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=@
$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=@
$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
--
Different results with ADVAN4 and ADVAN6
Dear Hanna,
You could perhaps try $SUBROUTINES ADVAN13 TOL=9 to check if this is related 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
Quoted reply history
________________________________
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. 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 MAXEVAL99 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 MAXEVAL99 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
--
Dear Hanna,
You could perhaps try $SUBROUTINES ADVAN13 TOL=9 to check if this is related 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
Quoted reply history
________________________________
From: [email protected] [[email protected]] on behalf of
Silber Baumann, Hanna [[email protected]]
Sent: Monday, December 12, 2016 10:13 AM
To: [email protected]
Subject: [NMusers] Different results with ADVAN4 and ADVAN6
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=@
$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=@
$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
--
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
[email protected]
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 applicable 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.
Quoted reply history
On 12 Dec 2016, at 10:13, Silber Baumann, Hanna
<[email protected]> 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=@
>
> $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=@
>
> $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
>
>
>
> --
>
Dear Hanna,
You might have hit a situation where the solver routine (ADVAN6) does not provide an adequate solution to your $DES system (even though a better fit). My advice would be to decrease the error tolerance of the solution by increasing the number that now says TOL=5 until (up to 12 or 15 even) you get the same result compared to ADVAN4. If that does not work, you could switch to ADVAN13 and try the same (the system might be partly stiff).
If you would like to diagnose in more detail, you could create a dataset with very dense interpolation entries (EVID=2) e.g. every 6 minutes. Any irregularities in the resulting curves could point to a solver problem. This might not give the complete picture, and you might suspect your large estimate of KA to have an impact in that case. Perhaps some different iterations with a simplified or partially fixed omega structure might help than. It seems rather heavily parameterized for what I guess is a phase 1 dataset.
Hope this helps,
Jeroen
http://pd-value.com
[email protected]
@PD_value
+31 6 23118438
-- More value out of your data!
Quoted reply history
On 12-12-16 10:13, Silber Baumann, Hanna 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=@
>
> $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=@
>
> $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
>
> --
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
Quoted reply history
2016-12-12 13:02 GMT+01:00 Silber Baumann, Hanna <
[email protected]>:
> 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 <
> [email protected]> 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>
>>
>> [email protected]
>>
>> 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 applicable
>> 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 <
>> [email protected]> 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=@
>>
>> $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=@
>>
>> $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
>>
>>
>>
>> --
>>
>>
>>
>
>
> --
>
>
> *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
> sender and delete this message. Any unauthorized use of the information
> contained in this message is prohibited.
>
> _________________________________________________________________________
>
>