Predicting antibiotic resistance
Date du début: 1 avr. 2010,
Date de fin: 31 mars 2013
"The aim of this proposal is to describe and predict the dynamics of antibiotic resistance development at the level of the drug target, the microbe and the host. That is, we want to generate the knowledge required to be able to connect resistance mechanism --> bacterial physiology and fitness --> bacterial survival within a host --> bacterial spread between hosts. To achieve this goal we will develop quantitative models that can capture these complex dynamics, obtain relevant parameter values and validate the models by testing them in suitable in vitro, animal models and in clinical settings. We will address three main areas (A-C) in 11 different work packages. A. Formation and emergence of resistant bacteria (WPs1-5). The rate of formation of resistant mutants is one major determinant that influences how rapidly resistant mutants will appear in response to antibiotic use. Depending on the type of resistance mechanism factors such as rates of mutation, recombination and transfer of plasmids will be important. In WPs 1-5 we will experimentally determine these rates for different combinations of bacterial species and resistance mechanisms. B. Survival and persistence of resistant strains (WPs 6-8). A key factor that affects the survival and persistence of resistant strains is the impact the particular resistance mechanism has on bacterial physiology, including bacterial growth rate and virulence. We want to determine the effect of various drug resistance mechanisms on cellular physiology and fitness to establish a link between the molecular alteration and potential effects at the cellular level. C. Transmission of resistant strains (WPs 9-11). Finally we want to understand and predict how resistance mechanisms (by their effect on bacterial physiology) influence the ability of the bacteria to be transmitted between hosts. Thus, we aim to generate both animal and clinical determinations of how resistance affects transmissibility and testable mathematical models."
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