Information for Researchers

To learn more about the study and the study team, please click on the relevant section below.

There are many disease modifying treatments (DMTs) for patients with Multiple Sclerosis (MS).听 Currently, there is no clinical tool to help patients听select the medication that will work best for them and the decision is often made according to factors such as drug administration, frequency of drug administration and risk of side effects.听 If patients continue to suffer from relapses or have serious side effects, they can switch to another DMT.听 The consequences of this policy, that requires a听DMT to 鈥渇ail鈥� before prescribing another one听include continuous relapses, accumulation of disability and unnecessary side effects.

We aim to collect all manifestations of MS that may influence individual treatment response and translate machine learning techniques into clinical practice in order to generate a tool which will predict treatment response, thereby guiding the choice of DMT and reducing the risk of adverse events given by unnecessary treatments.

Study Schedule

This is an observational study, where patients with MS initiating a DMT in the NHS will undergo clinical and MRI assessments and blood tests.听 We will recruit adult patients and children with MS from the clinics at UCLH and the UK Paediatric Neuroinflammation Services (Great Ormond Street Hospital, Evelina Children鈥檚 Hospital London and听Birmingham Women & Children鈥檚 Hospital).

Participants will be seen at three timepoints: baseline, 6 months and 18 months.听 At each visit they will undergo a standardised clinical assessment, blood tests for neurofilaments levels,听safety data and MRI scans.听听

The clinical assessment will comprise scales for physical disability, such as the EDSS and the Brief International Cognitive Assessment for MS (BICAMS).听 Participants will also be asked to complete some patient reported outcome measures (PROMs), as well as providing information on their medical history, demographics, comorbidities, diet,听education and employment.

Magnetic Resonance Imaging (MRI)

MRI will include standard-of-care protocols, which include 3D-FLAIR and T2-TSE axial imaging and research sequences such as a volumetric brain sequence (3D-T1).听

Genetics

Genetic factors may enable us to identify patients who are at higher risk of accumulation of brain pathology.听听The genetic testing will identify those variants previsouly associated with MS susceptibility and will be听undertaken at the UCL Institute of Neurology by Professor Henry Houlden, Professor of Neurogenetics, and his team.听听

Neurofilaments in Serum

Neurofilament levels in serum act as a blood biomarker of axonal damage, allowing for reliable, clinical monitoring of axonal damage in MS patients.听 They will be measured at each timepoint (baseline, 6 months and 18 months).

The concentration of neurofilaments in serum will be determined at the UCL Institute of Neurology by Professor Henrik Zetterberg, Professor of Neurochemistry, and his team, with the neurofilament-light assay.

Machine Learning

Machine learning will be employed to segment MS lesions.听 Additionally, high-dimensional models will be constructed to predict individual treatment response, using all the characteristics that define the disease in an individual.听听A positive treatment response will be defined as no evidence of disease activity or NEDA (no relapses, no progression of disability and no MRI activity.听 All predictions will be probabilistic.

High-dimensional models will be constructed in a tiered approach and the most parsimonious, efficient, predictive model will be created.听 Overfitting will be minimised in two ways and model training, validation and testing using external datasets will be performed.听