Bacteriophages have emerged as key solutions to bacterial challenges arising across human, animal, and harvest health. At the same time, artificial intelligence (AI) has revolutionised the pharma and biotech industries. The adoption of powerful AI tools has led to dramatic improvements in drug discovery, clinical trials, and personalised medicine.
Bacteriophage research is an area that stands to benefit greatly from AI, ultimately leading to further developments and innovations within human, animal, and harvest health. Having already covered the challenges and opportunities in bacteriophage research and the role of AI in phage therapy, we will now explore AI in phage research, unveiling the synergy between these two areas that can lead to the transformation of bacteriophage research.
Leveraging AI for Novel Bacteriophage Discoveries
In contrast to lab-based culture experiments, which are expensive and laborious (particularly high-throughput sequencing), AI and machine learning (ML) present a promising alternative for bacteriophage discovery. AI can be used in all stages of novel bacteriophage discovery, including phage recovery, prediction, and classification.
Unravelling phage genomes is critical to bacteriophage discovery. Several computational tools, such as MARVEL and VirFinder, have been developed to improve the automated recovery and prediction of bacteriophages. Using genomic data, these tools can reveal insights into bacteriophage composition and structure, as well as the dynamics of viral communities.
Determining the host species of identified phages is another important challenge that can be enhanced with AI and ML analysis of genomic data. An example of this is HostPhinder, a tool that can predict the bacterial host of phages by analysing the phage genome sequence.
AI-Driven Approaches to Bacteriophage-Host Interaction Analysis
Exploring phage-host interactions can help with understanding bacterial response mechanisms to phages and provide new insights into effective therapeutic approaches. With AI, it is possible to simplify the analysis of these complex phage-host interactions, providing a more efficient and economical approach compared to conventional lab experiments.
An example of AI-driven phage-host interaction analysis is the use of predictive computational models, which use deep-learning predictive frameworks to anticipate bacteriophage-bacteria dynamics. GSPHI is an example of a predictive model that identifies potential phage and target bacterium pairs through DNA and protein sequence information.
Encouraging results have also been shown when using AI to study the coevolution processes of phages and bacteria. Understanding host-virus coevolution is vital in the fight against antibiotic resistance. Various computational approaches have therefore been developed that exploit evolutionary information left behind in genomic sequences, revealing information relating to coevolution and adaptation mechanisms in bacteria.
Accelerating Bacteriophage Therapy Development with AI
Bacterial antibiotic resistance is a substantial threat to the control of infectious diseases and human health. Combining antibiotics with bacteriophages has the potential to tackle antibiotic resistance. This approach has been shown to enhance bacterial killing, overcome resistance mechanisms, and provide a more effective treatment for bacterial infections.
AI can be implemented across all stages of bacteriophage therapy development. Phage cocktails (rather than single phages) are commonly used as antibacterial agents because the hosts are unlikely to develop resistance to several phages simultaneously. Phage cocktail formulation constitutes a tradeoff between effectively reducing bacterial load and minimising the side effects associated with increased complexity.
AI in Bacteriophage Manufacturing and Production
Large quantities of bacteriophages are used in the food industry, agriculture, and in the treatment of bacterial infections. To meet the bacteriophage demand, a move away from traditional preparation processes towards optimised, continuous operations is required.
Mathematical models are essential in the design of bioreactor configurations, scale-up and optimisation. Bacteria-phage dynamics must also be considered, and are again factored into bacteriophage production using mathematical modelling. These modelling steps are a specific area in which AI-enhanced manufacturing can be employed, ensuring processes are scaled up with confidence.
Personalised Bacteriophage Solutions Through AI
Genetic engineering combined with the specificity of bacteriophages for targeting host cells makes them ideal candidates for targeted therapies within precision medicine. Phages can be engineered to broaden their host range, enhance their efficacy or allow them to target therapeutics to specific cells or tissues.
AI has the potential to guide and accelerate genome modification, generating phage variants with unique properties that can be used in precision medicine. Deep learning methodologies identifying genetic modifications have enabled phages to extend their host range, highlighting the impact AI can have in bacteriophage engineering.
The use of engineered bacteriophages in personalised medicine comes with both ethical and regulatory considerations. It has been suggested that phage therapy has stalled because of regulatory issues in the USA and the European Union. Ethical concerns have then been raised for when phage therapy is eventually approved as patients will have to consent to be infected with a “live” virus in order to treat bacterial infections.
AI-Powered Data Management and Analysis for Bacteriophage Research
As the name suggests, big data refers to massive and complex datasets. In the case of bacteriophages, this could relate to data generated from genome analysis. Big data is required to power the evolution of AI decision-making. AI and ML can be used to explore and analyse big data to identify patterns and extract actionable insights.
Collaborative Advancements: AI’s Role in Bacteriophage Research Networks
Even though phages are classed as biological medicine in the UK, none are actually licensed for use and clinical phage provision relies on networking with international sources. In order to establish sustainable, scalable and equitable phage therapy provision in the UK, new collaborations are required, such as the one between UK Phage Therapy, the Centre for Phage Research at the University of Leicester, CPI, and Nexabiome.
AI and ML approaches have had a profound impact on the pharma and biotech industries. Within bacteriophage research, AI is making waves in bacteriophage discovery, phage-host interaction analysis, and big data analysis, which can be applied to any phage-relevant section to drive innovation, including animal health and agricultural applications. As AI approaches mature and evolve, their uptake and impact is only set to grow, further propelling bacteriophage research forward in the future.