A mechanistic understanding of the postantibiotic effect and treatment strategies
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Although antibiotics have proven to be one of the great achievements of modern medicine, their efficacy has dramatically decreased over the past several decades. This is due, in part, to the rapid pace of natural bacterial evolution, but also to the overuse and misuse of antibiotics in general. This often selects for drug-resistant pathogens, and allows them to flourish in the face of antibiotic treatment. In addition to the emergence of genetic resistance, bacteria often utilize a number of population-level behaviors to survive antibiotic treatment. This is referred to as collective antibiotic tolerance (CAT). Taken together, antibiotic resistance and tolerance have led to the re-emergence of infectious diseases throughout the world. In general, there are two strategies to combat this risk: develop novel antibiotics, and/or use existing drugs more effectively, so as to minimize the chance of resistance emergence. Novel drug development is a time- and resource-intensive process, and pharmaceutical companies are not financially incentivized to develop these types of drugs. Therefore, it is of increasing importance to understand the population dynamics underlying various bacterial survival mechanisms, and exploit this knowledge to design better antibiotic treatment protocols.
My dissertation research focuses on a prevalent phenomenon called the postantibiotic effect (PAE), which refers to the transient suppression of bacterial growth following antibiotic treatment. Although PAE has been empirically observed in a wide variety of antibiotics and microbial species, heretofore there has not been a definitive mechanistic explanation for this pervasive observation.
In this work, I use a combination of high-throughput microfluidic experiments and computational modeling to examine the relationship between dosing parameters and the degree of bacterial inhibition, quantified by population recovery time. I found that recovery time is a function of total antibiotic, regardless of how the dose profile. Moreover, a minimal model of transport and binding kinetics was sufficient to recapture this trend, suggesting a unifying explanation for historical observations of PAE in a variety of contexts. I validated this modeling using both in silico and in vitro perturbation studies.
Moreover, I showed that efflux inhibition, a common strategy in antibiotic treatment, is effective in certain dynamic-dependent situations. This work puts forth a possible mechanism for PAE, which could serve as a clinical aid in selecting effective antibiotic/adjuvant combinations, as well as in designing periodic antibiotic treatments.
Srimani, Jaydeep (2017). A mechanistic understanding of the postantibiotic effect and treatment strategies. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/14534.
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