Rapid, Breath Volatile Metabolite-Based Diagnostic for In Vivo Identification and Antibiotic Resistance Profiling of Bacterial Pathogens in Ventilator-Associated Pneumonia

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Project Summary/Abstract: The lack of diagnostics that rapidly and accurately identify bacterial infections drives empiric antibiotic prescribing in patients with pneumonia – ultimately, 37-50% of these antibiotics are unnecessary. These issues are amplified in the intensive care unit (ICU), where antimicrobial resistance is common, the risk of imminent clinical deterioration and death is high, and clinicians are under pressure to make rapid treatment decisions. Ventilator-associated pneumonia (VAP) is the most common ICU hospital-acquired infection, responsible for approximately half of all ICU antibiotic prescribing. Time to effective antibiotic treatment is a critical determinant of outcome, but many patients with VAP receive inadequate empiric treatment due to the high prevalence of resistant organisms in VAP. Clinical findings in VAP are highly nonspecific, and 30-60% of antibiotics prescribed for suspected VAP are ultimately unnecessary. Despite a high pulmonary bacterial load in patients with VAP, the lung has traditionally been a particularly inaccessible space without the use of invasive diagnostic procedures. We have established proof of concept in murine VAP models that there are bacterial species-specific breath volatile metabolite signatures in VAP caused by Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli and Klebsiella pneumoniae, and that microbial breath volatile metabolites have markedly different responses to antibiotic exposure within a few hours in phenotypically susceptible (S) vs. non-susceptible (NS) organisms. In close collaboration with industry partners and a team of experts in antimicrobial resistance, microbiology, VAP, advanced statistical methods, and regulatory matters, we propose further development of an advanced, miniaturized gas chromatography-differential mobility spectrometry (GC-DMS) diagnostic platform for the rapid, noninvasive, breath-based diagnosis of VAP and its most common causative pathogens, S. aureus, P. aeruginosa, K. pneumoniae, E. coli, Enterobacter cloacae, and Acinetobacter baumannii, exploiting differential volatile metabolite responses to effective and ineffective antibiotic therapy to obtain in vivo phenotypic information about antibiotic susceptibility. Using thermal desorption-GC-tandem mass spectrometry and in parallel, a rapid GC-DMS diagnostic device, we will systematically characterize these species-specific breath signatures and early responses to antibiotic therapy in S vs. NS organisms in murine VAP models and in patients with suspected VAP, defining and validating breath signatures that (a) identify VAP, distinguishing it from other ventilator-associated conditions and respiratory tract colonization, (b) identify its underlying microbial etiology, and (c) determine whether the microbe is S or NS by examining its early response to antibiotics, and create GC-DMS algorithms that identify these signatures in breath data automatically, in preparation for a 510(k) clearance study. This diagnostic device will transform the care of patients with VAP and sharply reduce diagnostic delays, both facilitating early administration of appropriate antibiotics and reducing unnecessary antibiotic use.