Predicting responders to therapies for multiple sclerosis

Abstract

Therapies for relapsing-remitting multiple sclerosis (RRMS) are only partially effective, and, in most patients receiving such treatment, clinical activity persists. Accurately assessing the treatment response to disease-modifying agents enables non-responder patients to be identified at an early stage into therapy. Patients can then be switched to another, potentially more effective, therapy before too much neurological damage has occurred. Several criteria based on relapses, disability progression or both have been proposed for clinical evaluation of the treatment response to disease-modifying agents. These criteria have not been independently validated, however, and no consensus over which are the best to use currently exists among investigators. MRI can also be employed to detect disease activity in patients treated with disease-modifying agents. Changes on MRI can provide subclinical data relating to disease activity that can be of great benefit in patients monitoring, as inflammatory events occur more often than clinical events. Pharmacogenomic approaches are in the early stages of development for MS, but hold great promise for the eventual development of individually tailored therapies. In this Review, we discuss the proposed approaches for monitoring and predicting treatment responses to disease-modifying agents in patients with RRMS. We evaluate the roles of clinical measures, MRI and pharmacogenomics in these processes.