Design principles of collateral sensitivity-based dosing strategies

Abstract

Collateral sensitivity (CS)-based antibiotic treatments, where increased resistance to one antibiotic leads to increased sensitivity to a second antibiotic, may have the potential to limit the emergence of antimicrobial resistance. However, it remains unclear how to best design CS-based treatment schedules. To address this problem, we use mathematical modelling to study the effects of pathogen- and drug-specific characteristics for different treatment designs on bacterial population dynamics and resistance evolution. We confirm that simultaneous and one-day cycling treatments could supress resistance in the presence of CS. We show that the efficacy of CS-based cycling therapies depends critically on the order of drug administration. Finally, we find that reciprocal CS is not essential to suppress resistance, a result that significantly broadens treatment options given the ubiquity of one-way CS in pathogens. Overall, our analyses identify key design principles of CS-based treatment strategies and provide guidance to develop treatment schedules to suppress resistance.

Allele-specific collateral and fitness effects determine the dynamics of fluoroquinolone resistance evolution

“…These results indicate the potential effectiveness of CS-informed treatment combinations even with lower dosages for each of the coadministered antibiotics in eradicating infections while minimizing the risk for resistance development. This, in turn, suggests the exploitability of CS-informed treatment combinations especially for antibiotics having a narrow window between their effective doses and those doses at which they give rise to adverse toxic effects, which is in line with previous research ( 29 ).…”

Sequential antibiotic therapy in the lab and in the patient

“…While our observations are in line with those for mono-therapy, a comparison to other studies on sequential therapy is less clear. Both Udekwu, and Weiss [20] and Aulin et al [21] find that drug pairs with lower Hill coefficients (in their cases 1 and 0.5 respectively) can be superior to drug pairs with larger Hill coefficients (in their case 3) in terms of delaying the rise of double resistance to large numbers [20] or in reducing the probability of resistance [21]; the latter is observed for rapid cycling – for slow cycling, drug pairs with large Hill coefficients are superior. One reason for these differences could be the range of doses considered in the respective studies.…”

“…Mathematical models that take the pharmacokinetic and pharmacodynamic properties of drugs into account can simulate a patient-like environment and can help to close the gap between laboratory experiments and clinical applications. Such models have recently been applied to assess the benefits of collateral sensitivity for cycling strategies, showing that the benefits depend on the pharmacodynamic properties of the drugs and the drug dosing [20, 21]. However, these studies only compare a few cycling regimens and drug doses and, more importantly, do not include drug-drug interactions, which are one of the greatest differences between the lab and the patient.…”

Identification of antibiotic collateral sensitivity and resistance interactions in population surveillance data

“…These effects can be evaluated for specific antibiotic combinations and pathogen species, which may guide the design of CS-based dosing strategies of high clinical relevance and the selection of empirical treatment. 23 In addition, the quantification of CR in antimicrobial susceptibility surveillance datasets is of interest to identify antibiotic combinations that should be avoided as these could potentially lead to increased risk of treatment failure and the spread of antimicrobial resistance.…”