A phase 1b open-label dose-finding study of ustekinumab in young adults with type 1 diabetes
Bibliographic record
Abstract
OBJECTIVES: We assessed the safety of ustekinumab (a monoclonal antibody used in psoriasis to target the IL-12 and IL-23 pathways) in a small cohort of recent-onset (<100 days of diagnosis) adults with type 1 diabetes (T1D) by conducting a pilot open-label dose-finding and mechanistic study (NCT02117765) at the University of British Columbia. METHODS: We sequentially enrolled 20 participants into four subcutaneous dosing cohorts: (i) 45 mg loading weeks 0/4/16, (ii) 45 mg maintenance weeks 0/4/16/28/40, (iii) 90 mg loading weeks 0/4/16, and (iv) 90 mg maintenance weeks 0/4/16/28/40. The primary endpoint was safety as assessed by an independent data and safety monitoring board (DSMB) but we also measured mixed meal tolerance test C-peptide, insulin use/kg, and HbA1c. Immunophenotyping was performed to assess immune cell subsets and islet antigen-specific T cell responses. RESULTS: Although several adverse events were reported, only two (bacterial vaginosis and hallucinations) were thought to be possibly related to drug administration by the study investigators. At 1 year, the 90 mg maintenance dosing cohort had the smallest mean decline in C-peptide area under the curve (AUC) (0.1 pmol/ml). Immunophenotyping showed that ustekinumab reduced the percentage of circulating Th17, Th1, and Th17.1 cells and proinsulin-specific T cells that secreted IFN-γ and IL-17A. CONCLUSION: Ustekinumab was deemed safe to progress to efficacy studies by the DSMB at doses used to treat psoriasis in adults with T1D. A 90 mg maintenance dosing schedule reduced proinsulin-specific IFN-γ and IL-17A-producing T cells. Further studies are warranted to determine if ustekinumab can prevent C-peptide AUC decline and induce a clinical response.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".