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

BackgroundThe 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/DesignIn 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.DiscussionPANGAEA 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 registrationBfArM6532; Trial Registration Date: 20/05/2015.

[1]  M. Sormani,et al.  Optimizing therapy early in multiple sclerosis: An evidence-based view. , 2015, Multiple sclerosis and related disorders.

[2]  R. Bermel,et al.  Using MRI to make informed clinical decisions in multiple sclerosis care. , 2015, Current opinion in neurology.

[3]  V. Brinkmann.  FTY720 (fingolimod) in Multiple Sclerosis: therapeutic effects in the immune and the central nervous system , 2009, British journal of pharmacology.

[4]  À. Rovira,et al.  Relationship between MRI lesion activity and response to IFN-β in relapsing–remitting multiple sclerosis patients , 2008, Multiple sclerosis.

[5]  V. Tomassini,et al.  Predictors of long–term clinical response to interferon beta therapy in relapsing multiple sclerosis , 2006, Journal of Neurology.

[6]  B. Sharrack,et al.  The Guy's Neurological Disability Scale (GNDS): a new disability measure for multiple sclerosis , 1999, Multiple sclerosis.

[7]  D. Arnold,et al.  Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis , 2014, Annals of neurology.

[8]  J. Lechner-Scott,et al.  Comparison of switch to fingolimod or interferon beta/glatiramer acetate in active multiple sclerosis. , 2015, JAMA neurology.

[9]  H. Wiendl,et al.  Immune therapy of multiple sclerosis--future strategies. , 2012, Current pharmaceutical design.

[10]  M. Sormani,et al.  Combined MRI lesions and relapses as a surrogate for disability in multiple sclerosis , 2011, Neurology.

[11]  K. Fletcher,et al.  Handbook of Psychiatric Measures, 2nd Edition , 2008 .

[12]  Raimar Kern,et al.  The PANGAEA study design – a prospective, multicenter, non-interventional, long-term study on fingolimod for the treatment of multiple sclerosis in daily practice , 2015, BMC Neurology.

[13]  H. Reichmann,et al.  [Multiple sclerosis management system 3D. Moving from documentation towards management of patients]. , 2012, Der Nervenarzt.

[14]  H. Reichmann,et al.  Multiple-Sklerose-Dokumentationssystem 3D , 2011, Der Nervenarzt.

[15]  K. Schmierer,et al.  Assessing treatment response to interferon-β , 2014, Neurology.

[16]  A. Traboulsee,et al.  Utility of the Canadian Treatment Optimization Recommendations (TOR) in MS Care , 2013, Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques.

[17]  R. Rabin,et al.  EQ-SD: a measure of health status from the EuroQol Group , 2001, Annals of medicine.

[18]  W. Hopfenmüller,et al.  Hochrechnung der Zahl an Multiple Sklerose erkrankten Patienten in Deutschland , 2000, Der Nervenarzt.

[19]  C. Guttmann,et al.  Magnetic resonance disease severity scale (MRDSS) for patients with multiple sclerosis: A longitudinal study , 2012, Journal of the Neurological Sciences.

[20]  M. Benedetti,et al.  Treatment strategies for multiple sclerosis: When to start, when to change, when to stop? , 2015, World journal of clinical cases.

[21]  M. Freedman,et al.  Canadian treatment optimization recommendations (TOR) as a predictor of disease breakthrough in patients with multiple sclerosis treated with interferon β-1a: analysis of the PRISMS study , 2008, Multiple sclerosis.

[22]  Jeffrey A. Cohen,et al.  Mechanisms of fingolimod's efficacy and adverse effects in multiple sclerosis , 2011, Annals of neurology.

[23]  Alexander Suhrbier,et al.  Multiple sclerosis documentation system (MSDS): moving from documentation to management of MS patients , 2013, Journal of Neural Transmission.

[24]  B. Cree,et al.  Disease activity free status: a new end point for a new era in multiple sclerosis clinical research? , 2014, JAMA neurology.

[25]  Carsten Lukas,et al.  Towards the implementation of ‘no evidence of disease activity’ in multiple sclerosis treatment: the multiple sclerosis decision model , 2015, Therapeutic advances in neurological disorders.

[26]  J Sastre-Garriga,et al.  Measures in the first year of therapy predict the response to interferon β in MS , 2009, Multiple sclerosis.

[27]  M. Battaglini,et al.  Assessing brain atrophy rates in a large population of untreated multiple sclerosis subtypes , 2010, Neurology.

[28]  A. Waldman,et al.  Outcome Measures in Relapsing-Remitting Multiple Sclerosis: Capturing Disability and Disease Progression in Clinical Trials , 2014, Multiple sclerosis international.

[29]  Gilles Edan,et al.  Evidence for a two-stage disability progression in multiple sclerosis , 2010, Brain : a journal of neurology.

[30]  H. Wiendl,et al.  DGN-Leitlinie Diagnostik und Therapie der Multiplen Sklerose , 2014, Aktuelle Neurologie.

[31]  M. Dwyer,et al.  Thalamic atrophy is associated with development of clinically definite multiple sclerosis. , 2013, Radiology.

[32]  T. Ziemssen,et al.  Management of fingolimod in clinical practice , 2013, Clinical Neurology and Neurosurgery.

[33]  L. Durelli,et al.  MRI activity and neutralising antibody as predictors of response to interferon β treatment in multiple sclerosis , 2007, Journal of Neurology, Neurosurgery, and Psychiatry.

[34]  F. Jacques.  Defining the clinical course of multiple sclerosis: The 2013 revisions , 2015, Neurology.

[35]  Michael D. Davis,et al.  The Immune Modulator FTY720 Targets Sphingosine 1-Phosphate Receptors* , 2002, The Journal of Biological Chemistry.

[36]  T. Derfuss,et al.  Optimizing treatment success in multiple sclerosis , 2015, Journal of Neurology.

[37]  V. Dousset,et al.  MRI predictors of cognitive outcome in early multiple sclerosis , 2011, Neurology.

[38]  Lippincott Williams Wilkins,et al.  MRI predictors of cognitive outcome in early multiple sclerosis , 2012, Neurology.

[39]  Volker Brinkmann,et al.  Sphingosine 1-phosphate (S1P) , 2011, Neurology.

[40]  L. Balcer.  Clinical Outcome Measures for Research in Multiple Sclerosis , 2001, Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society.

[41]  C. Polman,et al.  Current approaches to the identification and management of breakthrough disease in patients with multiple sclerosis , 2009, The Lancet Neurology.

[42]  J. Lechner-Scott,et al.  Switch to natalizumab versus fingolimod in active relapsing–remitting multiple sclerosis , 2015, Annals of neurology.

[43]  Tjalf Ziemssen,et al.  Use and Acceptance of Electronic Communication by Patients With Multiple Sclerosis: A Multicenter Questionnaire Study , 2012, Journal of medical Internet research.

[44]  Tjalf Ziemssen,et al.  Multiple sclerosis beyond EDSS: depression and fatigue , 2009, Journal of the Neurological Sciences.

[45]  Olivier Gout,et al.  Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis , 2014, Annals of neurology.

[46]  S. Schwab,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 , 2011, European journal of neurology.

[47]  G. Comi,et al.  Freedom from disease activity in multiple sclerosis , 2010, Neurology.

[48]  Raimar Kern,et al.  Multiple sclerosis: clinical profiling and data collection as prerequisite for personalized medicine approach , 2016, BMC Neurology.

[49]  C. Polman,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 , 2009, The Lancet Neurology.

[50]  C. Pozzilli,et al.  One‐year MRI scan predicts clinical response to interferon beta in multiple sclerosis , 2009, European journal of neurology.

[51]  Jan Hillert,et al.  The importance of collecting structured clinical information on multiple sclerosis , 2016, BMC Medicine.

[52]  J. Herbert,et al.  Prognosis of the individual course of disease: the elements of time, hetereogeneity and precision , 2009, Journal of the Neurological Sciences.

[53]  H. Hartung,et al.  Evolution of multiple sclerosis treatment: next generation therapies meet next generation efficacy criteria , 2011, The Lancet Neurology.

[54]  B Stubinski,et al.  Scoring treatment response in patients with relapsing multiple sclerosis , 2013, Multiple sclerosis.

[55]  J. Kurtzke.  Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[56]  H. Wiendl,et al.  Clinical Relevance of Brain Volume Measures in Multiple Sclerosis , 2014, CNS Drugs.

[57]  J. Lechner-Scott,et al.  Fingolimod after natalizumab and the risk of short-term relapse , 2014, Neurology.

[58]  Maria Pia Sormani,et al.  Defining and scoring response to IFN-β in multiple sclerosis , 2013, Nature Reviews Neurology.

[59]  Raimar Kern,et al.  Designing an Electronic Patient Management System for Multiple Sclerosis: Building a Next Generation Multiple Sclerosis Documentation System , 2016, Interactive journal of medical research.

[60]  강연욱,et al.  Symbol Digit Modalities Test(SDMT)의 유용성 , 2007 .

[61]  Jeffrey A. Cohen,et al.  Long-term (up to 4.5 years) treatment with fingolimod in multiple sclerosis: results from the extension of the randomised TRANSFORMS study , 2015, Journal of Neurology, Neurosurgery & Psychiatry.

[62]  A. Zbrozek,et al.  The Validity and Reproducibility of a Work Productivity and Activity Impairment Instrument , 1993, PharmacoEconomics.

[63]  P. Vermersch,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 , 2011, The Lancet Neurology.

[64]  W. Hopfenmüller,et al.  [Projection of the number of multiple sclerosis patients in Germany]. , 2000, Der Nervenarzt.

[65]  J. Lechner-Scott,et al.  Risk of early relapse following the switch from injectables to oral agents for multiple sclerosis , 2016, European journal of neurology.

[66]  L. Kappos,et al.  The Fatigue Scale for Motor and Cognitive Functions (FSMC): validation of a new instrument to assess multiple sclerosis-related fatigue , 2009, Multiple sclerosis.