Drug-Induced Cheilitis Reports: 38 Pharmaceuticals and Molecular Targets

Functioning as derived from an analysis of 5,007 cheilitis adverse event reports from the U.S. FDA Adverse Event Reporting System (FAERS) spanning 2004–2025. It identifies 38 pharmaceuticals significantly associated with cheilitis, with isotretinoin most frequently reported and crisaborole showing the strongest signal. The study integrates pharmacovigilance, network toxicology, and molecular docking results, including binding affinities and molecular dynamics simulations.

Use Cases

• Identify high-risk pharmaceuticals for cheilitis using reported odds ratios (ROR) like those for isotretinoin (ROR=42.61) and crisaborole (ROR=550.48) from FAERS data.
• Analyze demographic risk factors such as female sex (OR=0.771) and protective effects of age and weight from multivariate logistic regression results.
• Validate binding interactions between high-risk drugs like afatinib and capecitabine with core targets EGFR and IL-6 using molecular docking affinity scores (ranging from -8.1 to -6.2 kcal/mol).
• Examine enriched signaling pathways (e.g., IL-17, TNF, PI3K-Akt) and central protein targets (e.g., IL6, TNF, AKT1, VEGFA, STAT3) identified through network toxicology.
• Assess drug safety profiles using ADMET predictions, such as the high risk of drug-induced liver injury for four compounds versus the favorable profile of lamotrigine.

Strengths

• Analysis is based on 5,007 cheilitis reports from the authoritative U.S. FDA Adverse Event Reporting System (FAERS).
• Identifies 38 pharmaceuticals with significant associations to cheilitis, providing specific risk metrics like ROR and OR.
• Integrates multiple methodologies: disproportionality analysis, multivariate regression, network toxicology, and molecular docking with 500 ns molecular dynamics simulations.
• Includes concrete molecular binding affinity scores ranging from -8.1 to -6.2 kcal/mol for key drug-target interactions.

Limitations

• The dataset is very small in file size (22.0 KB), suggesting it contains summary results and analysis, not the raw underlying report data.
• Temporal coverage (2004–2025) includes future data (2025), which may indicate projected or incomplete data for recent years.
• The primary data source is FAERS, a spontaneous reporting system subject to under-reporting and reporting biases.

Provenance

Source

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

Collection Method

Disproportionality analysis and multivariate logistic regression on FAERS reports, combined with network toxicology and molecular docking simulations.

Time Range

2004–2025

Freshness

Last updated 2026-03-20, indicating recent analysis.

Geography

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