Evaluating treatment protocols to prevent antibiotic resistance

Abstract

The spread of bacteria resistant to antimicrobial agents calls for population-wide treatment strategies to delay or reverse the trend toward antibiotic resistance. Here we propose new criteria for the evaluation of the populationwide effects of treatment protocols for directly transmitted bacterial infections and discuss different usage patterns for single and multiple antibiotic therapy. A mathematical model suggests that the long-term benefit of single drug treatment from introduction of the antibiotic until a high frequency of resistance precludes its use is almost independent of the pattern of antibiotic use. When more than one antibiotic is employed, sequential use of different antibiotics in the population (''cycling'') is always inferior to treatment strategies where, at any given time, equal fractions of the population receive different antibiotics. However, treatment of all patients with a combination of antibiotics is in most cases the optimal treatment strategy.

Impact of antimicrobial usage on the transmission dynamics of antimicrobial resistant bacteria among pigs

“…• It was assumed that, after treatment stops, pigs with drug-resistant bacteria spontaneously clear-off bacteria faster than those with drug-sensitive bacteria do but rather have a slower transmission rate due to fitness cost incurred by antimicrobial resistance (Banhoeffer et al 1997;Lipsitch and Samore, 2002). …”

“…It was assumed that drug-resistant pigs recovered spontaneously faster than drug-sensitive pigs although they have a lower transmission rate. The energy burden associated with mutations and plasmid transfer thus rendered drug-resistant bacteria less competitive (Austin et al, 1997;Banhoeffer et al, 1997;Lenski, 1998).…”

Studies of antibiotic resistance within the patient, hospitals and the community using simple mathematical models

“…Lond. B (1999) 354, 721^738 (including hospitals), with the key population genetic factors Bonhoe¡er et al 1997). The number of publications that meld these two areas, in combination with an understanding of the mechanisms of the evolution of resistance, patterns of drug consumption and the pharmacokinetics and pharmacodynamics of de¢ned drugs and pathogens, can be counted on one hand Austin et al 1998b).…”

“…In discussions of how best to combat resistance or how to slow its spread, the debates have largely centred on the microbiological and clinical issues. Very few papers in this important ¢eld address the key population genetic issues that underpin an understanding of the evolution and spread of resistant organisms (Bjo« rkmann et al 1998;Stilianakis et al 1998;Blower et al 1998;Levin et al 1997;Austin et al 1997a,b;Bonhoe¡er et al 1997;Schrag et al 1997;Lenski 1997;Lenski et al 1994;Massad et al 1993). An even smaller number attempt to meld current understanding of the transmission dynamics of bacteria in human communities Phil.…”

Simple models of antibiotic cycling

“…Bonhoeffer et al (1997) and Bergstrom et al (2004) use numerical simulation with random samplings of the feasible parameter space as grounds for preferring mixing strategies. While some medical studies suggest that cycling strategies are not harmful (Dominguez et al, 2000) and may improve on mixing strategies (Gruson et al, 2003), consensus opinion appears to favor mixing strategies.…”