Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes

Journal article

Melanie R. Shapiro, Erin M. Tallon, Matthew E Brown, A. Posgai, Mark A Clements, Todd M. Brusko
Diabetologia, 2024

Semantic Scholar DOI PubMedCentral PubMed

Cite

APA Click to copy
Shapiro, M. R., Tallon, E. M., Brown, M. E., Posgai, A., Clements, M. A., & Brusko, T. M. (2024). Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes. Diabetologia.

Chicago/Turabian Click to copy
Shapiro, Melanie R., Erin M. Tallon, Matthew E Brown, A. Posgai, Mark A Clements, and Todd M. Brusko. “Leveraging Artificial Intelligence and Machine Learning to Accelerate Discovery of Disease-Modifying Therapies in Type 1 Diabetes.” Diabetologia (2024).

MLA Click to copy
Shapiro, Melanie R., et al. “Leveraging Artificial Intelligence and Machine Learning to Accelerate Discovery of Disease-Modifying Therapies in Type 1 Diabetes.” Diabetologia, 2024.

BibTeX Click to copy

@article{melanie2024a,
  title = {Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes},
  year = {2024},
  journal = {Diabetologia},
  author = {Shapiro, Melanie R. and Tallon, Erin M. and Brown, Matthew E and Posgai, A. and Clements, Mark A and Brusko, Todd M.}
}

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. Graphical Abstract Supplementary Information The online version contains a slideset of the figures for download available at 10.1007/s00125-024-06339-6.