# Pharmacokinetics and Pharmacodynamics of Mechanistically Aware Phage Cocktails > **NIH NIH R01** · STATE UNIVERSITY OF NEW YORK AT BUFFALO · 2024 · $714,053 ## Abstract ABSTRACT: With only one mechanistically novel antibiotic developed in the past decade with activity versus resistant Gram-negatives, extensively-drug resistant Pseudomonas aeruginosa (XDR-PA) continues to pose an urgent threat to human health in the United States and globally. Phages are an encouraging class of complex biologics, which have substantial bactericidal activity, high target specificity, favorable toxicity profiles, and an ability to modulate bacterial virulence. Thus far, phage therapy has been limited to individual patient expanded- access, investigational new drug applications (eIND) as salvage therapy. Unclear consensus on phage therapy has led to a heterogeneity in dosing and regimen structures, necessitating additional development. Substantial innovations are required to quantify the unique aspects of phage pharmacokinetic/ pharmacodynamic (PK/PD) properties. Target-mediated phage disposition (TMPD) is a special case of target-mediated drug disposition, which addresses the unique aspects of self-dosing at peripheral sites due to phage binding to target bacteria. This project proposes optimizing the regimen structure to address the TMPD for five clinically relevant Pseudomonas phage therapeutics (Luz19, E215, PYO2, PAK_P1, and PaCe) with proven activity against MDR/XDR P. aeruginosa. Published preclinical and pilot eIND data demonstrate that Pseudomonas phages Luz19, E215, PYO2, PAK_P1, and PaCe exhibit high therapeutic potential in combination (i.e. an “N-phage cocktail”). The central hypothesis of this proposal is that optimal N-phage cocktail regimens can be generated using machine learning-led combination of individual phages based on their chief parameters of infectivity, and the disposition of each phage in the cocktail can be characterized by a novel, platform physiologically based pharmacokinetic (PBPK) model of target-mediated phage disposition. This project will address the challenges of phage therapeutics by: developing a translational LC-MS/MS assay for N-phage quantification using in vitro samples of each phage in antimicrobial growth media then extending the assay for in vivo blood and tissue matrices (Aim 1). Quantifying the effect of the chief parameters of phage infectivity on bacterial killing and identify optimal N-phage components and administration patterns using a machine-learning led optimization of PK/PD (Aim 2). Validating the optimal N-phage cocktail using the HFIM and our leading immunocompromised murine pneumonia model, while quantifying blood and tissue concentrations of each phage (Aim 3). And tuning and qualifying a human PBPK model, including descriptions of epithelial lining fluid in the lung and TMPD, based on whole blood and tissue concentrations of each phage from in vivo murine data (Aim 4). The culmination of these aims and the main product of this proposal will be the generation of a highly optimized N-phage cocktail and administration schedule, for use against clinical XDR P. aeruginosa. As... ## Key facts - **NIH application ID:** 10980990 - **Project number:** 1R01AI177997-01A1 - **Recipient organization:** STATE UNIVERSITY OF NEW YORK AT BUFFALO - **Principal Investigator:** Nicholas Michael Smith - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $714,053 - **Award type:** 1 - **Project period:** 2024-08-12 → 2029-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10980990 ## Citation > US National Institutes of Health, RePORTER application 10980990, Pharmacokinetics and Pharmacodynamics of Mechanistically Aware Phage Cocktails (1R01AI177997-01A1). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10980990. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*