Internal Medicine, Johns Hopkins University, Baltimore, MD 21210, United States.
World J Gastroenterol. 2022 Apr 7;28(13):1380-1383. doi: 10.3748/wjg.v28.i13.1380.
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.
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。