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DDMODEL00000006: Executable_Simeoni_2004_oncology_TGI

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PKPD model of Tumor Growth Kinetics in xenograft models after administration of anticancer agents
PharmML 0.8.x (0.8.1)
  • Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents.
  • Simeoni M, Magni P, Cammia C, De Nicolao G, Croci V, Pesenti E, Germani M, Poggesi I, Rocchetti M
  • Cancer research, 2/2004, Volume 64, Issue 3, pages: 1094-1101
  • Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, and Pharmacia Italia S.p.A., Nerviano. Milan, Italy.
  • The available mathematical models describing tumor growth and the effect of anticancer treatments on tumors in animals are of limited use within the drug industry. A simple and effective model would allow applying quantitative thinking to the preclinical development of oncology drugs. In this article, a minimal pharmacokinetic-pharmacodynamic model is presented, based on a system of ordinary differential equations that link the dosing regimen of a compound to the tumor growth in animal models. The growth of tumors in nontreated animals is described by an exponential growth followed by a linear growth. In treated animals, the tumor growth rate is decreased by a factor proportional to both drug concentration and number of proliferating tumor cells. A transit compartmental system is used to model the process of cell death, which occurs at later times. The parameters of the pharmacodynamic model are related to the growth characteristics of the tumor, to the drug potency, and to the kinetics of the tumor cell death. Therefore, such parameters can be used for ranking compounds based on their potency and for evaluating potential differences in the tumor cell death process. The model was extensively tested on discovery candidates and known anticancer drugs. It fitted well the experimental data, providing reliable parameter estimates. On the basis of the parameters estimated in a first experiment, the model successfully predicted the response of tumors exposed to drugs given at different dose levels and/or schedules. It is, thus, possible to use the model prospectively, optimizing the design of new experiments.
Paolo Magni
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  • Model owner: Paolo Magni
  • Submitted: Sep 25, 2014 5:40:49 PM
  • Last Modified: Oct 5, 2016 11:16:01 PM
Revisions
  • Version: 9 public model Download this version
    • Submitted on: Oct 5, 2016 11:16:01 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.
  • Version: 7 public model Download this version
    • Submitted on: Oct 5, 2016 10:45:35 PM
    • Submitted by: Paolo Magni
    • With comment: Update MDL syntax to the version 1.0 and R script to SEE version 2.0.0. Code automatically generated for NONMEM and MONOLIX
  • Version: 6 public model Download this version
    • Submitted on: May 24, 2016 11:10:38 PM
    • Submitted by: Paolo Magni
    • With comment: Updated model annotations.
  • Version: 5 public model Download this version
    • Submitted on: May 24, 2016 11:00:52 PM
    • Submitted by: Paolo Magni
    • With comment: Updated model annotations.
  • Version: 3 public model Download this version
    • Submitted on: Dec 10, 2015 9:38:58 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.
  • Version: 1 public model Download this version
    • Submitted on: Sep 25, 2014 5:40:49 PM
    • Submitted by: Paolo Magni
    • With comment: Import of Simeoni_2004_oncology_TGI
 
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