Controlling the evolution of antibiotic resistance
Author(s)
Pena-Miller, Rafael
Type
Thesis
Abstract
Modelling antibiotic resistance evolution is inherently a multiscale problem: from the physical
interactions between drug molecules and their targets to the epidemiology of drug resistance
in clinical settings. Although predicting the evolution of resistance is a difficult
and ongoing problem, it is known that pathogens are continually adapting to our drug prescription
patterns. For this reason, as well as the continual downturn in the discovery of
new drugs, the question of how to best deploy antibiotics has never been more pressing.
The purpose of this thesis is to use tools from control and systems theory to ask the following
fundamental question: How can we design rational antibiotic deployment strategies
that do not promote the evolution of antimicrobial resistance?
By re-examining epidemiological models from the literature, in the first part of this thesis
we show that the optimal drug deployment protocol has a universal structure not determined
by biological detail. This class of epidemiological models, however, provide insight into
the underlying mechanisms that influence the spread of disease at the population level but
fail to capture the complex molecular interactions between different antibiotics and bacteria,
as well as to provide an experimental system to test the efficacy of different treatment
protocols. Therefore in the second part of the thesis we pose an evolutionary model of an
experimental microbial system that allows us to study drug interactions and the effect that
combination treatments have on the evolution of multidrug resistance. Again, using optimal
control theory we design drug deployment protocols that minimise conditions promoting
the evolution of antimicrobial resistance in a single host.
Finally, in the last part of the thesis we propose an epidemiological model where patients
are considered as individual agents receiving antimicrobial treatment in a clinical setting.
This stochastic and spatially explicit model allows us the possibility to evaluate the efficacy
of different drug usage strategies. We conclude with a general principle: the best
performing drug usage policies utilise the highest quality of available information.
interactions between drug molecules and their targets to the epidemiology of drug resistance
in clinical settings. Although predicting the evolution of resistance is a difficult
and ongoing problem, it is known that pathogens are continually adapting to our drug prescription
patterns. For this reason, as well as the continual downturn in the discovery of
new drugs, the question of how to best deploy antibiotics has never been more pressing.
The purpose of this thesis is to use tools from control and systems theory to ask the following
fundamental question: How can we design rational antibiotic deployment strategies
that do not promote the evolution of antimicrobial resistance?
By re-examining epidemiological models from the literature, in the first part of this thesis
we show that the optimal drug deployment protocol has a universal structure not determined
by biological detail. This class of epidemiological models, however, provide insight into
the underlying mechanisms that influence the spread of disease at the population level but
fail to capture the complex molecular interactions between different antibiotics and bacteria,
as well as to provide an experimental system to test the efficacy of different treatment
protocols. Therefore in the second part of the thesis we pose an evolutionary model of an
experimental microbial system that allows us to study drug interactions and the effect that
combination treatments have on the evolution of multidrug resistance. Again, using optimal
control theory we design drug deployment protocols that minimise conditions promoting
the evolution of antimicrobial resistance in a single host.
Finally, in the last part of the thesis we propose an epidemiological model where patients
are considered as individual agents receiving antimicrobial treatment in a clinical setting.
This stochastic and spatially explicit model allows us the possibility to evaluate the efficacy
of different drug usage strategies. We conclude with a general principle: the best
performing drug usage policies utilise the highest quality of available information.
Date Issued
2011-01
Date Awarded
2011-03
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Beardmore, Robert
Sponsor
CONACYT and SEP
Creator
Pena-Miller, Rafael
Publisher Department
Mathematics
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)