Clinical microbiology often assumes a sample is resistant or susceptible. Making such a classification relies on applying a threshold (usually called a cutoff) to quantitative data, such as minimum inhibitory concentrations (MICs). If the MICs are strongly bimodal, then this is trivial and reproducibility is guaranteed. If the MICs are unimodal, then one is left […]
Although the population structure M. tuberculosis is clonal, one must be careful when inferring the effect of individual mutations on the effect of an antibiotic. Purely because a mutation appears to define a phylogeny does not mean it has no effect on the minimum inhibitory concentration. Read more here (Open Access).
The story behind this preprint goes back to the workshop on free energy methods run by BioExcel in Göttingen in May 2019. I gave a talk, based in part on the work I’d previously published showing how alchemical free energy methods are able to predict which mutations in S. aureus DHFR confer resistance to trimethoprim.
Usually, the protein that an antibiotic binds is essential for bacterial survival, which is how the drug has its effect. In this case, relatively few protein mutations arise that confer resistance, they are often subtle in nature and one can try to predict the phenotype of a protein mutation by considering how it affects the […]
Last year I coordinated a bid to the NIHR for capital to improve our research capacity to study antimicrobial resistance (AMR) at the Oxford Biomedical Research Centre. We were successful and were awarded £1.8 million to fund several different activities, including developing vaccines to prevent the spread of AMR. Previously in the John Radcliffe […]
Due to the rise of antibiotic resistance, it is increasingly important that your clinician knows which antibiotics will work (and which will not). Traditionally, this is done in hospital microbiology labs by growing a sample taken from the infection site, and then testing how a range of antibiotics affect its growth, or, ideally, kill it. […]