Machine Learning Mediated Advanced Phage and Antimicrobial Therapy- A Futuristic Approach

Abstract

The emergence of antimicrobial resistance (AMR) has overwhelmed the contemporary curatives and have turned into one of the major challenges in the biomedical sector. With increasing deaths being associated with AMR every year; early detection of pathogens and development of novel drugs and alternative therapies, have all become ad hoc in diagnosis, prognosis and patient survival. Bacteriophage therapy remains a viable strategy to counteract AMR, yet unduly restrained by phage resistance. Phage infection is a natural phenomenon and can be widely manipulated in vitro using advanced techniques including the CRISPR/Cas systems which renders phage therapy an upper hand in comparison to conventional drugs. Phage identification, host range detection, determination of phage-receptor binding efficiency, adsorption rate, phage genome analysis are crucial stages in phage selection and phage cocktail preparation and moreover pivotal in flourishing phage therapy. The ascent of translational research and omics has allowed the development of quick, reliable and precise strategies for phage-based diagnosis and treatment techniques. However, in vitro evaluation of AMR and phage factors as well as storing, processing and analyzing large laboratory data outputs are expensive, time-consuming and labor-intensive. Machine learning (ML) is a utilitarian strategy to organize, store, analyze data sets and more importantly allows prediction of certain features by recognizing patterns in the data sets. With the huge number of research been carried out around the globe and enormous data sets being published and stored in databases, ML can utilize the available data to perform and guide in developing alternative therapeutics. Several ML based tools have been developed to predict resistance in host, phage grouping for cocktail preparation, resistance and lysogenic genes detection, phage genomic evaluation and to understand phage-host interactions. ML also allows the in silico analysis of large samples (drug/phage) and reduces sample size for in vitro evaluation thereby reducing overall costs, time and labor. The present review summarizes the available ML algorithms and corresponding databases used in AMR and phage research. It also emphasizes the status quo of antimicrobial and phage resistance in the healthcare sector and analyses the role of ML in analyzing biological databases in order to predict possible phage/drug-host interaction patterns, phage susceptibility, suitability of phage strains for therapy and recommends the most efficient drug combinations and treatment strategies.