BACKGROUND: In other neurological diseases, the failure to translate pre-clinical findings to effective clinical treatments has been partially attributed to bias introduced by shortcomings in the design of animal experiments. OBJECTIVES: Here we evaluate published studies of interventions in animal models of multiple sclerosis for methodological design and quality and to identify candidate interventions with the best evidence of efficacy. METHODS: A systematic review of the literature describing experiments testing the effectiveness of interventions in animal models of multiple sclerosis was carried out. Data were extracted for reported study quality and design and for neurobehavioural outcome. Weighted mean difference meta-analysis was used to provide summary estimates of the efficacy for drugs where this was reported in five or more publications. RESULTS: The use of a drug in a pre-clinical multiple sclerosis model was reported in 1152 publications, of which 1117 were experimental autoimmune encephalomyelitis (EAE). For 36 interventions analysed in greater detail, neurobehavioural score was improved by 39.6% (95% CI 34.9-44.2%, p < 0.001). However, few studies reported measures to reduce bias, and those reporting randomization or blinding found significantly smaller effect sizes. CONCLUSIONS: EAE has proven to be a valuable model in elucidating pathogenesis as well as identifying candidate therapies for multiple sclerosis. However, there is an inconsistent application of measures to limit bias that could be addressed by adopting methodological best practice in study design. Our analysis provides an estimate of sample size required for different levels of power in future studies and suggests a number of interventions for which there are substantial animal data supporting efficacy.
BACKGROUND: In other neurological diseases, the failure to translate pre-clinical findings to effective clinical treatments has been partially attributed to bias introduced by shortcomings in the design of animal experiments. OBJECTIVES: Here we evaluate published studies of interventions in animal models of multiple sclerosis for methodological design and quality and to identify candidate interventions with the best evidence of efficacy. METHODS: A systematic review of the literature describing experiments testing the effectiveness of interventions in animal models of multiple sclerosis was carried out. Data were extracted for reported study quality and design and for neurobehavioural outcome. Weighted mean difference meta-analysis was used to provide summary estimates of the efficacy for drugs where this was reported in five or more publications. RESULTS: The use of a drug in a pre-clinical multiple sclerosis model was reported in 1152 publications, of which 1117 were experimental autoimmune encephalomyelitis (EAE). For 36 interventions analysed in greater detail, neurobehavioural score was improved by 39.6% (95% CI 34.9-44.2%, p < 0.001). However, few studies reported measures to reduce bias, and those reporting randomization or blinding found significantly smaller effect sizes. CONCLUSIONS: EAE has proven to be a valuable model in elucidating pathogenesis as well as identifying candidate therapies for multiple sclerosis. However, there is an inconsistent application of measures to limit bias that could be addressed by adopting methodological best practice in study design. Our analysis provides an estimate of sample size required for different levels of power in future studies and suggests a number of interventions for which there are substantial animal data supporting efficacy.
Authors: Story C Landis; Susan G Amara; Khusru Asadullah; Chris P Austin; Robi Blumenstein; Eileen W Bradley; Ronald G Crystal; Robert B Darnell; Robert J Ferrante; Howard Fillit; Robert Finkelstein; Marc Fisher; Howard E Gendelman; Robert M Golub; John L Goudreau; Robert A Gross; Amelie K Gubitz; Sharon E Hesterlee; David W Howells; John Huguenard; Katrina Kelner; Walter Koroshetz; Dimitri Krainc; Stanley E Lazic; Michael S Levine; Malcolm R Macleod; John M McCall; Richard T Moxley; Kalyani Narasimhan; Linda J Noble; Steve Perrin; John D Porter; Oswald Steward; Ellis Unger; Ursula Utz; Shai D Silberberg Journal: Nature Date: 2012-10-11 Impact factor: 49.962
Authors: Robert Fahed; Jean Raymond; Célina Ducroux; Jean-Christophe Gentric; Igor Salazkin; Daniela Ziegler; Guylaine Gevry; Tim E Darsaut Journal: Neuroradiology Date: 2016-01-08 Impact factor: 2.804