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Project

HavePhAIth: Human Phage therapy using AI to design phage cocktails against ESKAPE pathogens

Human Phage Therapy (PT) is a promising route for the treatment of increasingly drug-resistant bacterial infections. Belgium is leading the implementation of PT in Europe, and the technique is currently in operations at the Queen Astrid Military Hospital (QAMH), and was recently implemented at UZ Leuven. Indeed, a Multidisciplinary Phage Task Force (MPTF) has been set up within UZ Leuven to provide PT to patients with difficult-to-treat infections. However, the current design strategies of phage cocktails are blindfolded, relying on empirical rules that fail to leverage the rapidly expanding omics data to predict bacteria-phage interactions. In my doctoral research, I developed machine learning models of phage infectivity in Pseudomonas aeruginosa that predict which phages from a collection can infect given bacterial strains based on their genomic content. As a member of the MPTF and frequent collaborator of the QAMH, two entities that will generate big datasets of omics/clinical data on PT in vivo, I will translate these novel modeling approaches to the ESKAPE pathogens found in the patients. This will also put me in a unique position to assess the dynamics of bacteria-phage co-evolution in vivo, by inspecting longitunal isolates from given patients undergoing treatment. Importantly, these analyses will enable us to extract ground rules for the design of phage cocktail products, while productively translate our research towards 'sur-mesure' phage treatment of given patients. This effort will also result in the introduction of an 'AI doctor' that will rank therapeutic phages from our joint collections against ESKAPE, based on their predicted efficacy against the specific strain infecting the patient.
Date:1 Jan 2022 →  31 Oct 2022
Keywords:Bioinformatics, Host-virus interactions, Bacteriophage therapy, Omics data analysis
Disciplines:Infectious diseases, Computational evolutionary biology, comparative genomics and population genomics, Computational biomodelling and machine learning, Analysis of next-generation sequence data, Bacteriology