Delay From Treatment Start to Full Effect of Immunotherapies for Multiple Sclerosis

Izanne Roos; Emmanuelle Leray; Federico Frascoli; Romain Casey; J. William L. Brown; Dana Horakova; Eva K. Havrdova; Maria Trojano; Francesco Patti; Guillermo Izquierdo; Sara Eichau; Marco Onofrj; Alessandra Lugaresi; Alexandre Prat; Marc Girard; Pierre Grammond; Patrizia Sola; Diana Ferraro; Serkan Ozakbas; Roberto Bergamaschi; Maria José Sá; Elisabetta Cartechini; Cavit Boz; Franco Granella; Raymond Hupperts; Murat Terzi; Jeannette Lechner-Scott; Daniele Spitaleri; Vincent Van Pesch; Aysun Soysal; Javier Olascoaga; Julie Prevost; Eduardo Aguera-Morales; Mark Slee; Tunde Csepany; Recai Turkoglu; Youssef Sidhom; Riadh Gouider; Bart Van Wijmeersch; Pamela McCombe; Richard Macdonell; Alasdair Coles; Charles B. Malpas; Helmut Butzkueven; Sandra Vukusic; Tomas Kalincik


Brain. 2020;143(9):2742-2756. 

In This Article

Abstract and Introduction


In 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.


Multiple sclerosis is a complex neuroimmunological disease characterized by an interplay of inflammation and neurodegeneration throughout the disease course. Initiation or switch of therapy for multiple sclerosis is frequently prompted by disease activity, presenting as relapses, worsening of disability or new/active lesions on MRI. Whilst it is known that multiple sclerosis therapies reduce relapse rates and disability accrual (Tramacere et al., 2015; Lizak et al., 2017; Brown et al., 2019) the time of onset of treatment effect is often inferred from available information concerning pharmacodynamics of a given agent, typically available from preclinical or post-marketing trials. The delay in full biological effect of treatment, however, does not immediately translate into a delay to full clinical effect. This delay from starting a therapy to reaching its full clinical effect is termed 'therapeutic lag' (Giovannoni et al., 2017). Information about therapeutic lag is highly relevant to decisions regarding the use of multiple sclerosis therapies, in particular during the early weeks after the commencement of therapy.

The influence of therapeutic lag on treatment response has been briefly explored in multiple sclerosis. A post hoc analysis of two originally negative trials in progressive multiple sclerosis, the SPECTRIMS and PROMISE trials [Secondary Progressive Efficacy Clinical Trial of Recombinant Interferon-Beta-1a in MS (SPECTRIMS) Study Group, 2001; Wolinsky et al., 2007], suggested that treatment benefit on 3-month confirmed disability progression develops after a 2–2.5-year delay and is dependent on the degree of pre-existing disability (Sormani and Giovannoni, 2016). Thus, in clinical trials, the effect of therapy may be obscured by therapeutic lag, particularly when the duration of such trials is restricted to 3 years and include progressive multiple sclerosis. Further exploration of therapeutic lag and its determinants thereby depends on development of a robust method to detect when treatments attain full clinical effect.

In this study we used the two biggest multiple sclerosis registries, MSBase, the largest international observational cohort, and Observatoire Français de la Sclérose en Plaques (OFSEP), the largest national multiple sclerosis registry, to develop and externally validate an objective method to detect the onset of full clinically manifest effect of treatment on relapses and progression-of-disability events.