Objectives: The widespread use of immune checkpoint inhibitors (ICIs) has led to breakthrough advances for patients with various advanced solid tumors. As the skin is an important target organ of immune responses, it is the most commonly affected site of treatment-related adverse events associated with ICIs, with a relatively high incidence of ICI-related skin toxicity events. Immune-related adverse events induced by ICIs are increasingly becoming a bottleneck limiting their clinical application. To collect post-marketing adverse events and medication errors related to drugs and therapeutic biological products and to evaluate real-world drug safety, the United States Food and Drug Administration (FDA) established the FDA Adverse Event Reporting System (FAERS) database. Based on the FAERS database, this study aims to systematically evaluate differences in the risk of skin toxicity events among different drug subtypes, cytotoxic-T-lymphocyte-associated antigen-4 inhibitors, programmed death-1 (PD-1) inhibitors, and programmed death-ligand 1 (PD-L1) inhibitors, and to explore the limitations and potential improvements of existing pharmacovigilance methods.
Methods: Skin toxicity event data from the FAERS database between 2004 and 2024 were cleaned, standardized, and screened to identify adverse events associated with the target drugs. Pharmacovigilance signal detection methods, including the reporting odds ratio (ROR) method and Bayesian confidence propagation neural network (BCPNN) method, were used for the signals of ICIs-related skin toxicity event in the data, with stratified analyses by age and sex. For the ROR method, a skin toxicity event reporting count ≥3 and a lower bound of the 95% confidence interval (CI) >1 were used as criteria for a positive signal; for the Bayesian method, the information component was used as the core parameter. Subsequently, a systematic statistical analysis of the frequencies of different types of adverse events induced by different drugs was conducted, and outcomes associated with different drugs were summarized. Pharmacovigilance signal detection methods were applied for data analysis.
Results: A total of 15 768 reports of skin-related adverse events were collected. The reported population was predominantly male, with most patients aged ≥65 years, and a higher proportion of cases from Europe and the United States. Among the reported indications, the 3 most common were malignant melanoma, non-small cell lung cancer, and metastatic melanoma. Most adverse events occurred within 30 days after drug administration, during which the number of reports was the highest. Using the two signal detection methods, positive signals were identified for 5 of the 8 target drugs: nivolumab (ROR=1.20, 95% CI 1.17 to 1.23), pembrolizumab (ROR=1.31, 95% CI 1.27 to 1.35), ipilimumab (ROR=1.82, 95% CI 1.74 to 1.90), atezolizumab (ROR=1.06, 95% CI 1.01 to 1.12), and tislelizumab (ROR=3.05, 95% CI 2.71 to 3.43). Further analysis showed that the PD-1 inhibitors pembrolizumab and nivolumab had higher numbers of reported cases of immune-mediated dermatitis, vitiligo, psoriasis, and other skin toxicity events than other ICIs. Comparison of outcomes of skin toxicity reactions caused by different drugs revealed that nivolumab-related cases had the highest numbers of reports of hospitalization, death, and life-threatening, followed by pembrolizumab.
Conclusions: Five ICIs may induce skin toxicity events, which exhibit specific population characteristics, temporal patterns, and toxicity profiles. When using the PD-1 inhibitors nivolumab or pembrolizumab, particular vigilance is required for severe cutaneous toxicity to avoid unfavorable outcomes. Expanding the sample size and incorporating machine learning in the future may improve the precision and clinical translatability of signal detection, providing important evidence for optimizing clinical monitoring strategies for ICIs and establishing toxicity early-warning models.
目的: 免疫检查点抑制剂(immune checkpoint inhibitors，ICIs)的广泛应用使多种晚期实体肿瘤患者的治疗获得突破性进展。皮肤作为免疫应答的重要靶器官，是ICIs治疗相关不良事件最常累及的部位，ICIs相关皮肤不良事件发生率较高。ICIs引发的免疫相关不良事件正逐渐成为制约其临床应用的瓶颈问题。美国食品药品监督管理局(Food and Drug Administration，FDA)为收集上市后的药品及治疗性生物制品的不良事件及用药错误，评估真实世界的药物安全性，建立FDA不良事件报告系统(FDA Adverse Event Reporting System，FAERS)数据库。本研究以FAERS数据库为基础，拟对不同的药物亚型[细胞毒性T淋巴细胞相关抗原-4抑制剂、程序性死亡受体-1(programmed death-1，PD-1)抑制剂及程序性死亡受体配体-1(programmed death-ligand 1，PD-L1)抑制剂]的皮肤不良事件风险差异进行系统评估，并对现有的药物警戒方法的局限性和改进方向进行探讨。方法: 对FAERS数据库中2004至2024年的皮肤不良事件数据进行清洗、标准化及筛选，获得发生目标药品不良事件数据集。采用报告比值比(reporting odds ratio，ROR)法及贝叶斯法对数据的ICIs相关皮肤不良事件信号进行分析，并进行年龄及性别分层分析。ROR法采用皮肤不良事件报告数≥3例且95%置信区间(confidence interval，CI)下限>1作为阳性信号判定标准;贝叶斯法则以信息成分值为核心参数;接下来对不同药物引发的不同种类的不良事件发生频数进行系统的统计分析;最后总结不同药物的转归情况。结果: 共收集皮肤相关不良事件报告15 768例。报告人群以男性为主，年龄多集中于65岁以上，且以欧美地区患者占比较高。在所涉及的适应证中，最常见的3种为恶性黑色素瘤、非小细胞肺癌及转移性黑色素瘤。不良事件多发生在用药后30 d内，报告例数最多。通过ROR法及贝叶斯法，共识别出8种目标药物中5种具有阳性信号，分别为纳武利尤单抗(ROR=1.20，95% CI 1.17~1.23)、帕博利珠单抗(ROR=1.31，95% CI 1.27~1.35)、伊匹木单抗(ROR=1.82，95% CI 1.74~1.90)、阿替利珠单抗(ROR=1.06，95% CI 1.01~1.12)及替雷利珠单抗(ROR=3.05，95% CI 2.71~3.43)。进一步分析显示，PD-1抑制剂帕博利珠单抗与纳武利尤单抗报告免疫介导性皮炎、白癜风、银屑病等多种皮肤不良事件例数高于其他ICIs。对不同药物所致皮肤不良事件的转归情况进行比较发现，纳武利尤单抗相关病例中，住院、死亡及危及生命的报告数量均居首位，帕博利珠单抗次之。结论: 5种ICIs均可能诱发皮肤不良事件，其发生呈现特定人群特征、时序特征及毒性特征;使用PD-1抑制剂纳武利尤单抗或帕博利珠单抗时，需特别警惕重度皮肤毒性，避免引起不良转归。未来可扩大样本并引入机器学习，有望提升信号检测精度与临床可转化性，为优化ICIs临床监测方案及建立毒性预警模型提供重要循证依据。.
Keywords: adverse drug events; data mining; immune checkpoint inhibitors; signal detection; skin toxicity reactions.