Real-world pharmacovigilance insights into drug-induced risk of alopecia

Huixiang Li¹Haijian Wei²Qiaoqiao Shentu³Jianxin Cui²Jiaojiao Chen^1*

• ¹Department of Pharmacy, Yantai Yuhuangding Hospital, Yantai, China
• ²Department of Organ Transplantation, Yantai Yuhuangding Hospital, Yantai, China
• ³Department of pharmacy, Dongyang Red Cross Hospital, Dongyang, China

Background: Alopecia is a significant adverse effect that profoundly impacts quality of life. Although numerous medications are implicated, the real-world risk profiles across drug classes and patient demographics remain poorly quantified. Objective: To identify and characterize drugs associated with alopecia using real-world data from the FDA Adverse Event Reporting System (FAERS). Methods: FAERS reports from Q1 2004 to Q4 2024 were analyzed using four disproportionality methods (ROR, PRR, BCPNN, MGPS) to detect signals of drug-alopecia associations. Subgroup analyses were conducted by age, gender, and drug category. Time-to-onset (TTO) was analyzed using the Weibull distribution model. Results: A total of 181,838 reports with drug-associated alopecia were identified. The mean age was 53.84 ± 16.28 years, and 76.82% of reports were from females. Oncology medications showed strongest association (37.5%), especially docetaxel (ROR = 70.38). Endocrine (18.8%) and immune system medications (10.9%) were also prominent. The TTO analysis revealed a bimodal distribution, with 40.2% of cases occurring within 30 days and 13.1% manifesting at 240-360 days. Males experienced a significantly shorter onset latency compared to females (108 days vs. 236 days, P < 0.001). Oncology drugs also showed shorter latency than non-oncology agents (198 vs. 308 days, P < 0.001). Notably, comparison with United States prescribing information revealed that 23.4% of high-signal drugs lacked documentation of alopecia in their official labels. Conclusion: This large-scale pharmacovigilance study identified 64 drugs with significant alopecia signals, highlighting distinct demographic patterns and latency periods. The findings underscore the need for heightened clinical vigilance, gender-specific monitoring, and updates to labels to better reflect real-world risks.