RE: Antwort: PK/PD models to describe anti-ancer drug effect on the tumor volume
I think it's worth mentioning here that Simeoni's model is just as empirical as
other tumor growth models. Patrick should also consider Gompertz or logisitic
growth models. There is a large body of literature using these latter models
which oncologists and cancer pharmacologists are used to and familiar with.
Simeoni's model is still relatively new and, while it is couched in terms of
'mechanistic' models, there is still an empirical component to it. I would
still use the Gompertz model.
The second part of Patrick's question was how do you use the output from these
models to help guide human drug development. So here is what we have used.
Typically you get pk in mice and rats from the ADME group. In a separate study
by the cancer pharmacology group, you get tumor growth in mice without
collecting pk. I then fix the pk and model the tumor growth under the dosing
regimen in the xenograft study. The output from the Gompertz model is then an
IC50. This is the target you need to acheive in humans, usually assuming
trough. You could then allometrically scale the rodent models to get pk in
humans and simulate an approximate dosing regimen to acheive a trough
concentration or a 24 hour concentration above the IC50. And I don't usually
do this work in NONMEM. The mice pk data are usually single time-point studies
so a naive pooled approach is reasonable. You could model the xenograft data
in NONMEM, but I would expect that your IC50s would be similar regardless of
whether you model mean data or individual-level data.
Hope this helps
pete bonate
Peter L. Bonate, PhD, FCP
Genzyme Corporation
Senior Director
Clinical Pharmacology and Pharmacokinetics
4545 Horizon Hill Blvd
San Antonio, TX 78229 USA
[EMAIL PROTECTED]
phone: 210-949-8662
fax: 210-949-8219
crackberry: 210-315-2713
Quoted reply history
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of [EMAIL PROTECTED]
Sent: Monday, January 07, 2008 1:36 AM
To: [EMAIL PROTECTED]
Cc: [email protected]
Subject: Antwort: [NMusers] PK/PD models to describe anti-ancer drug effect on
the tumor volume
Dear Patrick,
the Simeoni model works well:
Simeoni M, Magni P, Cammia C, De Nicolao G, Croci V, Pesenti E, Germani M,
Poggesi I, Rocchetti M.
Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth
kinetics in xenograft models after administration of anticancer agents.
Cancer Res. 2004 Feb 1;64(3):1094-101.
I've attached a code using this model below. My code used the
concentrations in tumor tissue, but you can just as well use the serum
concentrations.
I also develowed a second model which uses linear tumor growth and
considers resistance development over time, which is less pronounced if
concentrations of the anti-tumor drug increase. I've attached that code as
well, maybe it is useful.
Considering the predictiveness of the model, in my case it seemed like the
use of physiological life-span seemed to be a good corrector (I got the
idea from Monro, Drug Toxicokinetics: Scope and Limitations that Arise
from Species Differences in Pharmacodynamic and Carcinogenic Responses, J
Pharmacokin Biopharm 22, 41-57, 1994). I.e. if you want your patients to
survive 6 months, then use human average age of 75 years and mouse age of
2 years, so a mouse would have to survive 6 months/75*2= 5 days. But this
might be different from drug to drug.
Best wishes
Nele
Simeoni:
$SUBROUTINES ADVAN6 TOL=3
$MODEL
COMP=(GUT)
COMP=(CENTRAL)
COMP=(TUMOR)
COMP=(PD)
$PK
TVCL=THETA(1)
CL=TVCL
;
TVV2=THETA(2)
V2=TVV2
;
TVKA=THETA(3)
KA=TVKA
TVF1=THETA(4)
F1=TVF1
TVPC=THETA(5)
PC=TVPC
TVKEO=THETA(6)
KEO=TVKEO
;
TVL0=THETA(7)
L0=TVL0*EXP(ETA(1))
;
TVL1=THETA(8)
L1=TVL1
TVK2=THETA(9)
K2=TVK2
W0=THETA(10)
F4=W0
S2=V2
K20=CL/V2
S4=1
PSI=20
;
$ERROR
IPRED=F
DEL=0
IF (IPRED.EQ.0) DEL=0.0001
W=F
IRES=DV-IPRED
IWRES=IRES/(W+DEL)
Y=F+SQRT(THETA(12)*THETA(12)+THETA(11)*THETA(11)*F**2)*EPS(1)
;
$DES
DADT(1)= -KA*A(1)
DADT(2)= KA*A(1) -K20*A(2)
DADT(3)= KEO*(PC*A(2)/V2 - A(3))
DADT(4)= L0*A(4)/(1+(L0/L1*A(4))**PSI)**(1/PSI)-K2*A(3)*A(4)
own code:
$SUBROUTINES ADVAN8 TOL=3
$MODEL
COMP=(GUT)
COMP=(CENTRAL)
COMP=(TUMOR)
COMP=(PD)
COMP=(AUC)
$PK
TVCL=THETA(1)
CL=TVCL*EXP(ETA(1))
;
TVV2=THETA(2)
V2=TVV2
;
TVKA=THETA(3)
KA=TVKA
TVF1=THETA(4)
F1=TVF1
TVPC=THETA(5)
PC=TVPC
TVKEO=THETA(6)
KEO=TVKEO
TVW0=THETA(7)
F4=TVW0
W0=F4 ; dataset: put a dummy dose of 1 into compartment 4, initial tumor
weight is then estimated as F4
TVKRES=THETA(8)
KRES=TVKRES
TVIC50=THETA(9)
IC50=TVIC50
TVKIN=THETA(10)
KIN=TVKIN
TVRED=THETA(13)
RED=TVRED
S2=V2
K20=CL/V2
S4=1
;
$ERROR
IPRED=F
DEL=0
IF (IPRED.EQ.0) DEL=0.0001
W=F
IRES=DV-IPRED
IWRES=IRES/(W+DEL)
Y=F+SQRT(THETA(12)*THETA(12)+THETA(11)*THETA(11)*F**2)*EPS(1)
;
$DES
CAV=A(5)/(T+0.01)
RES=IC50*EXP(KRES*T*(1-CAV/(RED+CAV)))
DADT(1)= -KA*A(1)
DADT(2)= KA*A(1) -K20*A(2)
DADT(3)= KEO*(PC*A(2)/V2 - A(3))
DADT(4)= KIN*(1-A(3)/(RES+A(3)))
DADT(5)= A(3)
_________________________
Dr. Nele Plock
Bayer Schering Pharma AG
Drug Metabolism & Pharmacokinetics
Development Pharmacokinetics
Scientific Expert Development Pharmacokinetics
D- 13342 Berlin
Phone : +49-30-468 15146
Fax: +49-30-468 95146
[EMAIL PROTECTED]
http://www.bayerscheringpharma.de
Vorstand: Arthur J. Higgins, Vorsitzender | Werner Baumann, Andreas Busch,
Ulrich Köstlin, Kemal Malik, Gunnar Riemann
Vorsitzender des Aufsichtsrats: Werner Wenning
Sitz der Gesellschaft: Berlin | Eintragung: Amtsgericht Charlottenburg 93
HRB 283
Patrick Zhou <[EMAIL PROTECTED]>
Gesendet von: [EMAIL PROTECTED]
06.01.2008 20:50
An
[email protected]
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Thema
[NMusers] PK/PD models to describe anti-ancer drug effect on the tumor
volume
Dear NMusers,
Does anyone aware any good published example (or non-public if you are
willing to share) of modeling the anti-cancer drug effect on the tumor
volume in nude mice model? And how this is normally used in the human dose
projection, and any published work of such? Please advice. Thank you very
much.
Patrick
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