Information for participants
For more information on the study, please click on the relevant section below. But please also note that our participant recruitment ended on 31 July 2024.
Multiple 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, the doctor and the NHS.
The aim of this study 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.
The design of the study includes a total of six research visits. The baseline visit (your first visit) and first two follow-up visits (after 6 months and 18 months) all include clinical and MRI assessments. There will be as many breaks as you like during these visits and we will try to make you as comfortable as possible.
After the initial three research visits, we will also follow up participants 30 months (third year), 42 months (fourth year) and 54 months (fifth year) after starting their treatment. The latter three visits will only be arranged for when participants are already attending other routine hospital appointments, and only clinical assessments which cannot be retrieved from NHS pathways (such as the memory tests) will be conducted. Due to lack of funding, MRI assessments will not be performed for the latter three visits, and participants' MRI data will be retrieved from NHS pathways.
Anyone over the age of 6 years who had been diagnosed with any type of MS and who was about to start a DMT (disease modifying treatment) was eligible to take part. Children under the age of 17 could give assent to take part but, for legal reasons, consent must also have been given by a parent or legal guardian.
Study Information Sheets
MS Clinics - UCLH
If you have any questions, you can find us in the MS clinics in the National Hospital for Neurology and Neurosurgery (NHNN) on weekdays (particularly Tuesdays). Please seek out a member of the PITMS team for any queries.
MS Clinics - Neuroimmunology Centre, Great Ormond Street Hospital
MS clinics for paediatric patients are run on Wednesdays and Fridays at the Neuroimmunology Centre at GOSH. If you have any queries regarding your participation, please look out for Dr. Yael Hacohen or Dr Riccardo Nistri to answer any questions about the study.
MRI or Research Appointments - UCLH
If you have been booked in for your Research Visit (on a day on which you have another hospital appointment), please come to the Institute of Neurology, Queen Square House, Queen Square, London, WC1N 3BG and report to reception. A member of the study team will then come to meet you and accompany you to the clinical rooms in the MRI suite. Map of the Institute of Neurology
Contact Us
uclh.pitms@nhs.net
UCL and UCLH
Professor Olga Ciccarelli, Principal Investigator
Dr Yael Hacohen, Co-Investigator
Professor Parashkev Nachev, Co-Investigator
Professor Daniel Alexander, Co-Investigator
Professor Henry Houlden, Co-Investigator
Professor Henrik Zetterberg, Co-Investigator
Suraya Mohamud, Study Coordinator and Research Manager
Dr Charmaine Yam, Clinical Research Fellow
Dr Anna He, Clinical Research Fellow
Dr Sarmad Al-Araji, Clinical Research Fellow (2019 - 2022)
Dr Alessia Bianchi, Clinical Research Fellow (2019 - 2023)
Dr Dimitris Champsas, Clinical Research Fellow (2021 - 2022)
Dr Anuriti Aojula, Clinical Research Fellow (2020 - 2022)
Ronja Christensen, Research Assistant and PhD student
Anna Reyes, Research Nurse
Eirini Samdanidou, Research Nurse (2021 - 2023)
Alvin Zapata, Research Nurse (2019 - 2022)
Dr Le Zhang, Research Associate – Computational Imaging (2019 - 2022)
Dr Baris Kanber, Senior Research Fellow – Machine Learning
Amber Strang, Study Coordinator and Research Manager (2019 - 2022)
Joy Song, Research Assistant (2023 - 2024)
Great Ormond Street Hospital
Dr Riccardo Nistri, Clinical Research Fellow
Dr Neena Kim, Clinical Research Fellow
Dr Cheryl Hemingway, Site Principal Investigator
Dr Omar Abdel-Mannan, Clinical Research Fellow
Katie Hanson, Clinical Nurse Specialist
Evelina Children’s Hospital
Dr Thomas Rossor, Site Principal Investigator
Dr Ming Lim, Site Principal Investigator (2019 - 2023)
Birmingham Women’s & Children’s Hospital
Dr Evangeline Wassmer, Site Principal Investigator
Magnetic Resonance Imaging (MRI)
MRI scan images will be processed and analysed not only to track the potential progression of your MS whilst you are on your medication but also to “teach” a computer programme to recognise lesions in your central nervous system, which are characteristic of MS.
Analysis of your scan images will be undertaken by our team in collaboration with Professor Parashkev Nachev, Professor of Neurology, supported by the NIHR UCLH BRC and Wellcome Trust and the Centre for Medical Image Computing at UCL, which is led by Professor Daniel Alexander, Professor of Imaging Science.
Genetics
We know that there are certain genetic markers that can predict response to certain disease modifying treatments (DMTs). We also know that certain genes are associated with particular types of MS such as progressive MS and relapsing onset MS.
By analysing participant blood samples, we will be able to learn more about the relationship between our environment (exposures) and our personal susceptibility to disease (genes). We may also come up with genetic tests that predict a positive response to a medication.
The genetic testing will be undertaken at the UCL Institute of Neurology by Professor Henry Houlden, Professor of Neurogenetics, and his team.
Neurofilaments in Serum
The neurofilaments represent the structure that supports the neurons (nerve cells). An increased level of neurofilaments in the blood indicates that there has been a large amount of neuronal loss. This may indicate that a particular treatment has not been effective and in future could help patients to avoid taking medications which are not necessary.
Serum is the clear liquid part of blood minus the agents which clot blood. Blood samples will first be processed in our lab to separate the serum from the blood cells. They will then be frozen and stored until the end of study recruitment when the concentration of neurofilaments in each sample from each visit will be analysed by Professor Henrik Zetterberg, Professor of Neurochemistry, and his team.
Machine Learning
All of the data collected over the course of the study including the results of clinical assessments, individual patient information such as age, gender, diet etc., MRI scan images, genetic biomarkers, neurofilament levels etc. will be combined. The combined data will then be programmed into a high-dimensional model which will be tested and retested to determine its accuracy at predicting individual participant responses to particular treatments.
This aspect of the study will be undertaken by Dr Arman Eshaghi, Dr Le Zhang and Dr Baris Kanber, with additional input from Professor Nachev’s team.
Abdel-Mannan O, and the UK-Childhood Inflammatory Disease Network. Use of Disease-Modifying Therapies in Pediatric Relapsing-Remitting Multiple Sclerosis in the United Kingdom. Neurol Neuroimmunol Neuroinflamm. 2021 May 21;8(4):e1008. doi: 10.1212/NXI.0000000000001008.
Al-Araji S, Bianchi A, Eshaghi A, et al, …Ciccarelli O. Assessing predictors of NEDA in RRMS patients initiating dimethyl fumarate in a real-world setting. P113. Association of British Neurologists (ABN) 2022.
Al-Araji S, Jha A, Zhang L, et al, …Ciccarelli O. Bayesian prediction of individualised treatment response in multiple sclerosis. ECTRIMS October 2022. Selected for poster award.
Moccia M, Al-Araji, Zhang L, et al…, Ciccarelli O. Comparing clinical and radiological effectiveness of disease modifying treatments in the real-world. O955. ECTRIMS October 2022.
Eshaghi A, Young AL, Wijeratne PA, et al…, Ciccarelli O. Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun. 2021 Apr 6;12(1):2078. doi: 10.1038/s41467-021-22265-2. (*)
Eshaghi A, Wijeratne P, Oxtoby N, et al…, Ciccarelli O. Predicting personalized risk of disability worsening in multiple sclerosis with machine learning. Nat Commun. 2022. Major revisions requested. (*)
Zhang L, Tanno R, Bronik K, et al. Ciccarelli O, Alexander DC. "Learning to segment when experts disagree." International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 179-190. Springer, Cham, 2020.
Zhang L, Tanno, R Xu MG, Ciccarelli O, Barkhof F, Alexander DC. "Disentangling Human Error from the Ground Truth in Segmentation of Medical Images." 34th Conference on Neural Information Processing Systems (NeurIPS 2020).