FEATURE MEDICAL & PHARMACEUTICAL
DEVELOPING AN INSTRUMENT for detecting diseases
A new research and analysis tool that features Field-asymmetric Ion Mobility Spectrometry (FAIMS) technology has been developed that enables breath and bodily fluids to be analysed, helping to detect diseases including cancer. Rachael Morling reports
W
e are increasingly hearing about the importance of early detection
and diagnosis of diseases such as cancer. It is, in fact, a topic which has been frequently talked about in the news in recent months, with scientists hoping to create solutions that make this a reality as quickly as possible. After all, the sooner it is diagnosed and treated, the better the chance there will be for the patient to make a full recovery. According to Owlstone Medical, researchers are increasingly finding that specific chemical compounds are present in the breath or bodily fluids of people suffering from various medical conditions such as TB, cancers or diabetes. The body produces compounds that
reflect cellular activity, of which a subset is volatile. These volatile organic compounds (VOCs) may be detected in media including breath, blood, urine, faeces, sputum and sweat. Virtually all diseases induce a change in these compounds which make them potential biomarkers for disease, the company explains. So, a solution that can test for the presence of these chemicals will help with the early diagnosis of these diseases without the need for costly and invasive medical procedures. For this to be a practical method of diagnosis, however, a solution is needed that is extremely sensitive, accurate and fast, yet is also portable, easy to use and affordable. This is where the Lonestar Medical Chemical Analyser comes in.
INTRODUCING LONESTAR Lonestar is a versatile gas analyser and chemical monitor in a portable, self- contained unit. Incorporating Owlstone’s proprietary Field-asymmetric Ion Mobility Spectrometry (FAIMS) technology, the instrument offers the flexibility to provide both high accuracy and rapid results. It can be trained to respond to a broad range of target chemical biomarkers and can be easily integrated with other sensors and third party systems to provide maximum flexibility. As a result, Lonestar is suitable for a broad variety of applications ranging from direct biomarker analysis
12 DEC/JAN 2016 | INSTRUMENTATION
in the clinic to lab based R&D. Ion Mobility Spectrometry separates
molecules according to the speed at which they move through a gas under the influence of an electric field. This depends upon the collision cross-section (i.e. the size), the electric charge and mass of the molecules. FAIMS uses an asymmetric alternating voltage to separate molecules according to their ion-mobility. Unlike mass spectrometry, it does not require molecules to move through a vacuum, avoiding the need for a vacuum generator and allowing a system footprint that is 1000 times smaller. For solid and liquid samples (urine,
blood, stool, etc), sample collection is relatively straightforward - Lonestar will generally require a sample volume of around 5-10ml, although the exact volume needed will vary with both the sample medium and disease under investigation. This sample can be collected in any inert sampling vessel. Breath sampling is a slightly more challenging proposition, with a more complex system required to capture the VOCs present in a patient’s breath so that they can be analysed using Lonestar. However, an international consortium of breath researchers has produced an open source breath sampling system designed to overcome these challenges (
www.breathe-
free.org) of which the ReCIVA is the productized version created by Owlstone (
owlstonemedical.com/products/reciva). Lonestar analyses VOCs in the
gas-phase, which means that for solid and liquid samples, the spectrum is generated by the volatile compounds that evaporate into the headspace above the sample. The rate at which this happens depends on temperature and pressure, so for solid and liquid samples, Lonestar has a complementary sampling system, ATLAS, which provides accurate control of the temperature and the flow rate of the gas taking VOCs from the headspace into the instrument. The output from the sample processing phase will be raw FAIMS spectra for patients with and without the disease of interest. The task is to
Researchers are
increasingly finding that specific chemical
compounds are present in the breath or bodily fluids of people suffering from various medical conditions such as TB, cancers or diabetes
then identify features present at different concentrations in the spectra of diseased patients. FAIMS data are multi-dimensional in
terms of the features present in each spectrum, and so it will usually be necessary to carry out some form of compression or data reduction (such as discrete wavelet transforms or principal component analysis) first. After this step, a classifications algorithm is developed and applied to the reduced data set which will test relevant features of the data, and assign each sample to the disease or disease-free category. The form of the classifier will depend upon the data-set, but random forest, sparse logistic regression and support vector machines have all been successfully deployed. The classifier will then ideally be tested on a new set of samples, to externally validate the sensitivity and specificity.
THE APPLICATIONS Three recent applications demonstrate the benefits of the Lonestar system. Brinkman et al showed at the European Respiratory
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44