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PubMed ID:
37186151
Public Release Type:
Journal
Publication Year: 2023
Affiliation: 1Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL, USA
2Critical Path Institute, Tucson, AZ, USA
3JDRF, New York, NY, USA
4Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA
5Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL, USA
*Current affiliations: R. Muse: FDA, J. Burton: Biogen, P. Lang: Arcus Biosciences, and I. O'Doherty: Engagement
DOI:
https://doi.org/10.1002/psp4.12973
Authors:
Morales JF,
Muse R,
Podichetty J,
Burton J,
David S,
Lang P,
Schmidt S,
Romero K,
O'Doherty I,
Martin F,
Campbell-Thompson M,
Haller MJ,
Atkinson MA,
Kim S
Request IDs:
23240
Studies:
Anti-CD3 Mab (Teplizumab) for Prevention of Diabetes in Relatives At-Risk for Type 1 Diabetes Mellitus
Type 1 diabetes (T1D) prevention clinical trials are particularly challenging in terms of identifying patient populations likely to progress to T1D within feasible trial durations. While it has been long established that islet autoantibodies (AAbs) are predictive biomarkers of T1D, the presence of different combinations of AAbs, genetic risk loci, and various other immune and environmental factors result in highly heterogeneous rates of progression through the stages of T1D. Optimization of subject selection for trials intended to delay or halt progression to T1D is needed to account for multiple patient features. The availability of extensive data, collected over the course of decades through multiple large-scale prospective observational trials, offers the potential to quantitatively delineate such features during disease progression using modeling and simulation. The innovative model-based clinical trial simulation (CTS) tool developed in this work utilizes data from large T1D natural history studies: TN01 and TEDDY, alongside a T1D disease progression joint model, to inform T1D prevention trial designs. The T1D disease progression joint model links the longitudinal glycemic measure (GLU120) to timing of T1D diagnosis through longitudinal and time-to-event sub-models with other patient features measured at derived baseline, including HbA1c and the presence of different islet AAbs. The T1D disease progression model was externally validated with placebo data from the phase 2 randomized controlled TrialNet TN10 Anti-CD3 (Teplizumab) Prevention Trial. This CTS tool provides a framework to quantitatively predict and simulate the time to T1D diagnosis in individuals at risk of developing the disease and thus, aligns with the needs of pharmaceutical companies and scientists seeking to advance therapies aimed at interdicting the T1D disease process.
