Study design of PANGAEA 2.0, a non-interventional study on RRMS patients to be switched to fingolimod

Abstract

Background:
The therapeutic options for patients with Multiple Sclerosis (MS) have steadily increased due to the approval of new substances that now supplement traditional first-line agents, demanding a paradigm shift in the assessment of disease activity and treatment response in clinical routine. Here, we report the study design of PANGAEA 2.0 (Post-Authorization Non-interventional GermAn treatment benefit study of GilEnyA in MS patients), a non-interventional study in patients with relapsing-remitting MS (RRMS) identify patients with disease activity and monitor their disease course after treatment switch to fingolimod (Gilenya®), an oral medication approved for patients with highly active RRMS.

Method/design:
In the first phase of the PANGAEA 2.0 study the disease activity status of patients receiving a disease-modifying therapy (DMT) is evaluated in order to identify patients at risk of disease progression. This evaluation is based on outcome parameters for both clinical disease activity and magnetic resonance imaging (MRI), and subclinical measures, describing disease activity from the physician's and the patient's perspective. In the second phase of the study, 1500 RRMS patients identified as being non-responders and switched to fingolimod (oral, 0.5 mg/daily) are followed-up for 3 years. Data on relapse activity, disability progression, MRI lesions, and brain volume loss will be assessed in accordance to 'no evidence of disease activity-4' (NEDA-4). The modified Rio score, currently validated for the evaluation of treatment response to interferons, will be used to evaluate the treatment response to fingolimod. The MS management software MSDS3D will guide physicians through the complex processes of diagnosis and treatment. A sub-study further analyzes the benefits of a standardized quantitative evaluation of routine MRI scans by a central reading facility. PANGAEA 2.0 is being conducted between June 2015 and December 2019 in 350 neurological practices and centers in Germany, including 100 centers participating in the sub-study.

Discussion:
PANGAEA 2.0 will not only evaluate the long-term benefit of a treatment change to fingolimod but also the applicability of new concepts of data acquisition, assessment of MS disease activity and evaluation of treatment response for the in clinical routine.

Trial registration:
BfArM6532; Trial Registration Date: 20/05/2015.

Full text

S T U D Y P R O T O C O L Open Access
Study design of PANGAEA 2.0, a non-
interventional study on RRMS patients
to be switched to fingolimod
Tjalf Ziemssen
1*
, Raimar Kern
1
and Christian Cornelissen
2
Abstract
Background: The therapeutic options for patients with Multiple Sclerosis (MS) have steadily increased due to the
approval of new substances that now supplement traditional first-line agents, demanding a paradigm shift in the
assessment of disease activity and treatment response in clinical routine. Here, we report the study design of
PANGAEA 2.0 (Post-Authorization Non-interventional GermAn treatment benefit study of GilEnyA in MS patients), a
non-interventional study in patients with relapsing-remitting MS (RRMS) identify patients with disease activity and
monitor their disease course after treatment switch to fingolimod (Gilenya®), an oral medication approved for
patients with highly active RRMS.
Method/Design: In the first phase of the PANGAEA 2.0 study the disease activity status of patients receiving a
disease-modifying therapy (DMT) is evaluated in order to identify patients at risk of disease progression. This
evaluation is based on outcome parameters for both clinical disease activity and magnetic resonance imaging (MRI)
, and subclinical measures, describing disease activity from the physician’s and the patient’s perspective. In the
second phase of the study, 1500 RRMS patients identified as being non-responders and switched to fingolimod
(oral, 0.5 mg/daily) are followed-up for 3 years. Data on relapse activity, disability progression, MRI lesions, and brain
volume loss will be assessed in accordance to ‘no evidence of disease activity-4’(NEDA-4). The modified Rio score,
currently validated for the evaluation of treatment response to interferons, will be used to evaluate the treatment
response to fingolimod. The MS management software MSDS3D will guide physicians through the complex
processes of diagnosis and treatment. A sub-study further analyzes the benefits of a standardized quantitative
evaluation of routine MRI scans by a central reading facility. PANGAEA 2.0 is being conducted between June 2015
and December 2019 in 350 neurological practices and centers in Germany, including 100 centers participating in
the sub-study.
Discussion: PANGAEA 2.0 will not only evaluate the long-term benefit of a treatment change to fingolimod but
also the applicability of new concepts of data acquisition, assessment of MS disease activity and evaluation of
treatment response for the in clinical routine.
Trial registration: BfArM6532; Trial Registration Date: 20/05/2015.
Keywords: Multiple sclerosis, Relapsing remitting, Fingolimod, Efficacy, Safety, Modified Rio score, NEDA, No evident
disease activity, Clinical routine
* Correspondence: tjalf.ziemssen@uniklinikum-dresden.de
1
Zentrum für klinische Neurowissenschaften, Klinik und Poliklinik für
Neurologie, Universitätsklinikum Carl Gustav Carus Dresden, Technische
Universität Dresden, Fetscherstr. 43, D-01307 Dresden, Germany
Full list of author information is available at the end of the article
© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Ziemssen et al. BMC Neurology (2016) 16:129
DOI 10.1186/s12883-016-0648-6
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Background
In recent years, the therapeutic options for patients with
Multiple Sclerosis (MS) have steadily increased. Sub-
stances such as Fingolimod (Gilenya®) supplement the
traditional first-line agents interferon (IFN) and glatira-
mer acetate, offering physicians the opportunity to
optimize individual MS-treatment [1, 2]. The safety and
tolerability profile of fingolimod and natalizumab is well
understood. However, experience with new treatment
options such as alemtuzumab, dimethylfumarate, and
teriflunomide is limited in comparison to former ap-
proved substances, and especially data on the safety and
tolerability of sequentially changed disease-modifying
therapies (DMTs) are mostly not available. Since defined
treatment algorithms for individual patients have not yet
been developed, many MS patients may continue to re-
ceive suboptimal treatment for long periods of time.
To optimize treatment, a switch to a more effective
medication generally needs to be considered if patients
do not respond to or fail with their current therapy [2].
It is well accepted that the earlier in MS pathogenesis
the therapy is adjusted (in the lower Expanded Disability
Status Scale [EDSS [3]] range up to 3), the higher would
be the benefit on long-term outcomes because MS pro-
gression might be more difficult to slow down at later
stages [4, 5]. Since magnetic resonance imaging (MRI)
parameters frequently assessed during therapy are sensi-
tive markers to identify patients who are insufficiently
responding to therapy [6], quantitative scoring systems
incorporating relapses and MRI activity have been sug-
gested as valuable diagnostic tools in clinical routine.
Among them, Lublin et al. [7] defined disease activity at
a particular time point on the basis of clinical relapses
and MRI activity in the previous 12 months. Sormani et
al. [8] modified the Rio score [9] to define treatment re-
sponse based on relapse activity and MRI activity over a
period of 1 year of treatment. However, the data under-
lying the modified Rio score was obtained from clinical
studies on IFN-β[10], and the modified Rio score has
not been evaluated for other therapies or under real-life
conditions. Other scoring systems have been developed
that assess parameters besides relapse and MRI activity,
but there is currently no consensus among MS experts
on the most sensitive measures applicable in clinical
practice for identifying patients on suboptimal treatment
[11, 12].
With the possibility to optimize treatment by sequen-
tially applying novel and highly effective MS therapeu-
tics, the MS community is increasingly accepting ‘no
evidence of disease activity’(NEDA) as an early objective
for individual treatment. This new treatment paradigm
is based on the view that the mere reduction of relapse
rate and the attenuation of disease progression can no
longer be accepted as sufficient in clinical routine.
Therefore, NEDA was defined as no relapse activity,
no EDSS progression, and no new MRI lesions (T1
Gd + and/or active T2 lesion; [13, 14]). Since these
measures may not be able to address all aspects of
the disease [7, 15, 16], brain volume loss (BVL) has
been suggested as fourth NEDA measure (NEDA-4)
to provide a more comprehensive and early picture of
the focal and diffuse damage occurring in MS. MS
experts recently proposed to further expand the
current concept of NEDA to include neuropsycho-
logical aspects as well as other subclinical measures
with a potential predictive value for treatment re-
sponse [12]. In daily clinical routine, implementation
of NEDA-4 as a treatment outcome goal, complemen-
ted by these subclinical measures might therefore
offer the possibility of an early optimization of
MStreatment [17].
Based on these considerations, we planned PANGAEA
2.0 (Post-Authorization Non-interventional GermAn
treatment benefit study of GilEnyA in MS patients), a
non-interventional study (NIS) to assess the benefits of a
treatment change to fingolimod in patients identified as
not responding to or having treatment failure with their
current therapy. Fingolimod is approved in over 80
countries for the treatment of adult patients with rapidly
progressing, severe RRMS or adult patients with high
levels of disease activity despite treatment with at least
one DMT [18–21]. As of Oct. 2015, it is estimated that
fingolimod has been used to treat approximately 134,000
patients, summing up to a total exposure of over
265,000 patient years [22]. The well-established safety
profile of fingolimod is currently being expanded by real-
world data obtained during our predecessor study PAN-
GAEA [23], which included RRMS patients who were
either untreated or pre-treated with medications available
at study initiation. Since further novel substances have
subsequently been approved [24], PANGAEA 2.0 will pro-
vide additional and more comprehensive data on the
safety of fingolimod in pretreated patients.
In this paper, we present the study protocol of PAN-
GAEA 2.0 and propose a comprehensive, multidimen-
sional approach for MS patient evaluation. By this
approach, we will assess the long-term benefits of a
treatment change to fingolimod in 1500 RRMS patients
identified as being non-responders or failing their
current first-line therapy. Disease activity at a given time
point will be determined according to the criteria of
Lublin [7] to identify sub-optimally treated patients.
During a 3-year observational phase, treatment response
to fingolimod will be evaluated by the modified Rio
score [8] and by parameters that are based on both the
treatment objectives of NEDA-4 [25] and the 2D
Focussed Disability Scale (2D FDS) as part of our multi-
dimensional approach for MS patient evaluation. The
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2D FDS comprises clinical and subclinical measures
representing both the patient’s and the physician’s per-
spectives. To handle the resulting amount of data and to
assist neurologists in executing the complex processes
required for MS diagnosis, treatment initiation, and
long-term therapy, the software-based MS management
system MSDS3D will be employed [23, 26]. Further-
more, a sub-study will assess the potential benefits of an
independent analysis of routine MRI scans by a central
reading facility. MRI analyses will additionally comprise
quantitative MRI parameters such as brain lesion vol-
ume and brain lesion volume loss (BVL) that are gener-
ally not part of routine MRI data analysis. Consequently,
PANGAEA 2.0 will expand the existing safety and effi-
cacy profile of fingolimod and evaluate the clinical ap-
plicability of novel concepts for the evaluation of the
patient status and treatment response in clinical routine
[27]. PANGAEA 2.0 was started in June 2015 and is
planned to continue until December 2019 in 350 neuro-
logical practices and centers in Germany, including 100
centers participating in the sub-study.
Methods/design
Study design
PANGAEA 2.0 is a multicenter non-interventional study
(NIS) in RRMS patients who switched to fingolimod
(oral, 0.5 mg daily [28]) because of non-responsiveness
to or treatment failure with their current therapy. Ac-
cordingly, the PANGAEA 2.0 main study is divided in
two phases, an evaluation of the current patient status
to identify sub-optimally treated RRMS patients and, if
these patients are switched to fingolimod, an observa-
tional prospective phase of up to 3 years (Fig. 1). The
aims of the study are to assess the clinical applicability
of the criteria of Lublin [7] to define disease activity as
well as the modified Rio score [8] to evaluate treatment
response (Fig. 2), and to investigate the long-term bene-
fits of a treatment change to fingolimod, as assessed by
parameters that are based on the treatment objectives of
NEDA-4 (Fig. 3; [25, 29]). The study further aims at inves-
tigating the power of a systematic collection of clinical
and subclinical measures that represent the physician’s
and the patient’s perspective (2D FDS, Fig. 4). The PAN-
GAEA 2.0 sub-study will additionally evaluate the benefits
of standardized MRI analyses obtained from a central
reading facility in daily clinical routine. The study started
in June 2015 and will end in December 2019. Recruitment
will end in December 2016 or after enrollment of 1500 pa-
tients who switched to fingolimod (Fig. 1).
PANGAEA 2.0 is conducted in line with the FSA
code [30], the joint recommendations of the BfArM
(Federal Institute for Drugs and Medical Devices) and
the Paul-Ehrlich-Institute on planning, conducting, and
evaluating observational studies [31], and the VFA (Re-
search-based Pharmaceutical Companies) recommen-
dations on improving the quality and transparency of
NIS [32]. The Ethics Committee of the Dresden Univer-
sity of Technology approved PANGAEA 2.0. The study
is registered at the BfArM as NIS 6532 (https://
awbdb.bfarm.de).
Study population
A total of 1500 female and male patients diagnosed
with RRMS [33] whose therapy is switched to fingoli-
mod (after evaluation of patient status) are being
included in PANGAEA 2.0. In Germany, MS-
prevalence is approximately 150 cases per 100,000
residents (122,000 cases; [34]). Approximately 70 % of
patients are currently receiving DMTs. In a retro-
spective analysis [35], 34 % of patients receiving
Fig. 1 Study design of PANGAEA 2.0. The study involves an evaluation of disease activity to identify sub-optimally treated RRMS patients (V0), the
switch of MS therapy to fingolimod (Gilenya®; V1), and a 3-year observational phase (V2–V14) to assess treatment response in several functional
domains. A sub-study analyzes the benefits of a standardized quantitative evaluation of routine MRI scans by a central reading facility (NEDA-4:
No Evidence of Disease Activity-4; 2D FDS: 2D Focused Disability Scale)
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DMTs experienced at least one relapse (28,900).
Therefore, approximately 5 % of patients with active
disease (1500) are considered to be appropriate to in-
vestigate the benefits of a treatment switch to fingoli-
mod. According to data provided by IMS Health, a
commercial vendor of prescription drug information
(source: IMS Xponent MAT 08/2014), most MS
patients (98 %) in Germany are treated in 2800 cen-
ters. Therefore, the number of 350 centers participat-
ing in PANGAEA 2.0 seems to be sufficient to ensure
the representativeness of MS treatment strategies.
For the first phase of PANGAEA 2.0 (evaluation of the
patient status), participants are eligible if they were diag-
nosed with RRMS [33] and have been treated with an ap-
proved DMT except fingolimod, or in case of rapidly
progressing, severe RRMS, currently untreated patients
will also be included. Disease activity is required to be
confirmed according to Lublin (Fig. 2; [7]), and patients
are required to provide informed consent. To include pa-
tients in the second phase of the study, the physician has
to decide to switch treatment to fingolimod or to pre-
scribe fingolimod as initial treatment due to high level of
disease activity. Prescription of fingolimod or other DMTs
is independent of the potential study participation.
Reimbursement of physicians was calculated in accord-
ance with governmental regulations and approved by an
independent ethics committee. There are no exclusion or
selection criteria except for the fingolimod contraindica-
tions as listed and described in the product characteristics
information [36]. Eligible patients will be enrolled in the
sequence in which they present at the physician’s
practice.
Only patients who switch to fingolimod will be pro-
spectively followed-up in the 3-year observational phase.
Patients not switching to fingolimod after the initial
study visit as well as patients who discontinue fingoli-
mod treatment during the 3-year follow-up can be docu-
mented as part of the MSDS3D database, but will
discontinue study documentation of PANGAEA 2.0.
Fig. 3 Individual treatment concept NEDA- 4 (no evidence of disease
activity-4). This new treatment concept comprises the criteria relapse
rate, MRI activity, loss of brain volume, and disability progression (EDSS:
Expanded Disability Status Scale). Individual treatment objective, "No
evidence of dicease activity-4" (NEDA-4)
Fig. 2 Assessment of disease activity and treatment response in PANGAEA 2.0. In the first study phase disease activity will be assessed according to
Lublin et al. [7]. Disease active patients who switch to fingolimod are subjected to a 3-year observation period. Disease progression and treatment
response will be assessed by using the modified Rio score [8] (figure adapted from [10]). Definition of active disease by Lublin et al. 2014 and evaluation
of disease progression and treatment response by the modified Rio Score
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Procedures
The study design of PANGAEA 2.0 is outlined in Table 1.
In a first phase at visit 0 the current patient status before
a potential switch to fingolimod is evaluated. Patients
that switch to fingolimod after the initial visit 0 enter a
3 year observational second phase starting with the
documentation of the first dose at visit 1. In the observa-
tional phase, study visits are scheduled every 3 months
(visit 2–14), as recommended by the German Society of
Neurology [33].
Fig. 4 Patient evaluation in PANGAEA 2.0. The software-based MS management system MSDS 3D is supporting patient management (1) and
assists physicians in executing all evaluations required for MS diagnosis, treatment initiation, assess safety, and long-term treatment outcome.
Patient status (2) is evaluated by means of the 2D Focused Disability Scale (2D FDS) that comprises clinical and subclinical measures representing
both the patient’s and the physician’s perspectives (UKNDS: United Kingdom Neurological Disability Scale; FSMC: Fatigue Scale for Motor Fatigue
and Cognitive Functions; WPAI-MS: Work Productivity and Activities Impairment; EQ-5D: EuroQuol-5D; EDSS: Expanded Disability Status Scale;
SDMT: Symbol Digit Modality Test; CGI: Clinical Global Impression). Standardized quantitative evaluation of routine MRI scans (3) is performed by a
central reading facility. The MRI protocol for MRI acquisition and the parameters for quantitative MRI evaluation of lesion load, lesion volume and
brain volume are shown. Propose patient evaluation in PANGAEA 2.0
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The evaluation of patient status (visit 0) includes the
documentation of demographic data, disease history
and clinical characteristics, the assessment of disease
activity according to Lublin et al. (Fig. 2, Table 1; [7]). It
also includes the assessment of a wide range of func-
tional domain parameters (2D FDS; Fig. 4), clinical
parameters as well as patient reported outcomes,
representing both the physician’s [3, 37, 38] and the pa-
tient’sperspectives[39–42].
Patients who are identified as non-responding to or fail-
ing treatment with their current therapy and are switched
to fingolimod treatment continue documentation in visit
1. According to the summary of product characteristics
(SmPC) the switch to fingolimod requires several pre- first
Table 1 Study visits PANGAEA 2.0
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dose observations as well as first dose monitoring as de-
scribed previously [23]. Clinical parameters such as blood
cell counts, liver function values, ophthalmological and
pulmonological examinations, the documentation and as-
sessment of cardiac diseases and concomitant medication
as well as the assessment of the Varicella zoster virus im-
mune status and the pregnancy status are recommended
before the first dose of fingolimod and documented in
visit1. The monitoring of the first dose of fingolimod in-
cludes a 12-channel ECG at before and 6 h after the first
dose. Heart rate, blood pressure, and symptoms of brady-
cardia are examined at 1 h intervals during the post-dose
period. In case of the occurrence of clinical relevant car-
diac symptoms, clinical management is initiated according
to the product information [36, 43]. First-dose monitoring
is also required if fingolimod therapy has been interrupted
and is re-initiated.
During the 3-year observational phase (visits 2–14
[Fig. 1, Table 1]), data on relapses and disability progres-
sion as well as MS activity, including MRI lesions and
MS-related BVL are regularly obtained and interpreted
according to the modified Rio-score (Fig. 2; [8]) and
NEDA-4 (Fig. 3; [25]). For the calculation of the modi-
fied Rio-score, a cut-off of four MRI lesions to identify
responder/non-responder was applied. In addition, clin-
ical and subclinical measures included in the 2D FDS
are evaluated at month 6, 12, 24, and 36 (Fig. 4, Table 1).
Premature discontinuation and interruption of therapy
along with the reasons therefor and the date of last ad-
ministration will be documented at any study visit. In-
vestigators will document the occurrence of adverse
events at every study visit beginning at visit 1 after
switch to fingolimod. Adverse events are defined and
will be handled as described previously [23].
In the PANGAEA 2.0 sub-study, 100 MS centers will
submit MRI data obtained in accordance with a stan-
dardized protocol to a central reading facility (Mediri
GmbH, Heidelberg) for qualitative and quantitative
evaluation (Fig. 4). Results including data on the number
and volume of Gd + T1 lesions, T2/FLAIR hyperintense
lesions, new or enlarging hyperintense T2/FLAIR le-
sions, T1 hypo-intense lesions, and changes of the brain
volume will then be reported to the treating physician
immediately after evaluation (within approximately 5–7
working days).
Data management
To collect data and to assist physicians to document and
manage all visits and examinations, the MSDS3D will be
used [26, 44]. Data will be recorded by the physician or
MS nurse responsible either using the web-based
MSDS3D electronic case report form or using the locally
installed MSDS3D software, both collecting data into the
same database. Anonymity of data and content
protection are ensured by a complex security process in-
cluding an encrypted data transfer [45].
Electronic measures of communication facilitate ana-
lysis and interpretation of data and are well accepted by
patients [45, 46]. The MSDS3D interface displays a verti-
cal timeline and horizontally arranged boxes represent-
ing procedures to be executed (e.g., documentation of
EDSS, patient questionnaires). The corresponding data
input menu can be directly opened from these boxes,
and additional procedures can be added to a selected
visit. Green color indicates that a procedure has been
completed by the MS nurse (e.g., patient questionnaire,
SDMT) or the treating neurologist (e.g., EDSS, adverse
effects). When all procedures of a visit have been com-
pleted, the visit is set as ‘approved’, and data can be
transferred to the central PANGAEA 2.0 database. En-
tries will be automatically controlled for plausibility at
the time of data entry and daily reviewed by the database
coordinator. All data management processes will be
overseen by the data management team of the Clinical
Research Organization responsible (Winicker Norimed
GmbH Medical Research).
Statistical analysis
Descriptive statistics will be used for analysis of data.
The full analysis set used for analysis includes all pa-
tients switching to fingolimod with at least one available
post-dose data recording. Median, mean ± standard devi-
ation, minimum, maximum, 5 % percentile, 1st quartile,
3rd quartile, 95 % percentile, number of valid and miss-
ing values will be presented in tabular form. For nominal
and ordinal-level data, distributions of absolute and rela-
tive frequencies will be reported. Incidence rates of all
safety outcomes will be evaluated for the patient popula-
tion switching to fingolimod. For all analyses, the SAS®
Version 9.2 will be used.
Discussion
Here, we report the study design of PANGAEA 2.0, a
multicenter NIS on disease active RRMS patients whose
therapy is switched to fingolimod. In its first phase, this
study evaluates the patient status to support the identifi-
cation of patients at risk of disease progression. In the
second phase, 1500 patients who switch to fingolimod
(after the first patient status evaluation) are entering a 3-
year observation period. The study is conducted at 350
neurological practices and MS centers in Germany, in-
cluding 100 centers participating in the PANGAEA 2.0
sub-study on the benefits of a standardized quantitative
MRI analysis in daily clinical routine. PANGAEA 2.0
aims not only to expand the fingolimod safety and ef-
fectiveness profile, but also to evaluate the applicability
of measures for the assessment of treatment response
and disease activity in routine clinical conditions.
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With the possibility of optimizing individual MS
treatment by switching to a more effective medication
before severe neurological deterioration occurs the
identification of non-responders to the current MS
therapy has gained fundamental importance. However,
without a standardized definition of non-responders
for clinical routine, the decision when to switch ther-
apy is challenging. Prediction of treatment efficacy
based exclusively on the traditional measures relapse
rate and EDSS progression has been shown to be of
limited value [47]. Since frequent MRI has been dem-
onstrated to predict non-response to IFN-βat early
stages [48–52], Rio et al. proposed a combined assess-
ment of clinical relapses, EDSS progression, and ac-
tive MRI lesions after 1 year of treatment [52]. Based
on the observation that patients who were positive
for two of these three parameters had a higher prob-
ability of disability progression and relapse activity,
Sormani et al. [8] proposed the modified Rio Score
(Fig. 2). The modified Rio score combines short-term
changes (during 1 year of IFN-βtreatment) in relapse
frequency and MRI lesions as a surrogate marker for
long-term disability progression [8, 53]. Patients are
classified as high, medium, and low-risk patients ac-
cording to the number of relapses and new T2 lesions
within 1 year of IFN-βtreatment. Medium-risk pa-
tients are then re-assessed after 1.5 years of treatment
[54]. The modified Rio score then allows to identify
patients at risk for non-responding to IFN-βtreat-
ment in the long-term [10].
In 2014, Lublin et al. [7] refined the established MS
phenotypes by adding disease activity as an additional
descriptor of MS pathogenesis. Active disease is defined
by relapses, acute or sub-acute episodes of new or in-
creasing neurological dysfunction during the previous
12 months, or contrast enhancing T1 or new or un-
equivocally enlarging T2 hyperintense lesions. In PAN-
GAEA 2.0, the criteria of Lublin will be employed to
assess disease activity at visit 0 in order to identify pa-
tients at risk of non-response, while the modified Rio
score will be used to evaluate treatment response during
the first year of treatment (Fig. 2). Signs of disease activ-
ity or progression might then indicate the need to initi-
ate therapy of treatment-naïve patients or to switch
therapy of patients who are not responding to their
current medication. Since the modified Rio score has
not been used to identify patients who are not respond-
ing to therapies other than IFN-β, PANGAEA 2.0 will
provide further insights into the applicability of this
score to evaluate treatment response to fingolimod in
clinical routine.
NEDA has evolved both as a concept for treatment
success of individual MStreatment [55] and as an out-
come measure of DMTs in clinical trials [56]. Cohen et
al. [57] assessed the proportion of IFN-β1a and
fingolimod-treated patients who achieved NEDA after
1 year and 2 years of treatment (defined as no relapses,
no 3-month confirmed disability progression, and no
MRI activity) and found a higher NEDA-proportion
among fingolimod-treated patients than among IFN-
β1a-treated patients, as well as an increased NEDA-
proportion among the IFN-β1a group after the switch to
fingolimod. Importantly, the authors demonstrated the
value of NEDA assessment during the first year of treat-
ment for the prediction of long-term outcomes. How-
ever, there is still controversy with regards to the most
relevant measurements for the assessment of treatment
response in RRMS patients. Other scores using different
algorithms taking disability progression, relapses, and
MRI assessments into account to evaluate a treatment
response have also been proposed [11, 12, 25, 58].
Due to the complexity and the heterogeneous
course of the disease, additional outcome measures
such as BVL have been suggested to complement the
NEDA criteria. BVL begins at early MS-stages and is
associated with disability progression and cognitive
decline [59–62]. Since treatment effects on BVL cor-
relate with those on disability progression, this par-
ameter might provide predictions of future outcomes
[56, 63]. In this study, we will therefore assess treat-
ment response according to the NEDA-4 parameters
(relapse rate, MRI activity, BVL, disability progression
[Fig. 3]) as well as subclinical measures summarized
in the 2D FDS (Fig. 4). Since deterioration not only
occurs in the motor, visual, and sensory systems, this
scale additionally includes cognitive changes, mood
swings, fatigue, bowel and bladder function, sexual
dysfunction, quality of life, as well as work productiv-
ity and activity to obtain a comprehensive picture of
the patient’s disease status [64]. All the above mea-
sures,, are integrated into our proposed approach for
patient evaluation aimed to comprise optimal patient
management, ‘state of the art’evaluation of patient
status, and quantitative evaluation of MRI in daily
clinical practice (Fig. 4).
For the assessment of NEDA-4 parameters a frequent
MRI monitoring (e.g., annual), carried out under stan-
dardized conditions is required. We have therefore
planned a sub-study that evaluates the value of routine
MRI scans performed in accordance with a standardized
protocol to ensure comparability of results. MRI scans
are examined by a central reading facility to consistently
obtain quality controlled standardized quantitative re-
sults according to lesion number, lesion volume and
brain volume (Fig. 4).
The processing of large quantities of data obtained in
this study demands intense data management [44]. The
MSDS3D software already employed in the predecessor
Ziemssen et al. BMC Neurology (2016) 16:129 Page 8 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

study PANGAEA [23] allows the documentation and
management of visits and examinations as well as the
integration of data input from different sources. The
MSDS3D-PANGAEA 2.0 module will assist physicians
in all processes required for the identification of pa-
tients at risk of disease progression and potential fingo-
limod switch patients (Fig. 4; [26, 43]).
The main objective of PANGAEA 2.0 is to expand
the knowledge on the safety and effectiveness of a
switch to fingolimod in RRMS patients who are not-
responding to or having treatment failure with their
current MS medication. In the predecessor study,
PANGAEA, most RRMS patients who started fingoli-
mod (Gilenya®) therapy had been pretreated with in-
jectable BRACE therapies (Betaferon®, Rebif®, Avonex®,
Copaxone®, Extavia®) or natalizumab (Tysabri®). Few
patients were treatment naïve at the time of inclusion or
pretreated with Azathioprine (Imuran®)/Mitoxantrone
(Novantron® and Mitoxantron Ebewe®). Other substances
such as alemtuzumab (Lemtrada®), dimethyl fumarate
(Tecfidera®), and teriflunomide (Aubagio®) have been ap-
proved since the end of the PANGAEA recruitment phase.
PANGAEA 2.0 will therefore provide new information on
the safety of fingolimod in RRMS patients pretreated with
these therapies in routine clinical practice. Furthermore,
the predecessor study, PANGAEA [23], focused on the
post approval fingolimod safety profile, comprising pre-
cautions to treatment and first dose monitoring, as well as
on parameters regarding global symptomatology (CGI
[38]) and disability (EDSS [3]). PANGAEA 2.0 will hence
add useful information on the effectiveness of fingolimod
by additionally assessing early and subtle signs of disease
activity, including data on MRI activity and BVL, as
well as on subclinical changes in, for example, cogni-
tion, fatigue, and activity (Fig. 4). Valuable data on
comparative DMT effectiveness have recently been
obtained by registry-based research such as MSBase
analyses [65–68]. Since registry- and trial-based re-
search are subject to different requirements, the com-
parison of results will provide additional information
on the effectiveness of different treatment algorithms.
In summary, PANGAEA 2.0 will assess not only the
long-term benefit of a treatment change to fingolimod,
but also the applicability of clinical and subclinical pa-
rameters and definitions for the assessment of disease
activity, as defined by Lublin et al., disability progression
and treatment response evaluated by the he modified
Rio score, the definition of individual treatment concepts
according to NEDA-4, and the clinical and subclinical
measures of 2D FDS [69]. The data to be obtained in
PANGAEA 2.0 will expand the existing safety and effect-
iveness profile of fingolimod and will contribute to the
establishment of novel concepts of decision making in
MS treatment.
Abbreviations
2D FDS, 2D Focussed Disability Scale; BfArM, Federal Institute for Drugs and
Medical Devices; BVL, brain volume loss; CGI, Clinical Global Impression; DMT,
disease-modifying therapy; EDSS, Expanded Disability Status Scale; EQ-5D,
EuroQuol-5D; FSMC, Fatigue Scale for Motor Fatigue and Cognitive Functions;
IFN, interferon; MRI, magnetic resonance imaging; MS, Multiple Sclerosis; NEDA-
4, no evidence of disease activity-4; NIS, non-interventional study; PANGAEA 2.0,
Post-Authorization Non-interventional GermAn treatment benefit study of
GilEnyA in MS patients; RRMS, relapsing-remitting MS; SDMT, symbol digit
modality test; SmPC, summary of product characteristics; UKNDS, United
Kingdom Neurological Disability Scale; VFA, Research-based Pharmaceutical
Companies; WPAI-MS, work productivity and activities impairment
Acknowledgements
Financial support for medical editorial assistance was provided by Novartis
Pharma GmbH. We thank Dr. Stefan Lang for his medical editorial assistance
with this manuscript.
Funding
This observational study is sponsored by Novartis Pharma GmbH,
Nuremberg, Germany.
Authors’contributions
TZ developed the study design, which is part of this manuscript, and
contributed to this manuscript. RK participated in the design of the study
and contributed to the manuscript. CC initiated the drafting of the report
and wrote the manuscript. All authors read and approved the final
manuscript.
Competing interests
Tjalf Ziemssen has served on scientific advisory boards, and has received scientific
grants and speaker honoraria from Bayer, Biogen Idec, Genzyme, TEVA, Merck
Serono and Novartis. Raimar Kern has received speaker honoraria from Bayer,
Biogen Idec, Genzyme, TEVA, Merck Serono and Novartis. Christian Cornelissen is
an employee of the Novartis Pharma GmbH, Nuremberg, Germany.
Ethics approval and consent to participate
The Ethics Committee of the Dresden University of Technology approved
PANGAEA 2.0.
Author details
1
Zentrum für klinische Neurowissenschaften, Klinik und Poliklinik für
Neurologie, Universitätsklinikum Carl Gustav Carus Dresden, Technische
Universität Dresden, Fetscherstr. 43, D-01307 Dresden, Germany.
2
Novartis
Pharma GmbH, Roonstr. 25, D-90429 Nuernberg, Germany.
Received: 11 February 2016 Accepted: 26 July 2016
References
1. Gajofatto A, Benedetti MD. Treatment strategies for multiple sclerosis: When
to start, when to change, when to stop? World J Clin Cases. 2015;3:545–55.
2. Ziemssen T, Derfuss T, De Stefano N, Giovannoni G, Palavra F, Tomic D, et
al. Optimizing treatment success in multiple sclerosis. J Neurol. 2016;263(6):
1053–65.
3. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded
disability status scale (EDSS). Neurology. 1983;33:1444–52.
4. Rudick RA, Polman CH. Current approaches to the identification and
management of breakthrough disease in patients with multiple sclerosis.
Lancet Neurol. 2009;8:545–59.
5. Leray E, Yaouanq J, Le Page E, Coustans M, Laplaud D, Oger J, et al.
Evidence for a two-stage disability progression in multiple sclerosis. Brain.
2010;133:1900–13.
6. Bermel RA, Naismith RT. Using MRI to make informed clinical decisions in
multiple sclerosis care. Curr Opin Neurol. 2015;28:244–9.
7. Lublin FD, Reingold SC, Cohen JA, Cutter GR, Sorensen PS, Thompson AJ, et
al. Defining the clinical course of multiple sclerosis: the 2013 revisions.
Neurology. 2014;83:278–86.
8. Sormani MP, Rio J, Tintore M, Signori A, Li D, Cornelisse P, et al. Scoring
treatment response in patients with relapsing multiple sclerosis. Mult Scler.
2013;19:605–12.
Ziemssen et al. BMC Neurology (2016) 16:129 Page 9 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

9. 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.
Mult Scler. 2009;15:848–53.
10. Sormani MP, De Stefano N. Defining and scoring response to IFN-beta in
multiple sclerosis. Nat Rev Neurol. 2013;9:504–12.
11. Grand’Maison F, Bhan V, Freedman MS, Myles ML, Patry DG, Selchen DH, et
al. Utility of the Canadian Treatment Optimization Recommendations (TOR)
in MS care. Can J Neurol Sci. 2013;40:527–35.
12. 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;8:3–13.
13. 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;8:254–60.
14. 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. Lancet Neurol. 2011;10:329–37.
15. Lavery AM, Verhey LH, Waldman AT. Outcome measures in relapsing-
remitting multiple sclerosis: capturing disability and disease progression in
clinical trials. Mult Scler Int. 2014;2014:262350.
16. Balcer LJ. Clinical outcome measures for research in multiple sclerosis.
J Neuroophthalmol. 2001;21:296–301.
17. Ziemssen T, De Stefano N, Pia Sormani M, Van Wijmeersch B, Wiendl H,
Kieseier BC. Optimizing therapy early in multiple sclerosis: an evidence-
based view. Mult Scler Relat Disord. 2015;4:460–9.
18. Brinkmann V. FTY720 (fingolimod) in Multiple Sclerosis: therapeutic effects in the
immune and the central nervous system. Br J Pharmacol. 2009;158:1173–82.
19. Brinkmann V, Davis MD, Heise CE, Albert R, Cottens S, Hof R, et al. The
immune modulator FTY720 targets sphingosine 1-phosphate receptors.
J Biol Chem. 2002;277:21453–7.
20. Cohen JA, Chun J. Mechanisms of fingolimod’s efficacy and adverse effects
in multiple sclerosis. Ann Neurol. 2011;69:759–77.
21. Hla T, Brinkmann V. Sphingosine 1-phosphate (S1P): physiology and the
effects of S1P receptor modulation. Neurology. 2011;76:S3–8.
22. Novartis Pharmaceuticals. Novartis Q2 and H1 2015 Condensed Interim
Financial Report. 2015. https://www.google.de/url?sa=t&rct=j&q=&esrc=s&
source=web&cd=3&ved=0CCwQFjACahUKEwi6rY7Op7DHAhVLiCwKHXq
MBHI&url=https%3A%2F%2Fwww.novartis.com%2Fsites%2Fwww.novartis.
com%2Ffiles%2F2015-07-interim-financial-report-en.pdf&ei=Ne3RVfq-
FsuQsgH6mJKQBw&usg=AFQjCNEbRZTdsYURM3IVZ4xdPmIzIbnJNw
&sig2=pOlxDShTRQqPI3EalXqWmw&cad=rja. Accessed 1 Dec 2015.
23. Ziemssen T, Kern R, Cornelissen C. The PANGAEA study design - a prospective,
multicenter, non-interventional, long-term study on fingolimod for the
treatment of multiple sclerosis in daily practice. BMC Neurol. 2015;15:93.
24. Meuth SG, Gobel K, Wiendl H. Immune therapy of multiple sclerosis–future
strategies. Curr Pharm Des. 2012;18:4489–97.
25. DeStefano N, Sprenger T, Sormani MP, Havrdova E, Radue EW, Bergvall N, et
al. Impact of fingolimod on achieving no evidence of disease activity and
worsening (NEDA)-4 in previously treated patients with high disease activity.
Washington: 67th AAN Annual Meeting; 2015. p. 3–246.
26. Schultheiss T, Kempcke R, Kratzsch F, Eulitz M, Pette M, Reichmann H, et al.
Multiple sclerosis management system 3D. Moving from documentation
towards management of patients. Nervenarzt. 2012;83:450–7.
27. Ziemssen T, Hillert J, Butzkueven H. The importance of collecting structured
clinical information on multiple sclerosis. BMC Med. 2016;14:81.
28. European Medicines Agency. Gilenya (fingolimod) Product information
EMEA/H/C/002202 -II-26-G. 2014. http://www.ema.europa.eu/ema/index.
jsp?curl=pages/medicines/human/medicines/002202/human_med_001433.
jsp&mid=WC0b01ac058001d125. Accessed 15 Sep 2015.
29. Hartung HP, Aktas O. Evolution of multiple sclerosis treatment: next
generation therapies meet next generation efficacy criteria. Lancet Neurol.
2011;10:293–5.
30. Freiwillige Selbstkontrolle für die Arzneimittelindustrie e.V. FSA-Kodex
zur Zusammenarbeit mit Fachkreisen (10.02.2010). Federal Gazette
(BAnz). 2008;68.
31. Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM), Paul-Ehrlich-
Institut (PEI). Empfehlungen des Bundesinstituts für Arzneimittel und
Medizinprodukte und des Paul-Ehrlich-Instituts zur Planung, Durchführung
und Auswertung von Anwendungsbeobachtungen (AWB) vom 7. Juli 2010.
2010. http://www.pei.de/DE/infos/pu/genehmigung-klinische-pruefung/
anwendungsbeobachtungen/awb-empfehlungen-2010-inhalt.html;
jsessionid=44372773F25161689BD9B7AC6D796F2B.1_cid344?nn=3266348.
Accessed 10 Nov 2014.
32. Verband Forschender Arzneimittelhersteller e.V. vfa-Empfehlungen zu
nichtinterventionellen Prüfungen mit Arzneimitteln. 2007. http://www.vfa.
de/de/arzneimittel-forschung/datenbanken-zu-arzneimitteln/nisdb/nis-
empfehlungen. Accessed 11 Dec 2014.
33. Deutsche Gesellschaft für Neurologie (DGN). Diagnose und Therapie der
Multiplen Sklerose. 2012. http://www.awmf.org/uploads/tx_szleitlinien/030-
050l_S2e_Multiple_Sklerose_Diagnostik_Therapie_2014-08.pdf. Accessed 3
Nov 2014.
34. Hein T, Hopfenmuller W. Projection of the number of multiple sclerosis
patients in Germany. Nervenarzt. 2000;71:288–94.
35. Maurer M, Dachsel R, Domke S, Ries S, Reifschneider G, Friedrich A, et al.
Health care situation of patients with relapsing-remitting multiple sclerosis
receiving immunomodulatory therapy: a retrospective survey of more than
9000 German patients with MS. Eur J Neurol. 2011;18:1036–45.
36. Committee for Medicinal Products for Human Use. Summary of Product
Characteristics GILENYA (Fingolimod). 2014. www.ema.europa.eu/docs/en_
GB/document_library/EPAR_-_Product_Information/human/002202/
WC500104528.pdf. Accessed 13 Jan 2015.
37. Smith A. Symbol Digit Modalities Test (SDMT). Manual (revised). Los
Angeles: Western Psychological Services; 1982.
38. Guy W. Clinical Global Impressions (CGI) Scale. In: Rush AJ, First MB, Blacker
D, editors. Handbook of psychiatric measures. 2nd ed. Arlington: American
Psychiatric Association; 2008. p. 90–2.
39. Sharrack B, Hughes RA. The Guy’s Neurological Disability Scale (GNDS): a
new disability measure for multiple sclerosis. Mult Scler. 1999;5:223–33.
40. Penner IK, Raselli C, Stocklin M, Opwis K, Kappos L, Calabrese P. The Fatigue
Scale for Motor and Cognitive Functions (FSMC): validation of a new
instrument to assess multiple sclerosis-related fatigue. Mult Scler. 2009;15:
1509–17.
41. Reilly MC, Zbrozek AS, Dukes EM. The validity and reproducibility of a work
productivity and activity impairment instrument. Pharmacoeconomics. 1993;
4:353–65.
42. Rabin R, de Charro F. EQ-5D: a measure of health status from the EuroQol
Group. Ann Med. 2001;33:337–43.
43. Thomas K, Ziemssen T. Management of fingolimod in clinical practice. Clin
Neurol Neurosurg. 2013;115 Suppl 1:S60–4.
44. Ziemssen T, Kempcke R, Eulitz M, Grossmann L, Suhrbier A, Thomas K, et al.
Multiple sclerosis documentation system (MSDS): moving from
documentation to management of MS patients. J Neural Transm. 2013;120
Suppl 1:S61–6.
45. Kern R, Haase R, Eisele JC, Thomas K, Ziemssen T. Designing an electronic
patient management system for multiple sclerosis: building a next
generation multiple sclerosis documentation system. Interact J Med Res.
2016;5:e2.
46. Haase R, Schultheiss T, Kempcke R, Thomas K, Ziemssen T. Use and
acceptance of electronic communication by patients with multiple sclerosis:
a multicenter questionnaire study. J Med Internet Res. 2012;14:e135.
47. Daumer M, Neuhaus A, Herbert J, Ebers G. Prognosis of the individual course
of disease: the elements of time, heterogeneity and precision. J Neurol Sci.
2009;287 Suppl 1:S50–5.
48. Dobson R, Rudick RA, Turner B, Schmierer K, Giovannoni G. Assessing
treatment response to interferon-beta: is there a role for MRI? Neurology.
2014;82:248–54.
49. Prosperini L, Gallo V, Petsas N, Borriello G, Pozzilli C. One-year MRI scan predicts
clinical response to interferon beta in multiple sclerosis. Eur J Neurol. 2009;16:
1202–9.
50. Durelli L, Barbero P, Bergui M, Versino E, Bassano MA, Verdun E, et al. MRI
activity and neutralising antibody as predictors of response to interferon
beta treatment in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2008;79:
646–51.
51. Tomassini V, Paolillo A, Russo P, Giugni E, Prosperini L, Gasperini C, et al.
Predictors of long-term clinical response to interferon beta therapy in
relapsing multiple sclerosis. J Neurol. 2006;253:287–93.
52. Rio J, Rovira A, Tintore M, Huerga E, Nos C, Tellez N, et al. Relationship
between MRI lesion activity and response to IFN-beta in relapsing-remitting
multiple sclerosis patients. Mult Scler. 2008;14:479–84.
Ziemssen et al. BMC Neurology (2016) 16:129 Page 10 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

53. Sormani MP, Li DK, Bruzzi P, Stubinski B, Cornelisse P, Rocak S, et al.
Combined MRI lesions and relapses as a surrogate for disability in multiple
sclerosis. Neurology. 2011;77:1684–90.
54. Freedman MS, Forrestal FG. Canadian treatment optimization
recommendations (TOR) as a predictor of disease breakthrough in patients
with multiple sclerosis treated with interferon beta-1a: analysis of the
PRISMS study. Mult Scler. 2008;14:1234–41.
55. Havrdova E, Galetta S, Stefoski D, Comi G. Freedom from disease activity in
multiple sclerosis. Neurology. 2010;74 Suppl 3:S3–7.
56. Bevan CJ, Cree BA. Disease activity free status: a new end point for a new
era in multiple sclerosis clinical research? JAMA Neurol. 2014;71:269–70.
57. 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. J Neurol
Neurosurg Psychiatry. 2015; doi: 10.1136/jnnp-2015-310597.
58. Moodie J, Healy BC, Buckle GJ, Gauthier SA, Glanz BI, Arora A, et al.
Magnetic resonance disease severity scale (MRDSS) for patients with
multiple sclerosis: a longitudinal study. J Neurol Sci. 2012;315:49–54.
59. De Stefano N, Airas L, Grigoriadis N, Mattle HP, O’Riordan J, Oreja-Guevara C,
et al. Clinical relevance of brain volume measures in multiple sclerosis. CNS
Drugs. 2014;28:147–56.
60. Zivadinov R, Havrdova E, Bergsland N, Tyblova M, Hagemeier J, Seidl Z, et al.
Thalamic atrophy is associated with development of clinically definite
multiple sclerosis. Radiology. 2013;268:831–41.
61. Deloire MS, Ruet A, Hamel D, Bonnet M, Dousset V, Brochet B. MRI
predictors of cognitive outcome in early multiple sclerosis. Neurology. 2011;
76:1161–7.
62. De Stefano N, Giorgio A, Battaglini M, Rovaris M, Sormani MP, Barkhof F, et
al. Assessing brain atrophy rates in a large population of untreated multiple
sclerosis subtypes. Neurology. 2010;74:1868–76.
63. Sormani MP, Arnold DL, De Stefano N. Treatment effect on brain atrophy
correlates with treatment effect on disability in multiple sclerosis. Ann
Neurol. 2014;75:43–9.
64. Ziemssen T. Multiple sclerosis beyond EDSS: depression and fatigue. J Neurol
Sci. 2009;277 Suppl 1:S37–41.
65. He A, Spelman T, Jokubaitis V, Havrdova E, Horakova D, Trojano M, et al.
Comparison of switch to fingolimod or interferon beta/glatiramer acetate in
active multiple sclerosis. JAMA Neurol. 2015;72:405–13.
66. Kalincik T, Horakova D, Spelman T, Jokubaitis V, Trojano M, Lugaresi A, et al.
Switch to natalizumab versus fingolimod in active relapsing-remitting
multiple sclerosis. Ann Neurol. 2015;77:425–35.
67. Jokubaitis VG, Li V, Kalincik T, Izquierdo G, Hodgkinson S, Alroughani R, et al.
Fingolimod after natalizumab and the risk of short-term relapse. Neurology.
2014;82:1204–11.
68. Spelman T, Mekhael L, Burke T, Butzkueven H, Hodgkinson S, Havrdova E, et
al. Risk of early relapse following the switch from injectables to oral agents
for multiple sclerosis. Eur J Neurol. 2016;23:729–36.
69. Ziemssen T, Kern R, Thomas K. Multiple Sclerosis: clinical profiling and data
collection as prerequisite for personalized medicine approach. BMC Neurol.
2016;16:124.
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