DDMODEL00000007: DelBene_2009_oncology_in_vitro
Short description:
PD model describing the effect of anticancer agents on in vitro human A2780 ovarian carcinoma cell growth
PharmML 0.8.x (0.8.1) 



Paolo Magni

Context of model development:  Candidate Comparison, Selection, Human Dose Prediction; 
Model compliance with original publication:  Yes; 
Model implementation requiring submitter’s additional knowledge:  No; 
Modelling context description:  PURPOSE: The use of in vitro screening tests for characterizing the activity of anticancer agents is a standard practice in oncology research and development. In these studies, human A2780 ovarian carcinoma cells cultured in plates are exposed to different concentrations of the compounds for different periods of time. Their anticancer activity is then quantified in terms of EC(50) comparing the number of metabolically active cells present in the treated and the control arms at specified time points. The major concern of this methodology is the observed dependency of the EC(50) on the experimental design in terms of duration of exposure. This dependency could affect the efficacy ranking of the compounds, causing possible biases especially in the screening phase, when compound selection is the primary purpose of the in vitro analysis. To overcome this problem, the applicability of a modeling approach to these in vitro studies was evaluated. METHODS: The model, consisting of a system of ordinary differential equations, represents the growth of tumor cells using a few identifiable and biologically relevant parameters related to cell proliferation dynamics and drug action. In particular, the potency of the compounds can be measured by a unique and drugspecific parameter that is essentially independent of drug concentration and exposure time. Parameter values were estimated using weighted nonlinear least squares. RESULTS: The model was able to adequately describe the growth of tumor cells at different experimental conditions. The approach was validated both on commercial drugs and discovery candidate compounds. In addition, from this model the relationship between EC(50) and the exposure time was derived in an analytic form. CONCLUSIONS: The proposed approach provides a new tool for predicting and/or simulating cell responses to different treatments with useful indications for optimizing in vitro experimental designs. The estimated potency parameter values obtained from different compounds can be used for an immediate ranking of anticancer activity.; 
Modelling task in scope:  estimation; 
Nature of research:  Preclinical development; In vitro; 
Therapeutic/disease area:  Oncology; 
Annotations are correct. 

This model is not certified. 
 Additional Files
 Simulated_delbene2009_data_MLX.csv
 Executable_DelBene_2009_oncology_in_vitro_MLX_project.mlxtran
 Executable_DelBene_2009_oncology_in_vitro.mdl
 Model_Accomodations.txt
 DDMODEL00000007.rdf
 Simulated_delbene2009_data.csv
 Executable_DelBene_2009_oncology_in_vitro_MLX_model.txt
 Executable_DelBene_2009_oncology_in_vitro.ctl
 Output_simulated_DelBene.pdf
 Command.txt
 Model owner: Paolo Magni
 Submitted: Sep 25, 2014 5:50:36 PM
 Last Modified: Oct 10, 2016 7:42:41 PM
Revisions

Version: 11
 Submitted on: Oct 10, 2016 7:42:41 PM
 Submitted by: Paolo Magni
 With comment: Edited model metadata online.

Version: 9
 Submitted on: May 24, 2016 11:25:29 PM
 Submitted by: Paolo Magni
 With comment: Edited model metadata online.

Version: 6
 Submitted on: Dec 10, 2015 10:16:45 PM
 Submitted by: Paolo Magni
 With comment: Edited model metadata online.

Version: 2
 Submitted on: Sep 25, 2014 5:50:36 PM
 Submitted by: Paolo Magni
 With comment: Edited model metadata online.
Name
Generated from MDL. MOG ID: delbene2009
Independent Variables

Function Definitions
$\mathrm{proportionalError}:\mathrm{real}\left(\mathrm{proportional}:\mathrm{real},f:\mathrm{real}\right)=\mathrm{proportional}\cdot f$

Covariate Model: $\mathrm{cm}$
Continuous Covariates
$\mathrm{CONC}$
Parameter Model: $\mathrm{pm}$
Random Variables
${\mathrm{eps\_RES\_W}}_{\mathrm{vm\_err.DV}}~\mathrm{Normal2}\left(\mathrm{mean}=0,\mathrm{var}=1\right)$
Population Parameters
$\mathrm{POP\_LAMBDA0}$
$\mathrm{POP\_K1}$
$\mathrm{POP\_K2}$
$\mathrm{POP\_N0}$
$\mathrm{CV}$
Individual Parameters
$\mathrm{LAMBDA0}=\mathrm{pm.POP\_LAMBDA0}$
$\mathrm{K1}=\mathrm{pm.POP\_K1}$
$\mathrm{K2}=\mathrm{pm.POP\_K2}$
$\mathrm{N0}=\mathrm{pm.POP\_N0}$
Structural Model: $\mathrm{sm}$
Variables
$\mathrm{NT}=\mathrm{sm.NP}+\mathrm{sm.N1}+\mathrm{sm.N2}+\mathrm{sm.N3}$
$\begin{array}{c}\frac{d}{dT}\mathrm{NP}=\mathrm{pm.LAMBDA0}\cdot \mathrm{sm.NP}\mathrm{pm.K2}\cdot \mathrm{cm.CONC}\cdot \mathrm{sm.NP}\\ \mathrm{NP}\left(T=0\right)=\mathrm{pm.N0}\end{array}$
$\begin{array}{c}\frac{d}{dT}\mathrm{N1}=\mathrm{pm.K2}\cdot \mathrm{cm.CONC}\cdot \mathrm{sm.NP}\mathrm{pm.K1}\cdot \mathrm{sm.N1}\\ \mathrm{N1}\left(T=0\right)=0\end{array}$
$\begin{array}{c}\frac{d}{dT}\mathrm{N2}=\mathrm{pm.K1}\cdot \mathrm{sm.N1}\mathrm{pm.K1}\cdot \mathrm{sm.N2}\\ \mathrm{N2}\left(T=0\right)=0\end{array}$
$\begin{array}{c}\frac{d}{dT}\mathrm{N3}=\mathrm{pm.K1}\cdot \mathrm{sm.N2}\mathrm{pm.K1}\cdot \mathrm{sm.N3}\\ \mathrm{N3}\left(T=0\right)=0\end{array}$
Observation Model: $\mathrm{om1}$
Continuous Observation
$Y=\mathrm{sm.NT}+\mathrm{proportionalError}\left(\mathrm{proportional}=\mathrm{pm.CV},f=\mathrm{sm.NT}\right)+\mathrm{pm.eps\_RES\_W}$
External Dataset
OID

$\mathrm{nm\_ds}$

Tool Format

NONMEM

File Specification
Format

$\mathrm{csv}$

Delimiter

comma

File Location

Simulated_delbene2009_data.csv

Column Definitions
Column ID  Position  Column Type  Value Type 

$\mathrm{ID}$ 
$1$

$\mathrm{id}$

$\mathrm{int}$

$\mathrm{TIME}$ 
$2$

$\mathrm{idv}$

$\mathrm{real}$

$\mathrm{DV}$ 
$3$

$\mathrm{dv}$

$\mathrm{real}$

$\mathrm{CONC}$ 
$4$

$\mathrm{covariate}$

$\mathrm{real}$

$\mathrm{MDV}$ 
$5$

$\mathrm{mdv}$

$\mathrm{int}$

Column Mappings
Column Ref  Modelling Mapping 

$\mathrm{ID}$ 
$\mathrm{vm\_mdl.ID}$ 
$\mathrm{TIME}$ 
$T$ 
$\mathrm{DV}$ 
$\mathrm{om1.Y}$ 
$\mathrm{CONC}$ 
$\mathrm{cm.CONC}$ 
Estimation Step
OID

$\mathrm{estimStep\_1}$

Dataset Reference

$\mathrm{nm\_ds}$

Parameters To Estimate
Parameter  Initial Value  Fixed?  Limits 

pm.POP_LAMBDA0 
$0.1$

false

$\left(0,\right)$

pm.POP_K1 
$0.1$

false

$\left(0,\right)$

pm.POP_K2 
$0.1$

false

$\left(0,\right)$

pm.POP_N0 
$1000$

false

$\left(0,\right)$

pm.CV 
$0.1$

false

$\left(0,\right)$

Operations
Operation: $1$
Op Type

generic

Operation Properties
Name  Value 

algo

$\text{foce}$

Step Dependencies
Step OID  Preceding Steps 

$\mathrm{estimStep\_1}$
