807. Using a Mathematical Model to Demonstrate the Effect of Variations in Immune Response Efficacy on Optimal Antibiotic Treatment Strategies for an Acute Bacterial Infection
Session: Poster Abstract Session: Use of PK/PD to optimize existing antibiotics and antifungals
Thursday, October 5, 2017
Room: Poster Hall CD
Background: While well-recognized that clearance of bacterial infections occurs through a collaboration between antibiotics and the immune system, existing strategies for evaluating optimal antibiotic treatment regimens, including pharmacodynamic/pharmacokinetic assessments and mathematical/computer simulation models, have been limited in their consideration of the immune response and variations therein.

Methods: This study develops a within-host mathematical model of an acute, potentially self-limiting bacterial infection and utilizes it to explore antibiotic dose regimens under conditions of varying immune response efficacy by comparing a normal response that would clear the infection to: (i) a hypoactive/suppressed, and (ii) a hyperactive/dysfunctional immune response, to examine how different regimens affect time to clearance of the infection, as well as de novo generation of antibiotic-resistant populations and the ascent of populations that exist prior to treatment initiation.

Results: Numerical analyses of the model demonstrate that there are threshold antibiotic doses required to mitigate the immunologic deficits of the abnormal immune responses to allow for clearance of the infection and decrease the likelihood of resistance evolving. Treating with low doses fosters the generation of high-level antibiotic-resistant populations at all levels of immune efficacy. However, more moderate dosing regimens can slow the rate of ascent of pre-existing resistant populations, particularly when immune responses are suboptimal.

Conclusion: This study demonstrates the effect of variations in immune response on optimal antibiotic treatment regimens that maximize rate of infection clearance and minimize the likelihood of resistance evolution in acute, potentially self-limiting infections. It illustrates the importance of incorporating quantitative immunological assessments into evaluations of antibiotic treatment regimens.

Pierre Ankomah, MD, PhD, Massachusetts General Hospital, Boston, MA

Disclosures:

P. Ankomah, None

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