Unveiling the hidden risk of caspofungin: insights from three adverse event reporting systems and network pharmacology integration
Bibliographic record
Abstract
Background: Caspofungin, the first FDA-approved echinocandin antifungal agent, plays a vital role in managing invasive fungal infections (IFIs). Despite its established efficacy, large-scale real-world safety evaluations remain limited. This study provides a comprehensive pharmacovigilance analysis of caspofungin's safety profile. Methods: Adverse drug events (ADEs) associated with caspofungin were extracted from the FDA Adverse Event Reporting System (FAERS), the Japanese Adverse Drug Event Reporting Database and the Canadian Vigilance Adverse Reaction Database (CVARD) databases. Signal detection utilized four methods: reporting odds ratio proportional reporting ratio Bayesian confidence propagation neural network and multiple gamma-Poisson shrinkage Time-to-onset (TTO) analysis was conducted using FDA Adverse Event Reporting System data, and network pharmacology approaches were employed to investigate potential molecular mechanisms, particularly in caspofungin-related liver injury. Results: A total of 2,270, 161, and 128 ADE reports were retrieved from FAERS, JADER, and CVARD, respectively. "Hepatobiliary disorders" and "infections and infestations" are overlapping positive signals from three databases at the system organ class level. ADEs such as hypokalemia, sepsis, and drug ineffectiveness were consistent with the drug label. Unexpected signals included prolonged QT interval, cardiac arrest, septic shock, and cholestasis. Cross-database overlap included "drug ineffective" and "toxic skin eruption" between FAERS and JADER, and "renal failure," "photodermatitis" between FAERS and CVARD. TTO analysis revealed that 89.95% of ADEs occurred within the first month, with a median onset time of 6 days. Network pharmacology identified PI3K/Akt and HIF-1 pathways as mechanisms underlying caspofungin-induced liver injury. Conclusion: This study highlights both expected and unexpected ADEs of caspofungin, emphasizing the importance of clinical vigilance and molecular research to enhance patient safety and therapeutic outcomes.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".