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Adverse event signal mining and serious adverse event influencing factor analysis of avacopan based on FAERS database
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Di LI, Xiao-chun YE, Lu-lu LI, Shao-hui ZHANG*
Chinese Journal of New Drugs | 2026, 35(7) : 772 - 779
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Chinese Journal of New Drugs | 2026, 35(7): 772-779
Adverse event signal mining and serious adverse event influencing factor analysis of avacopan based on FAERS database
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Di LI, Xiao-chun YE, Lu-lu LI, Shao-hui ZHANG*
Affiliations
  • Department of Pharmacy, Wuhan NO.1 Hospital, Wuhan 430022, China
Published: 2026-04-15 doi: 10.20251/j.cnki.1003-3734.2026.07.014
Outline
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Objective:

To explore post-marketing adverse drug events (ADE) of avacopan and explore the risk factors for severe ADE, providing a reference for clinical safe and rational drug use.

Methods:

Retrieved adverse event report information associated with avacopan from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database were extracted, covering a total of 12 quarters from the fourth quarter of 2021 to the third quarter of 2024. Suspicious AEs were screened using the reporting odds ratio (ROR), proportional reporting ratio (PRR), bayesian confidence propagation neural network (BCPNN), multiple item empirical bayesian gamma poisson shrinker (MGPS) based on disproportionality analysis. Univariate and multivariate logistic regression analyses were conducted on severe ADE to explore the risk factors associated with avacopan-induced severe ADE according to their severity.

Results:

A total of 2 886 ADE reports associated with avacopan were obtained, including 1 489 reports of severe AEs. Using the disproportionality analysis methods, a total of 69 positive ADE signals were identified for avacopan, involving 14 systems and organs. The ADE associated with avacopan were sorted by frequency of occurrence, with hospitalisation (272 cases) having the highest number of reports. By signal strength, antineutrophil cytoplasmic antibody increased showed the strongest signal (ROR=290.83, 95%CI=114.09~741.35). The results of the Logistic regression analysis showed that gender (OR=1.27, 95%CI=1.02~1.58, P<0.05), age (65~80 years: OR=2.20, 95%CI=1.61~2.99, P<0.001;≥80 years: OR=4.61, 95%CI=2.89~7.34, P<0.001) and multiple medications (<5 prescribed drugs: OR=1.32, 95%CI=1.05~1.66, P<0.05; ≥5 prescribed drugs: OR=12.22, 95%CI=6.83~21.87, P<0.001) were the influencing factors for occurrence of severe ADE associated with avacopan.

Conclusion:

Clinicians and pharmacists are advised topay greater attention to severe and new ADE signals not included in drug labeling. They should enhance management and pharmaceutical care for elderly, female, and polypharmacy patients to prevent serious ADE and ensure the patient medication safety.

avacopan  /  data mining  /  adverse drug events  /  FAERS database
李笛, 叶小春, 李璐璐, 张韶辉. 基于FAERS数据库的阿伐可泮不良事件信号挖掘与严重不良事件危险因素分析. 中国新药杂志, 2026 , 35 (7) : 772 -779 . DOI: 10.20251/j.cnki.1003-3734.2026.07.014
Di LI, Xiao-chun YE, Lu-lu LI, Shao-hui ZHANG. Adverse event signal mining and serious adverse event influencing factor analysis of avacopan based on FAERS database[J]. Chinese Journal of New Drugs, 2026 , 35 (7) : 772 -779 . DOI: 10.20251/j.cnki.1003-3734.2026.07.014
Year 2026 volume 35 Issue 7
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5
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Article Info
doi: 10.20251/j.cnki.1003-3734.2026.07.014
  • Online Date:2026-10-26
  • Published:2026-04-15
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  • Accepted:2025-10-10
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    Department of Pharmacy, Wuhan NO.1 Hospital, Wuhan 430022, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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