News & Events
June 28, 2022
How can natural language processing help model informed drug development?: a reviewCitation: Roopal Bhatnagar, Sakshi Sardar, Maedeh Beheshti, Jagdeep T Podichetty, JAMIA Open, Volume 5, Issue 2, July 2022, ooac043...
March 11, 2022
Leveraging Real-World Data for EMA Qualification of a Model-Based Biomarker Tool to Optimize Type-1 Diabetes Prevention Studies
The development of therapies to prevent or delay the onset of type 1 diabetes (T1D) remains challenging, and there is a lack of qualified biomarkers to identify individuals at risk of developing T1D or to quantify the time-varying risk of conversion to a diagnosis of T1D. To address this drug development need, the T1D Consortium (i) acquired, remapped, integrated, and curated existing patient-level data from relevant observational studies, and (ii) used a model-based approach to evaluate the utility of islet autoantibodies (AAs) against insulin/proinsulin autoantibody, GAD65, IA-2, and ZnT8 as biomarkers to enrich subjects for T1D prevention.
Jagdeep T. Podichetty, Patrick Lang, Inish M. O’Doherty, Sarah E. David, Rhoda N. Muse, Stephen R. Karpen, Laura Sue Song, Klaus Romero, Jackson K. Burton, on behalf of the Type-1 Diabetes Consortium (T1DC)
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