The cost of teriflunomide in the treatment of relapsing-remitting multiple sclerosis.
Bibliographic record
Abstract
AIMS: Teriflunomide, used globally to treat multiple sclerosis (MS) and widely subsidised for this indication including in Australia and New Zealand, is the main metabolite of leflunomide, an older immune-modulating drug. Leflunomide therefore represents a potential alternative therapy for MS. Teriflunomide is about 50-500 times more expensive than leflunomide, depending on prices in each jurisdiction. I wished to study how this situation arose. METHODS: Web search to obtain the publicly available minutes of eight international regulatory bodies that have approved teriflunomide for the governments of the US, Canada, Europe, England, Scotland, Australia (TGA and PBS) and New Zealand, and examination of the processes and minuted discussions concerning the metabolic, efficacy, toxicity and cost relationship between teriflunomide and leflunomide. RESULTS: The relationship between the two drugs and their relative efficacy or toxicity in MS was considered by three of eight agencies (Food and Drug Administration (FDA), European Medicines Agency (EMA) and the Canadian Agency for Drugs and Technology in Health (CADTH)). The remaining agencies accepted teriflunomide applications at face value, assessed cost-effectiveness against contemporaneous drugs used for treating MS, and did not discuss the potential role of leflunomide as a therapy for MS. No agency minuted the implications of the cost difference. CONCLUSIONS: Efficacy for leflunomide in MS is likely but unproven. The sponsor presented a case for teriflunomide that was within the established procedures for drug agencies in establishing cost-effectiveness, and agencies did not stray from their normal procedures. As a result, an opportunity to decrease the cost of treating MS has been missed. Though off-label use of leflunomide is possible, this is unlikely without a publicly-funded trial to demonstrate non-inferiority with regard to efficacy and safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".