Teriflunomide and Its Mechanism of Action in Multiple Sclerosis
Bibliographic record
Abstract
Treatment of multiple sclerosis (MS) is challenging: disease-modifying treatments (DMTs) must both limit unwanted immune responses associated with disease initiation and propagation (as T and B lymphocytes are critical cellular mediators in the pathophysiology of relapsing MS), and also have minimal adverse impact on normal protective immune responses. In this review, we summarize key preclinical and clinical data relating to the proposed mechanism of action of the recently approved DMT teriflunomide in MS. Teriflunomide selectively and reversibly inhibits dihydro-orotate dehydrogenase, a key mitochondrial enzyme in the de novo pyrimidine synthesis pathway, leading to a reduction in proliferation of activated T and B lymphocytes without causing cell death. Results from animal experiments modelling the immune activation implicated in MS demonstrate reductions in disease symptoms with teriflunomide treatment, accompanied by reduced central nervous system lymphocyte infiltration, reduced axonal loss, and preserved neurological functioning. In agreement with the results obtained in these model systems, phase 3 clinical trials of teriflunomide in patients with MS have consistently shown that teriflunomide provides a therapeutic benefit, and importantly, does not cause clinical immune suppression. Taken together, these data demonstrate how teriflunomide acts as a selective immune therapy for patients with MS.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".