Multiple sclerosis diagnostic/prognostic
For multiple sclerosis (MS) there is currently no reliable biomarker that can accurately stage disease, and there is often a long delay before clinicians are able to confirm the change from relapsing remitting (RR) to secondary progressive (SP) disease.
There is a strong need for accurate MS prognostic tests to track disease progression so that patients can be offered appropriate or new treatments as soon as possible after onset or according to their disease.
Oxford researchers have discovered metabolite signatures that accurately predict MS disease stage. Exploring differences between the MS phenotypes may identify key mechanisms driving progression, and is the first step in developing effective new therapies to prevent disability.
We have discovered that a combination of NMR and partial least squares discriminant analysis (PLS-DA) of biofluids, which makes no assumptions on underlying mechanisms of disease, can accurately diagnose MS and differentiate disease stages. This approach defines distinct patterns of metabolite variation characteristic of the disease as opposed to the identification of a unique biomarker.
Running our PLS-DA models on serum samples from MS patients highlights significant differences between the metabolic profiles of each of the primary progressive, RR and SP groups and the healthy controls, and provides clinicians with a means of allocating patients into the different disease groups with high specificity and sensitivity.
Critically, the method identifies a surrogate outcome measure for the RR to SP transition that is urgently required in the clinic. This approach will not only be of considerable benefit at the clinical interface by categorising patients for treatment, but also in evaluating patient based outcome measures in future clinical trials for MS.
The image above shows:
The PLS models can accurately diagnose new patients with MS and (BOTTOM) determine whether individuals have moved from relapsing remitting to secondary progressive disease. The model generating plots (monochrome) are overlaid with an unknown independent sample set (colour) that was used to test the models. RR patients are shown as diamonds, SP patients are shown as triangles, control samples are shown as circles. The models were able to accurately predict the identity of the unknown sample.
MS affects over 2.5 million people worldwide and there is a global market size of $7.3 billion for MS treatments that are highly sensitive for their efficacy on the stage of disease. The rate of disease incidence is also projected to rise, partly due to growing awareness of the disease.
Patent protection and opportunity
A UK priority patent application has been filed for this technology, claiming as in vitro diagnostic/prognostic method for MS.
We see this discovery as the beginning of what will become a standard method for diagnosis and monitoring across a spectrum of diseases. We are in the process of raising funds to scale up the clinical trial in order to realise its full potential as a routine diagnostic test.
Oxford University Innovation seeks a partner to develop and fully translate this high throughput technology. Request more information if you would like to discuss this further.
about this technology