DDMODEL00000274: Terranova_2017_oncology_TGI

  public model
Short description:
PKPD model of tumor growth inhibition and toxicity outcome after administration of anticancer agents in xenograft mice
PharmML 0.8.x (0.8.1)
  • Evaluation of a PK/PD DEB-based model for tumor-in-host growth kinetics under anticancer treatment
  • E.M.Tosca, E.Borella, N.Terranova, M.Rocchetti, P.Magni
  • PAGE 25, 6/2015
  • Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, Pavia, I-27100, Italy
  • Objectives: Mathematical models for describing the tumor growth in animals often neglect the relationship between tumor and host organism. To overcome this limitation, a more mechanistic model, based on energy balance between tumor and host, was developed. This PK/PD model, combining the Dynamic Energy Budget (DEB) theory with the Simeoni tumor growth inhibition (TGI) model, describes both the dynamics of the tumor-host interaction and the effect of anticancer treatments. Here a slightly revised model formulation and a new implementation are proposed. Moreover, a comparative study on the tumor growth in control groups between the DEB-TGI model and the widely used Simeoni TGI model is presented. Methods: Data used for model validation refer to xenograft experiments conducted on Harlan Sprague Dawley mice. Average data of tumor weight and mice net body weight were considered for the control and treated groups. The PKs were derived from separated studies. Monolix 4.3.3 was used for model identification, while Simulx was used to confirm the hypothesis emerged from a dynamic system analysis. Results: First of all, the model was identified on different experimental datasets with the following strategy: 1) physiological parameters of the tumor-free model were estimated on growth data of typical HSD mice; 2) estimated values were used to find the initial value for the energy reserve at the beginning of the experiment; 3) once fixed the tumor-free model parameters and energy initial value, the tumor-related and the drug-related parameters were simultaneously estimated. The mathematical analysis of the dynamic system showed that, as the Simeoni model, the DEB-TGI model predicts an exponential growth of the tumor in the early phases of its development. The exponential growth rate depends on several model parameters some of them related to the tumor cell lines and other to the host. We investigated also the relationship between the DEB-TGI model parameters and the decreasing of the tumor growth rate. Conclusions: The tumor-in-host DEB-based model confirmed its good capability in describing tumor growth and host body growth even when an anticancer drug is administered. Moreover, the affinities emerged from the comparative analysis with the Simenoni model provide a possible biological interpretation of the assumptions underlying the Simeoni model unperturbed (control) growth curve.
Elena Maria Tosca
Context of model development: Candidate Comparison, Selection, Human Dose Prediction;
Discrepancy between implemented model and original publication: Among the drugs considered in the paper, only PACLITAXEL has been used;
Model compliance with original publication: Yes;
Model implementation requiring submitter’s additional knowledge: No;
Modelling context description: Host features, such as cell proliferation rates, caloric intake, metabolism and energetic conditions, significantly influence tumor growth; at the same time, tumor growth may have a dramatic impact on the host conditions. For example, in clinics, at certain stages of the tumor growth, cachexia (body weight reduction) may become so relevant to be considered as responsible for around 20% of cancer deaths. Unfortunately, anticancer therapies may also contribute to the development of cachexia due to reduced food intake (anorexia), commonly observed during the treatment periods. For this reason, cachexia is considered one of the major toxicity findings to be evaluated also in preclinical studies. However, although various pharmacokinetic-pharmacodynamic (PK-PD) tumor growth inhibition (TGI) models are currently available, the mathematical modelling of cachexia onset and TGI after an anticancer administration in preclinical experiments is still an open issue. To cope with this, a new PK-PD model, based on a set of tumor-host interaction rules taken from Dynamic Energy Budget (DEB) theory and a set of drug tumor inhibition equations taken from the well-known Simeoni TGI model, was developed. The model is able to describe the body weight reduction, splitting the cachexia directly induced by tumor and that caused by the drug treatment under study. It was tested in typical preclinical studies, essentially designed for efficacy evaluation and routinely performed as a part of the industrial drug development plans. For the first time, both the dynamics of tumor and host growth could be predicted in xenograft mice untreated or treated with different anticancer agents and following different schedules. ;
Modelling task in scope: simulation; estimation;
Nature of research: Preclinical development; In vivo;
Therapeutic/disease area: Oncology;
Annotations are correct.
This model is not certified.
  • Model owner: Elena Maria Tosca
  • Submitted: Dec 22, 2017 8:10:56 AM
  • Last Modified: Dec 22, 2017 8:10:56 AM
Revisions
  • Version: 5 public model Download this version
    • Submitted on: Dec 22, 2017 8:10:56 AM
    • Submitted by: Elena Maria Tosca
    • With comment: Model revised without commit message

Name

Terranova_2017_oncology_TGI

Description

PKPD model of tumor growth inhibition and toxicity outcome after administration of anticancer agents in xenograft mice

Independent Variables

T

Function Definitions

additiveError:realadditive:real=additive

Parameter Model: pm

Random Variables

eps_RES_Wvm_err.DV~Normal2mean=0var=1
eps_RES_Wuvm_err.DV~Normal2mean=0var=1

Population Parameters

K10_POP
K12_POP
K21_POP
V1_POP
En_initial_POP
rho_b_POP
xi_POP
ni_POP
gr_POP
V1inf_POP
mu_POP
mu_u_POP
gu_POP
delta_Vmax_POP
W_initial_POP
Vu1_initial_POP
IC50_POP
k1_POP
k2_POP
b_W
b_Wu

Individual Parameters

K10=pm.K10_POP
K12=pm.K12_POP
K21=pm.K21_POP
V1=pm.V1_POP
En_initial=pm.En_initial_POP
rho_b=pm.rho_b_POP
xi=pm.xi_POP
ni=pm.ni_POP
gr=pm.gr_POP
V1inf=pm.V1inf_POP
mu=pm.mu_POP
mu_u=pm.mu_u_POP
gu=pm.gu_POP
delta_Vmax=pm.delta_Vmax_POP
W_initial=pm.W_initial_POP
Vu1_initial=pm.Vu1_initial_POP
IC50=pm.IC50_POP
k1=pm.k1_POP
k2=pm.k2_POP
density_V=1
density_Vu=1
omeg=0.75
m=pm.nipm.V1inf13pm.gr
Z_initial=pm.W_initial1+pm.En_initialpm.xi

Structural Model: sm

Variables

C=sm.Q1pm.V1
TQ1=pm.K21sm.Q2-pm.K10+pm.K12sm.Q1Q1T=0=0
TQ2=pm.K12sm.Q1-pm.K21sm.Q2Q2T=0=0
rho=pm.rho_b1-sm.Cpm.IC50+sm.C
ku=pm.mu_usm.Vu1sm.Z+pm.mu_usm.Vu1
switch1=1-sm.kupm.nism.Ensm.Z23-pm.grpm.msm.Zpm.gr+1-pm.mu_usm.Vu1sm.Z+pm.mu_usm.Vu1sm.En
switch2=1-sm.kupm.nism.Ensm.Z23-pm.grpm.msm.Z1-pm.mu_usm.Vu1sm.Z+pm.mu_usm.Vu1sm.En+pm.omegpm.gr
Wu=pm.density_Vusm.Vu1+sm.Vu2+sm.Vu3+sm.Vu4
W=pm.density_V1+pm.xism.Ensm.Z
W_err=pm.b_Wsm.W
Wu_err=pm.b_Wusm.Wu
TZ={1-sm.kupm.nism.Ensm.Z23-pm.grpm.msm.Zpm.gr+1-sm.kusm.Enifsm.switch101-sm.kupm.nism.Ensm.Z23-pm.grpm.msm.Z1-sm.kusm.En+pm.omegpm.grifsm.switch1<0sm.switch2-pm.delta_Vmax-pm.delta_VmaxotherwiseZT=0=pm.Z_initial
TEn=pm.nism.Z13sm.rhopm.V1infsm.Vu1+sm.Z23-sm.EnEnT=0=pm.En_initial
TVu1={pm.nism.Z23+pm.msm.Zpm.grsm.kusm.Enpm.grpm.gu+1-sm.kupm.gusm.En-pm.musm.Vu1-pm.k2sm.Vu1sm.Cifsm.switch10pm.grpm.mpm.mu_usm.Vu1pm.gu-pm.musm.Vu1-pm.k2sm.Csm.Vu1ifsm.switch1<0sm.switch2<0sm.switch2-pm.delta_Vmaxsm.kupm.gusm.Enpm.nism.Z23+pm.delta_Vmaxsm.En+pm.delta_Vmaxpm.omegpm.gr-pm.musm.Vu1-pm.k2sm.Csm.Vu1otherwiseVu1T=0=pm.Vu1_initial
TVu2=pm.k2sm.Csm.Vu1-pm.k1sm.Vu2Vu2T=0=0
TVu3=pm.k1sm.Vu2-pm.k1sm.Vu3Vu3T=0=0
TVu4=pm.k1sm.Vu3-pm.k1sm.Vu4Vu4T=0=0

Observation Model: om1

Continuous Observation

Y1=sm.W+additiveErroradditive=sm.W_err+pm.eps_RES_W

Observation Model: om2

Continuous Observation

Y2=sm.Wu+additiveErroradditive=sm.Wu_err+pm.eps_RES_Wu

External Dataset

OID
nm_ds
Tool Format
NONMEM

File Specification

Format
csv
Delimiter
comma
File Location
Simulated_DEB_TGI_data.csv

Column Definitions

Column ID Position Column Type Value Type
ID
1
id
int
TIME
2
idv
real
DV
3
dv
real
DVID
4
dvid
int
AMT
5
dose
real
EVID
6
evid
real
CMT
7
cmt
int

Column Mappings

Column Ref Modelling Mapping
TIME
T
DV
{om1.Y1ifDVID=1om2.Y2ifDVID=2
AMT
{sm.Q1ifAMT>0

Estimation Step

OID
estimStep_1
Dataset Reference
nm_ds

Parameters To Estimate

Parameter Initial Value Fixed? Limits
pm.K10_POP
20.832
true
pm.K12_POP
0.144
true
pm.K21_POP
2.011
true
pm.V1_POP
813.1
true
pm.En_initial_POP
1.3
true
pm.xi_POP
0.184
true
pm.ni_POP
1.2242
true
pm.gr_POP
12.2
true
pm.V1inf_POP
22.6
true
pm.rho_b_POP
1
true
pm.mu_POP
0.0223
false
0
pm.mu_u_POP
13.3
false
0
pm.gu_POP
11.7
false
0
pm.delta_Vmax_POP
0.185
false
0
pm.W_initial_POP
21.2
false
0
pm.Vu1_initial_POP
0.0023
false
0
pm.IC50_POP
0.461
false
0
pm.k1_POP
0.462
false
0
pm.k2_POP
6.53E-4
false
0
pm.b_W
0.101
false
0
pm.b_Wu
0.134
false
0

Operations

Operation: 1

Op Type
generic
Operation Properties
Name Value
algo
foce

Step Dependencies

Step OID Preceding Steps
estimStep_1
 
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