Use of dose–exposure–response relationships in Phase 2 and Phase 3 guselkumab studies to optimize dose selection in psoriasis
Bibliographic record
Abstract
BACKGROUND: Guselkumab is an anti-interleukin-23 monoclonal antibody for the treatment of moderate-to-severe psoriasis. OBJECTIVE: To evaluate the association between dose-response and exposure-response of guselkumab in Phase 2 and Phase 3 studies to optimize dose selection. METHODS: Serum guselkumab concentrations in Phase 2 and Phase 3 studies (VOYAGE 1 and VOYAGE 2) were measured using a validated immunoassay. Efficacy assessments included Physician's Global Assessment (PGA), Investigator's Global Assessment (IGA) and Psoriasis Area and Severity Index (PASI). RESULTS: In Phase 2, a positive dose-response relationship was observed for PASI and PGA (5-mg through 100-mg dose regimens). Exposure-response analysis showed that patients with steady-state trough serum guselkumab concentrations ≥0.67 μg/mL achieved the highest levels of efficacy (PGA 0/1: 90.0%; PGA 0: 70.0%). The guselkumab 100-mg every 8-week (q8w) dose regimen, safe and well-tolerated in Phase 2, provided the highest serum guselkumab concentrations among all regimens studied and was selected for Phase 3. In Phase 3, 72.5% of patients achieved guselkumab concentrations ≥0.67 μg/mL at week 28, the level associated with the highest clinical responses in Phase 2, with patients achieving response rates of IGA 0/1: 91.2%, IGA 0: 55.3%, PASI 90: 83.8% and PASI 100: 49.1% at week 28. CONCLUSION: The 100-mg guselkumab q8w dose regimen, based on the dose-exposure-response relationship from the Phase 2 study, produced the target serum concentration associated with high-level efficacy in the majority of patients in Phase 3. Phase 3 data further confirmed that guselkumab 100mg q8w is the optimum dosing regimen for treating patients with moderate-to-severe psoriasis.
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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 | 0.101 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.001 |
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".