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Therapeutic drug monitoring in inflammatory bowel disease: The dawn of reactive monitoring.炎症性肠病的治疗药物监测:反应性监测的曙光。
World J Gastroenterol. 2021 Oct 7;27(37):6231-6247. doi: 10.3748/wjg.v27.i37.6231.
2
Prospective randomised controlled trial of adults with perianal fistulising Crohn's disease and optimised therapeutic infliximab levels: PROACTIVE trial study protocol.前瞻性随机对照试验成人肛周瘘管性克罗恩病和优化治疗英夫利昔单抗水平:PROACTIVE 试验研究方案。
BMJ Open. 2021 Jul 1;11(7):e043921. doi: 10.1136/bmjopen-2020-043921.
3
Advances in Therapeutic Drug Monitoring in Biologic Therapies for Pediatric Inflammatory Bowel Disease.儿童炎症性肠病生物治疗中治疗药物监测的进展
Front Pediatr. 2021 May 26;9:661536. doi: 10.3389/fped.2021.661536. eCollection 2021.
4
Update on TDM (Therapeutic Drug Monitoring) with Ustekinumab, Vedolizumab and Tofacitinib in Inflammatory Bowel Disease.优特克单抗、维多珠单抗和托法替布在炎症性肠病中的治疗药物监测(TDM)进展
J Clin Med. 2021 Mar 17;10(6):1242. doi: 10.3390/jcm10061242.
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Proactive therapeutic drug monitoring of adalimumab for pediatric Crohn's disease patients: A cost-effectiveness analysis.对儿童克罗恩病患者进行阿达木单抗的前瞻性治疗药物监测:一项成本效益分析。
J Gastroenterol Hepatol. 2021 Sep;36(9):2397-2407. doi: 10.1111/jgh.15373. Epub 2021 Jan 23.
6
Optimizing biologic therapy in IBD: how essential is therapeutic drug monitoring?优化炎症性肠病的生物治疗:治疗药物监测有多重要?
Nat Rev Gastroenterol Hepatol. 2020 Nov;17(11):702-710. doi: 10.1038/s41575-020-0352-2. Epub 2020 Sep 2.
7
Effect of a Practice-wide Anti-TNF Proactive Therapeutic Drug Monitoring Program on Outcomes in Pediatric Patients with Inflammatory Bowel Disease.全实践范围抗 TNF 主动治疗药物监测方案对炎症性肠病儿科患者结局的影响。
Inflamm Bowel Dis. 2021 Mar 15;27(4):482-492. doi: 10.1093/ibd/izaa102.
8
Optimized timing of using infliximab in perianal fistulizing Crohn's disease.优化英夫利昔单抗在肛周瘘管性克罗恩病中的使用时机。
World J Gastroenterol. 2020 Apr 14;26(14):1554-1563. doi: 10.3748/wjg.v26.i14.1554.
9
Early Discontinuation of Infliximab in Pregnant Women With Inflammatory Bowel Disease.炎症性肠病孕妇中英夫利昔单抗的早期停药。
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Higher anti-tumor necrosis factor levels are associated with perianal fistula healing and fistula closure in Crohn's disease.较高的抗肿瘤坏死因子水平与克罗恩病患者肛周瘘管愈合及瘘管闭合相关。
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炎症性肠病的治疗药物监测:在适当的时间、适当的地点。

Therapeutic drug monitoring in inflammatory bowel disease: At the right time in the right place.

机构信息

Internal Medicine, Johns Hopkins University, Baltimore, MD 21210, United States.

出版信息

World J Gastroenterol. 2022 Apr 7;28(13):1380-1383. doi: 10.3748/wjg.v28.i13.1380.

DOI:10.3748/wjg.v28.i13.1380
PMID:35645545
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9099186/
Abstract

Therapeutic drug monitoring (TDM) was one of most sought-after objective tools to determine therapeutic efficiency of different biologics and its role in the management of patients with inflammatory bowel disease (IBD) was regarded with great anticipation. But implementation of the TDM in clinical practice was challenged by several factors including uncertainty of the optimal cut-off values, assay variable sensitivity in detecting drug levels and antibodies and, most importantly, individual pharmacokinetics. While reactive TDM was embraced in clinical practice as a useful tool in assessing lack of response to therapy, the utility of proactive TDM in managing IBD therapy is still challenged by the lack of consistency between evidence. Described here, there are four groups of IBD patients for whom proactive TDM has the potential to greatly impact their therapeutic outcomes: Patients with perianal Crohn's disease, patients with severe ulcerative colitis, pregnant women with IBD and children. As the future of IBD management moves towards personalizing treatment, TDM will be an important decision node in a machine learning based algorithm predicting the best strategy to maximize treatment results while minimizing the loss of response to therapy.

摘要

治疗药物监测(TDM)是一种最受关注的客观工具,可用于确定不同生物制剂的治疗效果,其在炎症性肠病(IBD)患者管理中的作用备受期待。但 TDM 在临床实践中的应用受到多种因素的挑战,包括最佳临界值的不确定性、检测药物水平和抗体的检测方法的敏感性,以及最重要的个体药代动力学。虽然反应性 TDM 作为评估治疗反应缺乏的有用工具在临床实践中得到了认可,但主动 TDM 在管理 IBD 治疗中的实用性仍受到证据缺乏一致性的挑战。这里描述了四组 IBD 患者,主动 TDM 有可能极大地影响他们的治疗效果:肛周克罗恩病患者、严重溃疡性结肠炎患者、患有 IBD 的孕妇和儿童。随着 IBD 管理的未来朝着个性化治疗的方向发展,TDM 将成为基于机器学习的算法中一个重要的决策节点,该算法可以预测最佳策略,最大限度地提高治疗效果,同时最大限度地减少治疗反应的丧失。