Journal article

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response

Tomas Kalincik, Ali Manouchehrinia, Lukas Sobisek, Vilija Jokubaitis, Tim Spelman, Dana Horakova, Eva Havrdova, Maria Trojano, Guillermo Izquierdo, Alessandra Lugaresi, Marc Girard, Alexandre Prat, Pierre Duquette, Pierre Grammond, Patrizia Sola, Raymond Hupperts, Francois Grand'Maison, Eugenio Pucci, Cavit Boz, Raed Alroughani Show all

BRAIN | OXFORD UNIV PRESS | Published : 2017


Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was a..

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Awarded by National Health and Medical Research Council

Awarded by National Health and Medical Research Council (centre for research excellence)

Funding Acknowledgements

This study was financially supported by National Health and Medical Research Council (practitioner fellowship 1080518, project grants 1129189, 1083539 and 1032484 and centre for research excellence 1001216) and University of Melbourne (Faculty of Medicine, Dentistry and Health Sciences research fellowship). The MSBase Foundation is a not-for-profit organization that receives support from Biogen, Novartis, Merck, Roche, Teva and Sanofi Genzyme. The study was conducted separately and apart from the guidance of the sponsors.