Title: Precision Treatment Strategies in Multiple Sclerosis Using Next-generation Machine Learning
Funder: Department of Health and Social Care

Abstract: Research question How can data-driven multiple sclerosis (MS) types be matched with specific treatment strategies in real-world settings to improve patient outcomes? BackgroundCurrently, we have more than 15 MS treatments with different risk profiles. There is a discrepancy between MS types and underlying disease mechanisms. Therefore, treatments selected based on MS type may be ineffective, as they imprecisely target disease mechanisms. I have recently developed a data-driven model to classify MS types with brain MRI to address this discrepancy in clinical trials. However, clinical trials do not represent the diversity of patients seen in routine care. I now want to develop novel models for the real world to enable widespread patient benefit. Aims, objectives and methods I aim to define biologically-driven MS types and match them with effective treatment strategies in real-world settings. This will enable us to (1) recommend the right treatments according to the likely treatment response and (2) enrol patients from real-world registries with specific disease biology into precision medicine studies to develop new treatments. I will use unique existing data sets from the UK (seven NHS Trusts) and two international collaborators (Montreal Neurological Institute, Canada and MS Center Amsterdam, the Netherlands). I will build novel machine learning models to: (Objective 1) Classify MS into MRI- and serum-derived types. I will combine MRI information with serum neurofilament light-chain (sNfL), an emerging biomarker for future use in clinical practice. This information will be combined with clinical data to stratify patients for the risk of disability, which can then be matched with treatment effects. (Objective 2) Predict individualised treatment effect. Unlike clinical trials, evaluating treatment effects is challenging in the real world. To assess and predict treatment effects, I will develop causal machine learning models to generate individualised 'simulated' outcomes to answer the question: How would a patient's outcome be different if they received a specific treatment? These models will perform individual-level predictions conditioned on a set of baseline characteristics. This will also improve predictions of treatment effects from data-driven types in objective (1). (Objective 3) Scale up models for future deployment in the NHS. Within objectives (1) and (2), I will build privacy-preserving models to learn from diverse MS populations at different centres without sharing patients' data. Across different centres, to make personalised solutions possible, I will also evaluate the effects of comorbidities, gender, age group and ethnicity on model performance. I will quantify the healthcare cost savings that a future diagnostic decision support system may bring by enabling earlier treatments and reducing adverse events. Timelines for deliveryOver five years, my fellowship will output research tools for (1) disease classification and risk-stratification models; (2) software to enrol MS patients from real-world routine care for precision medicine studies; and (3) privacy-preserving machine learning models. Anticipated impact and dissemination My fellowship will provide essential tools for future diagnostic decision support systems to choose the best treatment for patients and delay disability. I will disseminate outcomes through scientific publications, congresses, lay language summaries and Q&A video recordings.

Lay abstract: Background In multiple sclerosis (MS), the immune system attacks the brain and spinal cord. This causes problems in how we move, feel or think. In the UK, 130,000 people live with MS, costing the NHS more than 2.9 billion a year. MS types are based on when and how symptoms appear. Most cases (80%) begin as relapsing-remitting MS, with unpredictable attacks and remissions. After 10-20 years, about 50% of patients develop secondary progressive MS with gradually worsening symptoms without obvious attacks. In primary progressive MS, symptoms slowly worsen from the start and accumulate over many years. This classification does not reflect the underlying tissue changes (pathology), potential response to treatment or future disability. More than 15 treatments currently exist, each with different risks and strengths. Brain images, such as magnetic resonance imaging (MRI) and blood tests (serum neurofilament levels [sNfL]), help identify the underlying tissue changes. If types of MS were redefined based on the underlying tissue changes, doctors could recommend the most appropriate treatment with the fewest side effects. Artificial intelligence (AI) uses computers for tasks that otherwise rely on human intelligence. I have developed an AI tool to detect MS types based on MRI from clinical trials. However, clinical trials have strict criteria for taking part. They typically exclude people in certain age ranges or who have more than one lifelong condition. I now want to develop new tools for real-world use from diverse patient populations so that my tools can benefit anyone with MS. Aim I will identify patterns of MRI and serum neurofilament (where available) change that characterise specific types of MS. These types will predict how symptoms may change, when MS may worsen, and which drugs are most likely to help. Methods I will work with colleagues from seven NHS Trusts and two international clinical centres to: 1)Use MRI and, where available, sNfL to define MS types that reflect underlying abnormalities better than symptom-based types. 2)Build AI tools that measure and predict whether a treatment works at an individual level. This will make it easier to tell how new MS types predict treatment effects in NHS data. 3)Build AI tools based on objectives (1) and (2) that can learn from each other without sharing patients' data across centres to optimise and refine the predictions. This ensures that I can include more patients from diverse backgrounds for truly personalised predictions. I will measure how new MS types complement the existing MS types based on disability outcomes and calculate how my digital tools may save costs in future healthcare by recommending appropriate treatments earlier and delaying disability. Benefits The result will be research tools that predict whether a treatment works using widely available NHS data. It will give researchers information on the probability of treatment response and disease worsening. In the future, this could evolve into software that will be used in hospitals to help doctors recommend treatments, minimise disability accrual, reduce side effects and save money. Patient and public involvement (PPI) This proposal was developed in consultation with a PPI steering committee. The committee will meet twice a year to continue to shape the research and advise on the acceptability of the future medical device software tool, interface, risks and benefits. The committee will assist in designing a follow-on path for future use in clinical practice. Dissemination In addition to scientific articles and congress presentations, I will produce lay-language Q&A video summaries with the PPI committee. These will be published on a dedicated website and MS patient forums identified by the steering committee (e.g., MS Trust and Society) and promoted on social media.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: NEUROLOGICAL