From: Martin Fransson <*marfr*>

Date: Tue, 19 Dec 2006 09:32:37 +0100

Dear all,

I am a novice in NONMEM and am currently trying to fit data for a parent =

substance (P) and its two metabolites (Ma resp. Mb). As you can see =

below I use a nine compartment model with three compartments for each =

compound. The data (ln-transformed) is measured concentrations from =

infusion treatment and the total amount of data is approximately 20 =

(patients) x 10 (samples) x 3 (compounds), i.e., about 600 data records. =

Dose is applied to compartment P1. Since I am still trying to figure out =

the features of NONMEM (working with v. 6) perhaps you can help with me =

some questions I have.

1) Is it at all sensible to use nine compartment models in NONMEM? Is =

there an unspoken maximum complexity (in terms of no of parameters, no =

of compartments, "degree" of nonlinearity etc.) for the models one =

should use? As you can see there are already 10 unknown parameters with =

equally many random effects. If I a introduce nonlinearities such as =

MM-kinetics it is most likely that there will be even more parameters (I =

guess making the model nonlinear will also make things more difficult =

for the optimization algorithm). Investigating effects of covariates =

will also require additional parameters. (Some models I have tried did =

have up to 18 unknown parameters.)

2) Are there any obvious errors in the code? For instance when it comes =

to the aspect of modeling parent and metabolites at the same time?

3) It seems that it is almost impossible to get FOCE with interaction to =

work when using complex models, but if I assume CCV-error, perform =

ln-transformation of the data and use FO instead will it make any =

difference?

Notes:

1) I use Advan6 since I intend to make parts of the model nonlinear at =

later stage.

2) I do not capture any exceptions in the ERROR record in form of LOG(0) =

since I removed any zero valued data records.

All comments and critics are highly appreciated!

Best regards,

Martin

$PROBLEM Model 0 (linear),

$INPUT ID AMT=DOSE RATE TIME CP=DV CMT EVID MDV CO1 CO2 CO3 AGE BSA

$DATA DCPLog.txt

$SUBROUTINES ADVAN6 TOL=5

$MODEL COMP (P1) COMP (P2) COMP (P3)

COMP (Ma1) COMP (Ma2) COMP (Ma3)

COMP (Mb1) COMP (Mb2) COMP (Mb3)

$PK

V1=THETA(1)*EXP(ETA(1))

V2=THETA(2)*EXP(ETA(2))

V3=THETA(3)*EXP(ETA(3))

Q12=THETA(4)*EXP(ETA(4))

Q13=THETA(5)*EXP(ETA(5))

;Enzyme kinetics

kPMa=THETA(6)*EXP(ETA(6))

kPMb=THETA(7)*EXP(ETA(7))

kelP=THETA(8)*EXP(ETA(8))

kelMa=THETA(9)*EXP(ETA(9))

kelMb=THETA(10)*EXP(ETA(10))

$DES

x1=A(1)/V1

x2=A(2)/V2

x3=A(3)/V3

x4=A(4)/V1

x5=A(5)/V2

x6=A(6)/V3

x7=A(7)/V1

x8=A(8)/V2

x9=A(9)/V3

DADT(1)=-Q12*(x1-x2)-Q13*(x1-x3)

DADT(2)=Q12*(x1-x2)-V2*(kPMa+kPMb+kelP)*x2

DADT(3)=Q13*(x1-x3)

DADT(4)=-Q12*(x4-x5)-Q13*(x4-x6)

DADT(5)=Q12*(x4-x5)+V2*kPMa*x2-V2*kelMa*x5

DADT(6)=Q13*(x4-x6)

DADT(7)=-Q12*(x7-x8)-Q13*(x7-x9)

DADT(8)=Q12*(x7-x8)+V2*kPMb*x2-V2*kelMb*x8

DADT(9)=Q13*(x7-x9)

$ERROR CALLFL=0

IF (CMT.EQ.1) z=A(1)/V1

IF (CMT.EQ.4) z=A(4)/V1

IF (CMT.EQ.7) z=A(7)/V1

Y=LOG(z)+LOG(1+EPS(1))

$THETA

(3,5) ; V1

(1,3) ; V2

(30,70) ; V3

(0.01,1.5) ; Q12

(0.001,0.02) ; Q13

(0.0001,0.01)

(0.0001,0.01)

(0.0001,0.01)

(0.0001,0.01)

(0.0001,0.01)

$OMEGA .1,.1,.1,.1,.1,.1,.1,.1,.1,.1,

$SIGMA .15

$EST METHOD=0 MAXEVAL=9999 PRINT=1

$COV

;$TABLE TIME PRED

$SCAT CP VS TIME

$SCAT PRED VS TIME

$SCAT PRED VS CP UNIT

$SCAT RES VS TIME

==========================

======

Martin Fransson

Dept. of Computer and Information Science

Linköping University

581 83 LINKÖPING, SWEDEN

marfr

+46 13 281467

==========================

======

Received on Tue Dec 19 2006 - 03:32:37 EST

Date: Tue, 19 Dec 2006 09:32:37 +0100

Dear all,

I am a novice in NONMEM and am currently trying to fit data for a parent =

substance (P) and its two metabolites (Ma resp. Mb). As you can see =

below I use a nine compartment model with three compartments for each =

compound. The data (ln-transformed) is measured concentrations from =

infusion treatment and the total amount of data is approximately 20 =

(patients) x 10 (samples) x 3 (compounds), i.e., about 600 data records. =

Dose is applied to compartment P1. Since I am still trying to figure out =

the features of NONMEM (working with v. 6) perhaps you can help with me =

some questions I have.

1) Is it at all sensible to use nine compartment models in NONMEM? Is =

there an unspoken maximum complexity (in terms of no of parameters, no =

of compartments, "degree" of nonlinearity etc.) for the models one =

should use? As you can see there are already 10 unknown parameters with =

equally many random effects. If I a introduce nonlinearities such as =

MM-kinetics it is most likely that there will be even more parameters (I =

guess making the model nonlinear will also make things more difficult =

for the optimization algorithm). Investigating effects of covariates =

will also require additional parameters. (Some models I have tried did =

have up to 18 unknown parameters.)

2) Are there any obvious errors in the code? For instance when it comes =

to the aspect of modeling parent and metabolites at the same time?

3) It seems that it is almost impossible to get FOCE with interaction to =

work when using complex models, but if I assume CCV-error, perform =

ln-transformation of the data and use FO instead will it make any =

difference?

Notes:

1) I use Advan6 since I intend to make parts of the model nonlinear at =

later stage.

2) I do not capture any exceptions in the ERROR record in form of LOG(0) =

since I removed any zero valued data records.

All comments and critics are highly appreciated!

Best regards,

Martin

$PROBLEM Model 0 (linear),

$INPUT ID AMT=DOSE RATE TIME CP=DV CMT EVID MDV CO1 CO2 CO3 AGE BSA

$DATA DCPLog.txt

$SUBROUTINES ADVAN6 TOL=5

$MODEL COMP (P1) COMP (P2) COMP (P3)

COMP (Ma1) COMP (Ma2) COMP (Ma3)

COMP (Mb1) COMP (Mb2) COMP (Mb3)

$PK

V1=THETA(1)*EXP(ETA(1))

V2=THETA(2)*EXP(ETA(2))

V3=THETA(3)*EXP(ETA(3))

Q12=THETA(4)*EXP(ETA(4))

Q13=THETA(5)*EXP(ETA(5))

;Enzyme kinetics

kPMa=THETA(6)*EXP(ETA(6))

kPMb=THETA(7)*EXP(ETA(7))

kelP=THETA(8)*EXP(ETA(8))

kelMa=THETA(9)*EXP(ETA(9))

kelMb=THETA(10)*EXP(ETA(10))

$DES

x1=A(1)/V1

x2=A(2)/V2

x3=A(3)/V3

x4=A(4)/V1

x5=A(5)/V2

x6=A(6)/V3

x7=A(7)/V1

x8=A(8)/V2

x9=A(9)/V3

DADT(1)=-Q12*(x1-x2)-Q13*(x1-x3)

DADT(2)=Q12*(x1-x2)-V2*(kPMa+kPMb+kelP)*x2

DADT(3)=Q13*(x1-x3)

DADT(4)=-Q12*(x4-x5)-Q13*(x4-x6)

DADT(5)=Q12*(x4-x5)+V2*kPMa*x2-V2*kelMa*x5

DADT(6)=Q13*(x4-x6)

DADT(7)=-Q12*(x7-x8)-Q13*(x7-x9)

DADT(8)=Q12*(x7-x8)+V2*kPMb*x2-V2*kelMb*x8

DADT(9)=Q13*(x7-x9)

$ERROR CALLFL=0

IF (CMT.EQ.1) z=A(1)/V1

IF (CMT.EQ.4) z=A(4)/V1

IF (CMT.EQ.7) z=A(7)/V1

Y=LOG(z)+LOG(1+EPS(1))

$THETA

(3,5) ; V1

(1,3) ; V2

(30,70) ; V3

(0.01,1.5) ; Q12

(0.001,0.02) ; Q13

(0.0001,0.01)

(0.0001,0.01)

(0.0001,0.01)

(0.0001,0.01)

(0.0001,0.01)

$OMEGA .1,.1,.1,.1,.1,.1,.1,.1,.1,.1,

$SIGMA .15

$EST METHOD=0 MAXEVAL=9999 PRINT=1

$COV

;$TABLE TIME PRED

$SCAT CP VS TIME

$SCAT PRED VS TIME

$SCAT PRED VS CP UNIT

$SCAT RES VS TIME

==========================

======

Martin Fransson

Dept. of Computer and Information Science

Linköping University

581 83 LINKÖPING, SWEDEN

marfr

+46 13 281467

==========================

======

Received on Tue Dec 19 2006 - 03:32:37 EST