Document Type
Article
Publication Date
3-2025
Identifier
DOI: 10.1007/s00125-024-06339-6; PMCID: PMC11832708
Abstract
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.
Journal Title
Diabetologia
Volume
68
Issue
3
First Page
477
Last Page
494
MeSH Keywords
Humans; Diabetes Mellitus, Type 1; Machine Learning; Artificial Intelligence; Drug Discovery; Animals; Hypoglycemic Agents
PubMed ID
39694914
Keywords
Artificial intelligence; Digital twin; Drug discovery; Drug repurposing; Drug response; Immunotherapy; Machine learning; Pharmacogenetics; Precision medicine; Review; Type 1 diabetes
Recommended Citation
Shapiro MR, Tallon EM, Brown ME, Posgai AL, Clements MA, Brusko TM. Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes. Diabetologia. 2025;68(3):477-494. doi:10.1007/s00125-024-06339-6
Comments
Grants and funding
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Publisher's Link: https://link.springer.com/article/10.1007/s00125-024-06339-6