# 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
 Format: PharmML 0.8.x (0.8.1) Related Publication: .hiddenContent {display:none;} A model-based approach to the in vitro evaluation of anticancer activity. Del Bene F, Germani M, De Nicolao G, Magni P, Re CE, Ballinari D, Rocchetti M Cancer chemotherapy and pharmacology, 4/2009, Volume 63, Issue 5, pages: 827-836 Affiliation: Accelera, Nerviano Medical Sciences, Nerviano (MI), Italy. Università degli Studi di Pavia, Pavia, Italy Abstract: 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 drug-specific 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. Contributors: 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 drug-specific 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: In vitro; Preclinical development; Therapeutic/disease area: Oncology;
 Validation Status: Annotations are correct. Certification Comment: This model is not certified.
• Model owner: Paolo Magni
• Submitted: Sep 25, 2014 5:50:36 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

 T

### 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
$ID$
$\mathrm{vm_mdl.ID}$
$TIME$
$T$
$DV$
$\mathrm{om1.Y}$
$CONC$
$\mathrm{cm.CONC}$

## 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
$\mathrm{estimStep_1}$