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Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

The probability of treatment induced drug resistance

Rinaldo B Schinazi1

  • 1University of Colorado, Colorado Springs, USA. schinazi@math.uccs.edu

Acta Biotheoretica
|July 11, 2006
PubMed

Insights

This study models pathogen drug resistance using a discrete time branching process. Treatment success, preventing resistance, is only possible if pathogen division probability (p) is less than 1/2, decreasing exponentially with increased resistance parameters.

Area of Science:

  • Mathematical Biology
  • Evolutionary Biology
  • Pharmacology

Background:

  • Drug resistance is a major challenge in treating infectious diseases.
  • Understanding the evolutionary dynamics of resistance emergence is crucial for effective treatment strategies.
  • Branching processes offer a framework for modeling population growth and extinction events.

Purpose of the Study:

  • To develop a mathematical model simulating the emergence of drug resistance in pathogens during treatment.
  • To identify conditions under which treatment can be successful before resistance arises.
  • To analyze the impact of key parameters on the probability of treatment success.

Main Methods:

  • A discrete time branching process model was employed.
  • Pathogen dynamics were simulated with probabilities of death (1-p) and division (p).
  • Drug resistance in new pathogens was incorporated with probability mu, starting with N sensitive pathogens.

Main Results:

  • Treatment success is predicted to be possible only when the pathogen division probability (p) is less than 1/2.
  • The probability of successful treatment decreases exponentially as the parameter m (mu*N) increases.
  • Drug resistance is likely to emerge even with potent drugs if the parameter m is large.

Conclusions:

  • The discrete time branching process model provides insights into the dynamics of drug resistance.
  • Treatment success is highly sensitive to pathogen proliferation rates and the initial number/resistance of pathogens.
  • Minimizing the parameter m (mu*N) is critical for successful eradication of drug-sensitive pathogens before resistance develops.

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