Development of a stratification tool to predict Disease Modifying Treatment response in Paediatric Onset Multiple Sclerosis

Multiple sclerosis (MS) is a devastating condition, which is rare in children. It is caused by the body's immune system destroying parts of the myelin sheath, a fatty protective covering of nerves in the brain and spine, which is essential for transmission of messages from the brain to the rest of the body. Currently there is no cure, and over time individuals accumulate progressive disability. In children and young people this can affect movement, vision and particularly their thinking.

There has been an explosion in recent years in the treatments available, most of which have not been trialled in children. There are currently 14 different treatments available which aim to modify the body's inflammatory response. Choosing the right treatment for the individual child is difficult, as there are no tools to help us predict which medication is likely to work best for them. Much of the time paediatricians need to rely on adult data. MS in children though is different from that in adults; they have more relapses, more brain lesions and develop more learning problems than adults. The only paediatric trial so far completed, identified benefits and risks of treatment which are different from those seen in adults. This makes it crucial to gather information in children, rather than relying on information acquired in adults.

I am a Paediatric Neurologist who specialises in MS. In this project, I will partner with Prof Ciccarelli, who in 2018 was awarded a NIHR Research Professorship, to develop a tool to predict the best medicine to use for the individual with adult MS, by using special mathematical models that learn from the individual MS profile (demographic and diet, lifestyle, clinical findings, specific blood tests, genetics and MRI images) and make prediction about the future. I will extend this goal to children, and together we will develop a tool to help guide treatment choice for any patient with MS, independently of their age. I will take this unique opportunity to focus on cognitive impairment in children with MS.

MS is a highly specialised and complex condition in childhood, and NHS England has recently agreed to fund 5 Highly Specialist Services (HSS) across England, to ensure excellence in delivering care. I lead one of these services and will collaborate with the other centres.

In this project, I will look at two groups: the first group is the existing cohort of 100 children with MS across England, the second group includes 80 children with newly diagnosed MS. Children who do not wish to start a medication will still have their data recorded and will be used as a control group. We will use tablet computers in clinic to record information about diet, lifestyle, exposure to sunlight and nicotine, amongst other parameters, as well as quality of life. We will also document their clinical examination and their educational performance and academic ability. We will take blood to look for markers of inflammation which might provide important clues. We will also record their relapses, to document how well controlled their MS disease activity is.

All this information, together with repeat MRI scans acquired routinely as part of the NHS HSS, will be analysed by computer both separately and together with the adult data. We will use a tiered approach to identify factors which are likely to predict which medicine will work best for any one person with MS.

All these data will be stored in a national registry, thereby providing valuable information on the long-term outcome for these young people in England. All the medications and any serious side effects will also be recorded on the database. This will allow us over time to identify early any unexpected safety concerns with the medications.

The ultimate goal of the project is to learn about the individual treatment response and side effects in the clinical setting and to help the child and their parents choose the best medication for them as an individual.

14 disease-modifying treatments (DMTs) for multiple sclerosis (MS) are approved in UK, but most have not been trialled in children. The only paediatric trial so far identified benefits and risks different from those in adults. My aim, by partnering with Prof Ciccarelli, is to identify individual predictors of treatment response in Paediatric MS and correlate the response with long-term age-expected cognitive outcome. This, together with knowledge of side effects, will help individuals choose the best medication for them. The project will comprise i)a prospective cohort of 80 children (with 700 adults) with MS between 10-17y (studied over 24months)who initiate any DMT in England. Demographic and life-style factors (nicotine and UV exposure, diet and obesity) will be recorded, clinical examination, serological (PBMCs, Vitamin D) and immune markers (neurofilament-light chain (NfL) levels in serum (and CSF if possible), MRI scans and cognitive profile will be collected. Genetic factors will be obtained ii)a retrospective cohort of 150 children with MS on DMTs (with 1700 adults). Analysis of their clinical, paraclinical, genetic (HLA-DRB1*15) and MRI markers will provide predictors into individual treatment response. Neuropsychology assessment is assessed every 2 years as standard of care. The secure database OPTIMISE (Optimisation of Prognosis and Treatment in MultIple SclErosis), will be used to import patient information and clinical scans. Treatment response will be assessed by both "No Evidence of Disease Activity" on MRI and also by the presence or absence of a clinical relapse. Pharmacovigilance data will be captured by presence or absence of a serious adverse event from the DMT. This will be analysed together with patient demographics and genetic factors. Outputs will be i) identification of factors that predict both a specific treatment response and a worse cognitive outcome ii) a registry and iii) evidence based guidelines for treatment choice in Paediatric MS

By studying the natural history of children with demyelinating conditions together with analysis of the individual patients' genetic predisposition and immunophenotyping we will be able to determine whether the predictive accuracy of the model varies with age and if children will respond to one specific treatment better than another. This will enable a clinician to
o provide clear information based on paediatric data for patients and their families
o use this information together with the patients and families to make evidence based decisions on which treatment would be most effective for that individual
o to provide information on risks of treatment and counselling to the individual patients

Patient cohorts are extremely valuable because they provide data that cannot be captured in any other way and current MS registries in the UK do not include children nor do they provide much information on treatment effects. This project will address both aspects. The data and samples will also provide a biobank for future collaborations to address future challenges.

The project will enable clinicians in the future to provide the most appropriate early and effective therapies to reduce relapse-related disability.

The project will also lead to improved understanding in the MS research community of the mechanisms that lead to treatment resistance and the onset of MS related disabilities.

There has been long standing debate as to whether MS disability can be prevented by immune modulatory treatments alone, or whether neuro-degenerative strategies are needed in addition. This project will provide important data to evaluate if MS related cognitive impairment is relapse or non-relapse related. Children with MS are well suited to answer this question as their MS is more inflammatory, and they are less likely to have other comorbidities that impact their cognition.

By combing my data with Prof Ciccarelli's ongoing work on adult MS patient registries, funded by the NIHR, this will provide the necessary data for calculation of the cost-effectiveness of stratification with respect to DMT specific response to treatment (e.g. relapses, adverse events, disability).

Personalised therapy of children with MS, will lead to reduced costs for treatment of relapses and hospitalization and reduced costs incurred as a result of increased disability (home care).

Saving costs for the care of patients with MS means that resources can be invested where there is the greatest need, with benefit to all in society.