The prevalence of multidrug-resistance in Gram-negative bacteria (e.g., Pseudomonas aeruginosa, Acinetobacter baumannii), is rising at an alarming rate, rendering many (if not all) antibiotics ineffective when used alone. The rate of new drug development is unlikely to keep pace with the increase in multidrug resistance. Combination therapy is often used clinically as a last resort. However, considering the numerous possibilities, combination therapy is selected by clinicians mostly based on anecdotal experience and intuition. A robust method to guide rational selection of combination therapy would be crucial to delay returning to the pre-antibiotic era. Our long-term goal is to optimize clinical use of antibiotics to combat the emergence of resistance. The objective of this application is to improve understanding of the factors contributing to the effectiveness of combination therapy, by developing a computer-aided methodology that will guide the design of combination therapy. If we understand how well antimicrobial agents work together, effective treatment strategies could be formulated rationally by identifying the best possible combination, thus guiding clinicians in the selection of combination therapy. We plan to accomplish the objective of the application as follows: (1) predict the likelihood of various antibiotic combinations to suppress resistance development in wild-type bacteria; (2) maximize the potentiating effect of agents targeting specific mechanisms of resistance (e.g., beta-lactamase inhibitors) in drug-resistant bacteria; and (3) identify useful antibiotic combinations against multidrug resistant bacteria. In this application, the proposed approach will be illustrated by experimental data with P. aeruginosa, A. baumannii and Klebsiella pneumoniae. However, the proposed model-based system is not confined to a specific antimicrobial agent-pathogen combination. It could be easily extrapolated to other antimicrobial agents (e.g., antibacterials, antimycobacterials and antiretrovirals) with different mechanisms of action, as well as to other pathogens (e.g., Staphylococcus aureus, tuberculosis and HIV) with different microbiological characteristics.

R56AI111793

2014-08-01

2016-07-31