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Updated: Jun 10, 2026
Modeling Multiple Sclerosis in the Two Sexes: MOG35-55-Induced Experimental Autoimmune Encephalomyelitis
Published on: October 13, 2023
Improving the translational hit of experimental treatments in multiple sclerosis
Hanna M Vesterinen1, Emily S Sena, Charles ffrench-Constant
1Centre for Clinical Brain Sciences, Department of Clinical Neurosciences, University of Edinburgh, Western General Hospital, UK.
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.
Insights
Pre-clinical studies in multiple sclerosis animal models show promising drug efficacy but often lack bias reduction methods. Improving study design is crucial for reliable translation to clinical treatments.
Area of Science:
- Neuroscience
- Immunology
- Pharmacology
Background:
- Translational failure in neurological diseases is linked to flawed animal experiment design.
- Methodological shortcomings in pre-clinical studies can introduce bias.
- This impacts the reliability of translating findings to clinical treatments.
Purpose of the Study:
- Evaluate methodological design and quality of studies using animal models of multiple sclerosis.
- Identify candidate interventions with strong evidence of efficacy.
- Assess the impact of bias reduction on reported effect sizes.
Main Methods:
- Systematic review of published interventions in animal models of multiple sclerosis.
- Data extraction on study quality, design, and neurobehavioral outcomes.
- Meta-analysis of drug efficacy from studies reported in five or more publications.
Main Results:
- 1152 publications used drug interventions in pre-clinical multiple sclerosis models (1117 in EAE).
- 36 interventions showed a 39.6% improvement in neurobehavioral scores.
- Few studies reported bias reduction; randomization/blinding yielded smaller effect sizes.
Conclusions:
- Experimental autoimmune encephalomyelitis (EAE) is valuable for understanding multiple sclerosis pathogenesis and identifying therapies.
- Inconsistent application of bias-limiting measures necessitates methodological best practices.
- The study estimates sample sizes for future research and highlights interventions with strong animal data support.
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