FDA Adverse Event Reports on Drug-Induced Cheilitis with 38 High-Risk Pharmaceuticals

The dataset underpins a pharmacovigilance study analyzing 5,007 cheilitis reports from the U.S. FDA Adverse Event Reporting System (FAERS) from 2004–2025. It identifies 38 pharmaceuticals significantly associated with the adverse reaction, with disproportionality analysis results like ROR values for isotretinoin (42.61) and crisaborole (550.48). The study integrates these findings with network toxicology and molecular docking results, including binding affinities and identified core molecular targets.

Use Cases

• Identify high-risk pharmaceuticals for cheilitis by analyzing reported odds ratios (ROR) like those for isotretinoin and crisaborole from FAERS data.
• Investigate demographic risk factors such as female sex (OR = 0.771) and protective effects of age and weight using multivariate logistic regression results.
• Validate drug-target interactions through molecular docking binding affinities (e.g., -8.1 to -6.2 kcal/mol for afatinib-EGFR and capecitabine-IL-6).
• Explore enriched biological pathways (e.g., IL-17, TNF, PI3K-Akt) and core targets (e.g., IL6, TNF, AKT1) identified via network toxicology analysis.
• Assess drug safety profiles using ADMET predictions, such as the high liver injury risk for four compounds versus lamotrigine's favorable profile.

Strengths

• Analysis is based on 5,007 cheilitis reports from the authoritative U.S. FDA Adverse Event Reporting System (FAERS).
• Identifies 38 specific pharmaceuticals with significant associations to the adverse event, providing concrete drug candidates for study.
• Integrates multiple analytical methods: disproportionality analysis (ROR), multivariate regression, network toxicology, and molecular docking with binding affinity scores.
• Includes a defined temporal coverage from 2004 to 2025 for the pharmacovigilance data.

Limitations

• The underlying dataset is very small at 48.0 KB, suggesting it contains summary results or a limited subset, not the raw 5,007 FAERS reports.
• As a DOCX file, the data is likely presented in a narrative or tabular format within a document, requiring extraction for computational analysis.
• The pharmacovigilance data may suffer from inherent reporting biases present in spontaneous reporting systems like FAERS.

Provenance

Source

U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS).

Collection Method

Disproportionality analysis, multivariate logistic regression, network toxicology, and molecular docking conducted on extracted FAERS reports.

Time Range

2004–2025

Freshness

The study was last updated in March 2026, indicating recent analysis.

Geography

United States (data source is U.S. FDA FAERS).