TY - JOUR
T1 - Delay from treatment start to full effect of immunotherapies for multiple sclerosis
AU - Roos, Izanne
AU - Leray, Emmanuelle
AU - Frascoli, Federico
AU - Casey, Romain
AU - Brown, J. William L.
AU - Horakova, Dana
AU - Havrdova, Eva K.
AU - Trojano, Maria
AU - Patti, Francesco
AU - Izquierdo, Guillermo
AU - Eichau, Sara
AU - Onofrj, Marco
AU - Lugaresi, Alessandra
AU - Prat, Alexandre
AU - Girard, Marc
AU - Grammond, Pierre
AU - Sola, Patrizia
AU - Ferraro, Diana
AU - Ozakbas, Serkan
AU - Bergamaschi, Roberto
AU - Sá, Maria José
AU - Cartechini, Elisabetta
AU - Boz, Cavit
AU - Granella, Franco
AU - Hupperts, Raymond
AU - Terzi, Murat
AU - Lechner-Scott, Jeannette
AU - Spitaleri, Daniele
AU - Van Pesch, Vincent
AU - Soysal, Aysun
AU - Olascoaga, Javier
AU - Prevost, Julie
AU - Aguera-Morales, Eduardo
AU - Slee, Mark
AU - Csepany, Tunde
AU - Turkoglu, Recai
AU - Sidhom, Youssef
AU - Gouider, Riadh
AU - Van Wijmeersch, Bart
AU - McCombe, Pamela
AU - Macdonell, Richard
AU - Coles, Alasdair
AU - Malpas, Charles B.
AU - Butzkueven, Helmut
AU - Vukusic, Sandra
AU - Kalincik, Tomas
AU - MSBase
AU - OFSEP investigators
AU - Duquette, Pierre
AU - Grand' Maison, Francois
AU - Iuliano, Gerardo
AU - Ramo-Tello, Cristina
AU - Solaro, Claudio
AU - Cabrera-Gomez, Jose Antonio
AU - Edite Rio, Maria
AU - Fernandez Bolaños, Ricardo
AU - Shaygannejad, Vahid
AU - Oreja-Guevara, Celia
AU - Sanchez-Menoyo, Jose Luis
AU - Petersen, Thor
AU - Altintas, Ayse
AU - Barnett, Michael
AU - Fletcher, Shlomo
AU - Fragoso, Yara
AU - Amato, Maria Pia
AU - Moore, Fraser
AU - Ampapa, Radek
AU - Verheul, Freek
AU - Hodgkinson, Suzanne J.
AU - Cristiano, Edgardo
AU - Yamout, Bassem
AU - Laureys, Guy
AU - Andres Dominguez, Jose
AU - Zwanikken, Cees
AU - Deri, Norma
AU - Dobos, Eniko
AU - Vrech, Carlos
AU - Butler, Ernest
AU - Rozsa, Csilla
AU - Petkovska-Boskova, Tatjana
AU - Karabudak, Rana
AU - Rajda, Cecilia
AU - Alkhaboori, Jabir
AU - Saladino, Maria Laura
AU - Shaw, Cameron
AU - Shuey, Neil
AU - Vucic, Steve
AU - Perez Sempere, Angel
AU - Campbell, Jamie
AU - Piroska, Imre
AU - Taylor, Bruce
AU - Van der Walt, Anneke
AU - Kappos, Ludwig
AU - Roullet, Etienne
AU - Gray, Orla
AU - Simo, Magdolna
AU - Sirbu, Carmen-Adella
AU - Brochet, Bruno
AU - Cotton, Francois
AU - De Seze, Jerome
AU - Dion, Armelle
AU - Douek, Pascal
AU - Guillemin, Francis
AU - Laplaud, David
AU - Lebrun-Frenay, Christine
AU - Moreau, Thibault
AU - Olaiz, Javier
AU - Pelletier, Jean-Pierre
AU - Rigaud-Bully, Claire
AU - Stankoff, Bruno
AU - Marignier, Romain
AU - Debouverie, Marc
AU - Edan, Gilles
AU - Ciron, Jonathan
AU - Ruet, Aurelie
AU - Collongues, Nicolas
AU - Lubetzki, Catherine
AU - Vermersch, Patrick
AU - Labauge, Pierre
AU - Defer, Gilles
AU - Cohen, Mikael
AU - Fromont, Agnes
AU - Wiertlewsky, Sandrine
AU - Berger, Eric
AU - Clavelou, Pierre
AU - Audoin, Bertrand
AU - Giannesini, Claire
AU - Gout, Olivier
AU - Thouvenot, Eric
AU - Heinzlef, Olivier
AU - Al-Khedr, Abdullatif
AU - Bourre, Bertrand
AU - Casez, Olivier
AU - Cabre, Philippe
AU - Montcuquet, Alexis
AU - Creange, Alain
AU - Camdessanche, Jean-Philippe
AU - Faure, Justine
AU - Maurousset, Aude
AU - Patry, Ivania
AU - Hankiewicz, Karolina
AU - Pottier, Corinne
AU - Maubeuge, Nicolas
AU - Labeyrie, Celine
AU - Nifle, Chantal
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - multiple sclerosis
KW - therapeutic lag
KW - immunotherapies
UR - http://www.scopus.com/inward/record.url?scp=85090969423&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/NHMRC/1140766
UR - http://purl.org/au-research/grants/NHMRC/1129189
UR - http://purl.org/au-research/grants/NHMRC/1157717
U2 - 10.1093/brain/awaa231
DO - 10.1093/brain/awaa231
M3 - Article
C2 - 32947619
AN - SCOPUS:85090969423
SN - 0006-8950
VL - 143
SP - 2742
EP - 2756
JO - Brain : a journal of neurology
JF - Brain : a journal of neurology
IS - 9
ER -