{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T13:26:11Z","timestamp":1792070771440,"version":"4.4.0"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To identify and characterize drugs associated with alopecia using real-world data from the FDA Adverse Event Reporting System (FAERS).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 181,838 reports with drug-associated alopecia were identified. The mean age was 53.84 \u00b1 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\u2013360 days. Males experienced a significantly shorter onset latency compared to females (108 days vs. 236 days,\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.001). Oncology drugs also showed shorter latency than non-oncology agents (198 vs. 308 days,\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2025.1703423","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T06:30:10Z","timestamp":1764311410000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Real-world pharmacovigilance insights into drug-induced risk of alopecia"],"prefix":"10.3389","volume":"16","author":[{"given":"Huixiang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haijian","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaoqiao","family":"Shentu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxin","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaojiao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"S20","DOI":"10.1016\/j.jaad.2023.04.022","article-title":"Medication-induced hair loss: an update","volume":"89","author":"Alhanshali","year":"2023","journal-title":"J. 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