Description du projet
Modéliser l’évolution de la résistance bactérienne aux antibiotiques
La résistance bactérienne aux antibiotiques est un énorme défi de santé publique. La coexistence de génotypes sensibles et de génotypes résistants aux antibiotiques au sein d’une même population reste un problème non résolu. Les modèles épidémiologiques actuels prédisent la dominance de l’une ou l’autre des deux souches ou souffrent de généralité. Le projet PolyPath, financé par l’UE, vise à résoudre ce problème en couplant la dynamique des pathogènes au sein de l’hôte et la transmission des bactéries entre les hôtes. La modélisation du système intra-hôte permettra de prédire le taux d’apparition de la résistance et l’abondance des bactéries sensibles et résistantes avec ou sans traitement antibiotique. Les bactéries résistantes se développent sous traitement antibiotique, tandis que la souche sensible a l’avantage d’envahir et de coloniser les hôtes non traités. Le projet contribuera à l’identification des stratégies de traitement optimales pour résoudre le problème de la résistance aux antibiotiques.
Objectif
Understanding and controlling the evolution of antibiotic resistant strains is one of the biggest public health challenges of our time. Despite a vast amount of data gathered and models being developed, coexistence of antibiotic resistant and sensitive genotypes within the same bacterial pathogen is still an unresolved problem. Simple epidemiological models predict the dominance of either of the two strains while more complex models suffer from generality. Using empirical evidence, I set out to resolve this problem by coupling within-host pathogen dynamics and between-host transmission of bacteria. First, stochastically modelling the within-host system I will develop predictions for the rate of resistance emergence and abundance of sensitive and resistant individuals in hosts with or without antibiotic treatment. While resistant bacteria thrive under antibiotic treatment, the sensitive strain has an advantage in invading and colonising untreated hosts. The outcomes help to get a more detailed understanding of the within-host dynamics, e.g. identification of optimal treatment strategies to confine the evolution of antibiotic resistance. Feeding these results into the dynamics on the population level, the between-host level, will result in a within-between-host feedback. Fitting and confronting the model to empirical data on prevalence and resistance emergence in Streptococcus pneuomoniae and Escherichia coli will conclude this project. The mechanistic implementation of the dynamics can immediately be linked to data which is of great importance given the increasing amount of empirical studies in the field of epidemiology. Through the theoretical and applied results, the study will add new insights and predictions in the field of infectious disease evolution and be able to identify factors enabling the stable coexistence of antibiotic resistant and sensitive bacteria.
Champ scientifique
- natural sciencesbiological sciencesmicrobiologybacteriology
- medical and health scienceshealth sciencespublic healthepidemiology
- medical and health scienceshealth sciencesinfectious diseases
- medical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugsantibiotics
- medical and health sciencesbasic medicinepharmacology and pharmacydrug resistanceantibiotic resistance
Mots‑clés
Programme(s)
Régime de financement
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinateur
75006 Paris
France