Department of Gastroenterology, St George's University Hospitals NHS Foundation Trust, London, UK.
School of Immunology and Microbial Sciences, King's College London, London, UK.
Nat Rev Drug Discov. 2024 Jul;23(7):546-562. doi: 10.1038/s41573-024-00953-0. Epub 2024 May 22.
Inflammatory bowel disease (IBD) - consisting of ulcerative colitis and Crohn's disease - is a complex, heterogeneous, immune-mediated inflammatory condition with a multifactorial aetiopathogenesis. Despite therapeutic advances in this arena, a ceiling effect has been reached with both single-agent monoclonal antibodies and advanced small molecules. Therefore, there is a need to identify novel targets, and the development of companion biomarkers to select responders is vital. In this Perspective, we examine how advances in machine learning and tissue engineering could be used at the preclinical stage where attrition rates are high. For novel agents reaching clinical trials, we explore factors decelerating progression, particularly the decline in IBD trial recruitment, and assess how innovative approaches such as reconfiguring trial designs, harmonizing end points and incorporating digital technologies into clinical trials can address this. Harnessing opportunities at each stage of the drug development process may allow for incremental gains towards more effective therapies.
炎症性肠病（IBD）——包括溃疡性结肠炎和克罗恩病——是一种复杂的、异质的、免疫介导的炎症性疾病，其发病机制具有多因素性。尽管在这一领域取得了治疗进展，但单克隆抗体和先进小分子药物的单一治疗已经达到了疗效上限。因此，需要确定新的靶点，开发伴随生物标志物来选择应答者至关重要。在本观点中，我们探讨了机器学习和组织工程的进步如何在淘汰率较高的临床前阶段得到应用。对于进入临床试验的新型药物，我们研究了减缓疾病进展的因素，特别是炎症性肠病临床试验招募的下降，并评估了重新配置临床试验设计、协调终点以及将数字技术纳入临床试验等创新方法如何解决这一问题。在药物开发过程的每个阶段利用机会，可能会逐步实现更有效的治疗方法。
