Rocchetti_2013_oncology_TGI_combo

  public model
Short description:
PKPD model of tumor growth after administration of an anti-angiogenic agent, bevacizumab, as single-agent and combination therapy in tumor xenografts
Original code
  • Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth after administration of an anti-angiogenic agent, bevacizumab, as single-agent and combination therapy in tumor xenografts.
  • Rocchetti M, Germani M, Del Bene F, Poggesi I, Magni P, Pesenti E, De Nicolao G
  • Cancer chemotherapy and pharmacology, 5/2013, Volume 71, Issue 5, pages: 1147-1157
  • Pharmacokinetics and Modeling, Accelera S.r.l., Nerviano, MI, Italy. Università degli Studi di Pavia, Pavia, Italy.
  • PURPOSE: Pharmacokinetic-pharmacodynamic (PK-PD) models able to predict the action of anticancer compounds in tumor xenografts have an important impact on drug development. In case of anti-angiogenic compounds, many of the available models show difficulties in their applications, as they are based on a cell kill hypothesis, while these drugs act on the tumor vascularization, without a direct tumor cell kill effect. For this reason, a PK-PD model able to describe the tumor growth modulation following treatment with a cytostatic therapy, as opposed to a cytotoxic treatment, is proposed here. METHODS: Untreated tumor growth was described using an exponential growth phase followed by a linear one. The effect of anti-angiogenic compounds was implemented using an inhibitory effect on the growth function. The model was tested on a number of experiments in tumor-bearing mice given the anti-angiogenic drug bevacizumab either alone or in combination with another investigational compound. Nonlinear regression techniques were used for estimating the model parameters. RESULTS: The model successfully captured the tumor growth data following different bevacizumab dosing regimens, allowing to estimate experiment-independent parameters. A combination model was also developed under a 'no-interaction' assumption to assess the effect of the combination of bevacizumab with a target-oriented agent. The observation of a significant difference between model-predicted and observed tumor growth curves was suggestive of the presence of a pharmacological interaction that was further accommodated into the model. CONCLUSIONS: This approach can be used for optimizing the design of preclinical experiments. With all the inherent limitations, the estimated experiment-independent model parameters can be used to provide useful indications for the single-agent and combination regimens to be explored in the subsequent development phases.
Paolo Magni
Context of model development: Combination Therapy Dose Selection; Candidate Comparison, Selection, Human Dose Prediction;
Model compliance with original publication: Yes;
Model implementation requiring submitter’s additional knowledge: No;
Modelling context description: PURPOSE: Pharmacokinetic-pharmacodynamic (PK-PD) models able to predict the action of anticancer compounds in tumor xenografts have an important impact on drug development. In case of anti-angiogenic compounds, many of the available models show difficulties in their applications, as they are based on a cell kill hypothesis, while these drugs act on the tumor vascularization, without a direct tumor cell kill effect. For this reason, a PK-PD model able to describe the tumor growth modulation following treatment with a cytostatic therapy, as opposed to a cytotoxic treatment, is proposed here. METHODS: Untreated tumor growth was described using an exponential growth phase followed by a linear one. The effect of anti-angiogenic compounds was implemented using an inhibitory effect on the growth function. The model was tested on a number of experiments in tumor-bearing mice given the anti-angiogenic drug bevacizumab either alone or in combination with another investigational compound. Nonlinear regression techniques were used for estimating the model parameters. RESULTS: The model successfully captured the tumor growth data following different bevacizumab dosing regimens, allowing to estimate experiment-independent parameters. A combination model was also developed under a 'no-interaction' assumption to assess the effect of the combination of bevacizumab with a target-oriented agent. The observation of a significant difference between model-predicted and observed tumor growth curves was suggestive of the presence of a pharmacological interaction that was further accommodated into the model. CONCLUSIONS: This approach can be used for optimizing the design of preclinical experiments. With all the inherent limitations, the estimated experiment-independent model parameters can be used to provide useful indications for the single-agent and combination regimens to be explored in the subsequent development phases.;
Modelling task in scope: estimation;
Nature of research: Preclinical development; In vivo;
Therapeutic/disease area: Oncology;
Annotations are correct.
This model is not certified.
  • Model owner: Paolo Magni
  • Submitted: Sep 26, 2014 11:18:04 AM
  • Last Modified: Oct 10, 2016 7:53:05 PM
Revisions
  • Version: 6 public model Download this version
    • Submitted on: Oct 10, 2016 7:53:05 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.
  • Version: 4 public model Download this version
    • Submitted on: May 24, 2016 11:17:40 PM
    • Submitted by: Paolo Magni
    • With comment: Updated model annotations.
  • Version: 3 public model Download this version
    • Submitted on: Dec 10, 2015 10:37:36 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.
  • Version: 1 public model Download this version
    • Submitted on: Sep 26, 2014 11:18:04 AM
    • Submitted by: Paolo Magni
    • With comment: Import of Rocchetti_2013_oncology_TGI_antiangiogenic_combo
 
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