PROJECT SUMMARY/ABSTRACT
Antibiotics are life-saving, but their suboptimal use can lead to antibiotic-resistant bacteria, which contribute to
approximately 2,000,000 illnesses and 23,000 deaths in the United States annually. Unfortunately, the
development of new antibiotics is currently quite limited. Thus, immediate solutions are needed to preserve
existing therapies, in particular anti-pseudomonal beta-lactams, which are the backbone of treatment protocols
in the intensive care unit (ICU). A >500-fold intra-individual variability in drug levels has been observed using
our current standard of “one size fits all” dosing extrapolated from non-critically ill patients. In 30%-50% of ICU
patients beta-lactam levels are too low; this increases treatment failure 1.5-fold and propagates antibiotic
resistance. Therefore, the overall objective of this proposal is to develop a highly predictive, user-friendly,
individualized beta-lactam dosing and monitoring tool for use in the ICU. This will be accomplished through
three specific aims: (1) Design accurate individualized up-front beta-lactam dosing models for critically ill
patients, (2) Develop and validate a dynamic prediction model for use during therapy that identifies patients at
highest risk of either treatment failure/new resistance (drug level potentially too low) or neurotoxicity (drug level
potentially too high), and (3) Identify factors that enhance implementation of these individualized dosing and
monitoring models in practice. For Aim 1, we will prospectively assay beta-lactam levels from residual blood
samples from 300 ICU patients and use population pharmacokinetics to develop a novel individualized dosing
tool. For Aim 2, we will query the electronic records of 5,000 adult ICU patients treated with anti-pseudomonal
beta-lactams to develop and validate a highly-predictive model that classifies patients as either low-risk (high
likelihood of a favorable outcome, no drug level monitoring indicated) or high-risk (high likelihood of an
unfavorable outcome, drug level testing may be beneficial). Beta-lactam use is nearly ubiquitous in critically ill
patients. To optimize the potential for future clinical translation, in Aim 3 we will use focused ethnography
informed by implementation science to identify factors that influence implementation of these new tools in trials
and real-world practice. Dosing and monitoring protocols and interfaces will be iteratively refined based on
these insights. The proposed Career Development Award supports the NIAID mission by striving to improve
the care of patients with infections while providing the research skills, training, and mentorship necessary to
develop the candidate into an independent investigator conducting patient-oriented research. The proposed
training in pharmacokinetic modeling, bioinformatics, and dissemination and implementation science coupled
with an outstanding multidisciplinary mentorship team, and the exceptional resources available at Mayo Clinic
will allow the candidate to achieve her long-term goal of becoming an independent clinical researcher focused
on improving the health of critically ill patients with infections.