Teriflunomide: a novel oral treatment for relapsing multiple sclerosis
Bibliographic record
Abstract
INTRODUCTION: Multiple sclerosis is a disabling chronic inflammatory disease of the CNS. New emerging oral treatments can offer efficacy with higher levels of therapeutic adherence. Teriflunomide is one such oral agent that has recently been approved for the treatment of relapsing multiple sclerosis (RMS). AREAS COVERED: The aim of this review is to describe the pharmacological profile of teriflunomide and review the vast clinical development program that paved the way for its approval, with emphasis on its safety and tolerability. EXPERT OPINION: Teriflunomide is a safe new oral medication for treating RMS. It is effective at reducing relapses, MRI activity and slowing disability progression. It is well tolerated, with mild and transitory side effects. Although teriflunomide is given a pregnancy category 'X' by the FDA and an effective contraception is needed, to date, there has been no evidence of teratogenicity in humans and a rapid washout procedure can lead to a virtually complete elimination. Its effectiveness appeared to be at least comparable to that of high-dose IFN-β-1a, and although direct comparisons with other orals are still lacking, its tolerability and encouraging safety data suggest that teriflunomide could be considered an ideal first-line medication for RMS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".