Literature DB >> 16154360

Emergence and prevention of resistance against small molecule inhibitors.

Dominik Wodarz1, Natalia L Komarova.   

Abstract

Small molecule inhibitors target specific metabolic pathways in tumor cells and are a promising class of drugs for the treatment of cancers. The best known example is the treatment of chronic myeloid leukemia (CML) with Gleevec. This is a small molecule inhibitor of the Bcr-Abl kinase which has been shown to drive the initiation and progression of CML. While treatment of early stage CML with Gleevec has been quite successful, later stages of the disease (blast crisis) are not successfully treated due to the emergence of drug resistant cells. It is therefore important to understand the principles according to which drug resistant cells evolve, so that we can design treatment strategies which aim to prevent the rise of resistant cells. Such evolutionary dynamics can be studied with mathematical models, and this article reviews such an approach. We address three specific questions: (i) Do resistant cells emerge before or after the start of therapy? (ii) How does the turnover rate of cancer cells influence the evolution of drug resistant cells? (iii) Can combination therapy be used to prevent drug resistance? We apply our model to the treatment of CML with Gleevec, in order to demonstrate how this mathematical framework can be applied to the treatment of a specific cancer with small molecule inhibitors.

Entities:  

Mesh:

Substances:

Year:  2005        PMID: 16154360     DOI: 10.1016/j.semcancer.2005.07.002

Source DB:  PubMed          Journal:  Semin Cancer Biol        ISSN: 1044-579X            Impact factor:   15.707


  12 in total

1.  Controlling the Evolution of Resistance.

Authors:  Rutao Luo; Lamont Cannon; Jason Hernandez; Michael J Piovoso; Ryan Zurakowski
Journal:  J Process Control       Date:  2011-03-01       Impact factor: 3.666

2.  Use of oncolytic viruses for the eradication of drug-resistant cancer cells.

Authors:  Dominik Wodarz
Journal:  J R Soc Interface       Date:  2009-02-06       Impact factor: 4.118

3.  Characterization and quantification of clonal heterogeneity among hematopoietic stem cells: a model-based approach.

Authors:  Ingo Roeder; Katrin Horn; Hans-Bernd Sieburg; Rebecca Cho; Christa Muller-Sieburg; Markus Loeffler
Journal:  Blood       Date:  2008-09-22       Impact factor: 22.113

Review 4.  The dynamics of drug resistance: a mathematical perspective.

Authors:  Orit Lavi; Michael M Gottesman; Doron Levy
Journal:  Drug Resist Updat       Date:  2012-03-03       Impact factor: 18.500

Review 5.  Anaplastic lymphoma kinase: role in cancer pathogenesis and small-molecule inhibitor development for therapy.

Authors:  Thomas R Webb; Jake Slavish; Rani E George; A Thomas Look; Liquan Xue; Qin Jiang; Xiaoli Cui; Walter B Rentrop; Stephan W Morris
Journal:  Expert Rev Anticancer Ther       Date:  2009-03       Impact factor: 4.512

Review 6.  Pathogenesis, treatment effects, and resistance dynamics in chronic myeloid leukemia--insights from mathematical model analyses.

Authors:  Ingo Roeder; Ingmar Glauche
Journal:  J Mol Med (Berl)       Date:  2007-07-28       Impact factor: 4.599

7.  New experimental and theoretical investigations of hematopoietic stem cells and chronic myeloid leukemia.

Authors:  Ingo Roeder; Mark d'Inverno
Journal:  Blood Cells Mol Dis       Date:  2009-05-02       Impact factor: 3.039

Review 8.  Mathematical models of targeted cancer therapy.

Authors:  L H Abbott; F Michor
Journal:  Br J Cancer       Date:  2006-10-10       Impact factor: 7.640

9.  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

10.  Dynamical models of mutated chronic myelogenous leukemia cells for a post-imatinib treatment scenario: Response to dasatinib or nilotinib therapy.

Authors:  Clemens Woywod; Franz X Gruber; Richard A Engh; Tor Flå
Journal:  PLoS One       Date:  2017-07-05       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.