Shapiro Melanie R, Tallon Erin M, Brown Matthew E, Posgai Amanda L, Clements Mark A, Brusko Todd M
Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, USA.
Diabetes Institute, University of Florida, Gainesville, FL, USA.
Diabetologia. 2025 Mar;68(3):477-494. doi: 10.1007/s00125-024-06339-6. Epub 2024 Dec 19.
Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals who will progress faster to a clinical diagnosis of type 1 diabetes, and heterogeneous clinical responses in intervention trials. Classic placebo-controlled intervention trials often include monotherapies, broad participant populations and extended follow-up periods focused on clinical endpoints. While this approach remains the 'gold standard' of clinical research, efforts are underway to implement new approaches harnessing the power of artificial intelligence and machine learning to accelerate drug discovery and efficacy testing. Here, we review emerging approaches for repurposing agents used to treat diseases that share pathogenic pathways with type 1 diabetes and selecting synergistic combinations of drugs to maximise therapeutic efficacy. We discuss how emerging multi-omics technologies, including analysis of antigen processing and presentation to adaptive immune cells, may lead to the discovery of novel biomarkers and subsequent translation into antigen-specific immunotherapies. We also discuss the potential for using artificial intelligence to create 'digital twin' models that enable rapid in silico testing of personalised agents as well as dose determination. To conclude, we discuss some limitations of artificial intelligence and machine learning, including issues pertaining to model interpretability and bias, as well as the continued need for validation studies via confirmatory intervention trials.
1型糖尿病维持内源性胰岛素分泌或预防方面的治疗进展受到了多种因素的阻碍，包括动物模型有限、临床试验的时长和成本、难以识别将更快发展为1型糖尿病临床诊断的个体，以及干预试验中临床反应的异质性。经典的安慰剂对照干预试验通常包括单一疗法、广泛的参与者群体以及专注于临床终点的延长随访期。虽然这种方法仍然是临床研究的“金标准”，但人们正在努力采用利用人工智能和机器学习的力量来加速药物发现和疗效测试的新方法。在这里，我们回顾了用于重新利用治疗与1型糖尿病有共同致病途径疾病的药物以及选择药物协同组合以最大化治疗效果的新兴方法。我们讨论了新兴的多组学技术，包括对抗原加工和向适应性免疫细胞呈递的分析，如何可能导致发现新的生物标志物并随后转化为抗原特异性免疫疗法。我们还讨论了使用人工智能创建“数字孪生”模型的潜力，该模型能够对个性化药物进行快速的计算机模拟测试以及剂量确定。最后，我们讨论了人工智能和机器学习的一些局限性，包括与模型可解释性和偏差相关的问题，以及通过验证性干预试验进行验证研究的持续需求。
