Safety, Tolerability, and Pharmacokinetics of the Long‐Acting SARS‐CoV‐2–Neutralizing Monoclonal Antibody Combination AZD7442 (Tixagevimab/Cilgavimab) in Healthy Chinese Adults
Bibliographic record
Abstract
AZD7442, a combination of extended half-life monoclonal antibodies tixagevimab and cilgavimab, was shown to neutralize previously circulating SARS-CoV-2 variants. This study evaluated safety, tolerability, pharmacokinetics, and pharmacodynamics of AZD7442 in healthy Chinese adults. In this randomized, placebo-controlled, Phase 1 study, AZD7442 was administered intramuscularly or intravenously (300 or 600 mg). End points included safety, tolerability, pharmacokinetics, antidrug antibodies, and SARS-CoV-2-neutralizing antibody titers. Sixty participants were randomized and dosed (AZD7442, n = 49; placebo, n = 11). Adverse events occurred in 45 (91.8%) and 9 (81.8%) participants, serious adverse events occurred in 2 (4.1%) and 0 (0%) participants in AZD7442 and placebo groups, respectively, and there were no deaths. Tixagevimab and cilgavimab had mean half-lives of 82.4-88.1 (range across dosing groups) and 79.0-83.7 days, respectively. In participants who received AZD7442, 3 (6.1%) were treatment-emergent antidrug antibody positive. SARS-CoV-2-neutralizing antibody titers were more than 4-fold higher than baseline levels by Day 8, then decreased through Day 361 following AZD7442 administration. AZD7442 was well tolerated in healthy Chinese adults, demonstrating predictable pharmacokinetics and an extended half-life consistent with previous studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".