Navigating the complexities of drug development for inflammatory bowel disease

• Published in: Nature reviews. Drug discovery Vol. 23; no. 7; pp. 546 - 562
• Main Authors: Honap, Sailish, Jairath, Vipul, Danese, Silvio, Peyrin-Biroulet, Laurent
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01-07-2024; Nature Publishing Group
• Subjects: 631/154/155; 692/4020/1503/257; 692/4020/2199; Artificial intelligence; Biomedical and Life Sciences; Biomedicine; Biotechnology; Cancer Research; Clinical trials; Drug development; Inflammatory bowel disease; Machine learning; Medicinal Chemistry; Molecular Medicine; Perspective; Pharmacology/Toxicology
• ISSN: 1474-1776; 1474-1784; 1474-1784
• DOI: 10.1038/s41573-024-00953-0

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.
Drug discovery and development for inflammatory bowel disease (IBD) is hampered by various challenges including the insufficient mechanistic understanding of IBD immunopathology, disease heterogeneity, inadequate preclinical models and clinical trial inefficiencies. This Perspective assesses these limitations and presents strategies to overcome them, including the integration of artificial intelligence and machine learning approaches, organoid technology and innovative trial designs.