Gómez-Labrador Celia, Ricart Elena, Iborra Marisa, Iglesias Eva, Martín-Arranz María Dolores, de Castro Luisa, De Francisco Ruth, García-Alonso Francisco Javier, Sanahuja Ana, Gargallo-Puyuelo Carla J, Mesonero Francisco, Casanova María José, Mañosa Míriam, Rivero Montserrat, Calvo Marta, Sierra-Ausin Mónica, González-Muñoza Carlos, Calvet Xavier, García-López Santiago, Guardiola Jordi, Arias García Lara, Márquez-Mosquera Lucía, Gutiérrez Ana, Zabana Yamile, Navarro-Llavat Mercè, Lorente Poyatos Rufo, Piqueras Marta, Torrealba Leyanira, Bermejo Fernando, Ponferrada-Díaz Ángel, Pérez-Calle José L, Barreiro-de Acosta Manuel, Tejido Coral, Cabriada José Luis, Marín-Jiménez Ignacio, Roncero Óscar, Ber Yolanda, Fernández-Salazar Luis, Camps Aler Blau, Lucendo Alfredo J, Llaó Jordina, Bujanda Luis, Muñoz Villafranca Carmen, Domènech Eugeni, Chaparro María, Gisbert Javier P
Gastroenterology Unit, Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa (IIS-Princesa), Universidad Autónoma de Madrid and Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), 28006 Madrid, Spain.
Gastroenterology Unit, Hospital Clinic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and CIBEREHD, 08036 Barcelona, Spain.
Pharmaceutics. 2024 May 8;16(5):629. doi: 10.3390/pharmaceutics16050629.
Markers that allow for the selection of tailored treatments for individual patients with inflammatory bowel diseases (IBD) are yet to be identified. Our aim was to describe trends in real-life treatment usage. For this purpose, patients from the ENEIDA registry who received their first targeted IBD treatment (biologics or tofacitinib) between 2015 and 2021 were included. A subsequent analysis with Machine Learning models was performed. The study included 10,009 patients [71% with Crohn's disease (CD) and 29% with ulcerative colitis (UC)]. In CD, anti-TNF (predominantly adalimumab) were the main agents in the 1st line of treatment (LoT), although their use declined over time. In UC, anti-TNF (mainly infliximab) use was predominant in 1st LoT, remaining stable over time. Ustekinumab and vedolizumab were the most prescribed drugs in 2nd and 3rd LoT in CD and UC, respectively. Overall, the use of biosimilars increased over time. Machine Learning failed to identify a model capable of predicting treatment patterns. In conclusion, drug positioning is different in CD and UC. Anti-TNF were the most used drugs in IBD 1st LoT, being adalimumab predominant in CD and infliximab in UC. Ustekinumab and vedolizumab have gained importance in CD and UC, respectively. The approval of biosimilars had a significant impact on treatment.
能够为炎症性肠病（IBD）个体患者选择量身定制治疗方案的标志物尚未确定。我们的目的是描述实际治疗使用情况的趋势。为此，纳入了ENEIDA注册中心在2015年至2021年间接受首次靶向IBD治疗（生物制剂或托法替布）的患者。随后进行了机器学习模型分析。该研究纳入了10009名患者[71%为克罗恩病（CD），29%为溃疡性结肠炎（UC）]。在CD中，抗TNF（主要是阿达木单抗）是一线治疗（LoT）的主要药物，不过其使用随时间有所下降。在UC中，抗TNF（主要是英夫利昔单抗）在一线治疗中占主导地位，且随时间保持稳定。乌司奴单抗和维多珠单抗分别是CD和UC二线及三线治疗中处方最多的药物。总体而言，生物类似药的使用随时间增加。机器学习未能识别出能够预测治疗模式的模型。总之，CD和UC中的药物定位有所不同。抗TNF是IBD一线治疗中使用最多的药物，在CD中以阿达木单抗为主，在UC中以英夫利昔单抗为主。乌司奴单抗和维多珠单抗分别在CD和UC中变得越来越重要。生物类似药的获批对治疗产生了重大影响。
