Mechanistic and Transcriptomic Modeling of Aztreonam/Avibactam vs. Aztreonam Plus Ceftazidime/Avibactam in Stenotrophomonas maltophilia - Project Summary Stenotrophomonas maltophilia (SM) is a multidrug-resistant Gram-negative bacterium causing severe infections, such as hemorrhagic pneumonia and bacteremia, with mortality rates up to 91.2% in patients with hematologic malignancies and over 60% in those with chronic lung diseases like cystic fibrosis or chronic obstructive pulmonary disease. Despite its clinical impact, optimal treatment remains unclear. The Infectious Diseases Society of America recommends aztreonam plus ceftazidime/avibactam (ATM+CZA), while newly FDA-approved aztreonam/avibactam (ATM/AVI) shows promise, yet no data compare their effectiveness. Preliminary studies reveal a troubling gap: minimum inhibitory concentrations do not reliably predict bacterial killing, risking treatment failure and resistance. This K08 project aims to optimize SM therapy by comparing ATM+CZA and ATM/AVI, uncovering resistance mechanisms, and developing tools to guide personalized treatment. As a clinician- scientist, my goal is to advance precision infectious diseases therapeutics using isolate- and host-specific data. Specific Aim 1 will compare bacterial killing and resistance prevention of ATM+CZA versus ATM/AVI in four diverse SM isolates using hollow-fiber infection models (HFIMs) that mimic human drug levels. Over 168 hours, I will measure bacterial reduction and track resistance emergence to identify the more effective regimen. Specific Aim 2 will test these treatments in a neutropenic mouse lung model, assessing bacterial burden, biofilm reduction, and resistance. I will also measure immune responses and drug levels in plasma and lungs to understand outcomes. Specific Aim 3 will explore gene expression in SM after exposure to ATM+CZA and ATM/AVI using RNA sequencing from both HFIM and mouse models. By combining these data with exposure and bacterial response, I will build and validate a model to predict treatment success and pinpoint resistance- related genes. To achieve this, I will gain advanced training in in vivo drug modeling, transcriptomics, functional genomics, bioinformatics, and predictive modeling under the guidance of Dr. David Feola (mouse models, pathogenesis, immunology), Dr. Jill Turner (transcriptomics), Dr. Hunter Moseley (bioinformatics), and Dr. Gauri Rao (mechanistic modeling). This work addresses a critical gap: no head-to-head studies exist for these SM treatments, and MICs alone fail to guide therapy. By integrating bacterial killing dynamics, in vivo efficacy, and gene expression, this project will: (1) Identify which regimen, ATM+CZA or ATM/AVI, better kills SM and prevents resistance; (2) Uncover molecular clues to resistance via gene expression profiling; and (3) Develop a predictive tool to tailor therapy. These findings will improve care for high-risk patients, inform antibiotic stewardship, and advance precision medicine. The training will provide skills in animal models, gene analysis, and data integration, laying the groundwork for independent research and R01 funding. This project aligns with NIAID’s mission to combat resistant pathogens and reduce the burden of antimicrobial resistance. Through this K08, I will build a foundation to lead innovative research and improve treatment for SM and other challenging pathogens.
