Phase 2 Dose-Finding Study in Patients with Gout Using SEL-212, a Novel PEGylated Uricase (SEL-037) Combined with Tolerogenic Nanoparticles (SEL-110)
Bibliographic record
Abstract
INTRODUCTION: ), which mitigates the formation of anti-drug antibodies (ADAs) against uricase and SEL-037 (PEGylated uricase), thereby enabling sustained sUA control (sUA < 6 mg/dl). The aim of this study was to identify appropriate dosing for SEL-037 and SEL-110 for use in phase 3 clinical trials. METHODS: This open-label phase 2 study was conducted in adults with symptomatic gout and sUA ≥ 6 mg/dl. Participants received five monthly infusions of SEL-037 (0.2 or 0.4 mg/kg) alone or in combination with three or five monthly infusions of SEL-110 (0.05-0.15 mg/kg). Safety, tolerability, sUA, ADAs, and tophi were monitored for 6 months. RESULTS: A total of 152 adults completed the study. SEL-037 alone resulted in rapid sUA reductions that were not sustained beyond 30 days in most participants due to ADA formation and loss of uricase activity. Levels of ADAs decreased with increasing doses of SEL-110 up to 0.1 mg/kg, with anti-uricase titers < 1080 correlating with sustained sUA control and reductions in tophi. Overall, 66% of evaluable participants achieved sUA control at week 20 following five monthly doses of SEL-037 0.2 mg/kg + SEL-110 0.1-0.15 mg/kg, whereas only 26% achieved sUA control at week 20 when SEL-110 was withdrawn after week 12. Compared to other dose combinations, SEL-037 0.2 mg/kg + SEL-110 0.15 mg/kg achieved the greatest sUA control at week 12 and was well-tolerated with no safety concerns. CONCLUSION: Results provide continued support for the use of multiple monthly administrations of SEL-037 0.2 mg/kg + SEL-110 0.1-0.15 mg/kg in clinical trials for SEL-212. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT02959918.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".