.63 74.32657 0 44 1 =
  2 0
0 11001 24 24 0 5.55 0 2 24 1.7=
13798 5 54.8 20.63 74.32657 0 44 1 =
  2 0
0 11001 48 48 0 3.5 0 2 48 1.2=
52763 5 54.8 20.63 74.32657 0 44 1 =
  3 0
0 11001 72 72 0 1.86 0 2 72 0.6=
20576 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



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Received on Mon Dec 12 2016 - 05:37:11 EST

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