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.

Graphical Abstract

中文翻译：

---

利用人工智能和机器学习加速发现 1 型糖尿病的疾病缓解疗法

由于动物模型有限、临床试验的时间和成本、难以识别将更快进展为 1 型糖尿病临床诊断的个体以及干预试验中的异质性临床反应，开发维持 1 型糖尿病内源性胰岛素分泌或预防 1 型糖尿病的疗法的进展受到阻碍。经典的安慰剂对照干预试验通常包括单一疗法、广泛的参与者群体和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“黄金标准”，但正在努力实施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，我们回顾了用于治疗与 1 型糖尿病有共同致病途径的疾病的药物再利用的新兴方法，并选择药物的协同组合以最大限度地提高治疗效果。我们讨论了新兴的多组学技术，包括抗原加工和适应性免疫细胞呈递的分析，如何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，这些模型能够对个性化药物进行快速计算机测试和剂量测定。总而言之，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有关的问题，以及通过验证性干预试验进行验证研究的持续需求。

 图形摘要