Using one health approaches to study effects of antibiotic stewardship on AMR development

Antimicrobial resistance (AMR) is a growing global threat to public health expected to impact 10 million people by 2050, with a disproportionate effect on low- and middle- income countries, that is further exacerbated in communities living in urban informal settlements and refugee camps. As a result, there is a heightened urgency to understand how current antibiotic use is driving the spread of drug resistance in communities with high population density and those that are in proximity to wastewater settings and environmentally contaminated surroundings. Currently, there is a limited quantitative and mechanistic understanding of the evolution and spread of multidrug resistant (MDR) pathogens in these complex settings where there are a multitude of antibiotic residues and bacterial species present. Computational and experimental work in this area can lead to predictive outcomes and more effective strategies to prevent outbreaks of resistant pathogens. The goal of this thesis was to develop and test an integrated mathematical modeling and high-throughput experimental approach to quantitatively analyze AMR evolution in complex environments. The mathematical model captures predicted behavior for systems with multiple antibiotic residues and metal ions, incorporating the effects of both antibiotic-antibiotic interactions and metal-antibiotic interactions. This model is rooted in fundamental principles of biological systems modeling and was continuously integrated with a novel experimental workflow utilizing the eVOLVER for rapid iterative model development and validation. This work has resulted in the development of a robust method of understanding and predicting the development and spread of MDR bacteria in complex environments and has the potential to provide robust strategies to protect the health of vulnerable populations in these environments

Effects of antibiotic interaction on antimicrobial resistance development in wastewater

Abstract While wastewater is understood to be a critically important reservoir of antimicrobial resistance due to the presence of multiple antibiotic residues from industrial and agricultural runoff, there is little known about the effects of antibiotic interactions in the wastewater on the development of resistance. We worked to fill this gap in quantitative understanding of antibiotic interaction in constant flow environments by experimentally monitoring E. coli populations under subinhibitory concentrations of combinations of antibiotics with synergistic, antagonistic, and additive interactions. We then used these results to expand our previously developed computational model to account for the effects of antibiotic interaction. We found that populations grown under synergistic and antagonistic antibiotic conditions exhibited significant differences from predicted behavior. E. coli populations grown with synergistically interacting antibiotics developed less resistance than predicted, indicating that synergistic antibiotics may have a suppressive effect on resistance development. Furthermore E. coli populations grown with antagonistically interacting antibiotics showed an antibiotic ratio-dependent development of resistance, suggesting that not only antibiotic interaction, but relative concentration is important in predicting resistance development. These results provide critical insight for quantitatively understanding the effects of antibiotic interactions in wastewater and provide a basis for future studies in modelling resistance in these environments