Evolution of Drug Resistance: Bacteria

Please contact Roger Kouyos if you want to choose a paper within this topic.

Mechanisms of resistance

Michael N Alekshun, Stuart B Levy. 2007. Molecular mechanisms of antibacterial multidrug resistance. Cell [pdf]
Treatment of infections is compromised worldwide by the emergence of bacteria that are resistant to multiple antibiotics. Although classically attributed to chromosomal mutations, resistance is most commonly associated with extrachromosomal elements acquired from other bacteria in the environment. These include different types of mobile DNA segments, such as plasmids, transposons, and integrons. However, intrinsic mechanisms not commonly specified by mobile elements-such as efflux pumps that expel multiple kinds of antibiotics-are now recognized as major contributors to multidrug resistance in bacteria. Once established, multidrug-resistant organisms persist and spread worldwide, causing clinical failures in the treatment of infections and public health crises.

R Craig MacLean, Alex R Hall, Gabriel G Perron, Angus Buckling. 2010. The population genetics of antibiotic resistance: integrating molecular mechanisms and treatment contexts. Nat Rev Genet [pdf]
Despite efforts from a range of disciplines, our ability to predict and combat the evolution of antibiotic resistance in pathogenic bacteria is limited. This is because resistance evolution involves a complex interplay between the specific drug, bacterial genetics and both natural and treatment ecology. Incorporating details of the molecular mechanisms of drug resistance and ecology into evolutionary models has proved useful in predicting the dynamics of resistance evolution. However, putting these models to practical use will require extensive collaboration between mathematicians, molecular biologists, evolutionary ecologists and clinicians.

Epidemiology and population biology of resistance

Stuart B Levy, Bonnie Marshall. 2004. Antibacterial resistance worldwide: causes, challenges and responses. Nature Medicine [pdf]
The optimism of the early period of antimicrobial discovery has been tempered by the emergence of bacterial strains with resistance to these therapeutics. Today, clinically important bacteria are characterized not only by single drug resistance but also by multiple antibiotic resistance--the legacy of past decades of antimicrobial use and misuse. Drug resistance presents an ever-increasing global public health threat that involves all major microbial pathogens and antimicrobial drugs.

M Lipsitch, M Samore. 2002. Antimicrobial Use and Antimicrobial Resistance: A Population Perspective. EID [pdf]
The need to stem the growing problem of antimicrobial resistance has prompted multiple, sometimes conflicting, calls for changes in the use of antimicrobial agents. One source of disagreement concerns the major mechanisms by which antibiotics select resistant strains. For infections like tuberculosis, in which resistance can emerge in treated hosts through mutation, prevention of antimicrobial resistance in individual hosts is a primary method of preventing the spread of resistant organisms in the community. By contrast, for many other important resistant pathogens, such as penicillin-resistant Streptococcus pneumoniae, methicillin-resistant Staphylococcus aureus, and vancomycin-resistant Enterococcus faecium resistance is mediated by the acquisition of genes or gene fragments by horizontal transfer; resistance in the treated host is a relatively rare event. For these organisms, indirect, population-level mechanisms of selection account for the increase in the prevalence of resistance. These mechanisms can operate even when treatment has a modest, or even negative, effect on an individual host’s colonization with resistant organisms.

Treatment strategies

C Bergstrom, M Lo, M Lipsitch. 2004. Ecological theory suggests that antimicrobial cycling will not reduce antimicrobial resistance in hospitals. PNAS. [pdf]
Hospital-acquired infections caused by antibiotic-resistant bacteria pose a grave and growing threat to public health. Antimicrobial cycling, in which two or more antibiotic classes are alternated on a time scale of months to years, seems to be a leading candidate in the search for treatment strategies that can slow the evolution and spread of antibiotic resistance in hospitals. We develop a mathematical model of antimicrobial cycling in a hospital setting and use this model to explore the efficacy of cycling programs. We find that cycling is unlikely to reduce either the evolution or the spread of antibiotic resistance. Alternative drug-use strategies such as mixing, in which each treated patient receives one of several drug classes used simultaneously in the hospital, are predicted to be more effective. A simple ecological explanation underlies these results. Heterogeneous antibiotic use slows the spread of resistance. However, at the scale relevant to bacterial populations, mixing imposes greater heterogeneity than does cycling. As a consequence, cycling is unlikely to be effective and may even hinder resistance control. These results may explain the limited success reported thus far from clinical trials of antimicrobial cycling.

S Bonhoeffer, M Lipsitch, B R Levin. 1997. Evaluating treatment protocols to prevent antibiotic resistance. PNAS [pdf]
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 population-wide 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.