https://www.researchgate.net/profile/Ro ... atment.pdf
Abstract.
Multiple sclerosis (MS) is a chronic inflamma-tory disease of the central nervous system of autoimmune etiopathogenesis, and is characterized by various neurological symptoms. Glatiramer acetate and interferon-β are adminis-tered as first‑line treatments for this disease. In non‑responsive patients, several second-line therapies are available, including natalizumab; however, a percentage of MS patients does not respond, or respond partially.
Therefore, it is of the utmost importance to develop a diagnostic test for the prediction of drug response in patients suffering from complex diseases, such as MS, where several therapeutic options are already available. By a machine learning approach, the uncorrelated Shrunken centroid algorithm was applied to identify a subset of genes of cd4+ T cells that may predict the pharmacological response of relapsing-remitting MS patients to natalizumab, before the initiation of therapy.
The results from the present study may provide a basis for the design of personalized therapeutic strategies for patients with MS
T-cell analysis predicts response to Natalizumab
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