ObjectiveTo assess the value of blood neurofilament light chain (NfL) as a biomarker of recent, ongoing, and future disease activity and tissue damage and its utility to monitor treatment response in relapsing-remitting multiple sclerosis.MethodsWe measured NfL in blood samples from 589 patients with relapsing-remitting multiple sclerosis (from phase 3 studies of fingolimod vs placebo, FREEDOMS and interferon [IFN]-β-1a, TRANSFORMS) and 35 healthy controls and compared NfL levels with clinical and MRI-related outcomes.ResultsAt baseline, NfL levels (pg/mL) were higher in patients than in healthy controls (30.5 and 27.0 vs 16.9, p = 0.0001) and correlated with T2 lesion load and number of gadolinium-enhancing T1 lesions (p < 0.0001, both). Baseline NfL levels, treatment, and number of new or enlarging T2 lesions during the studies predicted NfL levels at the end of study (all p < 0.01). High vs low baseline NfL levels were associated (estimate [95% confidence interval]) with an increased number of new or enlarging T2 lesions (ratio of mean: 2.64 [1.51–4.60]; p = 0.0006), relapses (rate ratio: 2.53 [1.67–3.83]; p < 0.0001), brain volume loss (difference in means: −0.78% [−1.02 to −0.54]; p < 0.0001), and risk of confirmed disability worsening (hazard ratio: 1.94 [0.97–3.87]; p = 0.0605). Fingolimod significantly reduced NfL levels already at 6 months (vs placebo 0.73 [0.656–0.813] and IFN 0.789 [0.704–0.884]), which was sustained until the end of the studies (vs placebo 0.628 [0.552–0.714] and IFN 0.794 [0.705–0.894]; p < 0.001, both studies at all assessments).ConclusionsBlood NfL levels are associated with clinical and MRI-related measures of disease activity and neuroaxonal damage and have prognostic value. Our results support the utility of blood NfL as an easily accessible biomarker of disease evolution and treatment response.

Objectives To assess whether neurofilament light chain (NfL) could serve as an informative endpoint in Phase 2 studies in patients with relapsing–remitting multiple sclerosis (RRMS) and estimate the sample size requirements with NfL as the primary endpoint. Methods Using data from the Phase 3 FREEDOMS study, we evaluated correlation of NfL at Month 6 with 2‐year outcomes: relapses, confirmed disability worsening (CDW), new or enlarging T2 lesions (active lesions), and brain volume loss (BVL). We compared the proportion of treatment effect (PTE) on 2‐year relapses and BVL explained by 6‐month log‐transformed NfL levels with the PTE explained by the number of active lesions over 6 months. We estimated sample size requirements for different treatment effects. Results At Month 6, blood NfL levels (pg/mL, median [range]) were lower in the fingolimod arm (fingolimod (n = 132) 18 [8–247]; placebo (n = 114) 26 [8–159], P < 0.001). NfL at 6 months correlated with number of relapses (r = 0.25, P < 0.001), 6‐month CDW (hazard ratio 1.83, P = 0.012), active lesions (r = 0.46, P < 0.001), and BVL (r = −0.41, P < 0.001) at Month‐24. The PTE (95% CI) on 24‐month relapses and BVL explained by 6‐month NfL was 25% (8–60%) and 60% (32–132%), and by 6‐month active lesions was 28% (11–66%) and 45% (18–115%), respectively. Assuming a 20–40% treatment‐related reduction in NfL levels, 143‐28 patients per arm will be required. Conclusions Blood NfL may qualify as an informative and easy‐to‐measure endpoint for future Phase 2 clinical studies that captures both inflammatory‐ and noninflammatory‐driven neuroaxonal injury in RRMS.

ObjectiveTo assess the prognostic value of practice effect on Paced Auditory Serial Addition Test (PASAT) in multiple sclerosis.MethodsWe compared screening (day −14) and baseline (day 0) PASAT scores of 1009 patients from the FTY720 Research Evaluating Effects of Daily Oral therapy in Multiple Sclerosis (FREEDOMS) trial. We grouped patients into high and low learners if their PASAT score change was above or below the median change in their screening PASAT quartile group. We used Wilcoxon test to compare baseline disease characteristics between high and low learners, and multiple regression models to assess the respective impact of learning ability, baseline normalised brain volume and treatment on brain volume loss and 6-month confirmed disability progression over 2 years.ResultsThe mean PASAT score at screening was 45.38, increasing on average by 3.18 from day −14 to day 0. High learners were younger (p=0.003), had lower Expanded Disability Status Scale score (p=0.031), higher brain volume (p<0.001) and lower T2 lesion volume (p=0.009) at baseline. Learning status was not significantly associated with disability progression (HR=0.953, p=0.779), when adjusting for baseline normalised brain volume, screening PASAT score and treatment arm. However, the effect of fingolimod on disability progression was more pronounced in high learners (HR=0.396, p<0.001) than in low learners (HR=0.798, p=0.351; p for interaction=0.05). Brain volume loss at month 24 tended to be higher in low learners (0.17%, p=0.058), after adjusting for the same covariates.ConclusionsShort-term practice effects on PASAT are related to brain volume, disease severity and age and have clinically meaningful prognostic implications. High learners benefited more from fingolimod treatment.

Background Novartis and the University of Oxford's Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression. Method The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2,200 centres worldwide. For the 'IL-17' project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA)). Results A fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project. Conclusions An informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools.

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