A new Artificial Intelligence-based approach to Antibiotic Discovery

Since the discovery of penicillin, antibiotics have become the foundation of modern medicine. However, the lack of production of new antibiotics in the private sector together with an uncontrolled increase in antibiotic resistance represents a major global public health issue. It is projected that deaths attributable to resistant infections will reach 10 million per year by 2050.
Nowadays, a large proportion of biomedical research and the development of therapeutics focuses only on a small fraction of targets for which most lead drug compounds have shown relatively short-term effectiveness.
Infections originating from foodborne pathogens are becoming more difficult to treat due to increasing levels of antimicrobial resistance. Rapid and intensive farming practices promote indiscriminate use of broad-spectrum antimicrobials, providing ideal selection pressure for antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs). The presence of antibiotic-resistant bacteria of animal origin in the human food chain is thus a major global public health issue, with several studies having reported food animals and products being colonized and/or infected and contaminated by antibiotic-resistant strains, such as methicillin-resistant Staphylococcus aureus (MRSA), antibiotic-resistant Campylobacter spp, and extended spectrum-beta-lactamase (ESBL) producing-Enterobacteriaceae (viz. Salmonella spp., Escherichia coli).
Cholera is an acute diarrheal infection caused by ingestion of food or water contaminated with the bacterium Vibrio cholerae. Worldwide, 1.3 billion people are estimated to be at risk and approximately 1.3 to 4 million cases occur annually with 21,000 to 143,000 resulting in death. Also, for this bacterium, the indiscriminate use of wide-spectrum antibiotics creates an additional threat represented by the appearance and diffusion of antimicrobial resistance (AMR) profiles in the pathogen population.
The aim of this project is to develop an Artificial Intelligence (AI)-based approach to broaden the possibility to discover: (i) a wider range of therapeutic targets and (ii) individual lead drug molecules showing high binding affinity to the identified targets in the resistant bacteria.
Research Plan:
WP 1: Next-generation sequencing (NGS) bioinformatics analysis, data mining, and statistical modelling powered by machine learning will be used to scan the genomes of different foodborne pathogens (V. cholerae, E. coli, Salmonella, S. aureus, Enterococcus, Campylobacter) to find new genes (new therapeutic targets) associated to AMR. The PhD project will rely and build on data collected on recently awarded (2) GCRF and (2) InnovateUK-China grants by Dottorini. For all these isolates conventional culture-based screening, antibiotic susceptibility tests (AST), and whole-genome DNA sequencing have been carried out so that machine learning can be applied to correlate the genotype (AMR genes) to the phenotype (AMR profiles).
WP 2: The identified AMR-associated genes will be validated at the National Biofilms Innovation Centre (NBIC) labs in Nottingham using DNA recombinant techniques and microbiology.
WP3: Genes with a clear AMR function will undergo 3D structure modelling and by using deep neural network and ChEMBL, DrugTargetCommons, DrugBank, Broad Institute Drug Repurposing Hub databanks we will expand our antibiotic arsenal by identifying new drugs.
WP4. Drug efficiency together with the newly identified drugs will be validated at NBIC using bacteria inhibition growth assays.