Literature DB >> 16823607

The probability of treatment induced drug resistance.

Rinaldo B Schinazi1.   

Abstract

We propose a discrete time branching process to model the appearance of drug resistance under treatment. Under our assumptions at every discrete time a pathogen may die with probability 1-p or divide in two with probability p. Each newborn pathogen is drug resistant with probability mu. We start with N drug sensitive pathogens and with no drug resistant pathogens. We declare the treatment successful if all pathogens are eradicated before drug resistance appears. The model predicts that success is possible only if p<1/2. Even in this case the probability of success decreases exponentially with the parameter m=muN. In particular, even with a very potent drug (i.e. p very small) drug resistance is likely if m is large.

Mesh:

Year:  2006        PMID: 16823607     DOI: 10.1007/s10441-006-6545-4

Source DB:  PubMed          Journal:  Acta Biotheor        ISSN: 0001-5342            Impact factor:   1.774


  2 in total

1.  A stochastic model for cancer risk.

Authors:  Rinaldo B Schinazi
Journal:  Genetics       Date:  2006-07-18       Impact factor: 4.562

Review 2.  What should be considered if you decide to build your own mathematical model for predicting the development of bacterial resistance? Recommendations based on a systematic review of the literature.

Authors:  Maria Arepeva; Alexey Kolbin; Alexey Kurylev; Julia Balykina; Sergey Sidorenko
Journal:  Front Microbiol       Date:  2015-04-29       Impact factor: 5.640

  2 in total

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