Delay from treatment start to full effect of immunotherapies for multiple sclerosis
Authors: Roos Izanne , Leray Emmanuelle , Frascoli Federico , Casey Romain , Brown J William L , Horakova Dana , Havrdova Eva K , Trojano Maria , Patti Francesco , Izquierdo Guillermo , Eichau Sara , Onofrj Marco , Lugaresi Alessandra , Prat Alexandre , Girard Marc , Grammond Pierre , Sola Patrizia , Ferraro Diana , Ozakbas Serkan , Bergamaschi Roberto , Sá Maria José , Cartechini Elisabetta , Boz Cavit , Granella Franco , Hupperts Raymond , Terzi Murat , Lechner-Scott Jeannette , Spitaleri Daniele , Van Pesch Vincent , Soysal Aysun , Olascoaga Javier , Prevost Julie , Aguera-Morales Eduardo , Slee Mark , Csepany Tunde , Turkoglu Recai , Sidhom Youssef , Gouider Riadh , Van Wijmeersch Bart , McCombe Pamela , Macdonell Richard , Coles Alasdair , Malpas Charles B , Butzkueven Helmut , Vukusic Sandra , Kalincik Tomas ,   the MSBase, Duquette Pierre , Grand'Maison Francois , Iuliano Gerardo , Ramo-Tello Cristina , Solaro Claudio , Cabrera-Gomez Jose Antonio , Rio Maria Edite , Bolaños Ricardo Fernandez , Shaygannejad Vahid , Oreja-Guevara Celia , Sanchez-Menoyo Jose Luis , Petersen Thor , Altintas Ayse , Barnett Michael , Flechter Shlomo , Fragoso Yara , Amato Maria Pia , Moore Fraser , Ampapa Radek , Verheul Freek , Hodgkinson Suzanne , Cristiano Edgardo , Yamout Bassem , Laureys Guy , Dominguez Jose Andres , Zwanikken Cees , Deri Norma , Dobos Eniko , Vrech Carlos , Butler Ernest , Rozsa Csilla , Petkovska-Boskova Tatjana , Karabudak Rana , Rajda Cecilia , Alkhaboori Jabir , Saladino Maria Laura , Shaw Cameron , Shuey Neil , Vucic Steve , Sempere Angel Perez , Campbell Jamie , Piroska Imre , Taylor Bruce , van der Walt Anneke , Kappos Ludwig , Roullet Etienne , Gray Orla , Simo Magdolna , Sirbu Carmen-Adella ,   OFSEP investigators, Brochet Bruno , Cotton François , De Sèze Jérôme , Dion Armelle , Douek Pascal , Guillemin Francis , Laplaud David , Lebrun-Frenay Christine , Moreau Thibault , Olaiz Javier , Pelletier Jean , Rigaud-Bully Claire , Stankoff Bruno , Marignier Romain , Debouverie Marc , Edan Gilles , Ciron Jonathan , Ruet Aurélie , Collongues Nicolas , Lubetzki Catherine , Vermersch Patrick , Labauge Pierre , Defer Gilles , Cohen Mikaël , Fromont Agnès , Wiertlewsky Sandrine , Berger Eric , Clavelou Pierre , Audoin Bertrand , Giannesini Claire , Gout Olivier , Thouvenot Eric , Heinzlef Olivier , Al-Khedr Abdullatif , Bourre Bertrand , Casez Olivier , Cabre Philippe , Montcuquet Alexis , Créange Alain , Camdessanché Jean-Philippe , Faure Justine , Maurousset Aude , Patry Ivania , Hankiewicz Karolina , Pottier Corinne , Maubeuge Nicolas , Labeyrie Céline , Nifle Chantal 
Published: June 2020

AbstractIn multiple sclerosis, treatment start or switch is prompted by evidence of disease activity. Whilst immunomodulatory therapies reduce disease activity, the time required to attain maximal effect is unclear. In this study we aimed to develop a method that allows identification of the time to manifest fully and clinically the effect of multiple sclerosis treatments (‘therapeutic lag’) on clinical disease activity represented by relapses and progression-of-disability events. Data from two multiple sclerosis registries, MSBase (multinational) and OFSEP (French), were used. Patients diagnosed with multiple sclerosis, minimum 1-year exposure to treatment, minimum 3-year pretreatment follow-up and yearly review were included in the analysis. For analysis of disability progression, all events in the subsequent 5-year period were included. Density curves, representing incidence of relapses and 6-month confirmed progression events, were separately constructed for each sufficiently represented therapy. Monte Carlo simulations were performed to identify the first local minimum of the first derivative after treatment start; this point represented the point of stabilization of treatment effect, after the maximum treatment effect was observed. The method was developed in a discovery cohort (MSBase), and externally validated in a separate, non-overlapping cohort (OFSEP). A merged MSBase-OFSEP cohort was used for all subsequent analyses. Annualized relapse rates were compared in the time before treatment start and after the stabilization of treatment effect following commencement of each therapy. We identified 11 180 eligible treatment epochs for analysis of relapses and 4088 treatment epochs for disability progression. External validation was performed in four therapies, with no significant difference in the bootstrapped mean differences in therapeutic lag duration between registries. The duration of therapeutic lag for relapses was calculated for 10 therapies and ranged between 12 and 30 weeks. The duration of therapeutic lag for disability progression was calculated for seven therapies and ranged between 30 and 70 weeks. Significant differences in the pre- versus post-treatment annualized relapse rate were present for all therapies apart from intramuscular interferon beta-1a. In conclusion we have developed, and externally validated, a method to objectively quantify the duration of therapeutic lag on relapses and disability progression in different therapies in patients more than 3 years from multiple sclerosis onset. Objectively defined periods of expected therapeutic lag allows insights into the evaluation of treatment response in randomized clinical trials and may guide clinical decision-making in patients who experience early on-treatment disease activity. This method will subsequently be applied in studies that evaluate the effect of patient and disease characteristics on therapeutic lag.