BACKGROUND. Type 1 diabetes (T1D) results from loss of immune regulation, leading to the development of autoimmunity to pancreatic β cells, involving autoreactive T effector cells (Teffs). Tregs, which prevent autoimmunity, require IL-2 for maintenance of immunosuppressive functions. Using a response-adaptive design, we aimed to determine the optimal regimen of aldesleukin (recombinant human IL-2) to physiologically enhance Tregs while limiting expansion of Teffs. METHODS. DILfrequency is a nonrandomized, open-label, response-adaptive study of participants, aged 18–70 years, with T1D. The initial learning phase allocated 12 participants to 6 different predefined regimens. Then, 3 cohorts of 8 participants were sequentially allocated dose frequencies, based on repeated interim analyses of all accumulated trial data. The coprimary endpoints were percentage change in Tregs and Teffs and CD25 (α subunit of the IL-2 receptor) expression by Tregs, from baseline to steady state. RESULTS. Thirty-eight participants were enrolled, with thirty-six completing treatment. The optimal regimen to maintain a steady-state increase in Tregs of 30% and CD25 expression of 25% without Teff expansion is 0.26 × 106 IU/m2 (95% CI –0.007 to 0.485) every 3 days. Tregs and CD25 were dose-frequency responsive, Teffs were not. The commonest adverse event was injection site reaction (464 of 694 events). CONCLUSIONS. Using a response-adaptive design, aldesleukin treatment can be optimized. Our methodology can generally be employed to immediately access proof of mechanism, thereby leading to more efficient and safe drug development. TRIAL REGISTRATION. International Standard Randomised Controlled Trial Number Register, ISRCTN40319192; ClinicalTrials.gov, NCT02265809. FUNDING. Sir Jules Thorn Trust, the Swiss National Science Foundation, Wellcome, JDRF, and NIHR Cambridge Biomedical Research Centre.
Eleonora Seelig, James Howlett, Linsey Porter, Lucy Truman, James Heywood, Jane Kennet, Emma L. Arbon, Katerina Anselmiova, Neil M. Walker, Ravinder Atkar, Marcin L. Pekalski, Ed Rytina, Mark Evans, Linda S. Wicker, John A. Todd, Adrian P. Mander, Simon Bond, Frank Waldron-Lynch
Usage data is cumulative from December 2021 through December 2022.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.