Re: Antwort: [NMusers] PK/PD models to describe anti-ancer drug effect on the tumor volume

Just for suggestion... We recently analyzed data from quite aggressive
tumours in rabbits. Since there was no "plateau phase", we were not really
interested in trying a Gompertz model. Still talking about empirical model,
we tried a power law with low coefficient value: this has been originally
proposed by a computational model of cell colony growth which has been
validated in vitro (see Drasdo et al. Phys Biol. 2005;2(3):133-47). It
actually fitted quite well. This could be tried in addition to linear or
exponential growth models.

Kind regards,

Benjamin

Benjamin Ribba, PhD
Therapeutic Targeting in Oncology (EA3738)
University Claude Bernard Lyon 1
Faculty of Medicine Lyon-Sud
165, chemin du grand revoyet
69921 Oullins cedex

Tel: +33 4 26 23 59 57 / +33 6 66 06 15 21



Le 7/1/08 16:51, « Bonate, Peter » <Peter.Bonate_at_genzyme.com> a écrit :

> I think it's worth mentioning here that Simeoni's model is just as empiri=
cal
> 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 an=
d
> 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 use=
d.
> 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 with=
out
> collecting pk. I then fix the pk and model the tumor growth under the d=
osing
> regimen in the xenograft study. The output from the Gompertz model is th=
en 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 usu=
ally
> 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 sim=
ilar
> 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
> peter.bonate_at_genzyme.com
> phone: 210-949-8662
> fax: 210-949-8219
> crackberry: 210-315-2713
>
> -----Original Message-----
> From: owner-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com] =
On
> Behalf Of nele.plock_at_bayerhealthcare.com
> Sent: Monday, January 07, 2008 1:36 AM
> To: patrickmzhou_at_yahoo.com
> Cc: nmusers_at_globomaxnm.com
> Subject: Antwort: [NMusers] PK/PD models to describe anti-ancer drug effe=
ct 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 a=
s
> well, maybe it is useful.
> Considering the predictiveness of the model, in my case it seemed like th=
e
> 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
> nele.plock_at_bayerhealthcare.com
> http://www.bayerscheringpharma.de
>
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,
> Ulrich Köstlin, Kemal Malik, Gunnar Riemann
> Vorsitzender des Aufsichtsrats: Werner Wenning
> Sitz der Gesellschaft: Berlin | Eintragung: Amtsgericht Charlottenburg 93
> HRB 283
>
>
>
> Patrick Zhou <patrickmzhou_at_yahoo.com>
> Gesendet von: owner-nmusers_at_globomaxnm.com
> 06.01.2008 20:50
>
> An
> nmusers_at_globomaxnm.com
> Kopie
>
> 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 dos=
e
> projection, and any published work of such? Please advice. Thank you ver=
y
> much.
>
> Patrick
>
> Be a better friend, newshound, and know-it-all with Yahoo! Mobile. Try i=
t
> now.
>

Received on Tue Jan 08 2008 - 10:42:33 EST