Real-World Data on Severe Cutaneous Adverse Reactions to Drugs

TL;DR

This study identifies drugs commonly linked to severe skin reactions using real-world data from a Russian pharmacovigilance database.

Contribution

The study provides real-world evidence of drug-induced severe cutaneous adverse reactions using Russian spontaneous reporting data.

Findings

Antibacterial drugs, antineoplastic agents, and antiepileptics were the most frequently reported drug classes linked to SCARs.

Linagliptin, clindamycin, and piperacillin with beta-lactamase inhibitor showed the strongest signals for causing SCARs.

Pharmacovigilance databases are effective in identifying SCARs and their associated drugs to improve clinical outcomes.

Abstract

Background/Objectives: Cutaneous adverse drug reactions (CADRs) represent the most common manifestations of drug-induced allergy, with most unfavorable clinical outcomes seen in severe cutaneous adverse reactions (SCARs). To manage SCARs immediate cessation of the offending drug is needed; therefore, it is crucial to identify the list of medications associated with SCARs in real-world clinical practice. The objective of this study was to evaluate the structure of drugs associated with SCARs and to analyze drug-induced SCAR signals by calculating the reporting odds ratio (ROR) and proportional reporting ratio (PRR) based on spontaneous reports extracted from the Russian national pharmacovigilance database. Methods: A retrospective, descriptive pharmacoepidemiological analysis of spontaneous reports (SRs) registered in the pharmacovigilance database from 1 April 2019 to 31 March 2025. Results: A total of 7011 SRs with SCARs were finally revealed, with 907 identified drug triggers. The most frequently reported were antibacterial drugs for systemic use (22.8%), antineoplastic agents (17.8%), and antiepileptics (6.0%). The top five drugs involved in SCARs were dupilumab (2.14%, n = 244), piperacillin and beta-lactamase inhibitor (2.0%, n = 227), pembrolizumab (1.98%, n = 225), levofloxacin (1.95%, n = 222), and linagliptin (1.93%, n = 220). The strongest signals were detected for linagliptin (PRR = 15.37, 95% CI: 13.54–17.44; ROR = 17.24, 95% CI: 14.95–19.88), followed by clindamycin (PRR = 12.44, 95% CI: 10.89–14.21; ROR = 13.62, 95% CI: 11.77–15.77) and by piperacillin and beta-lactamase inhibitor (PRR = 10.02, 95% CI: 8.86–11.43; ROR = 10.81, 95% CI: 9.42–12.40). Conclusions: Pharmacovigilance databases facilitate the identification of diverse phenotypes of SCARs and the list of culprit drugs. The accumulated data serve as a valuable tool to enhance clinical practice outcomes and strengthen overall healthcare monitoring.

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