Title: Predicting individual treatment responses towards personalised medicine in Multiple Sclerosis
Funder: Department of Health and Social Care

Abstract: BackgroundMultiple sclerosis (MS) is the most widespread disabling disease of young people. Thirteen disease-modifying treatments (DMTs) are approved in the UK to reduce the risk of relapses. Patients can switch from one first-line DMT to a more effective medication, if they present with relapses.The consequences of this policy, that requires 'failure' of DMTs before using another DMT are continuous relapses and disability accumulation. We cannot currently predict which DMT will work best for an individual patient.The goal of this project is to predict the individual treatment response in MS by translating machine learning from the computer science field into clinical practice. This will bridge the gap between clinical trials, which focus on the 'average' response to a therapy, to clinical practice, where the focus should be on the individual treatment response.Aims To develop deep phenotyping and genotyping of patients initiating DMTs in the clinical setting To generate and validate a high-dimensional model that predicts treatment response earlyThe study design will offer the possibility to pursue additional aims, which are to: improve our understanding of disease pathobiology by defining responders' profiles collect data for the evaluation of the health-economic impact of stratificationPlan of investigation MS patients Retrospective cohort: 1,566 (1,494 adults, 72 children) currently on DMTs at UCLH NHS Trust and in the UK paediatric neuroinflammation centres, who are undergoing clinical and MRI assessments according to standard-of-care protocols. In the NIHR UCLH BRC Imaging initiative, we have already collected all the patients' MRI scans and carried out a pilot study that demonstrates the feasibility of our approach.Prospective cohort: 780 patients (700 adults, 80 children (>12yrs)) who initiate any DMT during the course of two years. Patients will be studied at baseline and after 6 and 18 months. A standardised acquisition of demographic factors, diet quality and life-style, clinical scales, comorbidities, MRI scans and blood tests for safety data and neurofilaments-light levels, will be collected. Genetic factors will be obtained through NIHR UCL BioResource biobank.Independent, international cohorts: MSBase (www.msbase.org,>55,000 patients); Swedish MS registry (http://www.neuroreg.se/en.html/multiple-sclerosis,>19,000 patients); FutureMS (www.stratmed.co.uk/programs-and-projects/future-ms/); EU Paediatric Demyelinating Disease Network.Predictive modelling Treatment response will be defined on clinical and MRI grounds. Machine learning techniques will be used to develop a high-dimensional model that predicts individual treatment response, using all the information collected. The model will be constructed from retrospective and prospective data, and then validated on independent cohorts. Benefit for patients and NHSThe outputs of this project are: Access to deep phenotyping and genotyping directly for NHS patients Prototype software running on local machines to guide treatment choice Improved understanding of the mechanisms that lead to a positive treatment response Data registry for the evaluation of health-economic impact of stratification Predicting individual treatment response is crucial to practice personalised medicine, which will have a huge impact at a personal level, societal level and on the NHS. This project will position the UK as a world leader for research in personalised medicine in neurology.

Lay abstract: BackgroundMultiple Sclerosis (MS) is a chronic condition that affects people at all ages, but people are more often diagnosed at a young age. There are over 100,000 people with MS in the UK. There is no cure for MS, and patients experience occasional bouts of symptoms, called relapses.There are 13 licensed drugs that reduce the risk of MS relapses, but doctors cannot predict which of these will work best for an individual. The decision about which drug to give to a patient is currently very complicated and is based on personal preference. Many elements that may influence treatment response, such as genetic factors, blood biomarkers, MRI markers, etc., are not taken into account when choosing a medication.Medications are changed only when a new relapse occurs. This approach is severely suboptimal for the patient, who experiences multiple relapses and increasing disability; for the doctor, who is unable to prescribe the most effective treatment the first time; and for the NHS, because of costs associated with treating relapses, and managing disability and side effects.My goal is to develop a computer tool that predicts whether an individual patient will respond to a medication by using special mathematical models that learn from the patient's individual MS profile and make predictions about the future. This is a crucial step towards 'personalised medicine', which means to be able to prescribe the right medication for the right patient. Aims To collect all manifestations of MS that may influence individual treatment response in the NHS; these include demographic and life-style factors, blood tests, genetics, clinical examinations, and MRI scans. To generate a tool which gives the probability that a patient will receive benefit from a treatment.This study will offer the possibility to address two additional aims: To identify whether there are groups of patients who respond similarly to the same medication, because they share similar mechanisms of the disease. To collect all the information necessary to calculate the savings obtained when using our new tool in the clinical setting.MethodsMy research strategy is to collect all the elements that constitute the individual MS profile (demographic and clinical information, life-style, diet, genetic factors, comorbidities, MRI scans) through assessments and tablet computers. We will study patients who are already on medications for MS and patients who are starting a new medication. Children will also be included, because they have the same biology of the disease as adults. We will develop a tool that predicts individual treatment response in these patients, and will test it in other patients studied in other centres in the UK and abroad, such as Scotland, Sweden, and Australia. Patient and public involvement and engagementPatients involved into the design of this research have suggested including life-style and diet questionnaires and collecting information on disability outside what is measurable in the consulting room. Patients and lay-members will participate in the management of this project. I have planned the production of an accessible short video and an art-science evening of talks (inspired by activities such as 'rewired' (http://www.movementforhope.org/rewired-2017/)), highlighting the main findings at the end of the project. Benefit for patients and NHSThis project will bring access to an advanced technology directly to NHS patients, and will help doctors to decide the most appropriate treatment for MS patients. The impact of a personalised approach to MS treatment will translate into benefits at a personal and societal level and on the NHS. This project will position the UK as a world leader for research in personalised medicine in MS, which is the most widespread disabling disease of young people.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: NEUROLOGICAL