Cohort: 2 

Location: Suresnes, France 

Application Title: AI-Designed Phage Therapy for Antibiotic-Free Animal Health 

Application Subtitle: An AI-guided platform that predicts phage–bacteria interactions from genomic data to rapidly design adaptive phage therapies, replacing antibiotics in livestock farming. 

Team Members: Adèle James, Alexandros Pantalis

The challenge 

Antimicrobial resistance is no longer a distant or abstract risk. It’s already affecting how food systems, farming and public health intersect. 

Nearly three-quarters of the world’s antibiotics are used in livestock farming. Over time, that scale of use has done what biology always does: it has adapted. As antibiotics lose their effectiveness, farmers face rising mortality rates, tighter regulations, and a shrinking set of options to protect animal health. 

One alternative that has regained attention is bacteriophage therapy. Bacteriophages, often referred to as phages, are viruses that infect and kill specific bacteria while leaving other organisms unaffected. They occur naturally and have been studied for decades. However, turning phages into reliable treatments has proven difficult. Traditional development relies on manual screening to identify which phages work against which bacteria, a process that can take up to two years. In environments where bacterial pathogens evolve quickly, that timeline limits practical deployment. 

The solution 

Predictive phage therapy, designed by AI 

Phagos approaches the problem from a different angle. Instead of testing phages one by one, its AI-powered platform predicts phage–bacteria interactions directly from genomic sequences, then designs multi-strain phage cocktails that can adjust as bacterial populations change. 

At the core of the system is a combination of generative AI and machine-learning ranking models to evaluate large numbers of potential phage–bacteria pairings.  Rather than producing a single static solution, these tools enable the continual updating of the phage mix used in the medicine, allowing it to remain effective as bacteria evolve.

The platform is trained on a large and diverse biological foundation; thousands of genomes and thousands of validated interactions. This scale allows the model to capture patterns in host–pathogen relationships that would be difficult to derive through manual testing alone. 

How it works 

Closing the loop between computation and the lab 

A central feature of the platform is the feedback loop between computational prediction and laboratory testing. Candidate phage cocktails generated in silico are tested in the lab, and the results are fed back into the model to improve subsequent predictions. 

The system operates on scalable infrastructure capable of processing genomic data across species and geographies. It is supported by a growing phage bank representing roughly 1% of all known phage genomes. As new experiments are conducted, both the dataset and the model continue to improve. 

This approach shortens discovery cycles and reduces reliance on repeated trial-and-error screening. 

How it differs from conventional approaches 

From reactive screening to anticipatory biology 

Phagos shifts phage therapy development away from a reactive mindset. Instead of responding to outbreaks after they occur as conventional phage development, the platform anticipates bacterial behaviour and adapts treatment strategies accordingly. 

By combining AI-driven prediction with experimental validation, the platform provides a structured way to explore phage therapies at scale. This makes development more systematic and less dependent on manual expertise alone, particularly in settings where rapid response is required. 

Impact 

• Phage therapy development reduced from two years to two months 
• Testing cycles shortened from days to minutes 
• Lab workflows compressed from 48 days to a single day per cocktail 
• Model accuracy reaching 95%, with success rates roughly double those of manual methods 

Clinical outcomes include: 

• Complete elimination of Salmonella 
• Measurable gains in poultry weight 
• Improved farmer's gross margins

Adoption to date: 

• Three billion-$ enterprise clients
• Five active pilots across Europe, Asia and the Americas

Why it matters 

An alternative to antibiotics in animal health 

Phagos demonstrates how AI can support the development of biologics that operate differently from conventional antibiotics. By reducing development time and improving the reliability of phage selection, the platform makes it easier to consider phage therapy as a practical option in animal health. 

In doing so, it contributes to global efforts to address antimicrobial resistance, not by stretching the lifespan of current drugs, but by supporting the development of treatments that operate differently from conventional antibiotics