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Exploring the collaboration between antibiotics and the immune response in the treatment of acute, self-limiting infections
- Ankomah, Peter, Levin, Bruce R.
- Proceedings of the National Academy of Sciences of the United States of America 2014 v.111 no.23 pp. 8331-8338
- adaptive immunity, antibiotic resistance, antibiotics, bacteria, bacterial infections, immune response, mathematical models, pathogens, pharmacokinetics, prediction, therapeutics
- The successful treatment of bacterial infections is the product of a collaboration between antibiotics and the host’s immune defenses. Nevertheless, in the design of antibiotic treatment regimens, few studies have explored the combined action of antibiotics and the immune response to clearing infections. Here, we use mathematical models to examine the collective contribution of antibiotics and the immune response to the treatment of acute, self-limiting bacterial infections. Our models incorporate the pharmacokinetics and pharmacodynamics of the antibiotics, the innate and adaptive immune responses, and the population and evolutionary dynamics of the target bacteria. We consider two extremes for the antibiotic-immune relationship: one in which the efficacy of the immune response in clearing infections is directly proportional to the density of the pathogen; the other in which its action is largely independent of this density. We explore the effect of antibiotic dose, dosing frequency, and term of treatment on the time before clearance of the infection and the likelihood of antibiotic-resistant bacteria emerging and ascending. Our results suggest that, under most conditions, high dose, full-term therapy is more effective than more moderate dosing in promoting the clearance of the infection and decreasing the likelihood of emergence of antibiotic resistance. Our results also indicate that the clinical and evolutionary benefits of increasing antibiotic dose are not indefinite. We discuss the current status of data in support of and in opposition to the predictions of this study, consider those elements that require additional testing, and suggest how they can be tested.