Literature DB >> 19135065

Modelling chemotherapy resistance in palliation and failed cure.

Helen C Monro1, Eamonn A Gaffney.   

Abstract

The goal of palliative cancer chemotherapy treatment is to prolong survival and improve quality of life when tumour eradication is not feasible. Chemotherapy protocol design is considered in this context using a simple, robust, model of advanced tumour growth with Gompertzian dynamics, taking into account the effects of drug resistance. It is predicted that reduced chemotherapy protocols can readily lead to improved survival times due to the effects of competition between resistant and sensitive tumour cells. Very early palliation is also predicted to quickly yield near total tumour resistance and thus decrease survival duration. Finally, our simulations indicate that failed curative attempts using dose densification, a common protocol escalation strategy, can reduce survival times.

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Year:  2008        PMID: 19135065     DOI: 10.1016/j.jtbi.2008.12.006

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  11 in total

Review 1.  Evolution of acquired resistance to anti-cancer therapy.

Authors:  Jasmine Foo; Franziska Michor
Journal:  J Theor Biol       Date:  2014-03-25       Impact factor: 2.691

Review 2.  Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development.

Authors:  L V Brown; E A Gaffney; J Wagg; M C Coles
Journal:  Clin Exp Immunol       Date:  2018-09       Impact factor: 4.330

Review 3.  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

4.  Is the Fixed Periodic Treatment Effective for the Tumor System without Complete Information?

Authors:  Jiali Wang; Yixuan Zhang; Xiaoquan Liu; Haochen Liu
Journal:  Cancer Manag Res       Date:  2021-11-30       Impact factor: 3.989

5.  Tumor growth instability and its implications for chemotherapy.

Authors:  Paolo Castorina; Daniela Carcò; Caterina Guiot; Thomas S Deisboeck
Journal:  Cancer Res       Date:  2009-10-27       Impact factor: 12.701

6.  Angiogenesis and chemotherapy resistance: optimizing chemotherapy scheduling using mathematical modeling.

Authors:  Mariusz Bodzioch; Piotr Bajger; Urszula Foryś
Journal:  J Cancer Res Clin Oncol       Date:  2021-05-29       Impact factor: 4.553

7.  How to Use a Chemotherapeutic Agent When Resistance to It Threatens the Patient.

Authors:  Elsa Hansen; Robert J Woods; Andrew F Read
Journal:  PLoS Biol       Date:  2017-02-09       Impact factor: 8.029

8.  Drug-induced resistance evolution necessitates less aggressive treatment.

Authors:  Teemu Kuosmanen; Johannes Cairns; Robert Noble; Niko Beerenwinkel; Tommi Mononen; Ville Mustonen
Journal:  PLoS Comput Biol       Date:  2021-09-23       Impact factor: 4.475

9.  How to mathematically optimize drug regimens using optimal control.

Authors:  Helen Moore
Journal:  J Pharmacokinet Pharmacodyn       Date:  2018-02-06       Impact factor: 2.745

10.  Turnover Modulates the Need for a Cost of Resistance in Adaptive Therapy.

Authors:  Philip K Maini; Alexander R A Anderson; Maximilian A R Strobl; Jeffrey West; Yannick Viossat; Mehdi Damaghi; Mark Robertson-Tessi; Joel S Brown; Robert A Gatenby
Journal:  Cancer Res       Date:  2020-11-10       Impact factor: 12.701

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