Modeling, Assessing, and Comparing Treatment Protocols to Prevent and Control Antibiotic Resistance

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The emergence and spread of antimicrobial resistance are considered one of the biggest threats to human health in the 21st century. Bacteria can develop defense mechanisms against antibiotics, and the misuse and overuse of antibiotics are accelerating this process; all antibiotics have lost effectiveness against their targeted bacteria as of now. The evolutionary mechanisms for bacteria developing resistance to antibiotics are multifactorial and differ widely with the bacterial species, antibiotic classes and generations, route of administration, and the specific antibiotic-bacteria combination. This project will combine experimental data and mathematical models to quantify and simulate treatment dynamics for specific antibiotic-bacteria pairs. The primary objective is to seek optimal antibiotic use protocols on the patient level and validate theoretical conclusions with experiments. The results will improve current understanding of antibiotic use strategies and help design an integrated plan for future antimicrobial stewardship programs. This project will engage undergraduate and graduate students with interests in interdisciplinary research from both the main and medicine campuses of the University of Miami.The first goal of the project is to develop models that reflect the resistance development mechanism for specific antibiotic-bacteria combinations. In vitro experimental data will be generated and linked with the models to estimate the bacterial growth and evolutionary parameters under different antibiotic concentrations. Second, considering the immune response and the periodical drug concentrations, within-host bacterial dynamic models will be developed with periodic coefficients. The investigators will study the nonlinear dynamics of these models and evaluate the effects of four major antibiotic protocols: (i) mono-drug therapy, (ii) combination therapy, (iii) sequential cycling therapy, and (iv) de-escalation therapy. Third, the investigators will develop novel bacterial population models formulated in terms of semilinear partial differential equations and structured with respect to two physiological features: bacterial age and plasmid copies. The analysis of these models will advance understanding of drug resistance development at the cellular level and will establish new mathematical results on partial differential equations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.