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ArticleNaunyn-Schmiedeberg's archives of pharmacology2026

Multi-database pharmacovigilance identifies disproportionate reporting of hepatobiliary events with avacopan: an integrative study with network pharmacology and interpretable machine learning.

Jing Zhang, Zhiwei Cui, Wenqian Zeng, Fan Zou, Xiyuan Zhang, Liang Shang, De Xie

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Article in Naunyn-Schmiedeberg's archives of pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jing ZhangDepartment of Geriatrics, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Zhiwei CuiDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Wenqian ZengDepartment of Tuberculosis, Shanghai Pulmonary Hospital, Shanghai, 200443, People's Republic of China.
Fan ZouDepartment of Tuberculosis, Shanghai Pulmonary Hospital, Shanghai, 200443, People's Republic of China.
Xiyuan ZhangDepartment of Geriatrics, The First Affiliated Hospital of Xi'an Medical University, Xi'an, Shaanxi, China.
Liang ShangDepartment of Geriatrics, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
De XieDepartment of Geriatrics, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China. xiede_xmu@126.com.

Funding

Shaanxi Association for Science and Technology 2026swyy17the Key Industrial Innovation Chain Project in Shaanxi Province of China (2023-ZDLSF-21
6 · The paper itself

Abstract

Avacopan is an oral C5a receptor antagonist approved for severe active granulomatosis with polyangiitis and microscopic polyangiitis. However, its real-world safety profile, particularly hepatobiliary safety signals, remains incompletely characterized. We analyzed avacopan-related adverse event reports from FAERS, JADER, and EudraVigilance. Disproportionality analyses were performed using reporting odds ratio and Bayesian Confidence Propagation Neural Network. Subgroup, sensitivity analysis, time-to-onset, Bradford Hill, network pharmacology, molecular docking, machine learning, and SHAP analyses were used to characterize safety signals, explore potential mechanisms, and identify factors associated with hepatobiliary disorder reporting. In FAERS, 5,293 avacopan-related individual case safety reports comprising 11,901 adverse event terms were identified. Positive signals were detected for infections, gastrointestinal disorders, and hepatobiliary disorders. Liver-related events, including drug-induced liver injury, jaundice, cholestasis, and vanishing bile duct syndrome, were consistently observed across FAERS, JADER, and EudraVigilance. In FAERS, most adverse events occurred early after treatment initiation. A Bradford Hill-informed contextual appraisal provided additional context for the observed DILI reporting signal; however, it did not permit causal inference. Network pharmacology identified 83 overlapping targets, with enrichment mainly involving xenobiotic response, oxidative stress, lipid metabolism, and PI3K-Akt signaling. Molecular docking supported favorable interactions between avacopan and key targets. Among machine learning models, NeuralNet showed the best validation performance for report-level classification, and SHAP analysis identified country as the dominant contributor to model predictions, suggesting substantial influence of regional reporting patterns. This real-world study expands the safety profile of avacopan and highlights hepatobiliary disorders as important signals requiring clinical attention. These findings support strengthened post-marketing surveillance and careful liver monitoring during avacopan therapy.

Indexed as

AvacopanDrug-induced liver injuryHepatobiliary disordersInterpretable machine learningMolecular dockingNetwork pharmacologyPharmacovigilance

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.