Literature DB >> 16516246

A mathematical model to study the effects of drugs administration on tumor growth dynamics.

P Magni1, M Simeoni, I Poggesi, M Rocchetti, G De Nicolao.   

Abstract

A mathematical model for describing the cancer growth dynamics in response to anticancer agents administration in xenograft models is discussed. The model consists of a system of ordinary differential equations involving five parameters (three for describing the untreated growth and two for describing the drug action). Tumor growth in untreated animals is modelled by an exponential growth followed by a linear growth. In treated animals, tumor growth rate is decreased by an additional factor proportional to both drug concentration and proliferating cells. The mathematical analysis conducted in this paper highlights several interesting properties of this tumor growth model. It suggests also effective strategies to design in vivo experiments in animals with potential saving of time and resources. For example, the drug concentration threshold for the tumor eradication, the delay between drug administration and tumor regression, and a time index that measures the efficacy of a treatment are derived and discussed. The model has already been employed in several drug discovery projects. Its application on a data set coming from one of these projects is discussed in this paper.

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Year:  2006        PMID: 16516246     DOI: 10.1016/j.mbs.2005.12.028

Source DB:  PubMed          Journal:  Math Biosci        ISSN: 0025-5564            Impact factor:   2.144


  17 in total

1.  Biomarker- versus drug-driven tumor growth inhibition models: an equivalence analysis.

Authors:  Maria Luisa Sardu; Italo Poggesi; Giuseppe De Nicolao
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-07-26       Impact factor: 2.745

2.  Improvement of Parameter Estimations in Tumor Growth Inhibition Models on Xenografted Animals: a Novel Method to Handle the Interval Censoring Caused by Measurement of Smaller Tumors.

Authors:  Philippe B Pierrillas; Michel Tod; Magali Amiel; Marylore Chenel; Emilie Henin
Journal:  AAPS J       Date:  2016-01-12       Impact factor: 4.009

3.  Improvement of Parameter Estimations in Tumor Growth Inhibition Models on Xenografted Animals: Handling Sacrifice Censoring and Error Caused by Experimental Measurement on Larger Tumor Sizes.

Authors:  Philippe B Pierrillas; Michel Tod; Magali Amiel; Marylore Chenel; Emilie Henin
Journal:  AAPS J       Date:  2016-06-21       Impact factor: 4.009

4.  Modeling the efficacy of trastuzumab-DM1, an antibody drug conjugate, in mice.

Authors:  Nelson L Jumbe; Yan Xin; Douglas D Leipold; Lisa Crocker; Debra Dugger; Elaine Mai; Mark X Sliwkowski; Paul J Fielder; Jay Tibbitts
Journal:  J Pharmacokinet Pharmacodyn       Date:  2010-04-28       Impact factor: 2.745

Review 5.  Array of translational systems pharmacodynamic models of anti-cancer drugs.

Authors:  Sihem Ait-Oudhia; Donald E Mager
Journal:  J Pharmacokinet Pharmacodyn       Date:  2016-10-22       Impact factor: 2.745

6.  Optimal Design for Informative Protocols in Xenograft Tumor Growth Inhibition Experiments in Mice.

Authors:  Giulia Lestini; France Mentré; Paolo Magni
Journal:  AAPS J       Date:  2016-06-15       Impact factor: 4.009

7.  Steering the Clinical Translation of Delivery Systems for Drugs and Health Products.

Authors:  Rosario Pignatello; Pietro Matricardi
Journal:  Pharmaceutics       Date:  2020-04-13       Impact factor: 6.321

8.  Comparison of two pharmacodynamic transduction models for the analysis of tumor therapeutic responses in model systems.

Authors:  Jun Yang; Donald E Mager; Robert M Straubinger
Journal:  AAPS J       Date:  2009-11-10       Impact factor: 4.009

9.  Modeling of tumor growth and anticancer effects of combination therapy.

Authors:  Gilbert Koch; Antje Walz; Gezim Lahu; Johannes Schropp
Journal:  J Pharmacokinet Pharmacodyn       Date:  2009-04-22       Impact factor: 2.745

10.  A predictive pharmacokinetic-pharmacodynamic model of tumor growth kinetics in xenograft mice after administration of anticancer agents given in combination.

Authors:  Nadia Terranova; Massimiliano Germani; Francesca Del Bene; Paolo Magni
Journal:  Cancer Chemother Pharmacol       Date:  2013-06-29       Impact factor: 3.333

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