This paper is in the following e-collection/theme issue:

Non-Randomized Study Protocols and Methods (Non-eHealth) (543) Pharmacology and Pharmaceutics (49) Neurology and Neurosciences (148)

Published on in Vol 10, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24969, first published .
Efficacy of Cladribine Tablets as a Treatment for People With Multiple Sclerosis: Protocol for the CLOBAS Study (Cladribine, a Multicenter, Long-term Efficacy and Biomarker Australian Study)

Efficacy of Cladribine Tablets as a Treatment for People With Multiple Sclerosis: Protocol for the CLOBAS Study (Cladribine, a Multicenter, Long-term Efficacy and Biomarker Australian Study)

Efficacy of Cladribine Tablets as a Treatment for People With Multiple Sclerosis: Protocol for the CLOBAS Study (Cladribine, a Multicenter, Long-term Efficacy and Biomarker Australian Study)

Authors of this article:

Vicki E Maltby1, 2, 3 Author Orcid Image ;   Rodney A Lea2, 3, 4 Author Orcid Image ;   Mastura Monif5, 6, 7 Author Orcid Image ;   Marzena J Fabis-Pedrini8 Author Orcid Image ;   Katherine Buzzard5, 7 Author Orcid Image ;   Tomas Kalincik7, 9 Author Orcid Image ;   Allan G Kermode8, 10 Author Orcid Image ;   Bruce Taylor11 Author Orcid Image ;   Suzanne Hodgkinson12, 13, 14 Author Orcid Image ;   Pamela McCombe15 Author Orcid Image ;   Helmut Butzkueven6, 9 Author Orcid Image ;   Michael Barnett16, 17 Author Orcid Image ;   Jeannette Lechner-Scott1, 2, 3 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

    Protocol

    1Department of Neurology, John Hunter Hospital, New Lambton Heights, Australia

    2School for Medicine and Public Health, University of Newcastle, Callaghan, Australia

    3Hunter Medical Research Institute, New Lambton Heights, Australia

    4Institute of Health and Biomedical Innovations, Genomics Research Centre, Queensland University of Technology, Kelvin Grove, Australia

    5Department of Neurosciences, Eastern Health Clinical School, Monash University, Box Hill Hospital, Melbourne, Australia

    6Department of Neurology, Alfred Health, Melbourne, Australia

    7Department of Neurology, Melbourne Multiple Sclerosis Centre, Melbourne Health, Melbourne, Australia

    8Centre for Neuromuscular and Neurological Disorders, Perron Institute for Neurological and Translational Science, University of Western Australia, Perth, Australia

    9Clinical Outcomes Research (CORe) Unit, Department of Medicine, University of Melbourne, Melbourne, Australia

    10Institute for Immunology and Infectious Disease, Murdoch University, Perth, Australia

    11Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia

    12Department of Medicine, University of New South Wales, Sydney, Australia

    13Department of Neurology, Liverpool Hospital, Sydney, Australia

    14Immune Tolerance Laboratory, Ingham Institute, Sydney, Australia

    15Centre for Clinical Research, University of Queensland, Brisbane, Australia

    16Brain and Mind Centre, University of Sydney, Sydney, Australia

    17Sydney Neuroimaging Analysis Centre, Sydney, Australia

    Corresponding Author:

    Jeannette Lechner-Scott, MD, PhD

    School for Medicine and Public Health

    University of Newcastle

    Medical Genetics, Hunter Medical Research Institute building

    c/- University Drive

    Callaghan, 2305

    Australia

    Phone: 61 240420286

    Email: jeannette.lechner-scott@health.nsw.gov.au


    Background: Cladribine tablets (marketed as Mavenclad) are a new oral therapy, which has recently been listed on the pharmaceutical benefits scheme in Australia for the treatment of relapsing multiple sclerosis (MS). The current dosing schedule is for 2 courses given a year apart, which has been shown to be effective for treatment of MS for up to 4 years in 75% of patients (based on annualized relapse rate). However, the reinitiation of therapy after year 4 has not been studied.

    Objective: This study aims to evaluate the safety and efficacy of cladribine tablets over a 6-year period, according to no evidence of disease activity 3.

    Methods: This will be a multicenter, 6-year, phase IV, low interventional, observational study that incorporates clinical, hematological, biochemical, epigenetic, radiological and cognitive biomarkers of disease. Participants considered for treatment with cladribine as part of their routine clinical care will be consented to take part in the study. They will be monitored at regular intervals during the initial course of medication administration in years 1 and 2. After year 3, patients will have the option of redosing, if clinically indicated, or to switch to another disease-modifying therapy. Throughout the duration of the study, we will assess blood-based biomarkers including lymphocyte subsets, serum neurofilament light chain, DNA methylation, and RNA analysis as well as magnetic resonance imaging findings (brain volume and/or lesion load) and cognitive performance.

    Results: This study has been approved by the Hunter New England Local Health District Human Research Ethics Committee. Recruitment began in March of 2019 and was completed by June 2021.

    Conclusions: This will be the first long-term efficacy trial of cladribine, which offers reinitiation of therapy in the 3rd year, based on disease activity, after the initial 2 courses. We expect that this study will indicate whether any of the assessed biomarkers can be used to predict treatment efficacy or the need for future reinitiation of cladribine in people with MS.

    Trial Registration: This study is registered with the Australian and New Zealand Clinical Trials Registry (ACTRN12619000257167) with Universal Trial Number (U1111-1228-2165).

    International Registered Report Identifier (IRRID): DERR1-10.2196/24969

    JMIR Res Protoc 2021;10(10):e24969

    doi:10.2196/24969

    Keywords

    multiple sclerosis; cladribine; biomarkers 


    Overview

    Multiple sclerosis (MS) is a common, immune-mediated, demyelinating disease that affects the central nervous system. MS has a highly variable course; therefore, patient-specific treatment decisions are becoming increasingly important. Currently, we have no way to differentiate between the patients who will acquire rapid disability progression and the ones who will remain stable over several years. Most studies point toward early intervention giving a better long-term outcome; however, long-term immunosuppression is associated with increased adverse risk [1,2]. Generally, MS therapies are long-term and some have a demonstrated rebound phenomenon precluding them from being stopped for long periods of time, such as fingolimod [3] and natalizumab [4]. However, there are currently 2 drugs that can potentially be used for immune reconstitution treatment: alemtuzumab and cladribine.

    Alemtuzumab is administered in 2 courses, via infusion, 1 year apart. In the Comparison of Alemtuzumab and Rebif Efficacy in Multiple Sclerosis Study (CARE-MS) extension trial, a redosing scheme was introduced, with up to 4 additional doses being offered in the event of recurring clinical activity [5]. Cladribine is a synthetic purine analog, and its mechanism of action is thought to be primarily via induction of apoptosis in lymphocytes [6]. It was shown in a phase III trial (A Safety and Efficacy Study of Oral Cladribine in Subjects with Relapsing-Remitting Multiple Sclerosis; CLARITY) to be highly effective in controlling disease activity [7-10]. With a no evidence of disease activity 3 (NEDA 3) rate of 44% over 2 years [7], it is placed in the range of the most efficacious disease-modifying therapies (DMTs) along with alemtuzumab (39%) [11,12] and ocrelizumab (47%) [13]. Furthermore, after 2 years of treatment and 2 years of follow-up (treatment free), 75% of patients with MS remained relapse free [8,14]. However, clinical stability beyond year 4, and additional doses of cladribine tablets based on clinical activity, has not yet been studied.

    In the study discussed below, we will offer additional doses of cladribine tablets after 3 years. We aim to compare the clinical outcomes of patients with MS who received additional doses of cladribine with those who changed DMT. We also aim to identify biomarkers for disease control that can be used for treatment decisions, such as redosing with cladribine tablets versus change of DMT.

    Justification of Outcome Measures: NEDA

    With the introduction of an ever-increasing number of DMTs, our treatment goals have shifted from simply reducing relapses to achieving NEDA.

    In MS, NEDA 3 is defined as no clinically confirmed progression, no relapse, and no new or enlarging or gadolinium (Gd)-enhancing lesions [15]. This has since been expanded to NEDA 4, which includes brain atrophy [16]. Although our current treatments are increasingly effective, only 30% of patients with MS reach NEDA 4 after 2 years [17]. The rate of NEDA 4 in patients with MS treated with cladribine has not yet been established.

    Magnetic Resonance Imaging for Assessing Progression of MS

    Magnetic resonance imaging (MRI) has been established as the most reliable indicator of long-term outcomes. Sormani et al [18] analyzed data from 13 large clinical trials, including 13,500 patients with MS, to show that new T2 lesions can predict disability progression. This prediction was significantly improved when combined with brain atrophy [18]. Some of the high efficacy treatments can change the rate of brain volume loss to that seen in the normal aging population [19]. As MRI technology advances, it has become clear that some substructures such as thalamic volume, lateral ventricle, and gray or white matter volume might be even more sensitive and correlate better with clinical outcomes [20].

    Lymphocytes as Biomarkers

    Blood-based biomarkers are attractive because of their ease of collection and cost-effectiveness. In addition, they may better reflect or even predict disease activity. Lymphocyte subsets may be good markers of disease stability, as many currently used therapies act by restoring the balance of immune cells in the periphery to a healthy state. Therapies affecting the B-cell population have been described as effective treatments for MS [21]. This may be related to the suppression of memory B cells. This theory is supported by the failure of Atacicept and tumor necrosis factor α inhibitors in treating MS, both of which result in disease activation [21]. In both ORACLE-MS and CLARITY, cladribine has been shown to markedly reduce B cells, while having a more modest reduction in T cells and natural killer cells [22,23]. A more recent study of the CLARITY cohort further characterized this reduction and demonstrated that memory B cells were the predominantly affected subtype [24].

    These studies investigated lymphocyte subsets over the course of 1-2 years. Therefore, a long-term investigation of lymphocyte subsets in response to cladribine treatment is warranted.

    Cognitive Dysfunction in MS

    Cognitive impairment is a prevalent symptom in MS, with rates of approximately 40%-70% [25]. The severity of cognitive deficits varies, but unlike the physical symptoms associated with MS, cognitive deficits are unlikely to remit and are associated with a higher risk of progression [26-28]. A recent systematic review evaluated the literature on cognitive impairment and employment status and found a consensus that patients with MS who are unemployed or have reduced work hours record weaker cognitive scores [29]. Loss of employment is a major concern for patients with MS, particularly as the disease is often diagnosed in people of working age, who are just establishing careers and families. Despite this, cognitive testing is often disregarded in terms of clinical trial outcomes. Several tools have been developed to evaluate cognitive function, including the Brief Cognitive Assessment for Multiple Sclerosis (BICAMS) [30]. The BICAMS is quick and easy to administer and has recently been validated in the Australian population [31].

    Global Epigenetic Profiles in MS

    Epigenetics is a rapidly developing area of medical research and refers to the potentially reversible regulation of genomic functions, particularly gene expression. This provides a mechanism whereby an organism can dynamically respond to a change in its environment (eg, DMTs) and alter its gene expression accordingly. DNA methylation is a well-characterized, relatively easy to study epigenetic modification and generally refers to the addition of a methyl group to a cytosine base, followed by a guanidine (referred to as a CpG dinucleotide).

    We, and others, have described differential DNA methylation profiles between healthy controls and patients with MS, as well as between relapsing remitting MS and secondary progressive MS [32-38]. In addition, we and others have performed longitudinal studies that found epigenetic profiles are altered after dimethyl fumarate treatment [39-41], making it a potentially specific biomarker for treatment response.

    Neurofilaments are a Promising Biomarker

    Neurofilament (NfL) light chains are found in neuronal cells but are shed into the cerebrospinal fluid upon neuronal damage and are detectable in the peripheral blood. Increased serum NfL levels have been identified in patients with MS compared with that in healthy controls [42]. These levels show a strong correlation not only with cerebrospinal fluid NfL levels but also with the presence and activity of focal lesions and clinical outcomes [42]. This makes them a promising biomarker not only of disease activity but also disease progression. These may be useful indicators of the need for additional courses of treatment.

    Objective

    The primary objective of the study is to evaluate the safety and efficacy of cladribine tablets over a 6-year period according to NEDA 3. We will also evaluate clinical outcomes and cognition over a 6-year period, blood-based and MRI-based biomarkers for their ability to predict treatment response, disease activity, and the need to redose cladribine subsequent to the 2-year, 2 initial courses.


    Study Design

    This study is a multicenter, 6-year, phase IV, low interventional trial. Enrollment of 150 patients with MS was planned across 9 specialist MS clinics in Australia. The Therapeutic Goods Australia approved a cumulative dose of cladribine (10 mg) tablets as 3.5 mg/kg body weight over 2 years administered as 1 treatment course of 1.75 mg/kg per year. After 3 years, patients with MS in consultation with their health care provider will discuss the option of redosing with cladribine tablets, if clinically indicated (by relapse or new MRI activity), switch to another DMT, or continue without change (without commencement of any DMT). For those who will be continued on cladribine, additional courses will be administered as per the previous dosing of 1.75 mg/kg per year.

    Eligibility Criteria

    Patients must meet the criteria as described below (Textbox 1).

    Inclusion and exclusion criteria.

    Inclusion criteria

    • Participants must be eligible for and already intend to commence cladribine tablets in accordance with the Australian Product Information (PI). Cladribine tablets are indicated for relapsing remitting patients with multiple sclerosis who do not have HIV infection, active chronic infection, are immunocompromised, have severe renal impairment, or are pregnant or breastfeeding.
    • Participants must have the ability to understand the purpose and risks of the study, as outlined in the patient informed consent form and provide signed and informed consent and authorization to use protected health information in accordance with national and local privacy regulations.
    • The participants must meet the McDonald criteria [43] for the diagnosis of relapsing remitting multiple sclerosis.
    • Male or female participants aged 18-70 years.
    • Be able to provide details for or consent to provide access to a stored minimum data set (ie, demographics, date of diagnosis, relapse information, and baseline expanded disability status scale score).
    • Be able and willing to comply with all study procedures, including magnetic resonance imaging scanning, as per the protocol.
    • Must agree to use contraception from baseline until 6 months after the last dose of cladribine tablets, unless they or their partners are infertile or surgically sterile.
    • Participants must be aware of all precautions listed in the PI for Mavenclad, and any subsequent disease-modifying therapy treatment received within this clinical study must be adhered to.

    Exclusion criteria

    • Participants must not have a concurrent diagnosis of neurological, psychiatric, or other diseases that, in the opinion of the investigator, could impair the capacity to provide informed consent, interfere with study assessments, or impair the participant’s ability to comply with the study protocol.
    • Any contraindication to magnetic resonance imaging scanning.
    • Participants who have any contraindications listed on the Australian PI or who have any of the listed precautions listed on the Australian PI.
    • The subject is considered by the investigator, for any reason, to be an unsuitable candidate for the trial.
    Textbox 1. Inclusion and exclusion criteria.

    Assessments

    NEDA Status

    NEDA-3 and NEDA-4 status (absence of clinical relapses, confirmed disability worsening, and new T2 lesions, including brain atrophy [NEDA-4 only]) will be assessed based on the clinical data uploaded into the database (MSBase) [44]. For this study, we will use the NEDA-4 criteria as defined by Kappos [16]. Physical examinations, vital sign assessments, and expanded disability status scale (EDSS) assessments will be performed throughout the study. Concomitant medications and adverse events are assessed at every study visit. On the basis of clinical and MRI parameters, patients with MS will be classified as NEDA-positive (responders) or NEDA-negative (nonresponders).

    MRI Analysis

    3D volumetric sequences will be performed annually in routine clinical practice and before treatment switch. The sequence will be performed on the same 3 tesla MRI scanner with a consistent protocol and without gadolinium administration. The images will be transmitted to the Sydney Neuroimaging Analysis Centre for volumetric analysis.

    BICAMS Test

    Cognition will be measured using BICAMS [30]. This test consists of the oral Symbol Digit Modalities Test (SDMT), the immediate word recall trials of the California Verbal Learning Test-2, and the Brief Visuospatial Memory Test Revised [30]. We chose the BICAMS as a cognitive tool over other available cognitive tests because of its ease of administration over multiple sites and a relatively short time frame in which it can be administered.

    Serum NfL Chain

    Serum NfL (sNfL) will be assessed using the Quanterix Simoa platform. This platform has a specialized immunoassay (NF-light) that allows for the detection of neurological biomarkers at very low levels (pg/mL) with excellent consistency and reproducibility. Given the low levels of sNfL, this was the only assay that could be used for this study at the time of study design.

    Lymphocyte Subsets
    Overview

    Lymphocyte subset analysis is a composite outcome that combines the results from 2 separate subset panels. Owing to the real-world nature of this study, the surface marker selection was based on the markers available at the local pathology services. The results are shown in Table 1.

    Table 1. Minimum data set from all sites.
    Surface markerAbsolute numbers (IUa)Proportional valuesHow proportional data is derived
    TBNKb panel

    		CD45✓c✓As per the FBEd

    		CD3✓✓CD45+CD3+ (T cells as a percentage of total lymphocytes)

    		CD4✓✓CD45+CD3+CD4+ (CD4+ [T helper class] as a percentage of T cells)

    		CD8✓✓CD45+CD3+CD8+ (CD8+ [cytotoxic T cells] as a percentage of T cells)

    		CD19✓✓CD45+CD3−CD19+ (B cells as a percentage of total lymphocytes)

    		CD56/16✓✓CD45+CD56+CD16+CD3− (natural killer cells as a percentage of total lymphocytes)
    B memory panel

    		CD45✓✓As per the FBE

    		CD19✓✓CD45+CD19+ (B cells as a percentage of total lymphocytes)

    		CD27
    		✓CD45+CD19+CD27+ (memory B cells as a percentage of total B cells)

    		Immunoglobulin D
    		✓CD45+CD19+CD27+IgD±(class switched or unswitched as a percentage of memory B cells)

    		CD38
    		✓CD45+CD19+CD27+CD38+ (proportion of plasmablasts as a percentage of memory B cells)

    aIU: International Units.

    bTBNK: T cells, B cells, and natural killer cells.

    cTick marks indicate where the subset will be presented as absolute numbers (column 2) or as proportional values (column 3).

    dFBE: full blood examination.

    T Cells, B Cells, and Natural Killer Cells Panel

    T cells, B cells, and natural killer cells (TBNK) panel will be evaluated at each site using the standardized TBNK cocktail from Becton Dickinson. All sites will use the same antibody cocktail for this panel. All results are presented as absolute numbers and percentages of total lymphocytes. Raw flow cytometry data will be obtained from all sites, and concordance measures will be performed to ensure that the reporting of total cells and subsets of cells is consistent between the sites.

    B Memory Panel

    The second panel will evaluate the B memory cell compartment using antibodies raised against CD45, CD19, CD27, immunoglobulin D, immunoglobulin M, CD21, CD24, and CD38 surface antigens. The minimum panel is gated on the lymphocyte population and assesses CD19, CD27, immunoglobulin D, and CD38. Table 1 lists the minimal data set for each site.

    DNA Methylation

    Epigenome-wide DNA methylation will be evaluated using DNA extracted from frozen whole blood samples. Genomic DNA will be bisulfite converted and hybridized to Illumina Infinium MethylationEPIC BeadChip arrays at the lead site. Changes in DNA methylation (differentially methylated positions) will be expressed as population medians. Medians will be used to identify changes between responders and nonresponders that meet both the significance cutoff of P<.05, and a threshold of >10% change.

    RNA or Gene Expression Analysis

    RNA will be collected in PaxGene tubes and stored for future analysis, as per funding.

    End Points and Outcomes

    Participants will attend visits or receive phone calls for assessments at baseline (before 1st dose) and months 1 (before 2nd dose), 3, 7, 12 (before 3rd dose), 13 (before 4th dose), 18, 24, 30, 36, 42, 48, 54, 60, 66, and 72 months, and a variable time point (Tv; at exacerbation of disease and/or before change in treatment or redosing). The schedule of the assessments is shown in Multimedia Appendix 1. BICAMS will be performed annually. MRI will be performed yearly, plus one additional scan 6 months after the first treatment course. Lymphocyte subsets (TBNK and B memory panels) will be performed at screening and at 3, 7, 12, 18, 24, 36, 48, 60, 72, and Tv. Serum NfL will be assessed at screening and at 3, 7, 12, 18, 24, 36, 72, and Tv. DNA methylation and RNA collection occur at screening and at 7, 24, 72, and Tv.

    End Points

    The primary end point for all outcome measures is the proportion of patients with MS achieving NEDA status (responders) at 6-years relative to screening. However, preliminary end points will also be considered at each of the major biomarker collection points (months 7, 24, 48, and Tv).

    The key outcome measures will be NEDA 3 or 4 responders after 6-years. Additional outcome measures will change over time from baseline in MRI parameters (brain volume loss, lesion load, and lesion volume), cognition, lymphocyte subsets, sNfL, and global DNA methylation profiles. In addition, the Tv time point will be used to determine if there are any changes in biomarker status that may predict disease activity. This will be compared with the time point at which the patient is deemed to have stable disease.

    Statistical Methods

    Sample Size Calculation

    This is a longitudinal study of 150 patients with MS entering the study and predicted to have no more than 20% dropout. Over the study period, we expected 120 patients with MS with longitudinal data for all measured factors: clinical, cellular, and omics. On the basis of CLARITY and CLARITY extension study data, we expect approximately 50% of patients with MS (n=60) to exhibit disease activity according to NEDA 3 (ie, nonresponders) [7-10].

    We performed a detailed simulation analysis to assess the power of our sample sizes to detect significant associations at a range of effect sizes and for a range of significance thresholds as per the recommendations of Tsai and Bell [45]. With 120 cases (60 responders vs 60 nonresponders), this study will have 80% power to detect a minimum mean biomarker difference of 10% at a Benjamini-Hochberg false discovery rate of 0.05, which will reasonably balance type I and type II errors. To detect more minor differences, we will use a penalized regression analysis within a machine learning framework using the GLMNet tool [46]. This method is designed for studies with large numbers of predictors and will further enhance the power to identify multi-marker signatures that are predictive of patient response (see details below in the Association Modeling section).

    Missing Data

    The trial data set comprises multiple outcome assessments made for each subject over a 72-month period. Therefore, because of the longitudinal nature of the data and the lengthy follow-up period, it is likely that missing outcome data will be present because of loss to follow-up. Patterns and degrees of missingness will be summarized and will inform the approach taken to deal with missing data (eg, missing at random analysis).

    Association Modeling

    Our primary outcome is treatment response status according to NEDA 3. We will perform parametric statistical analysis to determine whether any clinical parameters are associated with outcome—the dependent variable. Multiple repeated measurements of the same individual will be obtained. Therefore, we will apply generalized linear mixed models for binary outcomes, with random effects for time points to account for within-subject correlations and clustering. Clinical factors included in the primary analysis will be EDSS, MRI, and relapse rate measures regressed at baseline. Confounding factors (age, sex, disease duration, etc) will be included as required. By carefully defining covariate values in terms of lagged or leading indicators of study factors, longitudinal data will help us to establish the direction of causation and any lags involved. We will also use Mendelian randomization techniques to help dissect causation from the correlation of evidence for causality. In this analysis, all predictor biomarker variables will be modeled individually and yield a test-specific (unadjusted) P value. However, we will apply a Benjamini-Hochberg false discovery rate to adjust the raw P value for multiple testing while maintaining power. The secondary outcome variables will be modeled similarly using mixed models with specific parameters dependent on the type of data.

    To identify multi-biomarker signatures that predict response in this patient cohort, we will also use machine learning algorithms on the patient cohort data set. Specifically, elastic-net regularized generalized linear model analyses using logistic, linear, or multinomial or Poisson models (depending on the outcome variable) will be conducted within a cross-validation routine to avoid overfitting. This will be performed on the entire factor set using the GLMNet package in the R program [46]. Briefly, GLMNet fits a generalized linear model via penalized maximum likelihood and is akin to a stepwise forward regression with the added feature of being able to perform internal cross-validation. GLMNet allows for both binary and multinomial outcomes. GLMNet allows for the rapid discovery of reduced factor panels that are likely to be associated with outcomes. This complementary approach is not susceptible to multiple testing burdens and will facilitate the identification of the best fitting multifactor signature that is predictive of treatment response [46].

    The first analysis time point will be the 6-month assessment, which will be analyzed at this stage. The full data set up to and including the 6-month assessment will be subject to appropriate data cleaning for all variables involved in the primary analysis, consisting of postentry validation checks, and assessing the data for outliers.

    Additional interim analysis will be completed at 24 months, 48 months, and again at study termination when all data have been collected.


    Funding

    This study was funded in October 2018 as an investigator-initiated trial by Merck Healthcare Pty Ltd, KGgA, Darmstadt, Germany, to the lead site (John Hunter Hospital).

    Ethics Statement

    This study is registered with the Australian and New Zealand Clinical Trials Registry (ACTRN12619000257167) with Universal Trial Number (U1111-1228-2165). Ethical approval for conducting this study was granted on November 8, 2018, by the Hunter New England Local Health District Human Research Ethics Committee (protocol 2019/ETH08849). The study will be conducted in accordance with the revised Declaration of Helsinki, and all participants will provide written informed consent to participate in this study.

    Enrollment

    As of May 2021, 145 participants have been enrolled in the study. Recruitment was completed by June 2021, and the study is expected to conclude in 2027.


    Principal Findings

    Our study is designed to collect real-world outcomes, while filling the gaps of the previous randomized controlled trials (RCTs) of cladribine tablets for the treatment of MS. This study also offers the opportunity to assess the long-term use of cladribine tablets in a clinical setting and the effectiveness of additional courses of cladribine tablets based on disease activity, in ongoing treatment of MS.

    RCTs are the gold standard for evaluating treatment outcomes and providing efficacy data for new treatments. However, the strict inclusion criteria and protocol-driven approach may lead to low generalizability because these trials are not always reflective of real-life care [47,48]. In addition, the frequency of MRI in MS RCTs is often higher than that in routine clinical practice [47,48]. Real-world studies can complement data from RCTs by investigating patients who are receiving treatment and being monitored according to routine clinical practice [47,48], and the importance of real-world clinical data is becoming increasingly recognized. For example, MSBase, which is now following over 70,000 patients with MS worldwide with regular assessments, has shaped clinical decision-making in the MS field over the past 5 years using real-world data [49].

    The exclusion criteria for this study are deliberately minimal, so that most patients with MS who are planning to commence or choose to take cladribine tablets as part of their routine MS clinical care, are eligible to participate. This study imposes no limits on prior DMT exposure or immunosuppression, no upper limit on EDSS, and an upper age limit of 70. These relaxed inclusion criteria will allow us to capture the broadest range of patients with MS possible, and with the generation of data sets that are reflective of real-world MS demographics.

    In the CLARITY study, 75% of the participants were treatment-naïve before entering [7]. As with all pharmaceutical trials, there were restrictions on prior DMT use (no more than two failed DMTs were allowed before study entry, and anyone who had used prior immunosuppression was excluded) [7]. This leaves a scarcity of data relating to how patients with MS who have been on prior DMTs, particularly immune suppressants with long-term effects such as alemtuzumab or even dimethyl fumarate, will respond to cladribine tablets. In addition, this does not reflect clinical reality, where many patients with MS have trialled several treatments and often failed several other DMTs before starting cladribine tablets. Data on the safety and effectiveness of cladribine after other DMTs are needed for clinicians and patients with MS to be able to make appropriate decisions about therapy, particularly if they are switching from a prior immunosuppressant. Our study is well-positioned to provide such data.

    This trial will also investigate the effectiveness of additional courses of cladribine tablets based on disease activity. Alemtuzumab, another immunomodulatory therapy, is administered in a similar dosing scheme to cladribine tablets [12]. In the CARE-MS II trial, up to four additional courses of alemtuzumab were given to patients with MS as required, with success [5]. Starting in year 3, if patients with MS in this trial have a clinical or radiological relapse, they will be offered an additional course of cladribine. Although the CLARITY extension trial found that additional courses provided no additional benefit, these courses were not offered based on disease activity [9]. This will be the first investigation into an additional cladribine dose based on disease activity.

    There is evidence that clinicians are shifting their treatment goals away from relapse free to achieving NEDA status. For example, a recent study using MSBase data demonstrated that just one new subclinical T2 lesion was associated with 1.62 times odds of changing treatment compared with no new lesions [50]. Patients with MS taking cladribine tablets achieved 47% NEDA 3 rates in CLARITY after 2 years [7]; however, rates of NEDA 4 and data beyond 2 years have not been reported. The addition of accelerated brain volume loss to the NEDA criteria (NEDA 4) is an important parameter as it has been shown to be predictive of long-term disability progression and cognitive decline [26,51]. This is highlighted in the FREEDOM trials, where 31% of patients with MS on fingolimod sustained NEDA 3 status, but only 19.7% achieved NEDA 4 after 2 years [16].

    Achieving NEDA status may also differ outside the bounds of RCTs. A real-world study conducted in the USA (MS-MRIUS) evaluated nearly 600 patients with MS on fingolimod and found that NEDA 3 was achieved in approximately 58.7% of patients with MS, and 37.2% had achieved NEDA 4 [52]. The differences may be because of the shorter follow-up time (16 months vs 24 months), different patient populations (less severe disease course), or may be reflective of the real-world nature of the data collection versus an RCT [52]. Another real-world study also reported slightly higher levels of NEDA 3 (44%) after 2 years, but did not evaluate NEDA 4 [53]. There has been one small study of cladribine tablets from MSBase data, which compared outcomes over 1 year [54]. Although this study was shorter than CLARITY, the data were similar, with effects lasting at least 4 years [54] despite the majority of patients with MS receiving only one course of treatment. The MSBase study did not specifically report on NEDA 3 outcomes; therefore, it will be interesting to see if NEDA rates remain the same for cladribine use in the clinical setting.

    There have been recent criticisms of the current NEDA criteria [55-57]. Stangel et al [57] suggested an algorithm that incorporates cognitive (SDMT), patient-reported outcomes, depression and anxiety ratings, and other parameters. Other criticisms suggest that biomarkers of inflammation and neurodegeneration in body fluids need to be added for a true reflection of disease activity (NEDA-5 or minimal evidence of disease activity) [56]. The inclusion of blood-based biomarkers in this study, including sNfL, as well as cognitive assessments (the BICAMS includes the SDMT) will ensure that we are also able to assess disease activity based on these parameters and assess NEDA5 or minimal evidence of disease activity when a consensus is reached. Furthermore, the discovery of biomarkers that may indicate a response to treatment may allow us to prevent patients with MS from undergoing life-long immune suppression if not required. This could be translated to other MS therapies.

    There are minimal real-world data on the effectiveness of cladribine tablets for MS. This study will not only fill this knowledge gap, but also evaluate the efficacy of a clinically indicated additional course beyond the year 2 course. The regular collection of biospecimens for biomarker assessment will help identify biomarkers that may be indicators of treatment response and the need for additional dosing. This may help move clinical practices toward individualized dosing schedules.

    Data Sharing Statement

    Deidentified individual data sets will be available upon request to the authors following the publication of the results of the study.

    Conflicts of Interest

    VEM has received honoraria for presentations from Biogen and Merck Healthcare Pty Ltd. She received research funding from Merck KGgA and Biogen. RAL has no conflicts of interest. For author MM, her institute and health service receives funding from Merck KGaA. MFP has received travel sponsorship from Merck KGaA. KB has received honoraria for presentations and/or educational support from Roche, Biogen, Sanofi Genzyme, Teva, Novartis, and Merck KGaA and has served on advisory boards for Merck and received research funding from BioCSL. TK served on scientific advisory boards for Roche, Sanofi Genzyme, Novartis, Merck KGaA, and Biogen; steering committee for Brain Atrophy Initiative by Sanofi Genzyme; and received conference travel support and/or speaker honoraria from WebMD Global, Novartis, Biogen, Sanofi Genzyme, Teva, BioCSL, and Merck KGaA and received research or educational event support from Biogen, Novartis, Genzyme, Roche, Celgene, and Merck KGaA. AGK has recently received speaker honoraria and scientific advisory board fees from Bayer, BioCSL, Biogen-Idec, Lgpharma, Merck KGaA, Novartis, Roche, Sanofi-Aventis, Sanofi Genzyme, Teva, NeuroScientific Biopharmaceuticals, Innate Immunotherapeutics, and Mitsubishi Tanabe Pharma. BT has received travel assistance from Merck KGaA, Novartis, and Biogen-Idec and served on Ad Boards for Merck, Sanofi, Novartis, and Biogen. SH has received honoraria for consultancy, travel, and speaking fees from Emmanuel Merck, Darmstadt, Serono, Bayer, Biogen, Sanofi, Atara, and Novartis. PM received honoraria and travel grants from Biogen, Sanofi Genzyme, Novartis, and Merck KGaA. HB serves on steering committees and scientific advisory boards for Merck KGaA, Biogen, Novartis, and Roche. He received conference travel support from Merck KGaA. The institution has received honoraria for speaker engagements for Merck KGaA, Biogen, Roche, and Novartis. The institution has received research support from Biogen, Roche, Merck KGaA, Novartis, National Health and Medical Research Council, Medical Research Future Fund, Trish Foundation, and Multiple Sclerosis Research Australia. HB also receives personal compensation for serving the Brain Health Initiative Steering Committee. MB has received institutional support for research, speaking, and/or participation in advisory boards for Biogen, Merck KGaA, Novartis, Roche, Sanofi Genzyme, and Bristol Myers Squibb. He is a co-founder of RxMx and Research Director for the Sydney Neuroimaging Analysis Centre. For author JLS, the institution receives nondirected funding as well as honoraria for presentations and membership on advisory boards from Sanofi Genzyme, Biogen, Merck KGaA, Teva, Roche, and Novartis Australia.

    ‎
    Multimedia Appendix 1

    Study overview. Schedule of visits for study duration. The shaded area indicates when each study assessment was performed. Tv=clinical deterioration, Tv+=one month post redose (if applicable), Time point T15* month number is dependent on the year 2 dose (3 months post year 2 week 1). The Brief Cognitive Assessment for Multiple Sclerosis assessment will alternate between the standard and alternate versions. **Indicates magnetic resonance imaging scans to be used only if clinically applicable. X denotes the major analysis time point.

    PNG File , 46 KB
    1. Merkel B, Butzkueven H, Traboulsee AL, Havrdova E, Kalincik T. Timing of high-efficacy therapy in relapsing-remitting multiple sclerosis: a systematic review. Autoimmun Rev 2017 Jun;16(6):658-665 [FREE Full text] [CrossRef] [Medline]
    2. He A, Merkel B, Brown JW, Ryerson LZ, Kister I, Malpas CB, MSBase study group. Timing of high-efficacy therapy for multiple sclerosis: a retrospective observational cohort study. Lancet Neurol 2020 Apr;19(4):307-316. [CrossRef] [Medline]
    3. Barry B, Erwin AA, Stevens J, Tornatore C. Fingolimod rebound: a review of the clinical experience and management considerations. Neurol Ther 2019 Dec;8(2):241-250 [FREE Full text] [CrossRef] [Medline]
    4. Clerico M, Artusi C, Liberto A, Rolla S, Bardina V, Barbero P, et al. Natalizumab in multiple sclerosis: long-term management. Int J Mol Sci 2017 Apr 29;18(5):940 [FREE Full text] [CrossRef] [Medline]
    5. Coles AJ, Cohen JA, Fox EJ, Giovannoni G, Hartung H, Havrdova E, CARE-MS II and CAMMS03409 Investigators. Alemtuzumab CARE-MS II 5-year follow-up: efficacy and safety findings. Neurology 2017 Sep 12;89(11):1117-1126 [FREE Full text] [CrossRef] [Medline]
    6. Leist TP, Vermersch P. The potential role for cladribine in the treatment of multiple sclerosis: clinical experience and development of an oral tablet formulation. Curr Med Res Opin 2007 Nov;23(11):2667-2676. [CrossRef] [Medline]
    7. Giovannoni G, Comi G, Cook S, Rammohan K, Rieckmann P, Sørensen PS, CLARITY Study Group. A placebo-controlled trial of oral cladribine for relapsing multiple sclerosis. N Engl J Med 2010 Feb 04;362(5):416-426. [CrossRef] [Medline]
    8. Giovannoni G, Sorensen PS, Cook S, Rammohan K, Rieckmann P, Comi G, et al. Safety and efficacy of cladribine tablets in patients with relapsing-remitting multiple sclerosis: results from the randomized extension trial of the CLARITY study. Mult Scler 2018 Oct;24(12):1594-1604 [FREE Full text] [CrossRef] [Medline]
    9. Comi G, Cook S, Rammohan K, Sorensen PS, Vermersch P, Adeniji AK, et al. Long-term effects of cladribine tablets on MRI activity outcomes in patients with relapsing-remitting multiple sclerosis: the CLARITY Extension study. Ther Adv Neurol Disord 2018 Jan 23;11:1756285617753365 [FREE Full text] [CrossRef] [Medline]
    10. De Stefano N, Giorgio A, Battaglini M, De Leucio A, Hicking C, Dangond F, et al. Reduced brain atrophy rates are associated with lower risk of disability progression in patients with relapsing multiple sclerosis treated with cladribine tablets. Mult Scler 2018 Feb;24(2):222-226 [FREE Full text] [CrossRef] [Medline]
    11. Cohen JA, Coles AJ, Arnold DL, Confavreux C, Fox EJ, Hartung H, CARE-MS I investigators. Alemtuzumab versus interferon beta 1a as first-line treatment for patients with relapsing-remitting multiple sclerosis: a randomised controlled phase 3 trial. Lancet 2012 Nov 24;380(9856):1819-1828. [CrossRef] [Medline]
    12. Coles AJ, Twyman CL, Arnold DL, Cohen JA, Confavreux C, Fox EJ, CARE-MS II investigators. Alemtuzumab for patients with relapsing multiple sclerosis after disease-modifying therapy: a randomised controlled phase 3 trial. Lancet 2012 Nov 24;380(9856):1829-1839. [CrossRef] [Medline]
    13. Hauser SL, Bar-Or A, Comi G, Giovannoni G, Hartung H, Hemmer B, OPERA I and OPERA II Clinical Investigators. Ocrelizumab versus Interferon Beta-1a in relapsing multiple sclerosis. N Engl J Med 2017 Jan 19;376(3):221-234. [CrossRef] [Medline]
    14. Comi G, Cook SD, Giovannoni G, Rammohan K, Rieckmann P, Sørensen PS, et al. MRI outcomes with cladribine tablets for multiple sclerosis in the CLARITY study. J Neurol 2013 Apr;260(4):1136-1146. [CrossRef] [Medline]
    15. 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. Lancet Neurol 2009 Mar;8(3):254-260. [CrossRef] [Medline]
    16. Kappos L, De Stefano N, Freedman MS, Cree BA, Radue E, 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. Mult Scler 2016 Sep;22(10):1297-1305 [FREE Full text] [CrossRef] [Medline]
    17. Damasceno A, Damasceno BP, Cendes F. No evidence of disease activity in multiple sclerosis: implications on cognition and brain atrophy. Mult Scler 2016 Jan;22(1):64-72. [CrossRef] [Medline]
    18. Sormani MP, Arnold DL, De Stefano N. Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis. Ann Neurol 2014 Jan;75(1):43-49. [CrossRef] [Medline]
    19. Havrdova E, Arnold DL, Cohen JA, Hartung H, Fox EJ, Giovannoni G, CARE-MS I and CAMMS03409 Investigators. Alemtuzumab CARE-MS I 5-year follow-up: durable efficacy in the absence of continuous MS therapy. Neurology 2017 Sep 12;89(11):1107-1116 [FREE Full text] [CrossRef] [Medline]
    20. 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 Rev Neurother 2016 Jul;16(7):777-793. [CrossRef] [Medline]
    21. Baker D, Marta M, Pryce G, Giovannoni G, Schmierer K. Memory B cells are major targets for effective immunotherapy in relapsing multiple sclerosis. EBioMedicine 2017 Feb;16:41-50 [FREE Full text] [CrossRef] [Medline]
    22. Baker D, Herrod SS, Alvarez-Gonzalez C, Zalewski L, Albor C, Schmierer K. Both cladribine and alemtuzumab may effect MS via B-cell depletion. Neurol Neuroimmunol Neuroinflamm 2017 Jun;4(4):e360 [FREE Full text] [CrossRef] [Medline]
    23. Stuve O, Soerensen PS, Leist T, Giovannoni G, Hyvert Y, Damian D, et al. Effects of cladribine tablets on lymphocyte subsets in patients with multiple sclerosis: an extended analysis of surface markers. Ther Adv Neurol Disord 2019 Jun 18;12:1756286419854986 [FREE Full text] [CrossRef] [Medline]
    24. Ceronie B, Jacobs BM, Baker D, Dubuisson N, Mao Z, Ammoscato F, et al. Cladribine treatment of multiple sclerosis is associated with depletion of memory B cells. J Neurol 2018 May;265(5):1199-1209 [FREE Full text] [CrossRef] [Medline]
    25. Di Filippo M, Portaccio E, Mancini A, Calabresi P. Multiple sclerosis and cognition: synaptic failure and network dysfunction. Nat Rev Neurosci 2018 Oct;19(10):599-609. [CrossRef] [Medline]
    26. Deloire M, Ruet A, Hamel D, Bonnet M, Brochet B. Early cognitive impairment in multiple sclerosis predicts disability outcome several years later. Mult Scler 2010 May;16(5):581-587. [CrossRef] [Medline]
    27. Moccia M, Lanzillo R, Palladino R, Chang KC, Costabile T, Russo C, et al. Cognitive impairment at diagnosis predicts 10-year multiple sclerosis progression. Mult Scler 2016 Apr;22(5):659-667. [CrossRef] [Medline]
    28. Zipoli V, Goretti B, Hakiki B, Siracusa G, Sorbi S, Portaccio E, et al. Cognitive impairment predicts conversion to multiple sclerosis in clinically isolated syndromes. Mult Scler 2010 Jan;16(1):62-67. [CrossRef] [Medline]
    29. Clemens L, Langdon D. How does cognition relate to employment in multiple sclerosis? A systematic review. Mult Scler Relat Disord 2018 Nov;26:183-191. [CrossRef] [Medline]
    30. Langdon D, Amato M, Boringa J, Brochet B, Foley F, Fredrikson S, et al. Recommendations for a Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS). Mult Scler 2012 Jun;18(6):891-898 [FREE Full text] [CrossRef] [Medline]
    31. Maltby VE, Lea RA, Ribbons K, Lea MG, Schofield PW, Lechner-Scott J. Comparison of BICAMS and ARCS for assessment of cognition in multiple sclerosis and predictive value of employment status. Mult Scler Relat Disord 2020 Jun;41:102037. [CrossRef] [Medline]
    32. Maltby VE, Graves MC, Lea RA, Benton MC, Sanders KA, Tajouri L, et al. Genome-wide DNA methylation profiling of CD8+ T cells shows a distinct epigenetic signature to CD4+ T cells in multiple sclerosis patients. Clin Epigenetics 2015 Nov 05;7:118 [FREE Full text] [CrossRef] [Medline]
    33. Maltby VE, Lea RA, Graves MC, Sanders KA, Benton MC, Tajouri L, et al. Genome-wide DNA methylation changes in CD19 B cells from relapsing-remitting multiple sclerosis patients. Sci Rep 2018 Nov 27;8(1):17418 [FREE Full text] [CrossRef] [Medline]
    34. Maltby VE, Lea RA, Sanders KA, White N, Benton MC, Scott RJ, et al. Differential methylation at in CD4 T cells is associated with multiple sclerosis independently of HLA-DRB1. Clin Epigenetics 2017 Jul 18;9:71 [FREE Full text] [CrossRef] [Medline]
    35. Bos SD, Page CM, Andreassen BK, Elboudwarej E, Gustavsen MW, Briggs F, et al. Genome-wide DNA methylation profiles indicate CD8+ T cell hypermethylation in multiple sclerosis. PLoS One 2015 Mar 03;10(3):e0117403. [CrossRef] [Medline]
    36. Ewing E, Kular L, Fernandes SJ, Karathanasis N, Lagani V, Ruhrmann S, et al. Combining evidence from four immune cell types identifies DNA methylation patterns that implicate functionally distinct pathways during Multiple Sclerosis progression. EBioMedicine 2019 May;43:411-423 [FREE Full text] [CrossRef] [Medline]
    37. Kular L, Liu Y, Ruhrmann S, Zheleznyakova G, Marabita F, Gomez-Cabrero D, et al. DNA methylation as a mediator of HLA-DRB1*15:01 and a protective variant in multiple sclerosis. Nat Commun 2018 Jun 19;9(1):2397 [FREE Full text] [CrossRef] [Medline]
    38. Rhead B, Brorson IS, Berge T, Adams C, Quach H, Moen SM, et al. Increased DNA methylation of SLFN12 in CD4+ and CD8+ T cells from multiple sclerosis patients. PLoS One 2018 Oct 31;13(10):e0206511 [FREE Full text] [CrossRef] [Medline]
    39. Maltby VE, Lea RA, Ribbons KA, Sanders KA, Kennedy D, Min M, et al. DNA methylation changes in CD4 + T cells isolated from multiple sclerosis patients on dimethyl fumarate. Mult Scler J Exp Transl Clin 2018 Jul 17;4(3):2055217318787826 [FREE Full text] [CrossRef] [Medline]
    40. Carlström KE, Ewing E, Granqvist M, Gyllenberg A, Aeinehband S, Enoksson SL, et al. Therapeutic efficacy of dimethyl fumarate in relapsing-remitting multiple sclerosis associates with ROS pathway in monocytes. Nat Commun 2019 Jul 12;10(1):3081 [FREE Full text] [CrossRef] [Medline]
    41. Ntranos A, Ntranos V, Bonnefil V, Liu J, Kim-Schulze S, He Y, et al. Fumarates target the metabolic-epigenetic interplay of brain-homing T cells in multiple sclerosis. Brain 2019 Mar 01;142(3):647-661 [FREE Full text] [CrossRef] [Medline]
    42. Disanto G, Barro C, Benkert P, Naegelin Y, Schädelin S, Giardiello A, Swiss Multiple Sclerosis Cohort Study Group. Serum neurofilament light: a biomarker of neuronal damage in multiple sclerosis. Ann Neurol 2017 Jun;81(6):857-870 [FREE Full text] [CrossRef] [Medline]
    43. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 2018 Feb;17(2):162-173. [CrossRef] [Medline]
    44. Butzkueven H, Chapman J, Cristiano E, Grand'Maison F, Hoffmann M, Izquierdo G, et al. MSBase: an international, online registry and platform for collaborative outcomes research in multiple sclerosis. Mult Scler 2006 Dec;12(6):769-774. [CrossRef] [Medline]
    45. Tsai P, Bell JT. Power and sample size estimation for epigenome-wide association scans to detect differential DNA methylation. Int J Epidemiol 2015 Aug;44(4):1429-1441 [FREE Full text] [CrossRef] [Medline]
    46. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010;33(1):1-22 [FREE Full text] [Medline]
    47. Ziemssen T, Hillert J, Butzkueven H. The importance of collecting structured clinical information on multiple sclerosis. BMC Med 2016 May 31;14:81 [FREE Full text] [CrossRef] [Medline]
    48. Trojano M, Tintore M, Montalban X, Hillert J, Kalincik T, Iaffaldano P, et al. Treatment decisions in multiple sclerosis - insights from real-world observational studies. Nat Rev Neurol 2017 Feb;13(2):105-118. [CrossRef] [Medline]
    49. Kalincik T, Butzkueven H. Observational data: Understanding the real MS world. Mult Scler 2016 Nov;22(13):1642-1648. [CrossRef] [Medline]
    50. Min M, Spelman T, Lugaresi A, Boz C, Spitaleri DL, Pucci E, et al. Silent lesions on MRI imaging - shifting goal posts for treatment decisions in multiple sclerosis. Mult Scler 2018 Oct;24(12):1569-1577. [CrossRef] [Medline]
    51. Radue E, Barkhof F, Kappos L, Sprenger T, Häring DA, de Vera A, et al. Correlation between brain volume loss and clinical and MRI outcomes in multiple sclerosis. Neurology 2015 Feb 24;84(8):784-793 [FREE Full text] [CrossRef] [Medline]
    52. Weinstock-Guttman B, Medin J, Khan N, Korn JR, Lathi E, Silversteen J, MS-MRIUS Study Group. Assessing 'no evidence of disease activity' status in patients with relapsing-remitting multiple sclerosis receiving fingolimod in routine clinical practice: a retrospective analysis of the Multiple Sclerosis Clinical and Magnetic Resonance Imaging Outcomes in the USA (MS-MRIUS) study. CNS Drugs 2018 Jan;32(1):75-84 [FREE Full text] [CrossRef] [Medline]
    53. Baroncini D, Ghezzi A, Annovazzi PO, Colombo B, Martinelli V, Minonzio G, et al. Natalizumab versus fingolimod in patients with relapsing-remitting multiple sclerosis non-responding to first-line injectable therapies. Mult Scler 2016 Sep;22(10):1315-1326. [CrossRef] [Medline]
    54. Kalincik T, Jokubaitis V, Spelman T, Horakova D, Havrdova E, Trojano M, MSBase Study Group. Cladribine versus fingolimod, natalizumab and interferon β for multiple sclerosis. Mult Scler 2018 Oct;24(12):1617-1626. [CrossRef] [Medline]
    55. Pandit L. No Evidence of Disease Activity (NEDA) in multiple sclerosis - shifting the goal posts. Ann Indian Acad Neurol 2019;22(3):261-263 [FREE Full text] [CrossRef] [Medline]
    56. Londoño AC, Mora CA. Evidence of disease control: a realistic concept beyond NEDA in the treatment of multiple sclerosis. F1000Res 2017 Apr 25;6:566 [FREE Full text] [CrossRef] [Medline]
    57. 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. Ther Adv Neurol Disord 2015 Jan;8(1):3-13 [FREE Full text] [CrossRef] [Medline]

    ‎
    BICAMS: Brief Cognitive Assessment for Multiple Sclerosis
    CARE-MS Study: Comparison of Alemtuzumab and Rebif Efficacy in Multiple Sclerosis Study
    DMT: disease-modifying therapy
    EDSS: expanded disability status scale
    MRI: magnetic resonance imaging
    MS: multiple sclerosis
    NEDA: no evidence of disease activity
    NfL: neurofilament
    RCT: randomized controlled trial
    SDMT: Symbol Digit Modalities Test
    TBNK: T cells, B cells, and natural killer cells
    Tv: variable time point

    Edited by G Eysenbach; submitted 12.10.20; peer-reviewed by H Wiendl, N Hardikar, R Haase; comments to author 12.03.21; revised version received 04.05.21; accepted 28.05.21; published 19.10.21

    Copyright

    ©Vicki E Maltby, Rodney A Lea, Mastura Monif, Marzena J Fabis-Pedrini, Katherine Buzzard, Tomas Kalincik, Allan G Kermode, Bruce Taylor, Suzanne Hodgkinson, Pamela McCombe, Helmut Butzkueven, Michael Barnett, Jeannette Lechner-Scott. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 19.10.2021.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.