Currently, there is a critical clinical need for improvements in diagnostic techniques for neurodegenerative diseases, such as, Alzheimer’s disease. With AD affecting such a large number of people worldwide, it is imperative we make advances in this area for early diagnosis, as well as better treatment options. However, the latter is only useful if we can detect the disease early, before much of the physiological changes in the brain occur. For this reason, I have chosen to focus on a novel diagnostic method, using nanomedicine to find a new and hopefully improved approach to diagnosis.
As we are an ageing population, with a rising average life expectancy, dementia is becoming more of a problem. Alzheimer’s disease (AD) is the most common form of dementia, [1] affecting more than 520,000 people in the UK alone. [2]
AD is related to the presence of two neuropathological characteristics, which are extracellular amyloid plaques and intracellular neurofibrillary tangles. Amyloid plaques are formed from amyloid precursor proteins. This is present in everyone and when cut using the and -secretase enzymes, forms a soluble fragment which is neurotrophic. However, when cut with the and -secretase enzymes instead, it forms a longer fragment which can aggregate to form toxic oligomers. These oligomers can aggregate further to form insoluble fibrils and plaques. Amyloid-beta plaques are responsible for a lot of the neuronal damage seen in AD. For this reason, even though we are looking for new targets for drug treatment in AD, amyloid- would only be a good target if it is used in early stages. This is because in later stages, the plaques formed will already have caused irreversible neuronal damage hence the patient will have begun displaying symptoms.
Currently, only symptomatic treatment options are available and the only definitive way to diagnose Alzheimer’s is through post-mortem brain examination. Diagnosis is made when a patient reports symptoms to their doctor, and cognitive/neuropsychiatric tests can help to determine whether cognitive dysfunction is caused by dementia or simply the natural aging process. CT scans and cerebrospinal fluid analysis can also be used to aid with diagnosis, however, it is still hard to differentiate between natural aging, AD and other forms of dementia. Diagnosis is often subjective to patient’s symptoms and clinician’s opinions, as well as being both labour intensive and time-consuming. [3]
Therefore, there is a clear clinical need for better diagnostic methods, which can detect AD earlier. The current issue is, we are lacking a clear and simple marker for early detection. [4] AD is a progressive disease, so the longer it goes undiagnosed the more neuronal damage occurs which inevitably makes it harder to treat. Also, as the damage has already occurred, the patient begins to show more and more symptoms. It has been suggested that the biological onset of AD could actually come decades before any symptomatic expression of the disease. [5]
So, with a clear diagnosis, we can work towards offering treatments sooner in the disease progression before cognitive decline occurs. Earlier diagnosis is also important for symptomatic treatment, as if this was effective and started earlier it would mean more neurons would be left undamaged. We can also work towards finding better treatment methods as we can aim to prevent neuronal damage from occurring.
Aims:
Improvements need to be made in the diagnosis and treatment of AD in order to improve patient outcomes and prevent disease progression. Currently, we are lacking not only curative treatments but a definitive diagnostic method for this disease area. Consequently, I have chosen to focus on the development of a reliable method of diagnostics for early stage AD.
In this report, I aim to introduce a novel diagnostic tool which is currently being developed.
Using nanotechnology, we can perform a breath-test much earlier, hopefully providing a more objective diagnosis much earlier in AD. Nanomaterial-based sensors are used to identify the chemical signature of diseases through biomarkers present in exhaled breath. In exploring this option, I hope to show the promise in diagnostic tool development in AD.
Integrated analysis and discussion:
Currently, an electronic nose (eNose) device is being developed and tested in the hope that it will be able to diagnose neurodegenerative diseases through simple breath tests. The concept behind this is that specific diseases have particular chemical signatures which can be detected in exhaled breath. The eNose is able to detect the chemistry of disease by interpreting the effect on our usual chemical fingerprint.
Exhaled breath is analysed using nanomaterial based sensors such as organically functionalized gold nanoparticles and single walled carbon nanotubes. Volatile organic compounds present in the breath are detected by these sensors, as they cause a change in the electrical resistance. The sensors within the device each react in a different way to a particular VOC mixture. This, therefore, produces breath patterns that are unique and can be compared to identify which VOCs are present. [6]
Human breath generally holds thousands of VOCs which differ according to our physiological well-being. They can indicate neuronal metabolism in healthy and diseased states and for this reason, it is believed that this would be a good parameter to investigate in the diagnosis of neurodegenerative diseases. Even though they can be detected in breath, they are not limited to the respiratory system and are in fact blood-borne. This means they are able to be used to monitor many different physiological changes in the body. [7][8]
Given that AD causes many physiological changes to the brain very early in disease progression, it is hopeful that the eNose will be a useful tool for diagnosis. These physiological changes will lead to a different mixture of VOCs presenting in the persons exhaled breath. Therefore, if the eNose is able to accurately detect these compounds, an early diagnosis can be made, even before the patient displays symptoms.
Clinical studies:
Currently, studies have been done comparing breath samples gathered from healthy controls (HC) and patients with neurodegenerative diseases (Parkinson’s and Alzheimer’s). in a study of 57 volunteers, alveolar breath was collected and nano-material based sensors were used to analyse the samples. Discriminant factor analysis determined any significant statistical difference between the groups. Results collected during this study were supported by chemical analysis (gas chromatography/mass spectrometry) of the samples. This novel method was found to be quite accurate, as it was able to differentiate between HC and AD patients with 85% accuracy. Interestingly, it was also able to differentiate between types of neurodegenerative diseases as it could discriminate between PD and AD patients with 84% accuracy. [9]
Another study compared patients with age-matched HC, with no known co-morbities such as pulmonary disease, using a hand-held eNose device to sample VOC in exhaled breath. Samples were collected by allowing participants to breathe standardized medicinal air before exhaling in to a collection bag for 10 seconds at a controlled flow rate of 100-200ml/s. This found the eNose to be very reliable, being able to differentiate between HC and AD patients with 94% accuracy. [10]
Mazzatenta et al also researched this diagnostic technique, limiting the study to AD patients and healthy controls. Exhaled breath was measured for 10 continuous minutes to determine the VOC content. This study showed similarly promising results for the use of exhaled breath in diagnosis as you can see in the figures below. [12]
The common theme in these studies is that VOC analysis seems to be a useful marker in differentiating between patients with neurodegenerative diseases and healthy controls. These results encourage us to look further into the use of this diagnostic tool, as in the future it could potentially be used in earlier disease stages, for example, around biological onset. The VOCs identified in these studies, and breath patterns produced can hopefully be used to identify AD in all stages, as these mixtures of VOCs will be present from onset, when physiological changes begin to occur.
Why nanomaterials?
To be effective, a sensor material must be highly sensitive and able to detect analytes swiftly and efficiently in all environments. [13] Metal oxide conductors are often used as a sensing material, however, these are limited by temperature. At room temperature, metal-oxide sensors display poor sensitivity and lower precision. This means they require higher temperatures to operate and so must be heated by an extra heater, adding to the operational costs of the sensor.
Carbon nanotubes however have many unique properties which make them a much more sensitive sensor material. They possess electrical properties and also display high sensitivity to very small concentrations of analytes at a range of temperatures, including room temp. This means that CNTs do not require any assisting tools such as a heater and therefore, they have much lower operational costs whilst also making them more compact and simple. [14]
Nanomaterial sensors in general are much more sensitive due to their large surface area to volume ratio (rA/V) compared to larger sensor materials. Nanoparticles in sensors are very chemically reactive due to the fact that surface molecules have less allocation of covalent bonds due to their positioning. More of the nanoparticles will be on the surface, due to the large surface area, making them energetically unstable because of the decrease bonding. [15]
Also, sensor materials with higher rA/V allows for analytes to interact much easier, and so have higher sensitivity whilst shortening the response time. Nanofibers, such as nanotubes or wires, have a very large rA/V making them very effective sensor materials as they are both highly sensitive and have very short response times. [16]
Nanomaterial-based sensors can work in multiple ways to detect VOCs. One of which is the selective sensing method. In this approach, a very selective sensor is created for each specific analyte of relevance. This method is accurate in finding target analytes amongst other non-relevant species, and hence can be seen as a highly sensitive technique.
However, there are a number of flaws with this method. For example, selective sensing is often only viable when detecting analytes from within a controlled background, where any interfering species are known. Also, synthesizing these highly selective nanosensors for each specific VOC is time and cost consuming. To combat these limitations, a new method is being explored – cross-reactive nanomaterial-based sensors. In this technique, cross-reactive sensors are used which are able to identify a range of VOCs. These compounds produce a distinctive fingerprint on the sensors, which can be identified using pattern recognition processes. This therefore increases the range of compounds a particular sensor array is able to detect, making it more versatile and so more cost-effective. [17]
Overall, the advances in this diagnostic method are very promising. The current clinical evidence suggests that the use of an electronic nose device could very soon be put into practice and would have a great beneficial impact on neurodegenerative disease areas such as Alzheimer’s disease. The breath prints identified so far with nanomaterial-based sensors most definitely have potential in future practice to be used as biomarkers, helping to provide a fast, reliable and also cost-effective method of diagnosis for Alzheimer’s. [19]