Who Thrives on Fingolimod? Real-Life Insights into Treating Multiple Sclerosis

Fingolimod (brand name Gilenya®) has long been a key player in treating relapsing-remitting multiple sclerosis (RRMS). But how well does it work outside the tightly controlled environment of clinical trials? And just as importantly, can we predict which patients are most likely to benefit from it?

A comprehensive 2018 study from San Raffaele Hospital in Milan provides fresh, real-world insight. By analyzing over 360 RRMS patients over a two-year period, the researchers not only evaluated the effectiveness of fingolimod but also identified key baseline characteristics that could help guide treatment choices in clinical practice.

Study Snapshot
Participants: 367 RRMS patients from a single MS center in Milan

Follow-up: 2 years of clinical visits and MRI evaluations

Focus: Effectiveness of fingolimod and predictors of disease activity

Groups:

NTZ group: Patients previously treated with Natalizumab (within a year before fingolimod)

NO_NTZ group: All others

Key Findings
1. Fingolimod is effective in everyday clinical use
Overall, nearly half (46.6%) of patients had no evidence of disease activity (NEDA) after 2 years.

Annualized relapse rate (ARR) dropped significantly:

From 0.78 pre-treatment to 0.19 under fingolimod—a 75% reduction.

MRI scans showed a steady decline in active lesions, especially after the first year.

2. Switching from Natalizumab? Expect a bumpy start
Patients in the NTZ group experienced a temporary spike in relapses and MRI activity during the first 6 months of fingolimod treatment.

This “rebound” effect is likely tied to the pharmacological transition, as Natalizumab has a strong immunosuppressive profile that wears off over time.

However, by year two, disease activity in NTZ switchers stabilized to levels comparable with other patients.

3. Baseline disease activity matters—a lot
The study identified several predictors of fingolimod success or failure, especially in those not switching from NTZ:

Fewer gadolinium-enhancing lesions on MRI at baseline → greater chance of being NEDA

Lower ARR before starting treatment → better outcomes

Older age at disease onset → more favorable prognosis

In contrast, among NTZ switchers, the most important predictors of poor response were:

Presence of disease activity during the NTZ washout period

Younger age at disease onset

Clinical Implications: Towards Personalized MS Therapy
This study reinforces fingolimod’s real-world value—but more importantly, it underscores the need for personalized treatment strategies in MS:

Patients with less inflammatory activity at baseline (e.g., fewer MRI lesions, lower ARR) are more likely to benefit.

NTZ switchers with active disease post-withdrawal may require more aggressive or alternative therapies, at least in the short term.

Age at onset and prior disease behavior can provide early clues on likely treatment trajectories.

A Step Closer to Predictive MS Care
The takeaway is simple yet powerful: Not all MS patients respond the same, even to potent therapies like fingolimod. By incorporating simple baseline data—MRI results, relapse history, age—clinicians can better identify who stands to gain the most, and who may need a different approach.

This kind of predictive modeling moves MS treatment closer to the personalized medicine era—where therapy is tailored not just to the disease, but to the individual behind it.

Disclaimer: This blog post is based on the provided research article and is intended for informational purposes only. It is not intended to provide medical advice. Please consult with a healthcare professional for any health concerns.

References:
Saposnik, G., Monreal, E., Medrano, N., García-Domínguez, J. M., Querol, L., Meca-Lallana, J. E., Landete, L., Salas, E., Meca-Lallana, V., García-Arcelay, E., Agüera-Morales, E., Martínez-Yélamos, S., Gómez-Ballesteros, R., Maurino, J., Villar, L. M., & Caminero, A. B. (2024). Does serum neurofilament light chain measurement influence therapeutic decisions in multiple sclerosis?. Multiple sclerosis and related disorders, 90, 105838. https://doi.org/10.1016/j.msard.2024.105838