Project Summary:

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The rise of antimicrobial resistance (AMR) represents a significant public health challenge, often described as a silent pandemic. Bacteriophage therapy emerges as a promising strategy to combat infections caused by multi-drug resistant (MDR) bacteria. However, limitations such as narrow host specificity and the potential for phage resistance hinder its broader application.

 

This project aims to address these challenges by screening a robust phage cocktail and developing a dry powder formulation for treating MDR bacterial lung infections. We will establish a predictive framework that integrates both wet and dry laboratory methodologies, focusing on optimizing phage biomarker discovery pipelines. Initial efforts will involve collecting data on phage-bacteria interactions through controlled wet lab experiments. Subsequently, computational tools and bioinformatics will be employed to analyze these interactions, facilitating the identification of biomarkers for enhanced phage therapy, antibiotic resistance monitoring, and microbiome engineering. Predictive models, including machine learning algorithms, will be utilized to hypothesize potential phage biomarkers.

 

Key to the success of phage therapy is the stability of phage preparations and their effective delivery to infection sites. We aim to develop an intranasal dry powder formulation that ensures stability of phage cocktail and retains therapeutic levels at the lung infection site. This formulation will leverage engineering techniques such as spray drying, freeze drying, spray freeze drying, and thin-film freeze drying to achieve optimal aerodynamic performance and favorable physicochemical characteristics. To evaluate safety and therapeutic efficacy, we will implement an in-vitro bacteria-host cell co-culture system alongside a lung infection model.

 

The in-vivo therapeutic potency of the developed formulation will be assessed using a murine lung infection model, paving the way for innovative clinical solutions in the fight against MDR bacterial infections. Furthermore, this project embodies a strong interdisciplinary collaborative approach, integrating microbiology, computational biology, and pharmaceutical engineering.