Revealing the dynamics of polymicrobial infections: implications for antibiotic therapy.
As a new generation of culture-independent analytical strategies emerge, the amount of data on polymicrobial infections will increase dramatically. For these data to inform clinical thinking, and in turn to maximise benefits for patients, an appropriate framework for their interpretation is required. Here, we use cystic fibrosis (CF) lower airway infections as a model system to examine how conceptual and technological advances can address two clinical questions that are central to improved management of CF respiratory disease. Firstly, can markers of the microbial community be identified that predict a change in infection dynamics and clinical outcomes? Secondly, can these new strategies directly characterize the impact of antimicrobial therapies, allowing treatment efficacy to be both assessed and optimized?
Rogers
Geraint B
GB
King's College London, Molecular Microbiology Research Laboratory, Pharmaceutical Science Division, 150 Stamford Street, Franklin-Wilkins Building, King's College London, London, SE1 9NH, UK.
Hoffman
Lucas R
LR
Whiteley
Marvin
M
Daniels
Thomas W V
TW
Carroll
Mary P
MP
Bruce
Kenneth D
KD
D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D001424:Bacterial Infections / Q000188:drug therapy* / Q000382:microbiology*; D002908:Chronic Disease; D003550:Cystic Fibrosis / Q000150:complications*; D054884:Host-Pathogen Interactions; D006801:Humans; D016133:Polymerase Chain Reaction; D012141:Respiratory Tract Infections / Q000188:drug therapy* / Q000382:microbiology*
D000900:Anti-Bacterial Agents