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

Domain: neuroscience
Published: 2026-01-30
Editorial outcome: minor_revision

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.

Introduction
The ability of bacteria to evolve and acquire resistance to antibiotics poses a significant threat to global public health2–5. In 2019, an estimated 1.27 million deaths were attributed to bacterial antimicrobial resistance (AMR)2, and deaths associated with AMR are estimated to reach 8.22 (6.85–9.65) million by 20504,5. Escherichia coli (E. coli) was associated with the most bacterial AMR deaths—approximately 800,000—in 20192. Although broad-spectrum antibiotics are crucial tools for both preventing and treating a variety of infections, over-prescription has contributed to increasing rates of AMR6,7. Additionally, their non-specific nature may disrupt the natural balance of patient microbiomes, leading to a dysbiosis that allows opportunistic pathogens to fill niches opened by antibiotic treatment8. Despite efforts in place to prevent unnecessary antibiotic use9, alternative therapeutics are needed to provide specific and effective treatment while mitigating the rise of infections caused by resistant pathogens.

Urinary tract infections (UTIs) are a leading indication for antibiotic usage, representing between 1–6% of all medical visits10, and impacting over 400 million people annually11. Uncomplicated UTIs are disproportionately common in women, with a lifetime incidence of 50–60%10,12. Although many UTIs are relatively easy to treat with traditional antibiotics, recurrent UTIs are common, with 30–50% of women experiencing a repeat infection13. Uropathogenic E. coli (UPEC) is the primary causative agent in UTIs, with a prevalence of 75–80% in uncomplicated UTIs and 65% in complicated UTIs10,14.

Over the last decade, bacteriophage (phage) therapy has garnered increasing interest as an alternative treatment modality. Phages are ubiquitous viruses that infect bacteria and are the most abundant biological entity on Earth. Phages also possess an incredible spectrum of genetic diversity, with different phages exhibiting unique and highly selective mechanisms of action, giving them a subspecies level specificity for different bacterial strains. This specificity and their favorable safety profile15 make phages an attractive therapeutic target. However, the vast quantity and heterogeneity of phages pose both a significant opportunity and a challenge in discovering the appropriate phages and their optimal combination for therapeutic development. Phage therapy can be applied via personalized or fixed cocktails. Personalized phage cocktails are tailored to each individual, offering the advantage of targeted and specific development16,17. In contrast, fixed phage cocktails are designed for broad use across an indication, and although they require more extensive upfront development, ultimately can be deployed to more patients at lower costs.

In this work, our goal was to establish repeatable and automated unit operations that allow fixed phage cocktail development with efficacy across pathogenic strain panels. Here, we report an automated platform designed for high-throughput discovery and activity assessment of phages and phage cocktails. This platform has enabled the standardized assessment of more than 3.8 million phage and bacteria reactions. As an example of its utility, we describe the methods used to develop a phage cocktail targeting E. coli isolates from UTIs. The candidate cocktail demonstrates in vitro activity against 96.4% of 356 uropathogenic E. coli (UPEC) isolates and reduces bacterial burden by ≥99.999% in 94% of those isolates. Among 38 E. coli isolates obtained from participants in the Part 1 open-label portion of a Phase 2b trial, 97% (37/38) exhibit in vitro sensitivity to the cocktail.

Results
Defining a clinically relevant strain panel to represent E. coli urinary tract infection diversity
We first set out to define a target panel of strains to represent the epidemiologically relevant diversity of UPEC strains. We acquired 1773 strains of E. coli from commercial and academic sources, as well as our own clinical trials, including a Phase 0 surveillance study and a Phase 1b safety study. Approximately 10,000 E. coli strain genomes were acquired from the BV-BRC (formerly PATRIC) database18, and their genomic diversity was compared to genomes acquired from our E. coli strain bank (Supplementary Fig. 1a). Whole genome comparisons indicated that our bank contained representatives of almost all genomic clusters. Follow-on analyses indicated strong representation of sequencing types found in a meta-analysis of extraintestinal pathogenic E. coli strains19. Representatives of the top 20 sequencing types, which composed 85% of E. coli sequencing types found across 217 studies, were included in our collection (Supplementary Fig. 1b). In addition to genomic content, we also considered a variety of phenotypic and clinical attributes of strains, including patient gender, antibiotic resistance profile, geography of isolation site, and year of isolation.

Of the available strains, we selected 356 to establish a UTI Clinical Panel (Fig. 1). All strains were isolated from female urinary tracts and included isolates from patients living in 39 US states (see “Methods”). In total, 98% of isolates were collected between 2019 and 2021 (contemporaneous at the time of panel creation), and 29% were multidrug-resistant. To ensure repeatable growth of bacteria across assays, liquid handlers were used to normalize each strain titer via optical density (OD), and multiple aliquots from the same initial culture were generated and frozen (see “Methods”). Samples were added to pre-barcoded, automation-compatible tubes to maintain sample traceability and limit cross-sample contamination, which were then stored in automated biobanks (see “Methods”). This panel served as the source of strains for subsequent phage discovery and cocktail optimization.

High-throughput robotics design for engineered phage cocktail development
A major challenge in developing a phage cocktail for use across a patient population is identifying the optimal combination of phages with an ability to infect diverse pathotypes and prevent resistance development for the full range of pathogenic E. coli. As collections of phages increase, testing all possible combinations quickly becomes unfeasible with current technologies (Fig. 1b). For example, testing all cocktails composed of 6 phages from a 500-phage collection would require creating 2.1×1013 cocktails. The situation becomes even less tractable when one considers the number of replicates required to achieve statistical power and all of the infection-condition variables—including bacterial strains, growth media, phage input titer, and bacterial input titer—for which the antibacterial activity of each cocktail can be assessed.

To address this challenge, we designed a high-throughput robotic platform capable of monitoring phage and bacteria interactions across a range of phenotypic assays. We reasoned that a sufficiently sized database of interactions would enable machine learning approaches to predict cocktail efficacy of untested combinations. To this end, we designed two VANTAGE LIQUID HANDLING SYSTEM® work cells in collaboration with Hamilton Company (Fig. 1c). Each work cell contains a pipetting module consisting of 8 spanning pipette channels and a fixed 96-channel pipettor for flexible liquid handling. The base of the work cell contains a plate sealer, waste storage, and an Entry/Exit module allowing high-volume labware retrieval via built-in elevators. In the rear cabinet, dual shaking incubators provide 88-plate capacity and two plate readers allow wavelength readings (Fig. 1d). A sliding robotic arm between the front and rear cabinets is used to transfer labware throughout the work cell. In total, each system is capable of high-throughput execution of most microbiological assays.

Phage discovery and phenotypic characterization
We began phage discovery efforts by establishing relationships with wastewater facilities throughout the United States. We acquired 1084 environmental samples from 577 providers in 48 states. These samples were arrayed across individual bacteria strains from the Clinical Panel, incubated with shaking at 250 rpm to allow phage amplification, and plaqued via the double agar overlay method on indicator strains. Phages were isolated via triple plaque purification and amplified in liquid culture. E. coli targeting phages (n=1143) were banked, and 421 were selected based on their titer post-amplification. Each phage was sequenced, and its genome was screened for virulence genes, including antibiotic resistance genes, and used to predict phage lifestyle (i.e., temperate or obligately lytic).

To assess phage host range, we developed an automated optical-density-based assay on our work cells capable of testing 15,000 reactions per instrument per run (see “Methods”). In this method, the pipetting module adds various combinations of phages, bacterial strains, and media to 384-well plates accessed from the elevator system. Completed plates are moved into the shaking incubators for bacterial growth and transferred to plate readers for optical-density time-course readings routinely over a 20 h period. Growth curves from each well containing phages and bacterial strains were compared to a control well containing only bacteria via area-under-the-curve (AUC) measurements (Fig. 2a, see “Methods”). All analyses were performed by version-controlled Continuous Integration/Continuous Deployment (CI/CD) software pipelines that store run metadata and results in a centralized cloud database (“Methods”).

We began assessment of individual phage efficacy by testing the collection of 421 phages against stochastically chosen 44- or 88-strain subsets of our full Clinical Panel. Initially, the bacteria susceptibility profile for each phage was examined, and strains with limited phage susceptibility were prioritized for subsequent waves of phage discovery. Additionally, phages were assessed against bacteria grown in different matrices and at varying phage and bacterial input titers to better understand the consistency of phage activity in different environments.

We next set out to identify the minimum effective combination of phages that would have strong antibacterial activity and durable suppression of all UTI isolates in our panel. Because all cocktail combinations could not be tested, we developed a proprietary cocktail prediction model that leveraged our database of phage and bacteria reactions to recommend cocktails for additional testing. Outcomes from the tested cocktails were then reincorporated into the prediction engine. In total, we collected 1.5 million growth curves (Fig. 2c) assessing the efficacy of phages and phage cocktails. AUC ratio distributions were bimodal with peaks centered around 0.1 (the lowest optical density reading with media in the optical path) and 1 (no difference between phage-treated and control growth curves). The AUC ratio value of 0.65 was approximately equidistant from the two peaks and was used as a threshold value for classifying suitable phage activity. Using these data, we narrowed to 28 top-performing cocktails and tested their activity against the full Clinical Panel at a titer of 1×108 Plaque Forming Units per mL (PFU/mL). These cocktails possessed host ranges between 89–95% and exhibited similar bacteria susceptibility profiles (Fig. 2d). It is worth noting that the measured host range activity for the top-performing cocktails exceeds the expected host ranges that simple algorithmic cocktail selection by random choice or by simply selecting the top six phages with the highest host ranges would predict, as shown in Fig. 3.

Automated colony enumeration via computer vision
Because cocktail activity was similar between many of our top cocktails, we tested cocktail activity in complementary antibacterial assays to identify the best cocktail. A significant limitation of the liquid culture optical-density-based assay is a high lower limit of detection, which ranges between 107 and 108 Colony Forming Units per mL (CFU/mL). Alternative quantification approaches include qPCR and serial dilution, and spotting. We selected serial dilution and spotting to measure viable bacteria in a reaction rather than the measurement of DNA, which does not report on bacteria viability.

In this assay, phages, strains, and media were added to 96-well plates via liquid handlers and transferred to shaking incubators (see “Methods”). At varying time points, plates were extracted from the incubator, serially diluted across 7 additional microtiter plates using the work cell’s 96-channel pipettor (Fig. 4a, Supplementary Fig. 3), and 2 μL from each well was spotted onto barcoded agar plates to allow colony formation (Fig. 4b). Imaged plates were then subjected to a custom image analysis pipeline, written in Python, to enumerate the colony and plaque forming units from the images of the microtiter plates and to determine the original sample titer (Fig. 4c–e, “Methods”). The computer vision components of this pipeline leveraged three distinct algorithms to provide internal checks and balances to determine whether a human investigation of the final titers would be merited. The three algorithms employed were: 1) Watershed=basin finding and watershed marking, 2) Integrated=integrated optical density, and 3) Circle=feature detection by Hough circle transform. The sample enumeration was conducted on a high-performance cloud platform, managed by Apache Airflow for task scheduling and Dask for task-specific parallelism. Configuration parameters were managed through GitHub, triggering Google Cloud Build to execute tasks in pre-compiled containers, ensuring reproducibility and clear data provenance with automated cloud backups (see “Methods”).

Across all experiments, over 2.2 million spots have been automatically enumerated using this method. Figure 5 shows the aggregated log titering results for approximately 500,000 CFU measurements across 85 unique experiments. It is clear that while the vast majority of measured samples are within one log difference of one another for the different methods, a small set of enumerated samples show a greater than 1.5 log difference in the titer values between the three algorithms. In these instances where the different algorithms lead to disagreement between the values computed by the different approaches, the images were flagged for follow-up investigation by a qualified lab worker with training in manual enumeration and titer calculation to confirm the correct value and add corrections to the database.

In addition to the between-methods correlation for continuous evaluation of the pipeline’s consistency, a collection of 768 automated counts was compared to human manual counts from two analysts (see Supplementary Fig. 4). Of the 768 samples, 53 were identified that had at least a 1.5 log discrepancy between the two analysts or between an analyst and the automated counting method. Each of the 53 samples was manually reviewed by a third analyst who provided an additional data point to determine a reportable count, which was then converted to a bacterial titer, see (Fig. 5d). In all cases the third analyst calculated a titer within a 0.5 log range of either both manual analysts or one manual analyst and the automated method, providing additional confidence in the reportable counts. With respect to the refereed counts, the automated counting method was determined to be accurate (within 1 log) in 49 of the 53 reviewed samples (92.5%) (Supplementary Fig. 4). Analyst 1 was accurate in 10 of the 53 reviewed samples (18.9%), and analyst 2 was accurate in 31 of 53 samples (60.4%). Across all 768 samples, the automated counting method was accurate in 99.5% of samples, whereas analysts 1 and 2 were accurate in 94.4% and 97.1%, respectively. Overall, the watershed counting method provided counts with strong correlation and a greater agreement to corrected manual counts than either analyst (Fig. 5d, Supplementary Fig. 4). In sum, this pipeline provides automated counting and quality control flagging, allowing scalable assessment of bacteria and phage titers.

Automated phenotypic assessment of high-titer manufactured LBP-EC01
After evaluation of the top 28 cocktails through CFU reduction experiments, six phages were selected to compose LBP-EC01. Each phage was subjected to process and formulation development to develop the manufacturing conditions required for generating high-titer phage material while removing impurities such as endotoxin. After suitable conditions were established, each phage was manufactured under Good Manufacturing Practices (GMP) conditions. Manufactured phages were combined into a final cocktail with an aggregate titer of 2×1011 PFU/mL, which maintained titer within 0.5 log for 24 months at 4 °C.

Manufactured LBP-EC01 was tested in the optical-density-based assay against each of the 356 strains in the UTI Clinical Panel. Consistent with previous experiments of non-GMP material, LBP-EC01 exhibited activity against 96.4% of strains (Fig. 6). AUC ratios were heavily weighted towards the maximum detectable suppression. As a final assessment of LBP-EC01, this material was tested in a CFU reduction assay across the 356 strains. Each strain was exposed to a buffer control or three different titers of LBP-EC01 and incubated at 37 °C in a shaking incubator. Using the automated enumeration pipeline, the bacterial titer of each reaction was assessed at 0, 4, and 24 h. At 4 h, in the control reactions that lacked phage, bacterial samples grew to an average concentration of 1.2×109 CFU/mL. In contrast, the average concentration of bacterial strains treated with the highest concentration of drug product was reduced to 3.6×103 CFU/mL (Fig. 6a, b). 94.0% of strains were reduced by at least 4 logs compared to the control, and 97.2% of strains by at least one log. E. coli CFU reduction was dose dependent, with 88.8% and 77.0% of strains reduced by at least 4 logs when treated with 5×109 PFU/mL and 1×108 PFU/mL of drug product, respectively. At 24 h post-phage treatment, control reactions lacking phage but containing bacteria grew to an average concentration of 2.6×109 CFU/mL compared to 5.5×104 CFU/mL in the highest titer LBP-EC01-treated samples (Fig. 6c, d). 75.5%, 60.7%, and 40.7% of strains were reduced by at least 4 log in reactions treated with 2×1011, 5×109, and 1×108 PFU/mL, respectively.

Automated phenotypic assessment of isolates from a Phase 2b clinical trial
A Phase 2b clinical trial was designed to assess the efficacy of LBP-EC01 concomitant with trimethoprim/sulfamethoxazole (TMP/SMX) in female patients with acute, uncomplicated UTIs (NCT05488340). The trial was composed of two parts: 1) an unblinded and open-label lead-in study to assess dosing of LBP-EC01 in 39 patients, and 2) a controlled, double-blind study to assess LBP-EC01 efficacy in up to 288 patients. Part 1 of the trial completed 1 and was used to select the dosing regimen for Part 2. As part of the exploratory analyses in the trial, bacterial strains were isolated from urine and sequenced. In total, 166 E. coli isolates were collected from urine samples throughout the study time points, irrespective of being in a formal analysis population. Each isolate was tested for LBP-EC01 sensitivity via the double agar overlay assay20. Genomic analysis of sequencing data from each isolate confirmed the presence of 38 unique E. coli strains across all patient populations and time-points. Of the 38 strains, 37 were sensitive to the phages in LBP-EC01 (97.4%). Each strain was also tested via the optical-density-based assay, and the distribution of the resulting AUC ratio scores was compared to the distribution of scores obtained from screening against the full UTI Clinical Panel (Fig. 7a). The distributions were highly similar, with no statistical difference between the two strain collections, with 35/38 (92%) falling below the AUC threshold. Similarly, each of the E. coli isolates identified from urine samples prior to treatment was assessed via the CFU reduction assay and the automated enumeration pipeline. Again, there was no difference in the distribution of CFU reduction efficacy in strains isolated from the Phase 2b clinical trial in comparison to our UTI Clinical Panel (Fig. 7b). Finally, we assessed the sensitivity of all 166 E. coli isolates to LBP-EC01 by plaquing and optical density time course phage efficacy assays. For each patient, we compared the genome assemblies of urine-derived E. coli isolates collected pre-treatment and across a 34-day period after initial treatment. Because some patients harbored multiple E. coli strains, we grouped isolates by sequencing type within each patient. Resistance to LBP-EC01 was defined as a loss of phage efficacy within the same sequencing type over time—either a >1-log reduction in plaquing efficiency or a shift from susceptible to resistant classification in the optical density assay (AUC ratio threshold=0.65). Using this approach, we did not observe the development of genetic resistance to LBP-EC01 in any patient who enrolled in Part 1 of the study.

Methods
Automated strain panel generation
To ensure consistency in bacterial input titers and growth state across different assays, each bacterial strain on the UTI Clinical Panel was normalized and aliquoted into single-use tubes. Bacterial strains generally arrived in frozen cryovials, and upon intake, a sterile 10 μL loop was used to inoculate 1 mL of LB media in a deep well block. 48 strains were added in a checkerboard pattern per deep well block and were incubated in a shaking incubator at 37 °C for 4–16 h. Deep well blocks were then diluted and spotted using the liquid handling method described in the Automated Colony and Plaque Enumeration Assay section. Individual colonies were picked using a sterile 10 μL loop and added to a 14 mL sterile culture tube containing 5 mL of LB media or a 50 mL sterile, vented cap tube containing 20 mL of media. Tubes were then incubated with shaking at 37 °C overnight (12–16 h). All culture tubes were then loaded onto a Hamilton VANTAGE 2.0 liquid handler for normalization and aliquoting. A 200 μL aliquot of each culture was added to a 96-well plate, and the optical density of each sample was read at 600 nm and checked to ensure cultures were in the linear range of detection. Subsequently, the liquid handler normalized each culture to an OD of 0.02 in 4 identical deep well blocks, added glycerol as a cryoprotectant to a final percentage of 20%, and mixed the resulting samples. 180 μL was then transferred via the 96-channel pipettor to racks that each contain 96 pre-barcoded tubes with a 300 μL volume capacity from Hamilton Storage Technologies. Racks were loaded into a SAM HD −80 °C automated biobank, also from Hamilton Storage Technologies. This biobank has an 80,000-tube capacity and contains a tube picking module that allows custom organization and recall of individually banked tubes. This module was used to obtain smaller sub-panels of strains for different assays while maintaining consistency in starter material. Multi-Drug Resistant (MDR) strains were defined as resistant to at least one antibiotic in at least 3 distinct classes. All isolates were collected from female urinary tracts. In most cases, these isolates were from urine. A minority of isolates were collected from catheters (n=17) or were labeled by the providing vendor as Urinary Bladder (n=3).

Automated optical density time course phage efficacy assay
The optical density time course phage efficacy assay was run on a custom-designed Hamilton VANTAGE 2.0 with A, B, C, and D modules. Module A refers to the pipetting deck, whereas Module B is the logistics cabinet that supports the pipetting deck. Module D is the rear cabinet containing the incubators and plate readers. Positioned between Modules A/B and D, Module C is the track gripper or robotic arm that facilitates plate movement across the system. The assay was coded in INSTINCT V software. Plate additions, incubations, and plate readings can occur simultaneously through this scheduling software. User inputs for a unique experiment identifier, number of phages, number of optical density reads, phage identity and titer input file, bacteria identity input file, incubator temperature and shaking speed are recorded in a run metadata file. Serial numbers of all instruments used in a given run and the time of run initiation were also recorded in this file.

384-well plates containing media, phage, and bacteria were generated by the pipetting module of the liquid handler (Supplementary Fig. 2). Sterile, optically clear, non-surface-treated, and lidded microtiter plates were used and were stored in the Entry/Exit module of the instrument upon run start. Plates are processed in batches of two and are moved to an on-deck carrier position for liquid addition. The 96-channel pipettor then adds 40 μL of media to each well of the 384-well plate. Subsequently, individual spanning channels are used to add 5 μL of phage via a continuous surface dispense. Finally, the 96-channel pipettor adds 5 μL of bacteria from 4 racks of 96 pre-barcoded tubes and mixes the 50 μL reaction volume. 384-well plates are re-lidded and transferred to a StoreX Liconic shaking incubator with temperature control. Shaking rpm was varied for different bacteria species and was set at 250 rpm for E. coli. This shaking speed was selected based on initial growth optimization experiments for a random selection of E. coli strains normalized to a starting optical density of 0.02, in which various rpm speeds between 100 and 800 rpm were studied. Of these, 250 rpm was selected because it optimized for maximal area under the bacterial growth curve, while minimizing the speed. Minimizing speed was an optimization objective, as it reduces both the risk of cross-well contamination within the 384-well plates and the long-term stress on the automated shaking incubators. Orbital shaking amplitude was 2 mm. After the plate addition to the incubator, the timeframe of plate completion was recorded in the run metadata. After completion of all 384-well plates, scheduled transfers move plates from shaking incubators to a Biotek Powerwave HT plate reader. Before each read a 2 s plate shake was instituted to avoid cell settling during plate transfer. Wavelength dependent OD readings were taken at 600 nm and were written to an XML file in the run output folder with a corresponding timestamp. After the collection of all reads across all plates, typically between 20 and 40 h, the time of run completion was recorded.

Post-run schedulers move metadata and raw data captured by the instrument to cloud storage, which are then passed to CI/CD-based analysis software pipelines to ensure reproducible code execution through software version control and containerized computing environments (see Automated Colony and Plaque Enumeration Assay). Code for XML and run metadata parsing was generated in Python, and quality control reports were generated in R-based Jupyter notebooks triggered by Papermill. AUC measurements were taken by integrating under the optical density growth curve between the 3 and 20 h time points. The AUC ratio value is calculated by dividing the AUC measurement of the phage-treated growth curve by the AUC measurement of the control growth curve.

Automated colony and plaque enumeration assay
500–1000 μL mixtures of media, phage, and bacteria were generated in deep well blocks manually or with liquid handlers and were stored in shaking incubators at 37 °C. Phage and bacteria sample identifiers and concentrations were recorded in input files uploaded during automated run initiation. At pre-determined timepoints, dilution and spotting of the reaction plate were triggered using a Hamilton VANTAGE instrument pre-loaded with 50 μL nested, sterile tip racks, sterile microtiter dilution plates, and barcoded agar plates loaded in the Entry/Exit elevator module. An aliquot was taken from the reaction plate via the work cell’s 96-channel pipettor, and the reaction plate was returned to the shaking incubator. For colony enumeration, aliquots were taken directly into serial dilution, and for plaque enumeration, aliquots were passed through a 0.22 μm polyethersulfone (PES) filter. Each deep well block was serially diluted 1:10 into 1x phosphate-buffered saline (PBS) across 7 additional microtiter plates using the 96-channel pipettor. The neat plate and each of the 7 dilution plates were subsequently used to dispense 2 μL spots of each sample plate onto delidded, rectangular agar plates. For colony enumeration, neat and diluted samples were spotted on LB rectangular agar plates. In contrast, samples used for plaque enumeration were spotted onto a double-agar overlay. To generate this overlay, 100 μl of overnight bacterial culture was added to 11 mL of LB low-salt top agar (Teknova L7570) and thoroughly mixed by inversion. The mixture was then poured evenly across an LB rectangular agar plate and allowed to dry for 30–60 min. Barcode scanners integrated into the liquid handler were used to record the source plate and time stamp for each dispense. Scheduling software was used to control the dispense of each plate, a variable dry time under HEPA filtered air, restoration of the plate lid, and transfer back to the Entry/Exit module or the Liconic incubator on a static setting. Run input files, timestamps, and other relevant metadata were stored in a folder with a unique experiment identifier upon run completion. Agar plates were allowed to dry and were incubated upside down in a static incubator for an additional 12–16 h to allow colony formation. Finally, plates were imaged on a robotic work cell equipped with plate storage, a light table and IDS UI-3060CP Rev. 2 camera featuring a Sony IMX174LLJ monochrome imaging sensor with 5.86 μm pixels possessing a large full well depth of 31,278 electrons, and 12-bit ADC (4096 levels) readout that provides a large number of gray levels required to discriminate faint phage plaques from their surroundings. Metadata from the work cells that performed the assay, as well as the raw images, were ingested into cloud storage for further analysis.

The enumeration of the serially diluted sample was performed using a custom image analysis pipeline, written in Python, using a high-performance parallel cloud architecture orchestrated using Apache Airflow for large-scale task scheduling and Dask for parallelism within each task-specific allocated compute node. Each step in the enumeration pipeline was launched through the submission of configuration parameter files to a GitHub repository that, in turn, triggered Google Cloud Build processes to perform the actual computing tasks on pre-compiled container images. This overlay of GitHub with computation on cloud compute architecture enables highly reproducible computing with clean provenance and audit trails for all of the operations performed on the data. The combination of the git repository to version the evolution of the codebase, the container identifier to record the specific computing environment, and the cloud logging databases to provide long-term storage of all of the run-specific outputs or errors lead to a detailed record of all samples processed through the pipeline. Additionally, the use of cloud storage with well-defined retention policies enables the archival of metadata and computed measurements by leveraging the best current enterprise practices in multi-site and multi-method data backup through cloud automation.

Prior to evaluation by the three separate image enumeration algorithms, the images were background subtracted and aligned to a uniform coordinate system to account for slight variations in the position of the plates relative to the camera, or for adjustments in the camera zoom factor under normal operation due to machine maintenance or analyst intervention. The background subtraction and image optimization methods will be detailed in a separate following manuscript, given their general applicability to a wide variety of image processing tasks outside the scope of phage discovery and characterization. The major elements for the background subtraction consist of the following steps: 1) registration of the image to physical coordinates in millimeters using alignment marks from the plate imaging station on the robot, 2) subtraction of an average blank plate image from the image to be enumerated, 3) cropping of the image to the region within the plate boundaries, 4) subtraction on a pixel by pixel basis of the minimum intensity values at each x, y coordinate from all plate images within the enumeration plate image series, 5) median filtering of the image using a 5×5 median blurring kernel, 6) defining a triangular mesh that samples pixels in locations known to be outside the spotting locations in order to create a low spatial frequency model image of the background variation in the agar optical density or other light scattering artifacts such as condensation, 7) subtraction of this background model image from the image created by the preceding steps to create the final background subtracted image that will be passed to the enumeration algorithms. A representative background subtraction and image normalization processing series is shown in Supplementary Fig. 3, demonstrating the importance of removing non-uniform background features prior to analysis for counting. Following background subtraction and coordinate alignment, each of the spotted areas was segmented into smaller images for parallel processing across the cloud computing cluster for the Watershed and Integrated counting methods. Whole plates were processed for the Circle counting method.

The Watershed method leverages a marker-based modification to the original watershed algorithm31,32, which involves treating a gray-scale image as a topographic map, and seeks to segment based on the location of basins within the topography. The intensity of light transmission through agar growth plates is proportional to the optical density at a given position on the plate, and the OD increases with the number of bacteria in the surface layer of the agar. Given these aspects, the OD imaging measurements for phage plaques formed in the agar map excellently onto topological basins. For bacterial colonies, the image is simply inverted prior to the watershed calculations in order to convert the increased OD in regions containing colonies into basins instead of hilltops. The markers for each watershed were chosen by finding the local minima using a minima finding image filter with a radius commensurate with the average colony or plaque size of approximately 1 mm. The number of distinct watersheds found within the 9 mm region of a given spotting area is the count provided by this method.

The Integrated method uses a simple normalized sum of the total image intensity within the background-subtracted and sub-segmented image encompassing only the immediate 9 mm square area surrounding a single spot out of the full 8×12 plate of 96 spots, using the formula
I=A \sum_{x} \sum_{y} intensity(x,y), (1)
where x and y denote the horizontal and vertical coordinates of the image, intensity is pixel intensity at the image position in arbitrary units, and A is a normalization coefficient chosen by statistical analysis of calibration runs such that the total range of I roughly matches the integer counts provided by manual counting, Watershed counting, and Circle counting of the same regions.

The Circle method uses the circle Hough transform33 to find circles within the image at characteristic radii typical to individual phage plaques or bacterial colonies, approximately 0.5 to 2 mm, as well as for full zones of clearing (ZOCs) where the entire spotting area of bacterial lawn is eliminated by the phage, or the bacterial colonies are fully overlapping and indistinguishable, which is approximately 8 mm for the 2 μL volumes dispensed routinely in the assays. If a ZOC is found at a given dilution, the maximum distinguishable count of 30 is set for the region; otherwise, the number of smaller circles detected within the region is reported as the count for that region. 30 was chosen as a value, as it is typically too challenging to count 30 colonies or plaques within the diameter of a 2 μL spot.

The counts from the different methods are combined into separate vectors to represent the counts across the full dilution series for a given sample for each of the enumeration methods. These vectors are then converted into a titer value for each separate method that represents the series according to the following algorithm. The titer calculation algorithm iterates through the list of counts and corresponding dilution factors, ordered by increasing dilution factor, and selects the dilution factor of the first item in the list with a count value that is less than one-half of the preceding element in the list. In the event that no such greater than two-fold fall-off is found, the first item in the list is chosen as the count and dilution factor to represent the series. The titer is then calculated by the following formula
T= (W|I|C|M)×F / V, (2)
where T is the resulting titer in (C/P)FU per mL, V is the dispensed volume, W, I, C, or M are the Watershed, Integrated, Circle, or Manual counts depending on the method, and F is the dilution factor for the chosen element in the series. The log titer is calculated as the base ten logarithm of the titer as T =log10(T). Generally, for the measurement of relative variability in an assay that spans many orders of magnitude, statistical comparisons using a logarithmic basis have favorable mathematical properties34.

Clinical trial strain assessments
Strains were isolated from patient-provided samples as part of an ongoing two-part Phase 2 controlled trial named ELIMINATE (NCT05488340). Informed consent was obtained from patients providing urine cultures before trial enrollment. The study protocol and informed consent form were approved by the central Advarra Institutional Review Board (see Ethics Statement). Urine samples were collected by clean catch or catheter sampling across a 34-day period1. E. coli isolates were collected by International Health Management Associates (IHMA) and confirmed via matrix-assisted laser-desorption ionization time-of-flight (MALDI-TOF). Isolates were stored at −80 °C in cryovial tubes. Upon tube receipt, cultures were struck via sterile 1 μL loops, and allowed to incubate overnight at 37 °C. Subsequently, a single colony was picked and used to inoculate a 14 mL sterile culture tube. Cultures were normalized and aliquoted as described in the Automated Strain Panel Generation section.

A double agar overlay assay was performed by mixing 150 μL of an overnight culture of a given bacteria strain with 11 mL of soft agar, which was then poured over a rectangular agar plate and allowed to solidify for approximately 15 min. 1:10 serial dilutions of individual phage or the LBP-EC01 cocktail were then dispensed on top of the solidified agar in 2 μL spots. Spots were allowed to dry for 20–30 min before incubating at 37 °C overnight to allow formation of a bacterial lawn. Sensitivity to individual phages or to the cocktail was defined as observation of individual plaques or zones of clearing in the phage-exposed regions of the plate.

To sequence clinical isolates, 1 mL of overnight culture of each strain was pelleted at 4000g for 10 min, and the supernatant was removed. DNA was extracted using the MagMax Microbiome Ultra Nucleic Acid Isolation Kit (Thermo-Fisher). Isolates from urine samples were collected pre-LBP-EC01 treatment in the trial and were sequenced on a PacBio Sequel IIe using the SMRTbell prep kit 3.0. All other isolates were sequenced on an Illumina MiSeq using the Illumina DNA Prep library kit. Sequencing data from E. coli isolates were examined for each patient using whole genome alignment and review of sequencing type designations. E. coli strains were designated as unique in this study if they were from different patients or had a distinct sequencing type from another isolate within a given patient.

Statistics and reproducibility
Phage and phage cocktail efficacy were evaluated using a panel of 356 E. coli strains. This panel was established from genomic analyses of over 10,000 E. coli sequences, with representatives selected to capture broad biological and genetic diversity among clinical isolates. Each phage-bacteria combination was tested in 1–3 biological replicates to assess reproducibility, and the large number of strains ensured a comprehensive evaluation of phage efficacy across diverse genotypes. For enumeration analyses, a random subset of 500,000 colony counts was selected to enable clear visualization of titer distributions and method performance comparisons. Operators were blinded to the counts of the other operator and the computer vision model. Colony-forming unit (CFU) reduction experiments were performed in singlicate across the full 356-strain panel, with assay breadth compensating for replicate number to provide robust, population-level insight into phage activity. Phage cocktail efficacy on this panel was subsequently compared to all genomically unique clinical isolates from the Phase 2 trial using a Welch two-sample t-test to assess consistency of host range coverage.

For the simulation of cocktail combinations for simple additive host ranges, the number of simulated combinations was capped at (170/2)=14,365. The swarm and violin plots were capped at 340 samples of the total collection of simulated cocktails for the sake of visibility during plotting. These values were sufficient to illustrate the trends in the performance of naive algorithms for cocktail prediction relative to the final results from the full proprietary methods employed for clinical phage cocktail selection, as well as comparison to the performance against isolated clinical strains.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The research article by Penke et al. on “High-throughput methods leveraging robotics and computer vision for the\ndevelopment and assessment of therapeutic phage cocktails” describes the automated phage screening techniques to\nimprove the efficacy of phage selection in preclinical and clinical setting. Presenting this huge data set results are\ncommendable. The idea of using modern technologies like robotics and computer vision in phage therapy is attractive,\nespecially (methods) screening huge sets of pathogens and phages is impressive. Even though the efficacy of PT as a\nstand-alone therapy to treat AMR infection needs further investigation.\n1. One hypothetical question is the feasibility of using this method in personalized phage preparations when infections are\nmore complicated than E. coli.\n2. How effective is this method in selecting phage cocktails?\n[The combination of phages in cocktail should have different receptors to eliminate the development of resistant mutants.]\n3. What is the rate of (detection) error? Is it 100% accurate? As this article is all about the methods, it is important to discuss\nthe inaccuracies in the detection.\nMinor:\n1. Line no. 108: Are the isolates from urine samples? Need clarity.\n2. Line no. 109: Check: 98% of strains or isolates?\n3. Line no. 150: What are those phages belonging to?\n4. Line no. 169: To be more accurate, the growth media is related to bacteria which eventually relates to phage propagation.\n5. Line no. 246,247: LBP-EC01 is one of the 28 cocktails or different.\n6. Line no. 288: It should be ’38 stains’.\n7. Line no. 303-327: Correct the typo in degree sign.", "reviewer_2": "This manuscript contains some impressive results that move phage cocktail therapy toward clinical trials. The problem is\nthat large sections of the paper currently read like a press release. The bulk of the potential contribution to the field is in the\ndata itself on phage-bacteria interactions, which is not released, and the ‘proprietary cocktail prediction model’, which is not\ndescribed and can’t be replicated.\nWe think with some edits this could be a very nice paper. It is a powerful story that stretches all the way from the nuts and\nbolts of engineering a scalable data collection solution for the problem, using that data to solve the main barrier to\napplication, and then applying it to a problem of clinical relevance. The edits that would strengthen the paper is describing\nhow the data is acquired *and* how that data is used to add value.\nWe appreciated seeing the robotic methods you arrived at for gathering reproducible data on phage infection at high\nthroughput. This is the most technically detailed section of the paper, which we appreciate. With slightly more information\n(we make some suggestions below), these methods could be replicated and would be very useful for other phage biologists.\nSuggestions on edits to robotic methods for gathering phage infection data\nWould the authors be willing to release their automation code? They are presumably saved as a Venus file. The code\ncontains a lot of details that would be valuable for using this technique again, i.e. liquid classes that are used for different\nreagents, whether liquid handling steps have been optimized to avoid traversing over other plates to avoid contamination,\netc.\nFigure 1 contains renders of their instrument. It would be more helpful to have a photograph, a deck layout image, and/or a\nvisual depiction of the method itself (see an example in figure 2a of\nhttps://www.embopress.org/doi/full/10.15252/msb.20209942)\nCould the authors provide more details and the automated preparation of the plaque forming assays? At the moment, it is not\nclear how the PFU assays are prepared - as described later in the manuscript one would expect a double agar overlay\nmethod here to establish a bacterial lawn on some of the plates. For the PFUs, are the phages separated from the bacteria\nbefore having samples plated?\nSuggestions on edits to methods for using phage data to predict effective phage cocktails:\nCurrently the extent of detail is this sentence: “We developed a proprietary cocktail prediction model that leveraged our\ndatabase of phage and bacteria reactions to recommend cocktails for additional testing.” Figure 2 shows histograms of\ncocktail effectiveness, the top cocktails and their effectiveness against UTI strains. We understand that you can’t release this\nalgorithm, but it would be valuable to find other ways to discuss this part of the paper with more nuance.\nCould you show the performance of the proprietary method relative to simple control algorithms for selecting phage\ncocktails? For example, you should show Figure 2c for the proprietary cocktail selection method vs random vs another\nsimple method (say, regression based). This would help other scientists understand whether they could use a simpler, less\neffective technique and still have it work, or whether they should expect to need to develop something more sophisticated if\nthey wish to attempt this sort of technique.\nHow much better is the proprietary method? How many fewer experiments did it allow them to run? How iterative/closed\nloop was it? How did they handle the explore/exploit tradeoff? These are the fundamental questions that is of a lot of\ninterest\nto the field broadly. A lot of the potential value of your paper is interpreting it as a specific scientific example of the value of\nrobotics + data analysis in solving high-combinatorial problems in life science.\nThe dataset itself, if released, would be valuable: 421 phages to 356 E. coli, which is more than the current state of the art:\n96 phages to 403 E. coli from Pasteur recently (data https://zenodo.org/records/13831957, and paper\nhttps://www.nature.com/articles/s41564-024-01832-5). If it’s not possible to release the data, perhaps you could use your\ncomputational tools on these public datasets versus your private dataset\nOther comments by line:\nFig 3 d and e: What do the different colours mean in this figure?\nLine 47: Reference 5 estimates annual deaths attributable to AMR to be 1·91 million (1·56–2·26) and 8·22 million (6·85–\n9·65) deaths associated with AMR in 2050. Because of the context of the sentence which uses the language “an estimated\n1.27 million deaths were attributed to bacterial antimicrobial resistance (AMR) [2], and this number is estimated to be as high\nas 10 million by 2050”, I think that either the wording should be changed to show that the 10 million (from ref 4) is not\nreferring to attributable deaths or the 1.91 million number should be given.\nLine 70: “their favorable safety profile” - should have a reference\nLine 138, 147, 347: Shaking speed of the plates is important due to the effect on aeration (Shaer Tamar and Kishony 2022)\n(see additionally ref 30-38 of this paper) - shaking speed should be mentioned in the main text and orbital radius should be\ngiven in methods.\nIn line 347, why was 250rpm chosen for E. coli? What data informed this decision?\nLine 169 and 172: What conditions were tested? What components of the media were varied? How did this affect the growth\nof the different bacteria and phages?\nSome have observed phages showing limited efficacy in human serum - was this tested?\nLine 217: Figure 4 doesn’t have any PFUs in the axis titles but the text here says that PFUs are represented in the data - this\nis confusing\nLine 300: If including this line, then state what method was used to come to this conclusion and ideally show the data\nLine 330: I could not find any information online about what Modules A,B,C, and D of the Hamilton Vantage 2.0 system are", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "We are grateful to the editors and reviewers for their thoughtful and constructive feedback,\nwhich has significantly improved our manuscript. In response, we have made substantial\nrevisions to enhance the clarity, transparency, and reproducibility of our work. Key additions\ninclude 1) all of the raw data used to plot figures in tabular (.csv) form, 2) public release of\nautomation code used to execute the combinatorial optical density assay to support adoption by\nother researchers, 3) improved methodological detail for the requested assays, and 4) a\ndetailed response to all reviewers’ comments and questions. Furthermore, to address questions\nabout our cocktail selection model, we included a comparative analysis demonstrating its\nperformance relative to simpler algorithms. We also clarified methodological details, corrected\nerrors, revised the tone throughout the manuscript, and updated figures and legends for\nimproved readability. Below, we provide point-by-point responses to each of the reviewers’\ncomments, with our replies highlighted in blue. We hope we have sufficiently addressed all\nfeedback and welcome any further questions or suggestions.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe research article by Penke et al. on “High-throughput methods leveraging robotics and\ncomputer vision for the development and assessment of therapeutic phage cocktails” describes\nthe automated phage screening techniques to improve the efficacy of phage selection in\npreclinical and clinical setting. Presenting this huge data set results are commendable. The idea\nof using modern technologies like robotics and computer vision in phage therapy is attractive,\nespecially (methods) screening huge sets of pathogens and phages is impressive. Even though\nthe efficacy of PT as a stand-alone therapy to treat AMR infection needs further investigation.\nWe thank the reviewer for their encouraging comments and appreciate the recognition of our\nefforts to integrate robotics and computer vision into phage therapy workflows. We also agree\nwith the reviewer’s note that while the methodological infrastructure described here is a critical\nstep forward, additional work—including double-blind, placebo-controlled clinical trials—is\nessential to fully assess the efficacy of phage therapy as a stand-alone treatment for\nantimicrobial-resistant infections. Please see the following in-line comments to respond to the\nreviewer’s questions.\n1. One hypothetical question is the feasibility of using this method in personalized phage\npreparations when infections are more complicated than E. coli.\nWe assume here that the reviewer is referring to polymicrobial infections that may be due to\neither multiple species or multiple strains of a single species that are causing an infection. In\nthese cases, it may be necessary to target multiple pathogenic entities rather than a single\nentity. Although our company is not focused on personalized phage therapy, and rather, focuses\non development of a single cocktail for the treatment of a given type of infection, we believe that\nother groups’ development of personalized approaches could play an important role in the\ntherapeutic landscape. As such, our methods can also be applied to personalized phage\ntherapy development. Both the optical density-based assay and CFU reduction-based assay\ncan be run with either a panel of bacterial strains from different patients or a collection of\nbacterial strains from the same patient. In cases of personalized phage therapy where a single\nor smaller number of strains are the target for a phage cocktail, our platform would enable a\nhigher number of phages or phage cocktails to be assessed. In other words, the total number of\nbacteria x phage reactions per instrument run is fixed, but the number of phage or bacteria can\nbe flexibly changed to address the research question.\n2. How effective is this method in selecting phage cocktails?\n[The combination of phages in cocktail should have different receptors to eliminate the\ndevelopment of resistant mutants.]\nWe appreciate the reviewer’s question regarding the effectiveness of the method. Ultimately, we\nbelieve that this question is best answered in the clinic. We recently published clinical results in\nThe Lancet Infectious Diseases in which LBP-EC01 with concomitant trimethoprim/\nsulfamethoxazole (TMP/SMX) achieved a 100% clinical cure and 88% microbiological cure rate\n(14 of 16 patients) in patients with urinary tract infections caused by drug-resistant E. coli,\ndespite 69% of these patients’ baseline isolates being TMP/SMX resistant. This year we expect\nto complete enrollment for the double-blind, placebo-controlled part 2 of this Phase 2 trial, which\nwill allow us to answer this question directly. We intend to publish these data.\nIn the meantime, we must rely on in vitro assessments to determine cocktail effectiveness\nagainst a panel of bacterial strains (as in vivo models are limited in the number of strains that\ncan be assessed). We believe the most relevant metric to assess the effectiveness of the\nselected cocktail is the CFU reduction assay as it has the broadest dynamic range. In this\nassay, LBP-EC01 reduced bacterial titer by 4 log in 94% of strains at the 4-hour timepoint and\n10\n75% of strains at the 24-hour timepoint. Although there remains room for improvement, this\nphage cocktail has the broadest host range and most durable suppression of bactrial growth in\nvitro of which we are aware. Groups developing phage therapy do not typically perform the CFU\nreduction assay on a panel of several hundred bacterial strains due to its time- and\nconsumable-intensive nature. In most cases, groups will publish their total host range as\nassessed by an optical density assay and/or plaquing host range assay. We note that we have\nidentified cocktails that have similar total host ranges but have marked differences in abilities to\nreduce bacterial titer and prevent bacterial regrowth in the CFU reduction assays. We find that\nthis is often the case when a common mutation that confers resistance to one phage also\nconfers “cross resistance” to other phages in the cocktail, a situation that our cocktail selection\nalgorithms attempt to avoid and which we believe is related to shared aspects of the infection\nmechanism (i.e., adsorption to receptor or downstream infection).\nFinally, we have focused this paper on the automated methods used to collect phage\ncharacterization data rather than the cocktail recommendation algorithms, due to their\nproprietary nature. However, we agree with the reviewer that phages in the cocktail should\nleverage different receptors to prevent resistance development, and we note that our selection\nmetric optimizes for multi-phage coverage of each strain in the panel in a manner that prefers\ngreater genetic diversity of the phages that contribute to that coverage.\n3. What is the rate of (detection) error? Is it 100% accurate? As this article is all about the\nmethods, it is important to discuss the inaccuracies in the detection.\nFor the computer vision detection of colonies, the rate of error was calculated based on a\nmanual assessment of colony counts performed across 3 independent analysts. 768 colonies\nwere assessed and 0.5% of colony counts/titers were determined to be incorrectly calculated.\nWe defined an incorrect calculation as a bacterial titer that was greater than one log difference\n10\nfrom refereed counts provided by the third operator that was used to investigate differences\nobserved between manual counts and the computer vision method. For example, if the manual\ncount showed a titer 7.5e8, and the computer vision method showed 5e6, this would reflect a\ngreater than one log difference, and the calculation would be marked as incorrectly calculated.\nvision”. We are unsure if there are other rates of error that are requested by the reviewer, but we\ninvite any such requests.\nMinor:\n1. Line no. 108: Are the isolates from urine samples? Need clarity.\nWe have updated this line to include a reference to the methods section. In this section we have\nincluded the following clarifying sentences. “All isolates originated from female urinary tracts. In\nmost cases, these isolates were from urine. A minority of isolates were collected from catheter-\nobtained urine (n=17) or were labeled by the providing vendor as ‘Urinary Bladder’ (n=3).”\n2. Line no. 109: Check: 98% of strains or isolates?\nWe have updated this line to specify that these are isolates. Each isolate was not independently\nverified as being a unique strain; however, across the entire panel, isolates were selected to\nrepresent a broad genetic space.\n3. Line no. 150: What are those phages belonging to?\nIf the reviewer is referring to taxonomic classifications, we did not perform this type of\nassessment on every bacteriophage present in the bank. If this characterization is not what the\nreviewer is requesting, we invite further clarification and will include the information in the\nmanuscript if we have it.\n4. Line no. 169: To be more accurate, the growth media is related to bacteria which eventually\nrelates to phage propagation.\nWe thank the reviewer for pointing out the inaccuracy and have updated this line to specify that\nphages are assessed against bacteria grown in different media.\n5. Line no. 246,247: LBP-EC01 is one of the 28 cocktails or different.\nLBP-EC01 was one of the 28 cocktails that was assessed and was selected as it included the\nsmallest number of phages while maintaining maximum efficacy as assessed by the CFU\nreduction assay.\n6. Line no. 288: It should be ’38 stains’.\n“Isolates” has been corrected to “strains” in the revised manuscript.\n7. Line no. 303-327: Correct the typo in degree sign.\nThese types have been corrected in the revised manuscript.\nReviewer #2 (Remarks to the Author):\nThis manuscript contains some impressive results that move phage cocktail therapy toward\nclinical trials. The problem is that large sections of the paper currently read like a press release.\nThe bulk of the potential contribution to the field is in the data itself on phage-bacteria\ninteractions, which is not released, and the ‘proprietary cocktail prediction model’, which is not\ndescribed and can’t be replicated.\nWe think with some edits this could be a very nice paper. It is a powerful story that stretches all\nthe way from the nuts and bolts of engineering a scalable data collection solution for the\nproblem, using that data to solve the main barrier to application, and then applying it to a\nproblem of clinical relevance. The edits that would strengthen the paper is describing how the\ndata is acquired *and* how that data is used to add value.\nWe appreciated seeing the robotic methods you arrived at for gathering reproducible data on\nphage infection at high throughput. This is the most technically detailed section of the paper,\nwhich we appreciate. With slightly more information (we make some suggestions below), these\nmethods could be replicated and would be very useful for other phage biologists.\nWe thank the reviewer for their helpful feedback and agree with the commentary provided here.\nPlease see the below in line comments on the changes we have made to improve the\nmanuscript. Although we are restricted in our ability to share proprietary aspects of the platform,\nwe agree with the reviewer that additional code, methods, and data are needed to improve the\nreproducibility of the manuscript. We are now providing the fundamental pieces to enable other\ngroups to replicate the generalized process of using robotics and bioinformatics to capture data\ncharacterizing phage efficacy. This includes the release of our automation code to facilitate\nother researchers, as recommended by the reviewer.\nRegarding the cocktail prediction model, we respectfully note that this model incorporates\nproprietary design elements developed as part of an ongoing therapeutic program and cannot\nbe shared in full. However, we have added more detail in the in-line comments below to respond\nto the reviewer’s recommendations. We hope this provides sufficient transparency into the\nmodel's role in cocktail development while preserving necessary confidentiality. We offer the\nanalogy of publication of new foundational large language models, in which it is widely accepted\nthat authors will publish model architecture without sharing proprietary parameter weightings.\nWe appreciate the feedback regarding the tone of the paper and have updated several areas of\nthe manuscript to remove language that contributes to this sentiment. We used an LLM to\nperform sentiment analysis and highlight regions of the paper that should be modified. We have\nrevised the abstract as well as several other locations throughout the paper, which are\nhighlighted in the attached manuscript. Although we endeavored to be comprehensive in these\nrevisions, we are open to further revisions if we have missed any such instances.\nSuggestions on edits to robotic methods for gathering phage infection data\nWould the authors be willing to release their automation code? They are presumably saved as a\nVenus file. The code contains a lot of details that would be valuable for using this technique\nagain, i.e. liquid classes that are used for different reagents, whether liquid handling steps have\nbeen optimized to avoid traversing over other plates to avoid contamination, etc.\nFigure 1 contains renders of their instrument. It would be more helpful to have a photograph, a\ndeck layout image, and/or a visual depiction of the method itself (see an example in figure 2a of\nhttps://www.embopress.org/doi/full/10.15252/msb.20209942)\nCould the authors provide more details and the automated preparation of the plaque forming\nassays? At the moment, it is not clear how the PFU assays are prepared - as described later in\nthe manuscript one would expect a double agar overlay method here to establish a bacterial\nlawn on some of the plates. For the PFUs, are the phages separated from the bacteria before\nhaving samples plated?\nWe appreciate the reviewer’s interest in the technical aspects of our robotic workflows and fully\nagree that the reproducibility of high-throughput experimental methods is essential for broader\nadoption in the field. In response to this comment, we will make the INSTINCT V automation\ncode files used for the optical density assay publicly available. This method includes the\ndetailed parameters such as liquid classes for various reagents, deck layout, pipetting\nbehaviors, and safeguards implemented to minimize cross-contamination.\nWe believe that sharing this level of detail represents a meaningful and actionable contribution\nto the field, particularly for researchers aiming to implement or adapt similar high-throughput\nrobotic systems. The code will be made public via a persistent online repository at the time of\nmanuscript publication.\nFurthermore, we agree with the reviewer’s suggestion that including a deck layout image that\nhighlights the placement of labware will be useful for the manuscript. We have included an\nadditional supplementary figure that details this information. We anticipate this addition will\nassist other researchers in replicating or adapting our setup for similar high-throughput\napplications and complements the release of the automation code.\nFinally, we have added additional details into the methods section titled “Automated Colony and\nPlaque Enumeration Assay.” These additions should clarify the details of how the double agar\noverlay assay was performed.\nSuggestions on edits to methods for using phage data to predict effective phage cocktails:\nCurrently the extent of detail is this sentence: “We developed a proprietary cocktail prediction\nmodel that leveraged our database of phage and bacteria reactions to recommend cocktails for\nadditional testing.” Figure 2 shows histograms of cocktail effectiveness, the top cocktails and\ntheir effectiveness against UTI strains. We understand that you can’t release this algorithm, but\nit would be valuable to find other ways to discuss this part of the paper with more nuance.\nWe thank the reviewer for the constructive feedback that comparison to alternative choices\nwould be illustrative of the advantage to the proprietary methods that were employed. Details\nregarding the genomic elements of our proprietary methods are captured below. We agree that\nadding additional nuance would be helpful and have included this as part of our response to the\nfollowing reviewer comment.\nCould you show the performance of the proprietary method relative to simple control algorithms\nfor selecting phage cocktails? For example, you should show Figure 2c for the proprietary\ncocktail selection method vs random vs another simple method (say, regression based). This\nwould help other scientists understand whether they could use a simpler, less effective\ntechnique and still have it work, or whether they should expect to need to develop something\nmore sophisticated if they wish to attempt this sort of technique.\nHow much better is the proprietary method?\nHow many fewer experiments did it allow them to run?\nHow iterative/closed loop was it?\nHow did they handle the explore/exploit tradeoff?\nWe thank the reviewer for suggesting a comparison to simple control algorithms. We have\ncompleted and added an additional analysis to the manuscript that compares alternative cocktail\ngeneration methods to the results of the full proprietary methods. The two added simple\nalgorithms are based on the area under the curve for optical density up to 20 h of growth. These\ntwo methods do not contain the genomic analyses performed for final cocktail selection, or the\nproprietary ranking and host range distance metrics employed. Full details are in the revised\nmanuscript, but the added algorithms for comparison are \"random choice of 1 to 6 phages\ncombined into a cocktail\" and a \"ranked choice of 1 to 6 phages with highest host ranges\ncombined into a cocktail\". An additional plot, “Extended Data Fig. 9”, has been added to the\nsupplemental information to summarize this analysis. A few sentences summarizing the results\nof this additional analysis have been added to the body of the manuscript. The full proprietary\nmethod for cocktail development, including synthetic modifications, leads to a final cocktail with\n~97% host range activity, which is noticeably higher than the ~90% host range activity that\nwould be suggested from simple algorithmic wild-type phage selection and their predicted\ncombined efficacy when assembled into cocktails.\nWe did not directly calculate or extrapolate the reduction in experiment count that the\nproprietary methods should have allowed, as our target was generally to err on the side of\nadditional experiments to support the preclinical efforts whenever possible. The proprietary\nmethods were closed loop in the sense that we used rarefaction of both cocktail efficacy as well\nas rarefaction of genetic diversity to assess when continued effort led to diminishing returns.\nThis same rarefaction was used to balance the tradeoffs between exploration and exploitation.\nWhile we won’t be able to comment on the relative performance of different cocktail selection\nmethods for therapeutic efficacy in patients, our double-blind, placebo-controlled clinical trial\nusing the selected cocktail will read out later this year\n(https://clinicaltrials.gov/study/NCT05488340) and will be the subject of a subsequent\npublication. We will compare the predicted host range with the observed host range in the clinic.\nThese are the fundamental questions that is of a lot of interest to the field broadly. A lot of the\npotential value of your paper is interpreting it as a specific scientific example of the value of\nrobotics + data analysis in solving high-combinatorial problems in life science.\nThe dataset itself, if released, would be valuable: 421 phages to 356 E. coli, which is more than\nthe current state of the art: 96 phages to 403 E. coli from Pasteur recently (data\nhttps://zenodo.org/records/13831957, and paper https://www.nature.com/articles/s41564-024-\n01832-5). If it’s not possible to release the data, perhaps you could use your computational tools\non these public datasets versus your private dataset\nWe thank the reviewer for this suggestion. We are now sharing the liquid host range data,\nincluded in the plots of Fig. 2, to improve understanding of the analysis. Shared data sets\ninclude both early development screening (100 strains tested against 170 phages) as well as\nlater stage testing against the full 357 strain Clinical Panel (28 phage cocktails). However, due\nto our active pharmaceutical development program, we cannot share the entire phenotypic or\ngenomic dataset. It would be possible with significant effort to format and condition the data to\nrun our tools against the public dataset in the Pasteur paper, however, because the assays used\nas inputs for the prediction methods are distinct, we feel that an additional complete analysis\nagainst a novel dataset in the Pasteur publication exceeds the scope of this manuscript. We feel\nthe Pasteur data is complementary to the methods and results presented in our present\nmanuscript. We would be happy to participate in a follow up manuscript in collaboration with the\nPasteur institute, should the editors find merit in comparing results between tools, as well as\npotentially a larger meta-analysis of additional data sets across the growing community of\nphage and bacterial genomes researchers.\nOther comments by line:\nFig 3 d and e: What do the different colours mean in this figure?\nAdditional text was added to the figure legend to indicate that the different colors indicate\nuniquely identified colonies and plaques.\nLine 47: Reference 5 estimates annual deaths attributable to AMR to be 1·91 million (1·56–\n2·26) and 8·22 million (6·85–9·65) deaths associated with AMR in 2050. Because of the context\nof the sentence which uses the language “an estimated 1.27 million deaths were attributed to\nbacterial antimicrobial resistance (AMR) [2], and this number is estimated to be reach 8.22 (6.85–9.65) million\nby 2050 [4, 5].”\nLine 70: “their favorable safety profile” - should have a reference\nWe have added a citation to support this sentence from a publication created by the\nAntibacterial Resistance Leadership Group.\nLine 138, 147, 347: Shaking speed of the plates is important due to the effect on aeration\n(Shaer Tamar and Kishony 2022) (see additionally ref 30-38 of this paper) - shaking speed\nshould be mentioned in the main text and orbital radius should be given in methods.\nWe agree that this information is important to include and have added the shaking speed used\nfor the phage discovery process into the main text. Additionally, we have added information into\nthe methods about the orbital shaker amplitude, which is 2 mm.\nIn line 347, why was 250rpm chosen for E. coli? What data informed this decision?\nTo address this question, the following text was added to the manuscript at line 389:\n“This shaking speed was selected based on initial growth optimization experiments for a random\nselection of E. coli strains normalized to a starting optical density of 0.02, in which various\nspeeds between 100 and 800 rpm were studied. Of these, 250 rpm was selected because it\noptimized for maximal area under the bacterial growth curve while minimizing the speed.\nMinimizing speed was an optimization objective, as it reduces both the risk of cross well\ncontamination within the 384-well plates and the long-term stress on the automated shaking\nincubators.”\nLine 169 and 172: What conditions were tested? What components of the media were varied?\nHow did this affect the growth of the different bacteria and phages?\nSome have observed phages showing limited efficacy in human serum - was this tested?\nIn these lines, we note that multiple experimental conditions were tested. Our intent in this\nsection was to highlight that the conditions of our in vitro assay may not translate to conditions\npresent in acute urinary tract infections. A benefit of using automated systems is the ability to\napply scale to test many different conditions and mitigate this risk. We have added a line to this\nsection to better match our intent.\nKey conditions that we tested included: 1) varying bacterial titer and phage titer inputs, 2)\nvarious matrices such as lysogeny broth, synthetic urine, and pooled human urine, 3) presence\nof additives such as MgCl and CaCl , and 4) bacterial growth state at the time of phage\n2 2\nexposure. As expected, higher multiplicity of infection (MOI) led to improved phage efficacy, and\nbacteria grew more slowly and to a lower final optical density in urine compared to lysogeny\nbroth. We did not observe large changes in phage efficacy when comparing urine to lysogeny\nbroth. We intend to perform a comprehensive analysis of varying conditions and correlate them\nto the patient bacterial reduction data that we are capturing as part of our ongoing Phase 2 trial.\nE. coli isolates recovered from patient urine throughout the treatment time course will be\nexposed to LBP-EC01 under different experimental conditions, and we will determine the\nconditions that best correlate to observed E. coli level changes in patients. We believe these\ndata will be best suited for follow-on publication after the Phase 2 data collection is completed.\nRegarding testing in human serum, we recently published the resulting phage levels after IV\ndosing in part 1 of a Phase 2 trial1. In patients with an IV dose of 6 mL of 1x1011 PFU/mL drug\nproduct, phage levels in blood samples peaked between 1x106 and 1x107 PFU/mL. Assuming\nthe average patient has a blood volume of 5000 mL, the maximum theoretical blood\nconcentration would be ~1.2x108 PFU/mL, indicating that we are observing 1-10% of the\nmaximum theoretical pharmacokinetic signal. The reduction between our observation and the\nmaximum possible pharmacokinetic signal is likely due to rapid sequestration of phages to the\nspleen and liver, as previously reported in primates:\nhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10805017/.\nLine 217: Figure 4 doesn’t have any PFUs in the axis titles but the text here says that PFUs are\nrepresented in the data - this is confusing\nThank you for catching this error. We have removed the reference to PFU in this sentence.\nLine 300: If including this line, then state what method was used to come to this conclusion and\nideally show the data\nWe have updated this section to include more detailed language about the actual testing that\nwas completed and the thresholds that were used to determine whether resistance to the\ncocktail had developed. Additionally, we have added clarification to ensure that the reader\nunderstands that we were searching for genetic-based resistance mechanisms, as we cannot\nrule the possibility of transient resistance to LBP-EC01 in situ. The following text was added to\nthe manuscript:\n“Finally, we assessed sensitivity of all 166 E. coli isolates to LBP-EC01 by plaquing and\noptical density time course phage efficacy assays. For each patient, we compared the\ngenome assemblies of urine-derived E. coli isolates collected pre-treatment and across\na 34-day period after initial treatment. Because some patients harbored multiple E. coli\nstrains, we grouped isolates by sequencing type within each patient. Resistance to LBP-\nEC01 was defined as a loss of phage efficacy within the same sequencing type over\ntime—either a >1-log reduction in plaquing efficiency or a shift from susceptible to\nresistant classification in the optical density assay (AUC ratio threshold = 0.65). Using\nthis approach, we did not observe the development of genetic resistance to LBP-EC01 in\nany patient enrolled in Part 1 of the study”.\nLine 330: I could not find any information online about what Modules A,B,C, and D of the\nHamilton Vantage 2.0 system are\nWe have included an additional sentence in the methods section that describes the different\nmodules of the system, as Hamilton may not provide the module definitions on their website.\nWe assume the inclusion of the modules may help others examine potential similar systems.\n“Module A refers to the pipetting deck, while Module B is the logistics cabinet that\nsupports the pipetting deck. Module D is the rear cabinet containing the incubators and\nplate readers. Positioned between Modules A/B and D, Module C is the track gripper or\nrobotic arm that facilitates plate movement across the system.”\nReviewer #2 (Remarks on code availability):\nSome methods are described, but the code isn't linked in the methods unless we're missing\nsomething?\nPlease see the above section detailing the automation code we are releasing to improve the\nreproducibility of the methods.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #3 (Remarks on code availability):\nNot available\nCitation:\n1) Kim, Paul, et al. \"Safety, pharmacokinetics, and pharmacodynamics of LBP-EC01, a\nCRISPR-Cas3-enhanced bacteriophage cocktail, in uncomplicated urinary tract\ninfections due to Escherichia coli (ELIMINATE): the randomised, open-label, first part of\na two-part phase 2 trial.\" The Lancet Infectious Diseases 24.12 (2024): 1319-1332."}, {"round": 2, "reviewer_comments": {"reviewer_2": "These edits have greatly improved the paper and we appreciate the detailed answers to all of our comments.\nThank you for going to the effort of using OCR to get the code out of the INSTINCTV software for others to read and for\nsharing the GIFs of the simulations! We hope this makes your methods more widely accessible.\nCocktail selection analysis: The addition of Extended Data Figure 9, comparing your methods to random and top-k selection\nalgorithms, is very helpful. Given its significance in illustrating how your proprietary active learning algorithm enhanced\nexploration of the phage sequence space, this figure might warrant consideration for inclusion in the main text.\nCitation Correction: On a second read, we followed the citation chain for “The frequency and recurrence of UTIs are a\nsignificant health burden and are associated with an estimated $1.6 billion annual cost [12]”. The only reference in 12 that\nseems to be for the $1.6billion number is to a paper entitled “Risk factors for second urinary tract infection among college\nwomen” (from the line: “The estimated annual cost of community-acquired UTI is approximately $1.6 billion.[15]”. This paper\nis about how the sexual behaviours of college women from 1992 to 1994 affected their probabilities of getting a repeat UTI -\nvery interesting but there is no mention of the $1.6 billion number! We request this citation be removed before publication.\nRather than attempting to replace it with another economic estimate, we suggest the human health burden data already\npresented is sufficient to make your case.\nWe wish your clinical trial success and hope your future work leads to greatly improved patient outcomes.", "reviewer_2_code_availability": "The authors share Hamilton automation code for the three assays used in the paper. To help others who try to replicate or use the code, they share videos of the simulations of the experiments when run on their hardware and software.\nFurthermore, for those with different hardware and software setups they share the raw text of the automation code for each\nassay (outside of the hardware manufacturer's proprietary file format)."}, "author_response": "Response to Reviewers Round 2\nHigh-throughput methods leveraging robotics and computer vision for the development\nand assessment of therapeutic phage cocktails\nREVIEWER COMMENTS\nReviewer #2 (Remarks to the Author):\nThese edits have greatly improved the paper and we appreciate the detailed answers to all of our\ncomments.\nWe are grateful to the reviewers for facilitating the paper improvement, and we were happy to\nprovide details to the helpful questions and feedback.\nThank you for going to the effort of using OCR to get the code out of the INSTINCTV software for\nothers to read and for sharing the GIFs of the simulations! We hope this makes your methods more\nwidely accessible.\nWe were happy to provide this update and have included this code in the publicly facing repository.\nCocktail selection analysis: The addition of Extended Data Figure 9, comparing your methods to\nrandom and top-k selection algorithms, is very helpful. Given its significance in illustrating how your\nproprietary active learning algorithm enhanced exploration of the phage sequence space, this\nfigure might warrant consideration for inclusion in the main text.\nWe agree with the recommendation and have added this figure into the main text.\nCitation Correction: On a second read, we followed the citation chain for “The frequency and\nrecurrence of UTIs are a significant health burden and are associated with an estimated $1.6 billion\nannual cost [12]”. The only reference in 12 that seems to be for the $1.6billion number is to a paper\nentitled “Risk factors for second urinary tract infection among college women” (from the line: “The\nestimated annual cost of community-acquired UTI is approximately $1.6 billion.[15]”. This paper is\nabout how the sexual behaviours of college women from 1992 to 1994 affected their probabilities of\ngetting a repeat UTI - very interesting but there is no mention of the $1.6 billion number! We request\nthis citation be removed before publication. Rather than attempting to replace it with another\neconomic estimate, we suggest the human health burden data already presented is sufficient to\nmake your case.\nWe’re grateful to the reviewer for catching this error and agree with their recommendation to\nremove this citation. We have updated the manuscript accordingly.\nWe wish your clinical trial success and hope your future work leads to greatly improved patient\noutcomes.\nReviewer #2 (Remarks on code availability):\nThe authors share Hamilton automation code for the three assays used in the paper. To help others\nwho try to replicate or use the code, they share videos of the simulations of the experiments when\nrun on their hardware and software. Furthermore, for those with different hardware and software\nsetups they share the raw text of the automation code for each assay (outside of the hardware\nmanufacturer's proprietary file format).\nWe note that all of these provided files and simulations have been added to the publicly facing\nrepository."}]

Decision letter