Personalized treatment is ideal for multiple sclerosis (MS) owing to the heterogeneity of clinical features, but current knowledge gaps, including validation of biomarkers and treatment algorithms, limit practical implementation. The contemporary approach to personalized MS therapy depends on evidence-based prognostication, an initial treatment choice and evaluation of early treatment responses to identify the need to switch therapy. Prognostication is directed by baseline clinical, environmental and demographic factors, MRI measures and biomarkers that correlate with long-term disability measures. The initial treatment choice should be a shared decision between the patient and physician. In addition to prognosis, this choice must account for patient-related factors, including comorbidities, pregnancy planning, preferences of the patients and their comfort with risk, and drug-related factors, including safety, cost and implications for treatment sequencing. Treatment response has traditionally been assessed on the basis of relapse rate, MRI lesions and disability progression. Larger longitudinal data sets have enabled development of composite outcome measures and more stringent standards for disease control. Biomarkers, including neurofilament light chain, have potential as early surrogate markers of prognosis and treatment response but require further validation. Overall, attainment of personalized treatment for MS is complex but will be refined as new data become available.
Personalized treatment of multiple sclerosis (MS) depends on prognostication at baseline, a shared treatment decision between the physician and patient, and early assessment of response to therapy.
Prognosis can be evaluated soon after diagnosis on the basis of demographic and environmental factors, clinical features, MRI measures and biomarkers.
Individuals with poor prognostic features should be recommended high-efficacy therapies early on; studies are underway to investigate whether most patients with relapsing–remitting MS could benefit from initial aggressive therapy.
During the treatment discussion between the neurologist and patient, factors such as comorbidities, pregnancy planning, patient preferences, risk tolerance, safety, cost and treatment sequencing should be considered in addition to prognosis.
Early assessment of treatment response is important to identify the need to switch therapy; composite outcome measures that incorporate clinical and MRI data are best for predicting long-term disability.
Personalized MS therapy is currently limited by a lack of evidence-based biomarkers; newer biomarkers, such as neurofilament light chain, have potential, but further validation and standardization of assays are required.
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The authors acknowledge P. Mulero for her assistance with the literature review.
Nature Reviews Neurology thanks R. Bergamaschi, V. Martinelli and P. Vermersch for their contribution to the peer review of this work.
D.R. has served as a speaker or consultant for Biogen, EMD Serono, Novartis, Roche and Sanofi-Aventis. She has received research support from the Multiple Sclerosis Society of Canada and the Consortium of Multiple Sclerosis Centers (CMSC). X.M. has received speaking honoraria and travel expenses for scientific meetings and has been a steering committee member of clinical trials or participated in advisory boards of clinical trials in the past 3 years with Actelion, Biogen, Celgene, EXCEMED, Genentech, Genzyme, Merck Serono, the Multiple Sclerosis International Federation, the National Multiple Sclerosis Society, Novartis, Roche, Sanofi-Aventis and Teva.
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Rotstein, D., Montalban, X. Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis. Nat Rev Neurol 15, 287–300 (2019). https://doi.org/10.1038/s41582-019-0170-8
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