Ponesimod, a selective S1P1 receptor modulator: a potential treatment for multiple sclerosis and other immune-mediated diseases
Bibliographic record
Abstract
The first oral treatment for relapsing multiple sclerosis, the nonselective sphingosine-1-phosphate receptor (S1PR) modulator fingolimod, led to identification of a pivotal role of sphingosine-1-phosphate and one of its five known receptors, S1P1R, in regulation of lymphocyte trafficking in multiple sclerosis. Modulation of S1P3R, initially thought to cause some of fingolimod's side effects, prompted the search for novel compounds with high selectivity for S1P1R. Ponesimod is an orally active, selective S1P1R modulator that causes dose-dependent sequestration of lymphocytes in lymphoid organs. In contrast to the long half-life/slow elimination of fingolimod, ponesimod is eliminated within 1 week of discontinuation and its pharmacological effects are rapidly reversible. Clinical data in multiple sclerosis have shown a dose-dependent therapeutic effect of ponesimod and defined 20 mg as a daily dose with desired efficacy, and acceptable safety and tolerability. Phase II clinical data have also shown therapeutic efficacy of ponesimod in psoriasis. These findings have increased our understanding of psoriasis pathogenesis and suggest clinical utility of S1P1R modulation for treatment of various immune-mediated disorders. A gradual dose titration regimen was found to minimize the cardiac effects associated with initiation of ponesimod treatment. Selectivity for S1P1R, rapid onset and reversibility of pharmacological effects, and an optimized titration regimen differentiate ponesimod from fingolimod, and may lead to better safety and tolerability. Ponesimod is currently in phase III clinical development to assess efficacy and safety in relapsing multiple sclerosis. A phase II study is also ongoing to investigate the potential utility of ponesimod in chronic graft versus host disease.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".