Post-Marketing Safety Concerns with Efgartigimod alfa: A Pharmacovigilance Analysis Based on the Food and Drug Administration Adverse Event Reporting System Database
Bibliographic record
Abstract
Jinlong Huang,1,2 Hanyun Ye,1 Jingyang Lin,3 Dan Luo,1,2 Ping Huang,1 Xiaochun Zheng1,2 1Center for Clinical Pharmacy, Cancer Center, Department of Pharmacy, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, People’s Republic of China; 2School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang, People’s Republic of China; 3Heart Center, Department of Cardiovascular Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, People’s Republic of ChinaCorrespondence: Ping Huang, Center for Clinical Pharmacy, Cancer Center, Department of Pharmacy, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, 158 Shangtang Road, Gongsu District, Hangzhou, Zhejiang, 310014, People’s Republic of China, Email huangpwly@sina.com Xiaochun Zheng, Center for Clinical Pharmacy, Cancer Center, Department of Pharmacy, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, 158 Shangtang Road, Gongsu District, Hangzhou, Zhejiang, 310014, People’s Republic of China, Email 13868109173@126.comAim: Efgartigimod alfa (EA) is a novel US Food and Drug Administration (FDA) approved neonatal Fc receptor-targeting drug; however, its real-world adverse event (AE) profile remains underexplored.Methods: AE reports primarily related to EA were retrieved from the US FDA Adverse Event Reporting System database for the fourth quarter of 2021 to the third quarter of 2024. Disproportionality analysis using Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network, and Multi-item Gamma Poisson Shrinker algorithms was employed to detect signals of AEs.Results: Our study processed 3,182 AE reports related to EA, revealing 57 signals that met the criteria of the ROR, PRR, Bayesian Confidence Propagation Neural Network, and Multi-item Gamma Poisson Shrinker algorithms across 14 system organ classes. Notably, the most significant signal in the System Organ Class was “Surgical and medical procedures”, whereas the most significant signal in Preferred Term was “Bulbar Palsy”. Some unexpected over-the-counter AEs, including falls, choking, sepsis, nephrolithiasis, and atrial fibrillation, were also observed. The median onset time of EA-related AEs was 101.5 d (interquartile range 27– 260). The AE risk model associated with EA should be referred to as “early failure”, with the likelihood of AEs decreasing over time.Conclusion: This study highlights the potential AEs and risks associated with the clinical use of EA; the analysis provides significant evidence regarding the clinical safety of EA.Keywords: efgartigimod alfa, myasthenia gravis, signal mining, adverse events
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".