Philip Fowler

The Modernising Medical Microbiology Unit here in Oxford is pioneering genetics-based clinical microbiology. The central idea is to infer which antibiotics can be used to treat an infection by examining the genetics of a clinical sample. Our exemplar pathogen is Mycobacterium tuberculosis (MTB) which is the aetiology agent of tuberculosis due to its very slow growth rate and relatively straightforward genetics. I am working closely with the Ellison Institute of Technology (EIT) in Oxford to launch a free-to-use Mycobacterial genetics service that anyone worldwide can upload their raw genetics files (FASTQ) and get back in less than 30 minutes (i) the species contained, including subspecies/lineage, (ii) a prediction of which antibiotics will be effective and (iii) a list of which other samples are likely epidemiologically related.

Through the earlier CRyPTIC project, we amassed about 84,000 MTB genomes which have a range of different phenotypic antibiotic susceptibility data (AST). This data was used by the WHO to create their first catalogue of resistance associated mutations in MTB which was released in June 2021 and updated in November 2023. As part of the CRyPTIC project I developed image processing software that determined the minimum inhibitory concentrations of the dozen drugs on a 96-well broth micro dilution plate; this also enabled a very large and successful citizen science project, BashTheBug, through which 46,427 volunteers classified 4,746,420 images of MTB growing on different antibiotics. 

The current approach is inferential and therefore I am also developing predictive methods. These fall into two main classes: (1) protein structure-based machine learning and (2) calculating the effect of the mutation on the binding free energy of the antibiotic using alchemical free energy simulations. The former covers a range of machine learning methods, from relatively straightforward methods like random forest through to more advanced methods like graph-based convolution neural networks (gCNNs). Our hope with the latter is to begin to move into other species (like the ESKAPE pathogens) because we are predicting based off sequence rather than mutation.