Joanna,
I don't know of any way to request a positive SS solution. However I
wonder if the problem is related to the way you use the SS data item in
the code.
HIV = SS*THETA(9)*EXP(ETA(9))
Usually there is just one record with SS=1 at the start of each
subject's records. If this is the case then the value of HIV will be
zero at all times after the SS=1 record. Does this make sense for your
model?
I am not even sure that SS would have the value of 1 during the
calculation of the steady state solution -- although that would be a
reasonable assumption that could be confirmed by someone such as Alison
Boeckmann.
Nick
PS NONMEM calculates steady state amounts in compartments -- it does not
calculate steady state doses.
On 15/10/2011 3:07 a.m., Joanna Lewis wrote:
Dear NONMEM users,
I have a question about NONMEM's calculation of steady-state doses.
I am trying to model a system with two compartments. I want my t=0
boundary condition to be that the system is at steady-state, with a
rate of cell death/clearance from one of the compartments which is
higher than for all t>0. Unfortunately, I don't think there is an
analytic solution for this steady state (see code copied below).
From an old NMusers thread
(http://www.mail-archive.com/[email protected]/msg00584.html), I
found out that NONMEM will find the steady state if you give it SS=1,
RATE=0, AMT=0 in a t=0 record in the data file. When I tried this
though, NONMEM found the wrong root of the steady-state equation (a
negative one). Do you know if there is a way of making sure the steady
state it finds is the positive one? For the moment, I have been
initialising the compartments near to the steady state and at t=-(a
lot), and letting them equilibrate, but I think this is slowing down
my runs and would prefer to use another method.
Thank you very much in advance for any advice you can offer.
Joanna Lewis
PhD student
UCL Institute for Child Health
30 Guilford Street, London WC1N 1EH
$PROBLEM hapuarachchi0_15sep_1
$INPUT ID BAGE AGE WEEK TIME CD DV AMT RATE SS TYPE CMT IG
$DATA data.csv IGNORE=@
$SUBROUTINE ADVAN6 TOL=5
$MODEL
COMP=(X)
COMP=(Y)
$PK
ANO = THETA(1)*EXP(ETA(1))
EP = THETA(2)*EXP(ETA(2))
DEO = THETA(3)*EXP(ETA(3))
SIG = THETA(4)*EXP(ETA(4))
SCA = THETA(5)*EXP(ETA(5))
Q = THETA(6)*EXP(ETA(6))
THE = THETA(7)*EXP(ETA(7))
RHO = THETA(8)*EXP(ETA(8))
HIV = SS*THETA(9)*EXP(ETA(9))
$DES
ALP = ANO*EXP(-EP*SCA*A(1))
DEL = DEO*EXP(SIG*SCA*A(1))
MU = Q*A(2) + HIV
DADT(1) = THE + 2*RHO*A(2) - ALP*A(1) - DEL*A(1)
DADT(2) = ALP*A(1) - RHO*A(2) - MU*A(2)
$ERROR
IPCD = A(1)+A(2)
Y = IPCD + IPCD*EPS(1)
$THETA
...etc..
$OMEGA
...etc...
$SIGMA
...etc...
$ESTIMATION MAXEVAL=0 METHOD=1 INTER SIGDIG=1 PRINT=1
$TABLE
...etc...
--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology& Clinical Pharmacology
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53
email: [email protected]
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
