# High-throughput methods leveraging robotics and computer vision for the development of therapeutic phage cocktails

## TL;DR

The paper describes a high-throughput system using robotics and computer vision to develop effective phage cocktails for treating urinary tract infections.

## Contribution

The novel contribution is an automated platform for reproducibly screening and optimizing therapeutic phage cocktails at scale.

## Key findings

- The platform enabled systematic assessment of phage-bacteria interactions using standardized assays.
- A phage cocktail, LBP-EC01, was developed to be effective against over 96% of tested E. coli isolates.
- The methods address the lack of scalable frameworks for developing phage-based therapies.

## Abstract

We present the high-throughput automated screening techniques that are being used to develop bacteriophage-based therapeutic products currently under investigation in human clinical trials to combat urinary tract infections1. By integrating modern liquid handling robotics, standardized phenotypic assays, and computer vision-based enumeration, we established a platform capable of reproducibly screening large collections of phages against clinically derived bacterial strain panels. This approach enabled systematic assessment of phage-bacteria interactions at scale, facilitating the identification and optimization of phage cocktails with broad in vitro activity. Although bacteriophage therapy has long been investigated as a strategy for treating bacterial infections, few frameworks exist for developing phage combinations in a reproducible and scalable manner. The methods outlined here address this gap and aim to support the broader development of therapeutic assets available to combat antibiotic resistance.

In this work, authors present an automated, high-throughput platform that utilizes robotics and computer vision to enable creation of a therapeutic phage cocktail, LBP-EC01, effective against over 96% of tested Escherichia coli isolates from urinary tract infections.