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Exhaled breath condensate biomarkers in COPD

Medicines Evaluation Unit, Langley Building, The University of Manchester, Wythenshawe Hospital, Manchester, UK.

CORRESPONDENCE: Z. L. Borrill, Medicines Evaluation Unit, Langley Building, The University of Manchester, Wythenshawe Hospital, Southmoor Rd, Manchester, M23 9QZ, , UK. Fax: 44 1619461459. E-mail: zborril{at}meu.org.uk

Keywords: Biomarker, chronic obstructive pulmonary disease, clinical trials, exhaled breath condensate, methodology

Received: September 4, 2007
Accepted February 13, 2008

Biomarkers in chronic obstructive pulmonary disease may be useful in aiding diagnosis, defining specific phenotypes of disease, monitoring exacerbations and evaluating the effects of drugs. Exhaled breath condensate is a noninvasive means of sampling the airways, allowing biomarkers of airway inflammation and oxidative stress to be measured. In the present review, the use of exhaled breath condensate biomarkers in chronic obstructive pulmonary disease is explored and potential applications in diagnosis, disease phenotyping, exacerbation monitoring and clinical trials are considered. Exhaled breath condensate biomarkers are comprehensively reviewed in terms of method validation, reproducibility, disease specificity and sensitivity to detect changes in airway inflammation.

The commonly used exhaled breath condensate methodologies in chronic obstructive pulmonary disease patients are shown to have considerable variability, due to technical issues concerning both sample collection and analysis. Despite these issues, there is still data to support the use of exhaled breath condensate biomarkers for monitoring chronic obstructive pulmonary disease exacerbations and the response to pharmacological intervention. Further improvements to sample collection and analysis methods will improve the sensitivity of these biomarkers. The use of cytokine arrays, mass spectrometry and nuclear magnetic resonance profiling of exhaled breath condensate has opened a new avenue for analysis, as hypothesis generation from such profiling may lead to further selection of biomarkers for specific analysis.