Phase I, single-dose study to compare pharmacokinetics of depemokimab delivered by safety syringe device or autoinjector in healthy adults
Bibliographic record
Abstract
Background: Depemokimab is the first and only humanised anti-IL-5 antibody with enhanced binding affinity and high potency, resulting in an extended half-life, enabling 6-monthly dosing and sustained inhibition of broad inflammatory function. Aim: Compare pharmacokinetics (PK), immunogenicity and safety of depemokimab administered by safety syringe device (SSD) or autoinjector (AI). Methods: Participants randomised (1:1) received single-dose depemokimab 100 mg subcutaneously via SSD or AI, in the upper arm, abdomen or thigh (randomised 1:1:1). PK parameters were assessed up to Day 183 post dose and relative bioavailability calculated by AI:SSD geometric mean ratio (GMR). Immunogenicity (presence of anti-drug antibodies) and adverse events (AEs) were also assessed. Results: Each arm included 70 participants. PK was similar between devices. AI:SSD adjusted GMR and 90% CIs were within the bioequivalence range of 0.8 to 1.25 for Cmax (1.03 [0.96, 1.09]) and AUC(0-inf) (1.03 [0.96, 1.10]). When pooled across devices, depemokimab geometric mean (log SD) Cmax (µg/mL) and AUC0-inf (µg*day/mL) was 14.1 (0.25) and 1004.4 (0.30) for upper arm, 15.3 (0.22) and 1119.3 (0.22) for abdomen and 15.2 (0.23) and 1091.0 (0.28) for thigh, respectively. Immunogenicity incidence was low (1%) for both devices. Drug-related AE incidence was 19% (SSD) and 20% (AI). Conclusions: For single-dose depemokimab, PK and bioavailability were similar for SSD and AI; criteria for bioequivalence were met. PK was generally comparable across injection sites. Incidence of immunogenicity and AEs were low and similar for SSD and AI. Funding: GSK (214099, NCT05602025 )
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".