An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient’s characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters – including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient’s life circumstances and plans, and medical procedures – a digital twin paired to the patient’s characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients’ well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.

Objective To evaluate individual neurofilament light chain (NfL) variation over the time of disease course and the potential of NfL measurement to predict treatment response in patients with MS. Methods We investigated 15 patients with MS after immune reconstitution treatment with alemtuzumab (ATZ). Monthly serum NfL (sNFL) measurements were correlated with Expanded Disability Status Scale (EDSS), MRI, and relapse activity over an observational period of up to 102 months. Results Before ATZ, sNfL was significantly increased in correlation with previous relapse/MRI activity. After ATZ, sNfL decreased quickly within the first 6 months. In patients classified as NEDA-3, sNfL declined and persisted at an individual low steady-state level of 20 fold increase could be detected, which were associated with clinical or MRI disease activity. Even patient-reported relapse-suspicious symptoms, which have not been confirmed because relapses were accompanied by sNfL, increase, proposing sNfL assessment as a marker for relapse activity. sNfL started to increase earliest 5 months before, peaked at clinical onset, and recovered within 4–5 months. sNfL presented at higher levels in active patients requiring ATZ retreatment compared with responder patients. During 2 documented pregnancies, sNfL was at a low level, whereas a postpartum transient sNfL increase was seen without any signs of activity. Conclusions This study applied a long-term high-frequency sNfL assessment in an ATZ-treated cohort, allowing a holistic profiling on the individual level and highlighted that sNfL can eminently complement the individual clinical and MRI monitoring in clinical practice.

For incurable diseases, such as multiple sclerosis (MS), the prevention of progression and the preservation of quality of life play a crucial role over the entire therapy period. In MS, patients tend to become ill at a younger age and are so variable in terms of their disease course that there is no standard therapy. Therefore, it is necessary to enable a therapy that is as personalized as possible and to respond promptly to any changes, whether with noticeable symptoms or symptomless. Here, measurable parameters of biological processes can be used, which provide good information with regard to prognostic and diagnostic aspects, disease activity and response to therapy, so-called biomarkers Increasing digitalization and the availability of easy-to-use devices and technology also enable healthcare professionals to use a new class of digital biomarkers—digital health technologies—to explain, influence and/or predict health-related outcomes. The technology and devices from which these digital biomarkers stem are quite broad, and range from wearables that collect patients’ activity during digitalized functional tests (e.g., the Multiple Sclerosis Performance Test, dual-tasking performance and speech) to digitalized diagnostic procedures (e.g., optical coherence tomography) and software-supported magnetic resonance imaging evaluation. These technologies offer a timesaving way to collect valuable data on a regular basis over a long period of time, not only once or twice a year during patients’ routine visit at the clinic. Therefore, they lead to real-life data acquisition, closer patient monitoring and thus a patient dataset useful for precision medicine. Despite the great benefit of such increasing digitalization, for now, the path to implementing digital biomarkers is widely unknown or inconsistent. Challenges around validation, infrastructure, evidence generation, consistent data collection and analysis still persist. In this narrative review, we explore existing and future opportunities to capture clinical digital biomarkers in the care of people with MS, which may lead to a digital twin of the patient. To do this, we searched published papers for existing opportunities to capture clinical digital biomarkers for different functional systems in the context of MS, and also gathered perspectives on digital biomarkers under development or already existing as a research approach.