Science

The issue of antibiotic resistance is a critical concern for public health, prompting numerous investigations into the impact of treatment strategies on preventing or slowing down the emergence of resistance. While existing studies have predominantly focused on chromosomal resistance mutations, the consequences of often clinically more relevant plasmid-conferred resistance remain insufficiently explored. To address this gap, we conducted three extensive in vitro experiments utilizing a liquid-handling platform. These experiments evaluated the efficacy of five distinct treatment strategies using two antibiotics (tetracycline and ceftazidime) along with two horizontally transmissible clinical resistance plasmids conferring the respective resistances. Among the experimentally investigated treatment strategies, combination therapy proved to be the most effective in preventing the emergence of double resistance while minimizing the number of infections. To verify the reliability of these findings, we constructed a computational model of our experiments that we parameterized using the experimental data. We employed this model to augment the experimental data by conducting an in silico parameter sensitivity analysis. The sensitivity analysis corroborated our experimental results, demonstrating that combination therapy consistently outperformed other treatment strategies across a range of parameter values.

Bacterial population decline at antibiotic concentrations above the minimum inhibitory concentration (MIC) remains poorly characterized. This is because colony-forming units (CFU), the standard method to quantify inhibition, are slow, labor-intensive, and costly. Luminescence assays are widely used to quantify population dynamics at subinhibitory concentrations, yet their limitations and reliability at high concentrations remain underexplored. Here, we compared luminescence- and CFU-based rates in Escherichia coli across 20 antimicrobials. In our experiments, luminescence- and CFU-based rates did not differ significantly for half of them. For the other half, CFU-based decline rates were consistently higher. The estimates differed for two main reasons: First, because light intensity tracks biomass more closely than population size, luminescence declined more slowly than the population when bacteria filamented. Second, CFU-based estimates indicated a steeper decline when treatment reduced the number of colonies formed per plated bacterium. This can result from changes in clustering behavior, physiological changes that impair culturability, or antimicrobial carryover. Thus, the suitability of luminescence to quantify bacterial decline depends on the physiological effects of the antimicrobial and whether the quantity of interest is cell number or biomass. Within these limitations, luminescence can serve as an efficient, high-throughput alternative for quantifying bacterial dynamics at super-MIC concentrations.

Assessing whether drug combinations synergise or antagonise is difficult for several reasons: (i) measuring bacterial death rates at clinically relevant inhibitory drug concentrations is methodologically challenging, (ii) there is no unifying definition of what constitutes synergy or antagonism, and (iii) both synergy and antagonism may be concentration- and mixing-ratio-dependent. To assess how well sub-inhibitory measurements predict inhibitory behaviour, we quantified drug interactions for 15 pairwise drug combinations on a concentration checkerboard covering a wide range of inhibitory and sub-inhibitory concentrations. To this end, we tracked the population dynamics of 8640 bioluminescent E. coli cultures by recording their light-intensity trajectories. To handle time-varying treatment effects and allow fair comparisons between drugs with distinct killing dynamics, we used a time-weighted net growth rate ψ to summarise each trajectory and assigned interaction labels (synergistic/independent/antagonistic) based on Bliss independence and Loewe additivity. We found that the interaction label depends on both the concentration and the mixing ratio, frequently changing between the sub-inhibitory and inhibitory regimes. Characterising drug combinations at a single sub-inhibitory concentration is therefore not sufficient. Instead, their combined effects should be assessed at the conditions of their intended use.