Exhaled breath analysis for lung cancer detection using ion mobility spectrometry

Background: Conventional methods for lung cancer detection including computed tomography (CT) and bronchoscopy are expensive and invasive. Thus, there is still a need for an optimal lung cancer detection technique. Methods: The exhaled breath of 50 patients with lung cancer histologically proven by bronchoscopic biopsy samples (32 adenocarcinomas, 10 squamous cell carcinomas, 8 small cell carcinomas), were analyzed using ion mobility spectrometry (IMS) and compared with 39 healthy volunteers. As a secondary assessment, we compared adenocarcinoma patients with and without epidermal growth factor receptor (EGFR) mutation. Results: A decision tree algorithm could separate patients with lung cancer including adenocarcinoma, squamous cell carcinoma and small cell carcinoma. One hundred-fifteen separated volatile organic compound (VOC) peaks were analyzed. Peak-2 noted as n-Dodecane using the IMS database was able to separate values with a sensitivity of 70.0% and a specificity of 89.7%. Incorporating a decision tree algorithm starting with n-Dodecane, a sensitivity of 76% and specificity of 100% was achieved. Comparing VOC peaks between adenocarcinoma and healthy subjects, n-Dodecane was able to separate values with a sensitivity of 81.3% and a specificity of 89.7%. Fourteen patients positive for EGFR mutation displayed a significantly higher n-Dodecane than for the 14 patients negative for EGFR (p<0.01), with a sensitivity of 85.7% and a specificity of 78.6%. Conclusion: In this prospective study, VOC peak patterns using a decision tree algorithm were useful in the detection of lung cancer. Moreover, n-Dodecane analysis from adenocarcinoma patients might be useful to discriminate the EGFR mutation

Two different approaches for pharmacokinetic modeling of exhaled drug concentrations

Online measurement of drug concentrations in patient's breath is a promising approach for individualized dosage. A direct transfer from breath- to blood-concentrations is not possible. Measured exhaled concentrations are following the blood-concentration with a delay in non-steady-state situations. Therefore, it is necessary to integrate the breath-concentration into a pharmacological model. Two different approaches for pharmacokinetic modelling are presented. Usually a 3-compartment model is used for pharmacokinetic calculations of blood concentrations. This 3-compartment model is extended with a 2-compartment model based on the first compartment of the 3-compartment model and a new lung compartment. The second approach is to calculate a time delay of changes in the concentration of the first compartment to describe the lung-concentration. Exemplarily both approaches are used for modelling of exhaled propofol. Based on time series of exhaled propofol measurements using an ion-mobility-spectrometer every minute for 346 min a correlation of calculated plasma and the breath concentration was used for modelling to deliver R2 = 0.99 interdependencies. Including the time delay modelling approach the new compartment coefficient ke0lung was calculated to ke0lung = 0.27 min−1 with R2 = 0.96. The described models are not limited to propofol. They could be used for any kind of drugs, which are measurable in patient's breath

Measuring compounds in exhaled air to detect Alzheimer´s disease and Parkinson´s disease

Background Alzheimer’s disease (AD) is diagnosed based upon medical history, neuropsychiatric examination, cerebrospinal fluid analysis, extensive laboratory analyses and cerebral imaging. Diagnosis is time consuming and labour intensive. Parkinson’s disease (PD) is mainly diagnosed on clinical grounds. Objective The primary aim of this study was to differentiate patients suffering from AD, PD and healthy controls by investigating exhaled air with the electronic nose technique. After demonstrating a difference between the three groups the secondary aim was the identification of specific substances responsible for the difference(s) using ion mobility spectroscopy. Thirdly we analysed whether amyloid beta (Aβ) in exhaled breath was causative for the observed differences between patients suffering from AD and healthy controls. Methods We employed novel pulmonary diagnostic tools (electronic nose device/ion-mobility spectrometry) for the identification of patients with neurodegenerative diseases. Specifically, we analysed breath pattern differences in exhaled air of patients with AD, those with PD and healthy controls using the electronic nose device (eNose). Using ion mobility spectrometry (IMS), we identified the compounds responsible for the observed differences in breath patterns. We applied ELISA technique to measure Aβ in exhaled breath condensates. Results The eNose was able to differentiate between AD, PD and HC correctly. Using IMS, we identified markers that could be used to differentiate healthy controls from patients with AD and PD with an accuracy of 94%. In addition, patients suffering from PD were identified with sensitivity and specificity of 100%. Altogether, 3 AD patients out of 53 participants were misclassified. Although we found Aβ in exhaled breath condensate from both AD and healthy controls, no significant differences between groups were detected. Conclusion These data may open a new field in the diagnosis of neurodegenerative disease such as Alzheimer’s disease and Parkinson’s disease. Further research is required to evaluate the significance of these pulmonary findings with respect to the pathophysiology of neurodegenerative disorders

Signals of neutropenia in human breath?

Children undergoing systemic chemotherapy often suffer from severe immunosuppression usually associated to severe neutropenia (neutrophils < 0.5 x 109/l). Clinical courses during those periods range from asymptomatic to septic general conditions. Development of septic symptoms can be very fast and life-threatening. Swift detection of risk factors in those patients is therefore needed. So far no early, rapid and reliable marker or tool exists. Ion-Mobility-Spectrometry coupled with a Multi-Capillary-Column (IMS-MCC) can analyze more than 600 volatile components from exhaled air within a few minutes and hence is a potential, rapid detection-tool. As a proof of concept we measured the exhaled breath of 11 patients with neutropenia and 10 healthy controls ranging from 3 to 18 years of age at the time of measurement. Ten milliliters breath samples were taken at the outpatient clinic and analyzed with an onsite IMS-MCC (BreathDiscovery, B&S Analytik, Dortmund, Germany). Dead-space-volume was adapted to two groups (small 250 ml, large 500 ml). Interestingly 59 differing peaks were measured. Eleven were significantly different (p ≤ 0.05), three of which highly significant (p ≤ 0.01) in Mann-Whitney-Rank-Sum-testing. The corresponding analytes used in the decision tree are 2-Propanol, D-Limonene and Acetone. The analytes with the lowest rank sum identified are 2-Hexanone, Iso-Propylamine and 1-Butanol. Eventually we were able to show a three-step-decision-tree, which discerns the 21 samples except one from each group. Sensitivity was 90 % and specificity was 91 %. Naturally these findings need further confirmation within a bigger population. Our pilot-study proves that Ion-Mobility-Spectrometry coupled with a Multi-Capillary-Column is a feasible rapid diagnostic tool in the setting of a pediatric oncology out-patient clinic for patients 3 years and older. Our first results furthermore encourage additional analysis as to whether patients at risk for septic events during immunosuppression can be diagnosed in advance by rapidly assessing risk factors such as Neutropenia in exhaled breath

Decision tree algorithm to discriminate between healthy and lung cancer patients.

<p>Decision tree algorithm to discriminate between healthy and lung cancer patients.</p

Characteristics of ion mobility spectrometer (BioScout).

<p>Characteristics of ion mobility spectrometer (BioScout).</p

Detection of VOC peaks using Visual Now database.

<p>Lung cancer vs. healthy subjects.</p><p>Detection of VOC peaks using Visual Now database.</p

A decision tree algorithm could separate small cell carcinoma, squamous cell carcinoma and adenocarcinoma.

<p>A decision tree algorithm could separate small cell carcinoma, squamous cell carcinoma and adenocarcinoma.</p

Characteristics of patients.

<p>Characteristics of patients.</p

Multi-capillary column-ion mobility spectrometer (MCC-IMS) breath analysis in ventilated rats : a model with the feasibility of long-term measurements

Rats are commonly used in medical research as they enable a high grade of standardization. The exhalome of ventilated rats has not as yet been investigated using an ion mobility spectrometer coupled with a multi-capillary column (MCC-IMS). As a first step, a rat model has to be established to measure potential biomarkers in the exhale with long-term settings, allowing constant and continuous analysis of exhaled air in time series. Therefore, eight animals were anaesthetized, prepared and ventilated for 1 h. A total of 73 peaks were directly detected with the IMS chromatogram. Thirty five of them were assigned to the ventilator system and 38 to the animals. Peak intensity varied within three measurements. The intensity of analytes of individual rats varied by a factor of up to 18. This new model will also enable continuous measurements of volatile organic compounds (VOCs) from rat's breath in long-term experiments. It is hoped that, in the future, variability and progression of VOCs can be monitored in different models of diseases using this set-up