Exposure–Response Analysis of Tofacitinib in Active Psoriatic Arthritis: Results from Two Phase 3 Studies
Bibliographic record
Abstract
Abstract Tofacitinib is an oral Janus kinase inhibitor for the treatment of psoriatic arthritis (PsA). These post hoc exposure–response (E–R) analyses of pooled data from two Phase 3 studies (NCT01877668 and NCT01882439) characterized the relationships between tofacitinib exposure and efficacy (American College of Rheumatology [ACR] criteria), and changes in hemoglobin (Hgb) in patients with PsA. Efficacy data for the proportion of patients receiving tofacitinib 5 or 10 mg twice daily, or placebo, achieving ACR ≥20%, ≥50%, or ≥70% response criteria (ACR20, ACR50, and ACR70, respectively) at Month 3, were modeled jointly using a four‐category ordered categorical exposure–response model (ACR20 non‐responder, ACR20 responder but not ACR50 responder, ACR50 responder but not ACR70 responder, and ACR70 responder). A maximum drug effect (Emax) model (using average concentrations of tofacitinib at steady state [Cavg]) adequately described the exposure–ACR response rate relationship. Model‐predicted response rates for tofacitinib 5 and 10 mg twice daily were 51% and 58%, respectively, for ACR20; 29% and 36% for ACR50; and 15% and 20% for ACR70. The E–R relationship between tofacitinib exposure and changes in Hgb was assessed using an indirect response model, which generally predicted Hgb concentration–time profiles across treatments well. The proportions of patients experiencing a decrease in Hgb of >2 g/dL were similar with tofacitinib 5 mg twice daily or placebo. These results were generally consistent with previous analyses in rheumatoid arthritis and psoriasis, and support the use of tofacitinib 5 mg twice daily for active PsA.
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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 | 0.051 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".