The evolution of “No Evidence of Disease Activity” in multiple sclerosis

Author’s Accepted Manuscript The Evolution of “No Evidence of Disease Activity” in multiple sclerosis G Lu, HN Beadnall, J Barton, TA Hardy, C Wang, MH Barnett www.elsevier.com/locate/msard

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S2211-0348(17)30356-5 https://doi.org/10.1016/j.msard.2017.12.016 MSARD730

To appear in: Multiple Sclerosis and Related Disorders Received date: 4 October 2017 Revised date: 18 December 2017 Accepted date: 21 December 2017 Cite this article as: G Lu, HN Beadnall, J Barton, TA Hardy, C Wang and MH Barnett, The Evolution of “No Evidence of Disease Activity” in multiple s c l e r o s i s , Multiple Sclerosis and Related Disorders, https://doi.org/10.1016/j.msard.2017.12.016 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The Evolution of “No Evidence of Disease Activity” in multiple sclerosis Lu, G1,2, Beadnall, HN3,4, Barton, J3, Hardy, TA3,5, Wang, C3,6, Barnett, MH3,4,6* 1 University of Sydney, NSW, Australia 2 St Vincent’s Hospital Sydney, Australia 3 Brain and Mind Centre, University of Sydney, NSW, Australia 4 Neurology Department, Royal Prince Alfred Hospital 5 Neuroimmunology Clinic, Concord Hospital, University of Sydney, NSW, Australia 6 Sydney Neuroimaging Analysis Centre, Sydney, Australia * Corresponding author: Brain and Mind Centre, 94 Mallett St, Camperdown NSW 2050 Abstract The availability of effective therapies for patients with relapsing-remitting multiple sclerosis (RRMS) has prompted a re-evaluation of the most appropriate way to measure treatment response, both in clinical trials and clinical practice. Traditional parameters of treatment efficacy such as annualized relapse rate, magnetic resonance imaging (MRI) activity, and disability progression have an important place, but their relative merit is uncertain, and the role of other factors such as brain atrophy is still under study. More recently, composite measures such as “no evidence of disease activity” (NEDA) have emerged as new potential treatment targets, but NEDA itself has variable definitions, is not well validated, and may be hard to implement as a treatment goal in a clinical setting. We describe the development of NEDA as an outcome measure in MS, discuss definitions including NEDA-3 and NEDA-4, and review the strengths and limitations of NEDA, indicating where further research is needed. Keywords: No evidence of disease activity; NEDA; multiple sclerosis; magnetic resonance imaging Introduction Multiple sclerosis (MS) is an inflammatory demyelinating disorder of the central nervous system that affects more than two million people worldwide, with disease onset typically between 20 and 40 years of age.(1) Relapsing-remitting multiple sclerosis (RRMS), the most common form of the disease, is characterized by the subacute onset of neurological deficits lasting at least 24 hours followed by at least partial recovery.(2) The pathological hallmark of MS is the formation of multifocal inflammatory demyelinating lesions, but significant neurodegeneration occurs due to irreversible axonal, neuronal, and synaptic loss that persists throughout the natural course of the disease.(3) With major advances in the efficacy of MS disease modifying therapies (DMTs) over the past two decades, therapeutic goals are rapidly evolving. Rather than focusing solely on reducing relapse

rates and disability progression, the target is shifting to complete clinical and radiological disease quiescence. This is analogous to the evolution of treatment in systemic inflammatory diseases, such as rheumatoid arthritis, where complete disease remission can be achieved in around 50% of patients with an array of immunomodulatory/biologic therapies.(4, 5) Composite outcome measures of response to treatment that incorporate both clinical and radiological metrics of disease activity and progression, and are potentially more sensitive to the effects of DMTs(6), are now being incorporated into MS clinical trials, most recently in the form of ‘No Evidence of Disease Activity’ or ‘NEDA’. In this article, we discuss the development of the NEDA concept in MS, its applications and clinical utility, and directions for future research.

Traditional outcomes in MS treatment trials Clinical trials of MS DMTs must incorporate measures of disease activity that accurately assess treatment effects and predict long-term clinical outcomes. Traditionally, annualized relapse rate (ARR) has been the primary outcome measure in clinical trials of MS therapies, and the capacity of interferon beta (IFN-beta)-1b, the first available DMT, to lower ARR in RRMS was the basis for its regulatory approval.(7) While some recent MS DMT clinical trials have included head-to-head comparisons with an approved therapy(8-11), most focus on treatment outcomes compared to placebo. The recognition that inflammatory activity on magnetic resonance imaging (MRI) is associated with worse short to medium-term prognosis(12-14) led to the routine incorporation of lesion metrics, including the number of new T1 gadolinium-enhancing lesions and new and enlarging T2hyperintense lesions, into MS DMT clinical trials, as either primary or secondary outcomes. MRI changes, which are relatively objective and easy to measure, often underlie clinical relapses and as such have biological plausibility as a driver of short and longer-term disability. Prevention of disability progression is also a critical measure of therapeutic efficacy. In RRMS, measurement of accumulating, irreversible disability is confounded by transient relapse-associated neurological impairment. To account for this, disability progression in clinical trials is most commonly defined as an increase in the Expanded Disability Status Scale (15) (EDSS) confirmed

at 3 or 6 months. (16) However, extrapolation of relatively short-term improvements in disability progression to long-term patient outcomes is not straightforward. Increasingly, there has been recognition among MS clinicians that individual outcome measures such as ARR, disability progression and MRI lesions may not adequately reflect a patient’s overall response to therapy, generating an interest in more robust measures of treatment efficacy. NEDA and variants: an emerging treatment goal in MS clinical trials The importance of effectively evaluating therapeutic response to traditional DMTs has been heightened by the availability of newer, more effective DMT options together with a generally more aggressive approach to the management of MS. In a prospective longitudinal study by Rio et al., (17) the combination of clinical measures of disease activity and the presence of new active lesions on MRI had prognostic value for identifying disease activity after one year of IFN-beta therapy. Patients with a combination of relapse, EDSS confirmed disability progression, and new T2 lesions in the first year of treatment with IFN-beta were more likely to experience relapse or disability progression in the second and third year of therapy.(17) The nomenclature applied to composite measures of disease activity has varied between published studies and over time (Tables 1 and 2). The terms ‘absence of disease activity’ or ‘freedom of disease activity’ were first introduced in a post-hoc analysis of data from the AFFIRM trial of natalizumab in MS,(18) and explored a composite of the four established clinical and radiological measures of disease activity: clinical relapses, sustained (12 week) disability progression, gadolinium-enhancing lesions, and new or enlarging T2 hyperintense lesions. In a population of 942 RRMS patients, there was a significant difference in the proportion of natalizumab- vs placebo-treated subjects achieving freedom of disease activity (37% versus 7% respectively, p<0.0001).(18) This composite outcome measure was subsequently explored in posthoc analyses of data from the CLARITY study (19) (cladribine), and sub-group analyses of the active comparator TRANSFORMS study(20) (fingolimod vs interferonß-1a) (Table 2). Later studies employed the term ‘disease activity free status’ (DAFS) and included trials of dimethyl fumarate(21), alemtuzumab(22, 23), interferon-betaß-1a(22-24), glatiramer acetate(24), and daclizumab(25) (Table 1). The definition of DAFS has varied according to whether sustained disability progression was measured at 3-months or 6-months (Table 1). More recently, Kalincik et

al(16) have demonstrated the advantages of incorporating even longer confirmation periods into disability progression criteria, with the proportions of 3-, 6-, 12- or 24-month confirmed events persisting over 5 years reaching 70%, 74%, 80% and 89%, respectively.(26) Superseding the term DAFS was ‘NEDA’, a term first coined in 2014.(27) The definitions of the constituents of NEDA vary between published studies, particularly with improvements in MRI technology (Tables 1 and 2). The initial composite endpoint introduced by Havrdova et al.(18) consisted of three variables: (i) no clinical relapse, (ii) no confirmed EDSS disability progression sustained for 12 weeks (or six months in some studies), and (iii) no new gadolinium-enhancing; or new or enlarging T2 lesions(18).

This clinical-MRI triad has since been re-termed NEDA-3.

Recently the term NEDA-4 has been introduced, with the inclusion of a fourth variable, annualized whole brain volume loss less than or equal to 0.4%.(9) NEDA-3 has been used to evaluate traditional disease modifying therapy, such as IFN beta-1a and glatiramer acetate(24, 28). A three-year trial of combination therapy with IFN beta-1a plus glatiramer acetate revealed NEDA-3 rates of 33.3% and a low ARR over three years of 12%(24). As the on-study ARR is often low in the control arm of modern MS clinical trials, large cohorts studied over relatively long periods are necessary to adequately power clinical trials utilising this metric as the primary outcome. To overcome this limitation and measure the capacity of therapies to abolish disease activity, more sensitive outcomes are needed.(29) While a subsequent study of peginterferon-1a did find efficacy for single traditional clinical endpoints (30), favourable NEDA-3 outcomes were also realised, emphasising the potential utility of this composite measure in the clinical trial setting.(28) NEDA-3 has also been used to compare traditional DMTs with novel therapies in head-to-head MS clinical trials(14,16). In the CARE MS I study, alemtuzumab therapy was compared to IFN beta-1a in treatment naïve patients(16). In this study, NEDA-3 was used as a secondary endpoint in addition to ARR and time to 6 month sustained disability accumulation.(22) Alemtuzumab treatment resulted in a greater reduction in ARR (77.6% vs 58.7%, p<0.0001), and a higher likelihood of remaining free from combined clinical and MRI disease activity (i.e. NEDA-3) (39% vs 27%, p=0.006) over two years, compared with interferon beta-1a. However, the study did not demonstrate a difference in rates of sustained disability accumulation. In results from the extension

of the randomised TRANSFORMS study, which compared fingolimod and interferon beta-1a, the proportion of patients with NEDA-3 was significantly higher in the fingolimod treatment group than in the interferon beta-1a group (46% vs 34%, p<0.001).(31) To date, there have been no randomized head-to-head trials evaluating the relative efficacy of novel oral therapies in achieving NEDA-3. However, fingolimod, teriflunomide and dimethyl fumarate have been indirectly compared in a post-hoc analyses using a statistical modelling approach.(32) The model predicted the indirect relative risk of achieving NEDA-3 by using binomial regression models to adjust for different baseline characteristics, and then selecting baseline covariates that were most predictive of outcomes using a backward stepwise algorithm. The estimated relative risk of achieving NEDA-3 for fingolimod versus placebo was greater than dimethyl fumarate or teriflunomide versus placebo (relative risk greater than 1).(32) NEDA-4(33) outcomes have also been used to compare fingolimod with IFN beta-1a(9, 34). Conference abstracts have presented post-hoc analyses of data from the FREEDOMS, FREEDOMS II, and TRANSFORMS studies showing that patients on fingolimod were more likely to achieve NEDA-4 compared to IFN beta-1a; 21% vs. 8.7% (p<0.05) (9), and 27.9% vs. 16.7% (p=0.0002)(34). With the inclusion of less severely disabled subjects and increasingly strict definitions of relapse, modern MS clinical trials are characterized by low annualized relapse rates and infrequent disability progression. Under these circumstances NEDA may constitute a more sensitive measure of therapeutic efficacy than annualized relapse rate reduction alone (35) and result in smaller patient populations being required to adequately power studies.(19) However, standardization of study duration, frequency of assessments and methods used to obtain individual metrics would be required to facilitate comparison of NEDA outcomes between different trials.(35)

Long-term effects on disability The effect of short-term NEDA on long-term clinical outcomes in MS has been evaluated in only a small number of studies. The interval for analyzing NEDA or disease-free status has varied from only 12 weeks to 8 years depending on the duration of the clinical trial, and there is minimal research regarding the prognostic value of NEDA.(25, 36) The seven-year longitudinal CLIMB cohort study found that NEDA at two years had a positive predictive value of 78.3% for no disability

progression at 7 years, suggesting that NEDA at two years from a baseline visit, irrespective of treatment, may have significant prognostic value.(37) Almost half of the 219 patients in the CLIMB study were on no treatment at baseline (47.9%), and NEDA was not stratified according to the DMT received over the duration of the study. Similarly, analysis of the extension of the TRANSFORMS study, comparing fingolimod to interferon beta-1a supported the use of NEDA criteria in the first year of treatment for prognosis of longerterm outcomes because, irrespective of treatment allocation, patients with disease activity during the first year of treatment were more likely to have relapses and disability progression over the 4.5 years of follow up.(31) An important limitation in the comparison of specific therapies in an observational setting is that treatment selection is often biased by antecedent disease activity. The predictive value of NEDA for specific therapies remains an important question that needs to be further addressed in future studies. To this end, post-hoc analyses of data from the CLARITY study, which compared cladribine to placebo over 96 weeks, suggested that freedom from disease activity at earlier time points could have prognostic significance.(19) About two-thirds of patients treated with cladribine who were free from disease activity at 24 weeks remained so at 96 weeks, while more than 80% of patients free from disease activity at 48 weeks remained so at 96 weeks.(19) However, a lack of NEDA status may not necessarily be a poor prognostic sign. In the CLIMB cohort study the negative predictive value of NEDA at two years for no progression at 7 years was only 43.1%.(37) In this study, loss of NEDA was driven by clinical relapses rather than MRI findings.(37) However, in shorter studies the overall composite endpoint appeared to be driven largely by MRI findings rather than clinical endpoints.(35) Longer studies are needed to further clarify the dissociation between clinical and radiological disease activity, which has been noted in other natural history studies.(38, 39)

Table 1. NEDA and variants: Use in MS trials/studies NEDA Terminology

Components

Primary Studies

Medications

“Free from

No clinical relapse, no confirmed EDSS disability

Havrdova et al., 2009 (AFFIRM)(18)

Natalizumab

Disease Activity” or “Absence of disease activity” or “Disease Activity Free Status” (DAFS) or NEDA-3

progression sustained for 12 Giovannoni et al., 2011 weeks, no new gadolinium (CLARITY)(19) enhancing lesions, no new or Giovannoni et al., 2012 enlarging T2 lesions (DEFINE)(21)

No clinical relapse, no confirmed EDSS disability progression sustained for 6 months, no new gadolinium enhancing lesions, no new or enlarging T2 lesions

Cladribine Dimethyl fumarate

Cohen et al., 2013 (TRANSFORMS)(20)

Fingolimod IFN beta-1a

Havrdova et al., 2014 (SELECT)(25)

Daclizumab

Nixon et al., 2014(32)

Fingolimod Dimethyl fumarate Teriflunomide

Rudick et al., 2014 (AFFIRM)(27)

Natalizumab

Arnold et al., 2014(28)

IFN beta-1a

Nixon et al., 2014 (FREEDOMS and FREEDOMS II)(32)

Fingolimod

Coyle et al., 2014 (EVIDENCE)(40)

IFN beta-1a

Cohen et al., 2016 (TRANSFORMS)(31)

Fingolimod

Kappos et al., 2016 (DECIDE)(10)

Daclizumab IFN beta-1a

Traboulsee et al., 2016 (OPERA I and II)(41)

Ocrelizumab

Havrdova et al., 2017 (DEFINE/CONFIRM)(42)

Dimethyl fumarate Glatiramer acetate

Arnold et al., 2017 (ADVANCE)(43)

Peginterferon beta-1a

Coyle et al., 2017 (EVIDENCE)(11)

IFN beta-1a

Kappos et al., 2010 (FREEDOMS)(44)

Fingolimod

Cohen et al., 2012 (CARE-MS I)(22)

Alemtuzumab IFN beta-1a

Coles et al, 2012 (CARE- Alemtuzumab MS II)(23) IFN beta-1a Lublin et al., 2013 (CombiRx)(24)

IFN beta-1a Glatiramer acetate

Rotstein et al., 2015 (CLIMB)(37)

Independent of DMT

Giovannoni et al., 2015 (ENDORSE)(36)

Dimethyl fumarate

NEDA-4

Damasceno et al., 2016(8)

Daclizumab IFN beta-1a

No clinical relapse, no confirmed EDSS disability progression sustained for unspecified duration, no new gadolinium enhancing lesions, no new or enlarging T2 lesions

Ryerson et al., 2014(45)

Natalizumab

Nygaard et al., 2015(46)

Independent of DMT

No clinical relapse, no confirmed EDSS disability progression sustained for 6 months, no new gadolinium enhancing lesions, no new or enlarging T2 lesions, annualized percentage brain volume change less than or equal to -0.4%

Montalban et al., 2015 (TRANSFORMS)(34)

Fingolimod IFN beta-1a

Ghezzi et al., 2015(9)

Fingolimod IFN beta-1a

Kappos et al., 2016 (FREEDOMS I and II)(33)

Fingolimod

Table 2. NEDA Definitions Level Criteria NEDA-3 NEDA-4

No clinical relapses + no EDSS confirmed disability progression + no MRI activity NEDA-3 + no increased brain atrophy

NEDA-5

NEDA-4 + CSF neurofilament

MEDA

Minimal evidence of disease activity e.g. using the Rio Score(47), Modified Rio Score(48) No evidence of progression or active disease NEDA-3 + no 12-week confirmed progression of ≥20% on the timed 25-foot walk test and on the 9-hole peg test

NEPAD

The development of NEDA-4 The constituents of NEDA-3 have been criticized as inadequate measures of disease remission. NEDA-3 emphasizes the role of inflammatory pathology, reflecting the classical view of MS as a focal disease of the white matter. However there is growing evidence of diffuse pathology involving both grey and white matter, driven by both inflammatory demyelination and neurodegeneration (4957), that ultimately culminates in brain volume loss (BVL).(58-60) Brain atrophy is now routinely incorporated as a secondary outcome measure, and as a surrogate marker of neurodegeneration, in MS clinical trials. Brain atrophy is evident in early MS(58) and typically whole BVL progresses at a rate of 0.5-1.35% per annum, which is several times that observed in age-adjusted healthy individuals (0.1-0.3%/year).(58) Brain atrophy provides an objective measure of tissue damage in MS that, at the group level, is predictive of long-term disability progression and cognitive decline.(61-65) In individual patients, brain volume assessment

is confounded by methodological limitations, treatment-related changes in brain water and inflammatory cell content (pseudoatrophy), and biological/fluid-shift related fluctuations in brain volume. Recently, brain atrophy has been integrated into the four-parameter measure, NEDA-4, to provide a more comprehensive assessment of disease activity and progression.(9, 33) Based on a ‘pathological cut-off’ that discriminates BVL due to MS from that observed in healthy controls with high specificity (80%) and sensitivity (65%)(66), an annual BVL threshold of ≤0.4% has been proposed as an indicative measure of disease quiescence. BVL above this threshold is associated with a significant worsening EDSS after adjusting for age, disease duration and baseline EDSS.(66) NEDA-4 therefore incorporates; 1) no evidence of clinical relapses, 2) no 6-month confirmed disability progression, 3) no new or enlarged T2 lesions, and 4) annualized whole brain atrophy of less than or equal to 0.4%. (9, 33, 34, 67) Previous studies using NEDA-3 have allowed either 3month or 6-month EDSS progression, however, NEDA-4 uses 6-month EDSS progression as it is regarded as more specific for a permanent change in disability status.(68, 69)

Criticisms of NEDA Each of NEDA’s constituents have limitations; furthermore, while some constituents may exert proportionally greater influence on longer term MS outcomes, each have equal weight in current NEDA formulations, a potential source of bias.(36)

Limitations in Imaging Although focal inflammatory lesions and brain volume loss explain disability progression better in combination than alone(62, 70), current radiological markers defined within NEDA-4 are inherently limited. MRI markers of focal degeneration, such as persistent T1 hypointensities (“black holes”), may warrant inclusion in future NEDA definitions. These lesions are associated with severe axonal damage and have a stronger correlation with disability than gadolinium enhancing T1 lesions and new or enlarging T2 lesions.(71, 72) Similarly, higher cortical lesion burden also suggests a poorer prognosis(73), but is poorly discerned by routine clinical MRI protocols.(3) Novel MRI sequences,

such as double-inversion recovery may detect cortical lesions more reliably,(74) but are still only identify a small percentage of histopathologically detectable cortical lesions.(75) Whole brain atrophy is considered a surrogate marker of neurodegeneration, but in vivo measurement from MRI scans can be confounded by multiple factors. These include; physiological/biological factors, medication induced pseudoatrophy, and technical factors. DMTrelated pseudoatrophy is postulated to result from early anti-inflammatory effects and changes in electrolyte balance and vascular permeability in the first six months of treatment.(58, 76, 77) High dose pulsed steroids have also been shown to cause pseudoatrophy.(78) Other factors that may effect brain volumes include; alcohol, smoking, dehydration, apolipoprotein E expression, other medications, diabetes mellitus, and cardiovascular risk factors.(79) Technical factors that compromise optimal MRI acquisition and measurement errors inherent to post-processing techniques also can introduce systematic biases, particularly in multi-center studies. These factors have even greater impact at the individual patient level, hindering translation of outcome measures that incorporate brain atrophy measurement to clinical practice.(58, 79) The most commonly employed method for estimation of BVL in MS clinical trials to date is SIENA (Structural Image Evaluation using Normalisation of Atrophy)(80), an open source registrationbased tool that measures longitudinal whole BVL, but does not provide substructure analysis. Atrophy of specific substructures, in particular grey matter, may be more informative than whole BVL.(81) Grey matter atrophy appears to occur more rapidly than white matter atrophy during both early and late disease(82-85), and some structures of the brain may be more vulnerable to MSrelated neurodegeneration than others. Thalamic volume loss is thought to be detectable as the earliest surrogate of pathologic change and may reveal neurodegeneration before the onset of clinical symptoms.(86-89) Novel tools that facilitate longitudinal analysis with relatively low measurement error, such as SIENAX-MTP(90), may drive the refinement of the MRI metrics incorporated into future iterations of NEDA.

Limitations in Measuring Disability Progression Although the EDSS is the reference standard for disability assessment in MS, it is prone to significant inter-examiner and intra-examiner variability, with differences of between 1.0 and 2.0

points in some studies(19, 91, 92). This variability can arise due to subjective components of the clinical scale, which may increase or decrease the EDSS result by one point, particularly at the lower end of the scale.(93) Furthermore, the scale focuses strongly on motor impairment and mobility, while domains such as cognition are weighted less heavily. Currently, disability confirmation periods of 3-months or 6-months are used to estimate irreversible long-term accumulation of disability within the limited timeframe of MS treatment trials.(94) However, it is uncertain whether 3-month or 6-month confirmed EDSS progression correlates with long-term disability outcomes.(35) Recently a large prospective observational cohort study evaluated the long-term persistence of the identified EDSS progression.(16) The study recommended the implementation of longer disability confirmation periods of 12 to 24 months, as disability outcomes based on 3-month or 6-month progression could overestimate the accumulation of permanent disability by up to 30%.(16)

Application of NEDA in the clinic Clinical Practice There is currently no standardized way to monitor RRMS disease activity in the clinic.(95) In practice, NEDA may have value in guiding patient management decisions, as NEDA status provides an overall composite assessment of disease activity in individual MS patients.(96) Neurologists routinely incorporate MRI outcomes in order to aid clinical decision-making.(97) However, in routine clinical practice there are multiple challenges from an imaging perspective, both between and within centers. These challenges include; (i) a lack of standardized MRI acquisition, (ii) a lack of standardized reporting and radiologist expertise, and (iii) technical limitations. Another barrier to NEDA status being applied to individuals in routine MS clinical care, is that the EDSS is not universally used by neurologists.(98) The application of NEDA-4 in MS clinical practice is limited by logistical and technical factors, and by the lack of validated MRI brain atrophy measures in individual MS patients. (99) Implementation of longitudinally stable image acquisition protocols is mandatory, and software applications for the measurement of BVL may not be readily available to treating neurologists. While freely available, the SIENA/X pipelines require expertise to implement and maintain.(80) Web-based software

packages such as NeuroQuant(100) and MSmetrix(101) have ‘user-friendly’ interfaces but despite their reported accuracy, are not validated as clinical decision-guiding tools.(101-104) To facilitate the integration of NEDA-3 into clinical practice, an individualized treatment paradigm has recently been proposed.(36) Clinicians are encouraged to treat to target NEDA-3, with disease activity monitored with a baseline MRI and then at least annual repeat MRI studies to monitor for subclinical MRI disease activity. In the correct circumstances, the demonstration of MRI disease activity may warrant therapy escalation or retreatment.(6, 22, 23) Questions remain regarding whether NEDA is a realistic treatment target in MS, since longitudinal studies have shown that NEDA is lost over time, and less than 50% of treated patients achieve NEDA status in clinical practice.(37) Another issue is that even if NEDA is achievable, clinicians and patients may be hesitant to select/implement more efficacious, aggressive therapies due to treatment-related risks, and not all patients acquire major disability from their MS. Conversely, switching patients to high-efficacy therapies may significantly impact the course of the disease and long-term remission may be achieved. A less stringent goal, ‘Minimal Evidence of Disease Activity’ (MEDA), may be more appropriate in some patients in whom maintenance of NEDA is difficult.(74, 96, 105) MEDA allows a level of residual disease activity as a treatment goal, for example using the Rio(47) and Modified Rio scores(48).

Future outcome markers In future, NEDA may evolve to include MRI volumetric measures of specific brain substructures or incorporate fluid biomarkers, such as cerebrospinal fluid (CSF) or serum neurofilament levels.(106108) CSF neurofilament light chains are a marker of neurodegeneration in MS and some commentators have already proposed the term ‘disease-free status score’ (D-FREESC)(108) or NEDA-5(105), which incorporates normalisation of CSF neurofilament as a treatment goal in addition to the other NEDA-4 parameters. The inclusion of neuropsychological outcomes in NEDA definitions has also been proposed.(109, 110) In a recent two-year cohort study, maintenance of NEDA-3 did not preclude cognitive deterioration in patients with RRMS, which occurred in 58.3% of otherwise stable patients.(8) To address these shortcomings, it has been suggested that the EDSS be replaced with the Multiple Sclerosis Function Composite (MSFC), a more sensitive measure

that includes the timed 25 foot walk, 9 peg-hole test and the paced auditory serial addition test-3 (PASAT-3).(109, 111) A recently proposed treatment goal of NEPAD (No evidence of progression or active disease) for progressive MS uses the composite endpoint of NEDA-3 with the addition of no 12-week confirmed progression of ≥20% on the timed 25-foot walk test and on the 9-hole peg test(112).

Conclusion For the past six years, NEDA has been a significant composite outcome measure in MS clinical trials and is starting to become an important consideration in clinical practice. The definition of NEDA has evolved in light of research and technological advancements; in particular the analysis of MRI brain volume loss as a surrogate marker of neurodegeneration. While the implementation of NEDA-4 targeted management into clinical practice is currently hindered by logistical and technical difficulties, NEDA-3 is becoming integrated into clinical decision-making models and is an attractive target for RRMS patients. Future iterations of NEDA definitions will almost certainly incorporate novel imaging, blood and CSF biomarkers of disease activity and progression, as future research elucidates their place in MS treatment monitoring.

Declaration of interests GL: No declaration of interests. HNB: Has received compensation for education travel, speaker honoraria and consultant fees from Biogen, Novartis, Merck, Sanofi-Genzyme and Roche. JLB: Has received compensation for education travel, honoraria for talks and advisory boards from Biogen, Teva, Merck-Serono, Sanofi-Genzyme, Novartis and Roche. TAH: Has received honoraria for talks and advisory boards and support for scientific meetings from Novartis, Biogen Idec, Merck-Serono, Roche, Teva, and Sanofi-Genzyme. MHB: Has received institutional support from Biogen, Sanofi-Genzyme, Novartis and Teva; and travel support from Novartis and Biogen.

Funding This work received no specific grant from any funding agency in the public, commercial or not-forprofit sectors.

Contributorship statement GL and MHB conceived the idea for the article and drafted the manuscript. All authors critically revised the manuscript for important intellectual content, and gave final approval of the version to be published. CONFLICT OF INTEREST AND FUNDING GL: No declaration of interests. HNB: Has received compensation for education travel, speaker honoraria and consultant fees from Biogen, Novartis, Merck, Sanofi-Genzyme and Roche. JLB: Has received compensation for education travel, honoraria for talks and advisory boards from Biogen, Teva, Merck-Serono, Sanofi-Genzyme, Novartis and Roche. TAH: Has received honoraria for talks and advisory boards and support for scientific meetings from Novartis, Biogen Idec, Merck-Serono, Roche, Teva, and Sanofi-Genzyme. MHB: Has received institutional support from Biogen, Sanofi-Genzyme, Novartis and Teva; and travel support from Novartis and Biogen. References 1. Noseworthy JH, Lucchinetti, C., Rodriguez, M. and Weinshenker, B.G. Multiple sclerosis. N Engl J Med. 2000;343:938-52. 2. Brownlee WJ, Hardy TA, Fazekas F, Miller DH. Diagnosis of multiple sclerosis: progress and challenges. Lancet (London, England). 2016. 3. Matthews PM, Roncaroli F, Waldman A, Sormani MP, De Stefano N, Giovannoni G, et al. A practical review of the neuropathology and neuroimaging of multiple sclerosis. Practical neurology. 2016;16(4):279-87. 4. Breedveld FC, Weisman MH, Kavanaugh AF, Cohen SB, Pavelka K, van Vollenhoven R, et al. The PREMIER study: A multicenter, randomized, double-blind clinical trial of combination therapy with adalimumab plus methotrexate versus methotrexate alone or adalimumab alone in patients with early, aggressive rheumatoid arthritis who had not had previous methotrexate treatment. Arthritis and rheumatism. 2006;54(1):26-37. 5. van der Heijde D, Klareskog L, Rodriguez-Valverde V, Codreanu C, Bolosiu H, MeloGomes J, et al. Comparison of etanercept and methotrexate, alone and combined, in the treatment of rheumatoid arthritis: two-year clinical and radiographic results from the TEMPO study, a doubleblind, randomized trial. Arthritis and rheumatism. 2006;54(4):1063-74. 6. Dobson R, Rudick RA, Turner B, Schmierer K, Giovannoni G. Assessing treatment response to interferon-beta: is there a role for MRI? Neurology. 2014;82(3):248-54. 7. IFNB Multiple Sclerosis Study Group and the University of British Columbia MS/MRI Analysis Group. Interferon beta-1b in the treatment of multiple sclerosis: final outcome of the randomized controlled trial. Neurology. 1995;45(7):1277-85. 8. Damasceno A, Damasceno BP, Cendes F. No evidence of disease activity in multiple sclerosis: Implications on cognition and brain atrophy. Multiple sclerosis (Houndmills, Basingstoke, England). 2016;22(1):64-72. 9. Ghezzi A, Karlsson G, Haering D, Gärtner J, Chitnis T, Pohl D, et al. Effect of Fingolimod on No Evidence of Disease Activity (NEDA-4) and Safety in Young Adult Patients with RelapsingRemitting Multiple Sclerosis American Academy of Neurology Annual Meeting; Washington, DC: Neurology 2015. p. Supplement P3.277. 10. Kappos L, Havrdova E, Giovannoni G, Khatri B, Greenberg S, Wang P, et al. DACLIZUMAB HYP VS IM INTERFERON BETA-1A IN MS: NEDA RESULTS FROM DECIDE. Journal of Neurology Neurosurgery and Psychiatry 2016;87(12):e1. 11. Coyle PK, Reder AT, Freedman MS, Fang J, Dangond F. Early MRI results and odds of attaining 'no evidence of disease activity' status in MS patients treated with interferon β-1a in the EVIDENCE study. J Neurol Sci 2017. 2017;15(379 ):151-6. 12. Tintore M, Rovira A, Rio J, Otero-Romero S, Arrambide G, Tur C, et al. Defining high, medium and low impact prognostic factors for developing multiple sclerosis. Brain. 2015;138(Pt 7):1863-74.

13. Fisniku LK, Brex PA, Altmann DR, Miszkiel KA, Benton CE, Lanyon R, et al. Disability and T2 MRI lesions: a 20-year follow-up of patients with relapse onset of multiple sclerosis. Brain. 2008;131(Pt 3):808-17. 14. Sormani MP, Bruzzi P. MRI lesions as a surrogate for relapses in multiple sclerosis: a meta-analysis of randomised trials. The Lancet Neurology. 2013;12(7):669-76. 15. Kurtzke J. Rating neurologic impairment in multiple sclerosis: an Expanded Disability Status Scale (EDSS). Neurology. 1983;33(11):1444-52. 16. Kalincik T, Cutter G, Spelman T, Jokubaitis V, Havrdova E, Horakova D, et al. Defining reliable disability outcomes in multiple sclerosis. Brain 2015;138(11):3287-98. 17. Rio J, Castillo J, Rovira A, Tintore M, Sastre-Garriga J, Horga A, et al. Measures in the first year of therapy predict the response to interferon beta in MS. Multiple sclerosis (Houndmills, Basingstoke, England). 2009;15(7):848-53. 18. Havrdova E, Galetta S, Hutchinson M, Stefoski D, Bates D, Polman CH, et al. Effect of natalizumab on clinical and radiological disease activity in multiple sclerosis: a retrospective analysis of the Natalizumab Safety and Efficacy in Relapsing-Remitting Multiple Sclerosis (AFFIRM) study. The Lancet Neurology. 2009;8(3):254-60. 19. Giovannoni G, Cook S, Rammohan K, Rieckmann P, Sorensen PS, Vermersch P, et al. Sustained disease-activity-free status in patients with relapsing-remitting multiple sclerosis treated with cladribine tablets in the CLARITY study: a post-hoc and subgroup analysis. The Lancet Neurology. 2011;10(4):329-37. 20. Cohen JA, Barkhof F, Comi G, Izquierdo G, Khatri B, Montalban X, et al. Fingolimod versus intramuscular interferon in patient subgroups from TRANSFORMS. J Neurol. 2013;260(8):2023-32. 21. Giovannoni G, Gold R, Kappos L, Arnold DL, Bar-Or A, Selmaj K, et al. Analysis of clinical and radiological disease activity-free status in patients with relapsing-remitting multiple sclerosis treated with BG-12: findings from the DEFINE study. Journal of Neurology. 2012;259:S106-S. 22. Cohen JA, Coles AJ, Arnold DL, Confavreux C, Fox EJ, Hartung HP, et al. Alemtuzumab versus interferon beta 1a as first-line treatment for patients with relapsing-remitting multiple sclerosis: a randomised controlled phase 3 trial. Lancet (London, England). 2012;380(9856):181928. 23. Coles AJ, Twyman CL, Arnold DL, Cohen JA, Confavreux C, Fox EJ, et al. Alemtuzumab for patients with relapsing multiple sclerosis after disease-modifying therapy: a randomised controlled phase 3 trial. Lancet (London, England). 2012;380(9856):1829-39. 24. Lublin FD, Cofield SS, Cutter GR, Conwit R, Narayana PA, Nelson F, et al. Randomized study combining interferon and glatiramer acetate in multiple sclerosis. Annals of neurology. 2013;73(3):327-40. 25. Havrdova E, Giovannoni G, Stefoski D, Forster S, Umans K, Mehta L, et al. Diseaseactivity-free status in patients with relapsing-remitting multiple sclerosis treated with daclizumab high-yield process in the SELECT study. Multiple sclerosis (Houndmills, Basingstoke, England). 2014;20(4):464-70. 26. Kalincik T, Cutter G, Spelman T, Jokubaitis V, Havrdova E, Horakova D, et al. Defining reliable disability outcomes in multiple sclerosis. BRAIN. 2015;138:3287–98. 27. Rudick R, Fisher E, Goodman A, Phillips JT, Pace A, Belachew S. No evident disease activity (NEDA) in the AFFIRM study: association with brain atrophy and functional outcomes. European Journal of Neurology. 2014;21(Suppl. 1):388-713. 28. Arnold DL, Calabresi PA, Kieseier BC, Sheikh SI, Deykin A, Zhu Y, et al. Effect of peginterferon beta-1a on MRI measures and achieving no evidence of disease activity: results from a randomized controlled trial in relapsing-remitting multiple sclerosis. BMC Neurology. 2014;14:240(240). 29. Lublin FD. Disease activity free status in MS. Multiple sclerosis and related disorders. 2012;1(1):6-7. 30. Calabresi P, Kieseier B, Arnold D, Balcer L, Boyko A, Pelletier J, et al. Pegylated interferon beta-1a in relapsing-remitting multiple sclerosis: results from ADVANCE, a randomized, phase 3, double-blind study. . The Lancet Neurology. 2014;13:657–65. 31. Cohen JA, Khatri B, Barkhof F, Comi G, Hartung HP, Montalban X, et al. Long-term (up to 4.5 years) treatment with fingolimod in multiple sclerosis: results from the extension of the

randomised TRANSFORMS study. Journal of neurology, neurosurgery, and psychiatry. 2016;87(5):468-75. 32. Nixon R, Bergvall N, Tomic D, Sfikas N, Cutter G, Giovannoni G. No evidence of disease activity: indirect comparisons of oral therapies for the treatment of relapsing-remitting multiple sclerosis. Advances in therapy. 2014;31(11):1134-54. 33. Kappos L, De Stefano N, Freedman MS, Cree BA, Radue EW, Sprenger T, et al. Inclusion of brain volume loss in a revised measure of 'no evidence of disease activity' (NEDA-4) in relapsing-remitting multiple sclerosis. Multiple sclerosis (Houndmills, Basingstoke, England). 2016;22(10):1297-305. 34. Montalban X, Cohen J, Barkhof F, Khatri B, Hartung H, Ritter S, et al. Effect of fingolimod versus interferon-beta1a on no evidence of disease activity or worsening (NEDA-4) in the TRANSFORMS study (P4.001). Neurology. 2015;84(14):Supplement P4.001. 35. Bevan CJ, Cree BA. Disease activity free status: a new end point for a new era in multiple sclerosis clinical research? JAMA neurology. 2014;71(3):269-70. 36. Giovannoni G, Havrdova E, Gold R, Fox R, Kappos L, Phillips JT, et al. Long-term Followup of the Effect of Delayed-Release Dimethyl Fumarate on No Evidence of Disease Activity in Patients with Multiple Sclerosis (P7.280). Neurology. 2015 84 (14):Supplement P7.280. 37. Rotstein DL, Healy BC, Malik MT, Chitnis T, Weiner HL. Evaluation of no evidence of disease activity in a 7-year longitudinal multiple sclerosis cohort. JAMA neurology. 2015;72(2):1528. 38. Barkhof F. The clinico-radiological paradox in multiple sclerosis revisited. Current opinion in neurology. 2002;15(3):239-45. 39. Strasser-Fuchs S, Enzinger C, Ropele S, Wallner M, Fazekas F. Clinically benign multiple sclerosis despite large T2 lesion load: can we explain this paradox? Multiple sclerosis (Houndmills, Basingstoke, England). 2008;14(2):205-11. 40. Coyle PK, Fang J, Hassan A, Cha C, Dangond F, Reder AT. P102 No evident disease activity at 24 weeks in patients with relapsing MS treated with interferon β-1a SC vs. interferon β1a IM in the EVIDENCE study. Joint ACTRIMS-ECTRIMS Meeting MS Journal Online: Poster Session 1; MSBoston: Multiple Sclerosis (Houndmills, Basingstoke, England); 2014 p. 113. 41. Traboulsee A, Arnold D, Bar-Or A, Comi G, Hartung H-P, Kappos L, et al. Ocrelizumab No Evidence of Disease Activity (NEDA) Status at 96 Weeks in Patients with Relapsing Multiple Sclerosis: Analysis of the Phase III Double-Blind, Double-Dummy, Interferon beta-1a-Controlled OPERA I and OPERA II Studies (PL02.004). Neurology. 2016;86(16 Supplement). 42. E. Havrdova, Giovannoni G, Gold R, Fox RJ, Kappos L, Phillips JT, et al. Effect of delayedrelease dimethyl fumarate on no evidence of disease activity in relapsing–remitting multiple sclerosis: integrated analysis of the phase III DEFINE and CONFIRM studies. European Journal of Neurology. 2017;24:726-33. 43. Arnold DL, Calabresi PA, Kieseier BC, Liu S, You X, Fiore D, et al. Peginterferon beta-1a improves MRI measures and increases the proportion of patients with no evidence of disease activity in relapsing-remitting multiple sclerosis: 2-year results from the ADVANCE randomized controlled trial. BMC Neurology. 2017;17(29). 44. Kappos L, Radue EW, O'Connor P, Polman C, Hohlfeld R, Calabresi P, et al. A placebocontrolled trial of oral fingolimod in relapsing multiple sclerosis. The New England journal of medicine. 2010;362(5):387-401. 45. Ryerson LZ, Herbert J, Tornatore C, Foley J, Weinstock-Guttman B, Kister I, et al. P287 Efficacy of Natalizumab extended dosing in multiple sclerosis: a retrospective multicenter analysis. 2014 Joint ACTRIMS-ECTRIMS Meeting MSBoston: Multiple Sclerosis Journal; 2014. p. 194. 46. Nygaard G, Celius E, de Rodez Benavent S, Sowa P, Gustavsen M, Fjell A, et al. A Longitudinal Study of Disability, Cognition and Gray Matter Atrophy in Early Multiple Sclerosis Patients According to Evidence of Disease Activity. PloS one. 2015;10(8):e0135974. 47. Rio J, Nos C, Tintore M, Tellez N, Galan I, Pelayo R, et al. Defining the Response to Interferon-’ in Relapsing-Remitting Multiple Sclerosis Patients. Annals of neurology. 2006;59:344– 52. 48. Sormani MP, Rio J, Tintore M, Signori A, Li D, Cornelisse P, et al. Scoring treatment response in patients with relapsing multiple sclerosis. Multiple sclerosis (Houndmills, Basingstoke, England). 2013;19(5):605-12.

49. Kutzelnigg A, Lassmann H. Pathology of multiple sclerosis and related inflammatory demyelinating diseases. Handb Clin Neurol. 2014;122:15-58. 50. Kutzelnigg A, Lucchinetti C, Stadelmann C, et al. Cortical demyelination and diffuse white matter injury in multiple sclerosis Brain. 2005;128:2705-12. 51. Frischer J, Bramow S, Dal-Bianco A, et al. The relation between inflammation and neurodegeneration in multiple sclerosis brains. Brain. 2009;132:1175-89. 52. Lassmann H. Mechanisms of white matter damage in multiple sclerosis. Glia. 2014;62:1816-30. 53. Trapp B, Peterson J, Ransohoff R, et al. Axonal transection in the lesions of multiple sclerosis. . The New England journal of medicine. 1998;338(278-85). 54. Evangelou N, Konz D, Esiri M, et al. Size-selective neuronal changes in the anterior optic pathways suggest a differential susceptibility to injury in multiple sclerosis. Brain. 2001;124:1813– 20. 55. Helms G. T2-based segmentation of periventricular paragraph sign volumes for quantification of proton magnetic paragraph sign resonance spectra of multiple sclerosis lesions. MAGMA. 2003;16:10-6. 56. Whittall K, MacKay A, Li D, et al. Normal-appearing white matter in multiple sclerosis has heterogeneous, diffusely prolonged T(2). Magn Reson Med. 2002;47:403-8. 57. Evangelou N, Esiri M, Smith S, et al. Quantitative pathological evidence for axonal loss in normal appearing white matter in multiple sclerosis. Annals of neurology. 2000;47:391-5. 58. De Stefano N, Airas L, Grigoriadis N, Mattle H, O'Riordan J, Oreja-Guevara C, et al. Clinical relevance of brain volume measures in multiple sclerosis. CNS Drugs 2014;28(146-156). 59. Filippi M, Rocca M. MRI evidence for multiple sclerosis as a diffuse disease of the central nervous system. J Neurol. 2005;252:(Suppl 5):v16–24. 60. Filippi M, Rocca M, Barkhof F, et al. Association between pathological and MRI findings in multiple sclerosis. The Lancet Neurology. 2012;11:349–60. 61. Fisher E, Rudick R, Simon J, Cutter G, Baier M, Lee J, et al. Eight-year follow-up study of brain atrophy in patients with MS. Neurology. 2002;59:1412-20. 62. Popescu V, Agosta F, Hulst H, Sluimer I, Knol D, Sormani M, et al. Brain atrophy and lesion load predict long term disability in multiple sclerosis. Journal of neurology, neurosurgery, and psychiatry. 2013;84:1082–91. 63. Horakova D, Dwyer M, Havrdova E, Cox J, Dolezal O, Bergsland N, et al. Gray matter atrophy and disability progression in patients with early relapsing–remitting multiple sclerosis: A 5year longitudinal study. . J Neurol Sci 2009;282 112–9. 64. Deloire M, Ruet A, Hamel D, Bonnet M, Dousset V, Brochet B. MRI predictors of cognitive outcome in early multiple sclerosis. . Neurology. 2011;76:1161–7. 65. Radue E, Barkhof F, Kappos L, Sprenger T, Haring D, de Vera A, et al. Correlation between brain volume loss and clinical and MRI outcomes in multiple sclerosis. . Neurology. 2015;84 784–93. 66. De Stefano N, Stromillo ML, Giorgio A, Bartolozzi ML, Battaglini M, Baldini M, et al. Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis. Journal of neurology, neurosurgery, and psychiatry. 2016;87(1):93-9. 67. Kappos L, De Stefano N, Freedman MS, Cree BA, Radue EW, Sprenger T, et al. Inclusion of brain volume loss in a revised measure of 'no evidence of disease activity' (NEDA-4) in relapsing-remitting multiple sclerosis. Multiple sclerosis (Houndmills, Basingstoke, England). 2015. 68. Rudick R, Lee J, Cutter G, Miller D, Bourdette D, Weinstock-Guttman B, et al. Disability progression in a clinical trial of relapsing–remitting multiple sclerosis: Eight-year follow-up. . Arch Neurol 2010;67:1329–35. 69. Liu C, Blumhardt L. Disability outcome measures in therapeutic trials of relapsing–remitting multiple sclerosis: Effects of heterogeneity of disease course in placebo cohorts. . Journal of neurology, neurosurgery, and psychiatry. 2000;68: 450–7. 70. Sormani MP, Arnold DL, De Stefano N. Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis. Annals of neurology. 2014;75(1):43-9. 71. MAGNIMS study group. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis — establishing disease prognosis and monitoring patients. Nat Rev Neurol. 2015;11:597–606.

72. Wattjes M, Steenwijk M, Stangel M. MRI in the diagnosis and monitoring of multiple sclerosis: an update. Clin Neuroradiol. 2015;25(2):157-65. 73. Geurts J, Calabrese M, Fisher E, et al. Measurement and clinical effect of grey matter pathology in multiple sclerosis. The Lancet Neurology. 2012;11:1082–92. 74. Andr Palma da Cunha Matta OJMN, Ana arolina Andorinho de F Ferreira ha s Nascimento Magalh es a s ais esende enevides Arielle irmse oao abriel Dib enri ue al Marco rsini ucas Masi ro Araujo. No evidence of disease activity in multiple sclerosis patients. Expert Review of Neurotherapeutics. 2016;16(11):1279-84. 75. Filippi M, Rocca M, Ciccarelli O, De Stefano N, Evangelou N, Kappos L, et al. MRI CRITERIA FOR THE DIAGNOSIS OF MULTIPLE SCLEROSIS: MAGNIMS CONSENSUS GUIDELINES. The Lancet Neurology. 2016;15(3):292-303. 76. Zivadinov R, Reder A, Filippi M, et al. Mechanisms of action of disease-modifying agents and brain volume changes in multiple sclerosis. Neurology. 2008;71:136–44. 77. Jasperse B, Minneboo A, de Groot V, et al. Determinants of cerebral atrophy rate at the time of diagnosis of multiple sclerosis. Arch Neurol. 2007;64:190–4. 78. Zivadinov R, Reder AT, Filippi M, Minagar A, Stuve O, Lassmann H, et al. Mechanisms of action of disease-modifying agents and brain volume changes in multiple sclerosis. Neurology. 2008;71(2):136-44. 79. Enzinger C, Fazekas F, Matthews P, et al. Risk factors for progression of brain atrophy in aging: six-year follow-up of normal subjects. . Neurology. 2005;64:1704-11. 80. Smith S, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. NeuroImage. 2002;17(1):479–89. 81. Zivadinov R, Jakimovski D, Gandhi S, Ahmed R, Dwyer MG, Horakova D, et al. Clinical relevance of brain atrophy assessment in multiple sclerosis. Implications for its use in a clinical routine. Expert Review of Neurotherapeutics. 2016;16(7):777-93. 82. Minagar A, Barnett M, Benedict R, et al. The thalamus and multiple sclerosis: modern views on pathologic, imaging, and clin- ical aspects. Neurology. 2013;80(2):210–9. 83. Fisher E, Lee J, Nakamura K, et al. Gray matter atrophy in multiple sclerosis: a longitudinal study. Annals of neurology. 2008;64(3):255–65. 84. Zivadinov R, Uher T, Hagemeier J, et al. A serial 10-year follow-up study of brain atrophy and disability progression in RRMS patients. Mult Scler. 2016;22 (13):1709-18. 85. Van Munster C, Jonkman L, Weinstein H, et al. Gray matter damage in multiple sclerosis: impact on clinical symptoms. Neuroscience. 2015;303:446–61. 86. Okuda D, Mowry E, Beheshtian A, et al. Incidental MRI anomalies suggestive of multiple sclerosis: the radiologically isolated syndrome. Neurology. 2009;72(9):800-5. 87. Henry R, Shieh M, Okuda D, et al. Regional grey matter atrophy in clinically isolated syndromes at presentation. Journal of neurology, neurosurgery, and psychiatry. 2008;79(11):1236–44. 88. ivadinov avrdov E, Bergsland N, et al. Thalamic atrophy is associated with development of clinically definite multiple sclerosis. . Radiology. 2013;268(3):831-41. 89. Calabrese M, Rinaldi F, Mattisi I, et al. The predictive value of gray matter atrophy in clinically isolated syndromes. Neurology. 2011;77(3):257–63. 90. Dwyer MG, Bergsland N, Zivadinov R. Improved longitudinal gray and white matter atrophy assessment via application of a 4-dimensional hidden Markov random field model. Neuroimage. 2014;90:207-17. 91. Goodkin DE, Cookfair D, Wende K, Bourdette D, Pullicino P, Scherokman B, et al. Interand intrarater scoring agreement using grades 1.0 to 3.5 of the Kurtzke Expanded Disability Status Scale (EDSS). Multiple Sclerosis Collaborative Research Group. Neurology. 1992;42(4):859-63. 92. Verdier-Taillefer MH, Roullet E, Cesaro P, Alperovitch A. Validation of self-reported neurological disability in multiple sclerosis. International journal of epidemiology. 1994;23(1):14854. 93. Fragoso YD. Why some of us do not like the expression "no evidence of disease activity" (NEDA) in multiple sclerosis. Multiple sclerosis and related disorders. 2015;4(4):383-4.

94. Filippini G, Del Giovane C, Vacchi L, D’Amico R, Di Pietrantonj C, Beecher D, et al. Immunomodulators and immunosuppressants for multiple sclerosis: a network meta-analysis. . Cochrane Database Syst Rev. 2013;6:CD008933. 95. Hartung HP, Aktas O. Evolution of multiple sclerosis treatment: next generation therapies meet next generation efficacy criteria. The Lancet Neurology. 2011;10(4):293-5. 96. Imitola J, Racke MK. Is No Evidence of Disease Activity a Realistic Goal for Patients With Multiple Sclerosis? JAMA neurology. 2015;72(2):145-6. 97. Traboulsee A, Simon JH, Stone L, Fisher E, Jones DE, Malhotra A, et al. Revised Recommendations of the Consortium of MS Centers Task Force for a Standardized MRI Protocol and Clinical Guidelines for the Diagnosis and Follow-Up of Multiple Sclerosis. AJNR American journal of neuroradiology. 2016;37(3):394-401. 98. Meyer-Moock S, Feng Y, Maeurer M, et al. Systematic literature review and validity evaluation of the Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis Functional Composite (MSFC) in patients with multiple sclerosis. BMC Neurol . 2014;14:58. 99. Minneboo A, Jasperse B, Barkhof F, Uitdehaag BM, Knol DL, de Groot V, et al. Predicting short-term disability progression in early multiple sclerosis: added value of MRI parameters. Journal of neurology, neurosurgery, and psychiatry. 2008;79(8):917-23. 100. Brewer J, Magda S, Airriess C, et al. Fully-automated quantification of regional brain volumes for improved detection of focal atrophy in Alzheimer disease. . AJNR Am J Neuroradiol. 2009;30:578-80. 101. Jain S, Sima DM, Ribbens A, Cambron M, Maertens A, Van Hecke W, et al. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. NeuroImage Clinical. 2015;8:367-75. 102. Lysandropoulos AP, Absil J, Metens T, Mavroudakis N, Guisset F, Van Vlierberghe E, et al. Quantifying brain volumes for Multiple Sclerosis patients follow-up in clinical practice - comparison of 1.5 and 3 Tesla magnetic resonance imaging. Brain and Behavior. 2016;6(2):e00422. 103. Smeets D, Ribbens A, Sima DM, Cambron M, Horakova D, Jain S, et al. Reliable measurements of brain atrophy in individual patients with multiple sclerosis. Brain and Behavior. 2016;6(9). 104. Wang C, Beadnall H, Hatton S, Bader G, Tomic D, Silva D, et al. Automated brain volumetrics in multiple sclerosis: a step closer to clinical application . Journal of neurology, neurosurgery, and psychiatry. 2016(0):1–4. 105. Giovannoni G, Bermel R, Phillips T, Rudick R. A brief history of NEDA. Multiple sclerosis and related disorders. 2017:(In press). 106. Giovannoni G. Cerebrospinal fluid neurofilament: the biomarker that will resuscitate the 'Spinal Tap'. Multiple sclerosis (Houndmills, Basingstoke, England). 2010;16(3):285-6. 107. Giovannoni G, Nath A. After the storm: neurofilament levels as a surrogate endpoint for neuroaxonal damage. Neurology. 2011;76(14):1200-1. 108. Bonnan M, Marasescu R, Demasles S, Krim E, Barroso B. No evidence of disease activity (NEDA) in MS should include CSF biology - Towards a 'Disease-Free Status Score'. Multiple sclerosis and related disorders. 2017;11:51-5. 109. Stangel M, Penner IK, Kallmann BA, Lukas C, Kieseier BC. Towards the implementation of 'no evidence of disease activity' in multiple sclerosis treatment: the multiple sclerosis decision model. Therapeutic advances in neurological disorders. 2015;8(1):3-13. 110. Ziemssen T, De Stefano N, Sormani MP, Wijmeersch BV, Wiendl H, Kieseier BC. Optimizing therapy early in multiple sclerosis: An evidence-based view. Multiple sclerosis and related disorders. 2015;4:460-9. 111. Bosma L, Kragt JJ, Polman CH, Uitdehaag BM. Walking speed, rather than Expanded Disability Status Scale, relates to long-term patient-reported impact in progressive MS. Multiple sclerosis (Houndmills, Basingstoke, England). 2013;19(3):326-33. 112. Wolinsky J, Montalban X, Kappos L, Hauser S, Giovannoni G, Seze Jd, et al. Evaluation of No Evidence of Progression or Active Disease (NEPAD) in Patients with Primary Progressive Multiple Sclerosis in the ORATORIO Trial (P4.384). Neurology. 2017;88(16):Supplement P4.384.

Highlights  “No evidence of disease activity” (NEDA) has emerged as a potential treatment target in patients with RRMS. 

NEDA-3 consists of a composite of clinical relapse, EDSS, and MRI outcomes, and has been shown to be predictive of long term disability.



Whole brain atrophy has been integrated into NEDA-4 in order to provide a more comprehensive assessment of disease activity and progression.



NEDA-3 is becoming integrated into clinical decision-making models, however, the implementation of NEDA-4 in clinical practice is currently hindered by logistical and technical difficulties.