Advertisement

psychopraxis. neuropraxis

, Volume 19, Issue 4, pp 131–140 | Cite as

„Time is brain“ bei der schubförmigen Multiplen Sklerose

Aktuelle Behandlungskonzepte in der Immuntherapie
  • R. LinkerEmail author
  • B.-A. Kallmann
  • C. Kleinschnitz
  • P. Rieckmann
  • M. Mäurer
  • S. Schwab
Neurologie
  • 107 Downloads

Zusammenfassung

Hintergrund

Trotz der diametral unterschiedlichen Zeitskalen, in denen sich die Krankheitsprozesse und -folgen bei der Multiplen Sklerose (MS) und beim ischämischen Schlaganfall manifestieren, zeigen sich konzeptionelle Parallelen zwischen beiden Erkrankungen, die für das Management der MS richtungsweisend sind: Entzündliche Erkrankungsaktivität und konsekutive Neurodegeneration führen zu potenziell irreversibler Gewebeschädigung und damit zu bleibender Behinderung. Dementsprechend sind eine frühzeitige Detektion von Erkrankungsaktivität und eine zeitnahe Intervention bzw. Therapieoptimierung ausschlaggebend für eine Verbesserung der langfristigen Prognose, was sich mit dem in der Akuttherapie des ischämischen Schlaganfalls geprägten Begriff „time is brain“ griffig beschreiben lässt.

Ergebnisse und Diskussion

Bei der MS beinhaltet ein „Time-is-brain“-Konzept den Wert einer frühen Immuntherapie nach Diagnosestellung sowie die Notwendigkeit eines sensitiven strukturierten Monitorings in Verbindung mit frühzeitiger Therapieoptimierung bei Nachweis von Krankheitsaktivität unter Therapie. Das übergeordnete Ziel der Therapie im gesamten Erkrankungsverlauf der MS ist dabei die bestmögliche und anhaltende Kontrolle jeglicher erfassbarer Krankheitsaktivität, insbesondere in der frühen Erkrankungsphase. Im Alltag kann die intersektorale Kooperation in regionalen MS-Netzwerken mit spezialisierten Zentren dazu beitragen, die verfügbaren hochwirksamen Medikamente mit ihren jeweils eigenen Nutzen-Risiko-Profilen optimal einzusetzen und damit die Aussicht auf eine langfristige Stabilisierung der Lebensqualität der MS-Patienten zu erhöhen.

Schlüsselwörter

Multiple Sklerose Immuntherapie Therapieoptimierung Netzwerk Kooperation 

“Time is brain” in relapsing remitting multiple sclerosis

Current treatment concepts in immunotherapy

Abstract

Background

Despite highly divergent time scales of disease evolution in multiple sclerosis (MS) and ischemic stroke, clear analogies are apparent that may point the way to optimization of MS treatment. Inflammatory disease activity and neurodegeneration may induce potentially irreversible damage to central nervous system structures and thus lead to permanent disability. For the treatment of MS early detection of disease activity and early immunotherapy or treatment optimization are pivotal determinants of long-term outcomes. Such therapeutic concepts may be described with the catchy phrase “time is brain” as coined for the acute thrombolytic treatment of ischemic stroke.

Results and discussion

For MS a “time is brain” concept would comprise an early initiation of first line therapy as well as sensitive and structured monitoring of disease activity under therapy in conjunction with a low threshold for timely treatment optimization to achieve sustained freedom from measurable disease activity. This approach may substantially improve the long-term outcome in patients who show insufficient response to platform therapies. The intersectorial collaboration in regional MS care networks involving office-based neurologists and specialized MS centers may facilitate the timely use of highly active therapies with their specific benefit-risk profiles thus supporting sustained stabilization of patient quality of life.

Keywords

Multiple sclerosis Immunotherapy Treatment optimization Network Collaboration 

Notes

Einhaltung ethischer Richtlinien

Interessenkonflikt

R.A. Linker erhielt Honorare für Referenten- bzw. Beratertätigkeiten von folgenden Firmen: Bayer, Biogen, Fresenius, Sanofi-Genzyme, Merck-Serono, Novartis und Roche Pharma. Er besetzt eine von der Novartis Stiftung für therapeutische Forschung unterstützte Stiftungsprofessur. B.-A. Kallmann war als Referent für die Firma Genzyme, Teva, Biogen, Merck Serono tätig und erhielt Beraterhonorare von den Firmen Sanofi-Genzyme, Teva, Biogen, Merck Serono, Novartis. C. Kleinschnitz erhielt in den letzten 5 Jahren persönliche Zuwendungen als Referent und Berater und/oder Forschungsförderung von Bayer Healthcare, Biogen, Biotronik, Boehringer Ingelheim, Bristol-Myers Squibb, Eisai, Merck Serono, Novartis, Pfizer, Sanofi-Genzyme, Siemens, Teva Pharma. P. Rieckmann erhielt in den letzten 5 Jahren Honorare für Referenten- und Beratertätigkeiten von Bayer Healthcare, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Genpharm, Merck Serono, Novartis, Pfizer, Sanofi-Genzyme, Siemens, Teva Pharma. M. Mäurer erhielt Honorare für Referenten- und Beratertätigkeiten von folgenden Firmen: Biogen, Böhringer Ingelheim, Bayer, Merck Serono, Genzyme, Sanofi-Genzyme, Talecris, TEVA, Novartis. S. Schwab erhielt Honorare für Referenten- und Beratertätigkeiten von folgenden Firmen: Bayer Healthcare, Biogen, Bristol-Myers Squibb, Boehringer Ingelheim, Daiichi-Sanky, Novartis, Pfizer.

Dieser Beitrag beinhaltet keine Studien an Menschen oder Tieren.

Literatur

  1. 1.
    Heesen C, Bohm J, Reich C et al (2008) Patient perception of bodily functions in multiple sclerosis: gait and visual function are the most valuable. Mult Scler 14:988–991CrossRefPubMedGoogle Scholar
  2. 2.
    Feuillet L, Reuter F, Audoin B et al (2007) Early cognitive impairment in patients with clinically isolated syndrome suggestive of multiple sclerosis. Mult Scler 13:124–127CrossRefPubMedGoogle Scholar
  3. 3.
    Amato MP, Hakiki B, Goretti B et al (2012) Association of MRI metrics and cognitive impairment in radiologically isolated syndromes. Neurology 78:309–314CrossRefPubMedGoogle Scholar
  4. 4.
    Deloire M, Ruet A, Hamel D et al (2010) Early cognitive impairment in multiple sclerosis predicts disability outcome several years later. Mult Scler 16:581–587CrossRefPubMedGoogle Scholar
  5. 5.
    Trapp BD, Peterson J, Ransohoff RM et al (1998) Axonal transection in the lesions of multiple sclerosis. N Engl J Med 338:278–285CrossRefPubMedGoogle Scholar
  6. 6.
    Kuhlmann T, Lingfeld G, Bitsch A et al (2002) Acute axonal damage in multiple sclerosis is most extensive in early disease stages and decreases over time. Brain 125:2202–2212CrossRefPubMedGoogle Scholar
  7. 7.
    Kern S, Kuhn M, Ziemssen T (2013) Chronically ill and unemployed? A review on vocational status in multiple sclerosis. Fortschr Neurol Psychiatr 81:95–103CrossRefPubMedGoogle Scholar
  8. 8.
    Kobelt G, Berg J, Lindgren P et al (2006) Costs and quality of life of patients with multiple sclerosis in Europe. J Neurol Neurosurg Psychiatry 77:918–926CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Scalfari A, Neuhaus A, Degenhardt A et al (2010) The natural history of multiple sclerosis: a geographically based study 10: relapses and long-term disability. Brain 133:1914–1929CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Weinshenker BG, Bass B, Rice GP et al (1989) The natural history of multiple sclerosis: a geographically based study. 2. Predictive value of the early clinical course. Brain 112:1419–1428CrossRefPubMedGoogle Scholar
  11. 11.
    Tremlett H, Yousefi M, Devonshire V et al (2009) Impact of multiple sclerosis relapses on progression diminishes with time. Neurology 73:1616–1623CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Hirst C, Ingram G, Pearson O et al (2008) Contribution of relapses to disability in multiple sclerosis. J Neurol 255:280–287CrossRefPubMedGoogle Scholar
  13. 13.
    Scalfari A, Neuhaus A, Daumer M et al (2013) Early relapses, onset of progression, and late outcome in multiple sclerosis. JAMA Neurol 70:214–222CrossRefPubMedGoogle Scholar
  14. 14.
    Prosperini L, Gallo V, Petsas N et al (2009) One-year MRI scan predicts clinical response to interferon beta in multiple sclerosis. Eur J Neurol 16:1202–1209CrossRefPubMedGoogle Scholar
  15. 15.
    Fisniku LK, Brex PA, Altmann DR et al (2008) Disability and T2 MRI lesions: a 20-year follow-up of patients with relapse onset of multiple sclerosis. Brain 131:808–817CrossRefPubMedGoogle Scholar
  16. 16.
    Tintore M, Rovira A, Rio J et al (2006) Baseline MRI predicts future attacks and disability in clinically isolated syndromes. Neurology 67:968–972CrossRefPubMedGoogle Scholar
  17. 17.
    Lukas C, Knol DL, Sombekke MH et al (2015) Cervical spinal cord volume loss is related to clinical disability progression in multiple sclerosis. J Neurol Neurosurg Psychiatry 86:410–418CrossRefPubMedGoogle Scholar
  18. 18.
    Sumowski JF, Rocca MA, Leavitt VM et al (2014) Brain reserve and cognitive reserve protect against cognitive decline over 4.5 years in MS. Neurology 82:1776–1783CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Fisher E, Rudick RA, Simon JH et al (2002) Eight-year follow-up study of brain atrophy in patients with MS. Neurology 59:1412–1420CrossRefPubMedGoogle Scholar
  20. 20.
    Barkhof F, Jong R de, Sfikas N et al (2014) The influence of patient demographics, disease characteristics and treatment on brain volume loss in Trial Assessing Injectable Interferon vs FTY720 Oral in Relapsing-Remitting Multiple Sclerosis (TRANSFORMS), a phase 3 study of fingolimod in multiple sclerosis. Mult Scler 20:1704–1713CrossRefPubMedGoogle Scholar
  21. 21.
    Coles AJ, Twyman CL, Arnold DL et al (2012) Alemtuzumab for patients with relapsing multiple sclerosis after disease-modifying therapy: a randomised controlled phase 3 trial. Lancet 380:1829–1839CrossRefPubMedGoogle Scholar
  22. 22.
    Fisniku LK, Chard DT, Jackson JS et al (2008) Gray matter atrophy is related to long-term disability in multiple sclerosis. Ann Neurol 64:247–254CrossRefPubMedGoogle Scholar
  23. 23.
    Weinshenker BG, Rice GP, Noseworthy JH et al (1991) The natural history of multiple sclerosis: a geographically based study. 3. Multivariate analysis of predictive factors and models of outcome. Brain 114:1045–1056CrossRefPubMedGoogle Scholar
  24. 24.
    Edan G, Kappos L, Montalban X et al (2014) Long-term impact of interferon beta-1b in patients with CIS: 8-year follow-up of BENEFIT. J Neurol Neurosurg Psychiatry 85:1183–1189CrossRefPubMedGoogle Scholar
  25. 25.
    Hughes S, Spelman T, Trojano M et al (2012) The Kurtzke EDSS rank stability increases 4 years after the onset of multiple sclerosis: results from the MSBase Registry. J Neurol Neurosurg Psychiatry 83:305–310CrossRefPubMedGoogle Scholar
  26. 26.
    Weinshenker BG, Rice GP, Noseworthy JH et al (1991) The natural history of multiple sclerosis: a geographically based study. 4. Applications to planning and interpretation of clinical therapeutic trials. Brain 114:1057–1067CrossRefPubMedGoogle Scholar
  27. 27.
    Leray E, Yaouanq J, Le PE et al (2010) Evidence for a two-stage disability progression in multiple sclerosis. Brain 133:1900–1913CrossRefPubMedCentralGoogle Scholar
  28. 28.
    Confavreux C, Vukusic S, Adeleine P (2003) Early clinical predictors and progression of irreversible disability in multiple sclerosis: an amnesic process. Brain 126:770–782CrossRefPubMedGoogle Scholar
  29. 29.
    Confavreux C, Vukusic S (2006) Natural history of multiple sclerosis: a unifying concept. Brain 129:606–616CrossRefPubMedGoogle Scholar
  30. 30.
    Leray E, Coustans M, Le PE et al (2013) ‚Clinically definite benign multiple sclerosis’, an unwarranted conceptual hodgepodge: evidence from a 30-year observational study. Mult Scler 19:458–465CrossRefPubMedGoogle Scholar
  31. 31.
    Goldschmidt T, Antel J, Konig FB et al (2009) Remyelination capacity of the MS brain decreases with disease chronicity. Neurology 72:1914–1921CrossRefPubMedGoogle Scholar
  32. 32.
    Gold R, Hartung HP, Stangel M et al (2012) Therapeutic goals of baseline and escalation therapy for relapsing-remitting multiple sclerosis; Therapeutic goals of baseline and escalation therapy for relapsing-remitting multiple sclerosis. Akt Neurol 39:342–350CrossRefGoogle Scholar
  33. 33.
    Havrdova E, Galetta S, Hutchinson M et al (2009) 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 8:254–260CrossRefPubMedGoogle Scholar
  34. 34.
    Rotstein DL, Healy BC, Malik MT et al (2015) Evaluation of no evidence of disease activity in a 7-year longitudinal multiple sclerosis cohort. JAMA Neurol 72:152–158CrossRefPubMedGoogle Scholar
  35. 35.
    Comi G, Martinelli V, Rodegher M et al (2009) Effect of glatiramer acetate on conversion to clinically definite multiple sclerosis in patients with clinically isolated syndrome (PreCISe study): a randomised, double-blind, placebo-controlled trial. Lancet 374:1503–1511CrossRefPubMedGoogle Scholar
  36. 36.
    Comi G, De SN, Freedman MS et al (2012) Comparison of two dosing frequencies of subcutaneous interferon beta-1a in patient with a first clinical demyelinating event suggestive of multiple sclerosis (REFLEX): a phase 3 randomised controlled trial. Lancet Neurol 11:33–41CrossRefPubMedGoogle Scholar
  37. 37.
    Jacobs LD, Beck RW, Simon JH et al (2000) Intramuscular interferon beta-1a therapy initiated during a first demyelinating event in multiple sclerosis. CHAMPS Study Group. N Engl J Med 343:898–904CrossRefPubMedGoogle Scholar
  38. 38.
    Kappos L, Freedman MS, Polman CH et al (2007) Effect of early versus delayed interferon beta-1b treatment on disability after a first clinical event suggestive of multiple sclerosis: a 3-year follow-up analysis of the BENEFIT study. Lancet 370:389–397CrossRefPubMedGoogle Scholar
  39. 39.
    Miller AE, Wolinsky JS, Kappos L et al (2014) Oral teriflunomide for patients with a first clinical episode suggestive of multiple sclerosis (TOPIC): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Neurol 13:977–986CrossRefPubMedGoogle Scholar
  40. 40.
    Rudick RA, Lee JC, Cutter GR et al (2010) Disability progression in a clinical trial of relapsing-remitting multiple sclerosis: eight-year follow-up. Arch Neurol 67:1329–1335CrossRefPubMedGoogle Scholar
  41. 41.
    Goodin DS, Reder AT, Ebers GC et al (2012) Survival in MS: a randomized cohort study 21 years after the start of the pivotal IFNbeta-1b trial. Neurology 78:1315–1322CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Shirani A, Zhao Y, Karim ME et al (2012) Association between use of interferon beta and progression of disability in patients with relapsing-remitting multiple sclerosis. JAMA 308:247–256PubMedGoogle Scholar
  43. 43.
    Derfuss T, Kappos L (2012) Evaluating the potential benefit of interferon treatment in multiple sclerosis. JAMA 308:290–291CrossRefPubMedGoogle Scholar
  44. 44.
    Prosperini L, Gianni C, Barletta V et al (2012) Predictors of freedom from disease activity in natalizumab treated-patients with multiple sclerosis. J Neurol Sci 323:104–112CrossRefPubMedGoogle Scholar
  45. 45.
    Cohen JA, Khatri B, Barkhof F et al (2015) 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 (im Druck)Google Scholar
  46. 46.
    Spelman T, Kalincik T, Zhang A et al (2015) Comparative efficacy of switching to natalizumab in active multiple sclerosis. Ann Clin Transl Neurol 2:373–387CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Rio J, Nos C, Tintore M et al (2006) Defining the response to interferon-beta in relapsing-remitting multiple sclerosis patients. Ann Neurol 59:344–352CrossRefPubMedGoogle Scholar
  48. 48.
    Rio J, Castillo J, Rovira A et al (2009) Measures in the first year of therapy predict the response to interferon beta in MS. Mult Scler 15:848–853CrossRefPubMedGoogle Scholar
  49. 49.
    Sormani MP, Bruzzi P (2013) MRI lesions as a surrogate for relapses in multiple sclerosis: a meta-analysis of randomised trials. Lancet Neurol 12:669–676CrossRefPubMedGoogle Scholar
  50. 50.
    Kinkel RP, Simon JH, O’Connor P et al (2014) Early MRI activity predicts treatment nonresponse with intramuscular interferon beta-1a in clinically isolated syndrome. Mult Scler Relat Disord 3:712–719CrossRefPubMedGoogle Scholar
  51. 51.
    Giovannoni G, Turner B, Gnanapavan S et al (2015) Is it time to target no evident disease activity (NEDA) in multiple sclerosis? Mult Scler Relat Dis 4:329–333CrossRefGoogle Scholar
  52. 52.
    Freedman MS, Selchen D, Arnold DL et al (2013) Canadian MS Working Group updated recommendations. Can J Neurol Sci 40:307–323CrossRefPubMedGoogle Scholar
  53. 53.
    Stangel M, Penner IK, Kallmann BA et al (2015) Development of a multifactorial model to monitor treatment response and disease course in relapsing remitting multiple sclerosis; Multiple Sclerosis Decision Model (MSDM): development of a multifactorial model to monitor treatment response and disease course in relapsing remitting multiple sclerosis. Akt Neurol 40:486–493Google Scholar
  54. 54.
    Maurer M, Dachsel R, Domke S et al (2011) 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 18:1036–1045CrossRefPubMedGoogle Scholar
  55. 55.
    Rio J, Comabella M, Montalban X (2011) Multiple sclerosis: current treatment algorithms. Curr Opin Neurol 24:230–237CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • R. Linker
    • 1
    Email author
  • B.-A. Kallmann
    • 2
  • C. Kleinschnitz
    • 3
  • P. Rieckmann
    • 4
  • M. Mäurer
    • 5
  • S. Schwab
    • 1
  1. 1.Neurologische KlinikUniversitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-NürnbergErlangenDeutschland
  2. 2.Neurologische Praxis und multiple sklerose zentrum BambergBambergDeutschland
  3. 3.Neurologische Klinik und Poliklinik des Universitätsklinikums WürzburgWürzburgDeutschland
  4. 4.Neurologische KlinikKlinikum am Bruderwald der Sozialstiftung Bamberg BambergDeutschland
  5. 5.NeurologieCaritas-Krankenhaus Bad Mergentheim gGmbHBad MergentheimDeutschland

Personalised recommendations