Alessandro Gerada

Amoxicillin-clavulanic acid (AMX-CLV) is a widely used oral β-lactam/β-lactamase inhibitor combination against Escherichia coli . Clinical success is largely confined to urinary tract infections. The mechanistic basis for this site-specific efficacy remains unclear. Using a hollow-fibre infection model to replicate human plasma and urinary pharmacokinetics, we show that plasma-like exposures rapidly select for pre-existing resistant subpopulations; whereas, urinary exposures produce sustained bactericidal activity without resistance emergence. Genomic and transcriptomic analyses following plasma drug exposure reveal that treatment selectively enriches pre-existing resistant lineages already harbouring oxidative-stress-associated mutations that activate the SOS response and drive IS-mediated amplification of blaTEM-1 , leading to β-lactamase hyperproduction and treatment failure. In contrast, the high urinary concentrations of clavulanic acid exert direct antibacterial activity, eradicating these subpopulations. Our findings demonstrate that local pharmacokinetic environments fundamentally shape evolutionary trajectories under β-lactam/β-lactamase inhibitor therapy, explaining the restricted efficacy of AMX-CLV and revealing a dynamic interplay between stress responses, genome plasticity, and drug partitioning that governs treatment outcome.

Although machine learning models can predict antimicrobial susceptibility from bacterial whole genome sequencing (WGS), state-of-the-art approaches are computationally demanding or dependent on knowledge of genetic resistance determinants. Here, we describe an efficient data-driven approach to predicting minimum inhibitory concentration (MIC) by progressively extending and refining predictive genome segments, independent of prior knowledge of resistance determinants. Resultant models had high interpretability — known and potentially novel resistance determinants were captured. Using 762 clinical E. coli strains, 71.6% of predictions were within one dilution of the measured MIC. Models trained with this algorithm generalised better onto external data (F1 score = 0.85) compared with alternative models trained on annotated resistance determinants (F1 = 0.82) or k -mer counts (F1 = 0.74). Computational demands were low (RAM usage 23.6GB vs 38.8GB for k -mer model). These advantages represent an important advance in predicting antimicrobial susceptibility from WGS, with potential applications for clinical diagnostics, drug development, and surveillance.

Amoxicillin-clavulanic acid (AMX-CLV) is a widely used oral β-lactam/β-lactamase inhibitor combination against Escherichia coli . Clinical success is largely confined to urinary tract infections. The mechanistic basis for this site-specific efficacy remains unclear. Using a hollow-fibre infection model to replicate human plasma and urinary pharmacokinetics, we show that plasma-like exposures rapidly select for pre-existing resistant subpopulations; whereas, urinary exposures produce sustained bactericidal activity without resistance emergence. Genomic and transcriptomic analyses following plasma drug exposure revealed that treatment selectively enriches pre-existing resistant lineages already harbouring oxidative-stress-associated mutations that activate the SOS response and drive IS-mediated amplification of blaTEM-1 , leading to β-lactamase hyperproduction and treatment failure. In contrast, the high urinary concentrations of clavulanic acid exert direct antibacterial activity, eradicating these subpopulations. Our findings demonstrate that local pharmacokinetic environments fundamentally shape evolutionary trajectories under β-lactam/β-lactamase inhibitor therapy, explaining the restricted efficacy of AMX-CLV and revealing a dynamic interplay between stress responses, genome plasticity, and drug partitioning that governs treatment outcome.

Objectives: Infection diagnoses are critical to the personalized interpretation of EUCAST aminopenicillin breakpoints for Enterobacterales, but microbiology laboratories cannot predict diagnosis when specimens are received. Here, we assess whether machine learning could facilitate personalized antimicrobial susceptibility breakpoint reporting by predicting urinary tract infection (UTI) diagnoses. Methods: XGBoost models were trained using open-source electronic healthcare record data to predict complicated UTI in patients with Enterobacterales bacteriuria and to predict UTI in patients with Enterobacterales bacteraemia. These models were validated and used to provide simulated aminopenicillin dosing/regimen recommendations based on antimicrobial susceptibility results for patients with bacteriuria and bacteraemia in a holdout dataset. The main outcomes were the proportions of patients recommended appropriate aminopenicillin dosages/regimens according to EUCAST guidelines based on their diagnosis. Results: The area under the receiver operating characteristic curve was 0.62 for predicting both complicated UTI in patients with bacteriuria and UTI in patients with bacteraemia. In the simulation study, 79.3% (n = 276) and 72.7% (n = 8) of patients with ampicillin-susceptible Enterobacterales bacteriuria and bacteraemia, respectively, were recommended appropriate aminopenicillin dosages/regimens for their infection diagnosis according to EUCAST guidelines. Adjusting the probability threshold for predicting complicated UTI increased the proportion of appropriate recommendations in bacteriuria to 96.6% (n = 336). Conclusions: Using machine learning models to predict the probability of complicated UTI in patients with bacteriuria and the probability of UTI in patients with bacteraemia resulted in appropriate aminopenicillin dosages/regimens being recommended in most cases. These results provide proof-of-concept for how machine learning could facilitate the personalized implementation of EUCAST aminopenicillin breakpoints.

Analyse, plot, and tabulate antimicrobial minimum inhibitory concentration (MIC) data. Validate the results of an MIC experiment by comparing observed MIC values to a gold standard assay, in line with standards from the International Organization for Standardization (2021) . Perform MIC prediction from whole genome sequence data stored in the Pathosystems Resource Integration Center (2013) database or locally.

Antimicrobial susceptibility testing is a key weapon against antimicrobial resistance. Diagnostic microbiology laboratories use one-size-fits-all testing approaches that are often imprecise, inefficient, and inequitable. Here, we report a personalised approach that adapts laboratory testing for urinary tract infection to maximise the number of appropriate treatment options for each patient. We develop and assess susceptibility prediction models for 12 antibiotics on real-world healthcare data using an individual-level simulation study. When combined with decision thresholds that prioritise selection of World Health Organisation Access category antibiotics (those least likely to induce antimicrobial resistance), the personalised approach delivers more susceptible results (results that encourage prescription of that antibiotic) per specimen for Access category antibiotics than a standard testing approach, without compromising provision of susceptible results overall. Here, we show that personalised antimicrobial susceptibility testing could help tackle antimicrobial resistance by safely providing more Access category antibiotic treatment options to clinicians managing urinary tract infection.