Literature DB >> 21077714

An elementary approach to modeling drug resistance in cancer.

Cristian Tomasetti1, Doron Levy.   

Abstract

Resistance to drugs has been an ongoing obstacle to a successful treatment of many diseases. In this work we consider the problem of drug resistance in cancer, focusing on random genetic point mutations. Most previous works on mathematical models of such drug resistance have been based on stochastic methods. In contrast, our approach is based on an elementary, compartmental system of ordinary differential equations. We use our very simple approach to derive results on drug resistance that are comparable to those that were previously obtained using much more complex mathematical techniques. The simplicity of our model allows us to obtain analytic results for resistance to any number of drugs. In particular, we show that the amount of resistance generated before the start of the treatment, and present at some given time afterward, always depends on the turnover rate, no matter how many drugs are simultaneously used in the treatment.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 21077714      PMCID: PMC3877932          DOI: 10.3934/mbe.2010.7.905

Source DB:  PubMed          Journal:  Math Biosci Eng        ISSN: 1547-1063            Impact factor:   2.080


  38 in total

1.  Evolution of resistance during clonal expansion.

Authors:  Yoh Iwasa; Martin A Nowak; Franziska Michor
Journal:  Genetics       Date:  2006-04       Impact factor: 4.562

2.  The dynamics of gene amplification described as a multitype compartmental model and as a branching process.

Authors:  L E Harnevo; Z Agur
Journal:  Math Biosci       Date:  1991-02       Impact factor: 2.144

Review 3.  Use of mathematical models for understanding the dynamics of gene amplification.

Authors:  L E Harnevo; Z Agur
Journal:  Mutat Res       Date:  1993-08       Impact factor: 2.433

4.  The Norton-Simon hypothesis revisited.

Authors:  L Norton; R Simon
Journal:  Cancer Treat Rep       Date:  1986-01

Review 5.  Gene amplification in cultured cells.

Authors:  R T Schimke
Journal:  J Biol Chem       Date:  1988-05-05       Impact factor: 5.157

6.  Rationale for the use of alternating non-cross-resistant chemotherapy.

Authors:  J H Goldie; A J Coldman; G A Gudauskas
Journal:  Cancer Treat Rep       Date:  1982-03

7.  Drug resistance in cancer: principles of emergence and prevention.

Authors:  Natalia L Komarova; Dominik Wodarz
Journal:  Proc Natl Acad Sci U S A       Date:  2005-06-24       Impact factor: 11.205

8.  Role of mathematical modeling in protocol formulation in cancer chemotherapy.

Authors:  A J Coldman; J H Goldie
Journal:  Cancer Treat Rep       Date:  1985-10

9.  Quantitative model for multiple levels of drug resistance in clinical tumors.

Authors:  J H Goldie; A J Coldman
Journal:  Cancer Treat Rep       Date:  1983-10

10.  Effect of cellular quiescence on the success of targeted CML therapy.

Authors:  Natalia L Komarova; Dominik Wodarz
Journal:  PLoS One       Date:  2007-10-03       Impact factor: 3.240

View more
  16 in total

1.  On the probability of random genetic mutations for various types of tumor growth.

Authors:  Cristian Tomasetti
Journal:  Bull Math Biol       Date:  2012-02-07       Impact factor: 1.758

2.  Large population solution of the stochastic Luria-Delbruck evolution model.

Authors:  David A Kessler; Herbert Levine
Journal:  Proc Natl Acad Sci U S A       Date:  2013-07-01       Impact factor: 11.205

3.  Scaling solution in the large population limit of the general asymmetric stochastic Luria-Delbrück evolution process.

Authors:  David A Kessler; Herbert Levine
Journal:  J Stat Phys       Date:  2014-11-15       Impact factor: 1.548

Review 4.  An argument for mechanism-based statistical inference in cancer.

Authors:  Donald Geman; Michael Ochs; Nathan D Price; Cristian Tomasetti; Laurent Younes
Journal:  Hum Genet       Date:  2014-11-09       Impact factor: 4.132

5.  Optimal Strategy and Benefit of Pulsed Therapy Depend On Tumor Heterogeneity and Aggressiveness at Time of Treatment Initiation.

Authors:  Deepti Mathur; Bradford P Taylor; Walid K Chatila; Howard I Scher; Nikolaus Schultz; Pedram Razavi; Joao B Xavier
Journal:  Mol Cancer Ther       Date:  2022-05-04       Impact factor: 6.009

6.  The impact of cell density and mutations in a model of multidrug resistance in solid tumors.

Authors:  James Greene; Orit Lavi; Michael M Gottesman; Doron Levy
Journal:  Bull Math Biol       Date:  2014-02-20       Impact factor: 1.758

7.  A data-driven predictive approach for drug delivery using machine learning techniques.

Authors:  Yuanyuan Li; Scott C Lenaghan; Mingjun Zhang
Journal:  PLoS One       Date:  2012-02-23       Impact factor: 3.240

8.  Modeling drug resistance in a conjoint normal-tumor setting.

Authors:  Mitra Shojania Feizabadi; Tarynn M Witten
Journal:  Theor Biol Med Model       Date:  2015-01-15       Impact factor: 2.432

9.  Modeling multi-mutation and drug resistance: analysis of some case studies.

Authors:  Mitra Shojania Feizabadi
Journal:  Theor Biol Med Model       Date:  2017-03-21       Impact factor: 2.432

10.  Applying ecological and evolutionary theory to cancer: a long and winding road.

Authors:  Frédéric Thomas; Daniel Fisher; Philippe Fort; Jean-Pierre Marie; Simon Daoust; Benjamin Roche; Christoph Grunau; Céline Cosseau; Guillaume Mitta; Stephen Baghdiguian; François Rousset; Patrice Lassus; Eric Assenat; Damien Grégoire; Dorothée Missé; Alexander Lorz; Frédérique Billy; William Vainchenker; François Delhommeau; Serge Koscielny; Raphael Itzykson; Ruoping Tang; Fanny Fava; Annabelle Ballesta; Thomas Lepoutre; Liliana Krasinska; Vjekoslav Dulic; Peggy Raynaud; Philippe Blache; Corinne Quittau-Prevostel; Emmanuel Vignal; Hélène Trauchessec; Benoit Perthame; Jean Clairambault; Vitali Volpert; Eric Solary; Urszula Hibner; Michael E Hochberg
Journal:  Evol Appl       Date:  2012-11-16       Impact factor: 5.183

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.