T1dCteGui: A User‐Friendly Clinical Trial Enrichment Tool to Optimize T1D Prevention Studies by Leveraging AI/ML Based Synthetic Patient Population

Abstract

Whereas islet autoantibodies (AAs) are well‐established risk factors for developing type 1 diabetes (T1D), there is a lack of biomarkers endorsed by regulators to enrich clinical trial populations for those at risk of developing T1D. As such, the development of therapies that delay or prevent the onset of T1D remains challenging. To address this drug development need, the Critical Path Institute's T1D Consortium (T1DC) acquired patient‐level data from multiple observational studies and used a model‐based approach to evaluate the utility of islet AAs as enrichment biomarkers in clinical trials. An accelerated failure time model was developed, discussed in our previous publication, which provided the underlying evidence required to receive a qualification opinion for islet AAs as enrichment biomarkers from the European Medicines Agency (EMA) in March 2022. To further democratize the use of the model for scientists and clinicians, we developed a Clinical Trial Enrichment Graphical User Interface. The interactive tool allows users to specify trial participant characteristics, including the percentage of participants with a specific AA combination. Users can specify ranges for participant baseline age, sex, blood glucose measurement from the 120‐minute timepoints of an oral glucose tolerance test, and HbA1c. The tool then applies the model to predict the mean probability of a T1D diagnosis for that trial population and renders the results to the user. To ensure adequate data privacy and to make the tool open‐source, a deep learning‐based generative model was used to generate a cohort of synthetic subjects that underpins the tool.