Michael Heathman(1), Zachary Skrivanek(1), Brenda Gaydos(1), Mary Jane Geiger(2), Jenny Chien(1)
(1)Eli Lilly and Company, Indianapolis, IN, USA; (2)Relypsa, Inc, Redwood City, CA, USA
Objectives: A two-stage, adaptive dose-finding, inferentially seamless Phase 2/3 study was designed to optimize the development of dulaglutide (dula), a new therapeutic for the treatment of type 2 diabetes mellitus. Integrated models of dula pharmacokinetics and pharmacodynamics (PD) of key clinical and safety measures were developed, leveraging early phase clinical data and literature data of marketed comparators. These models were used to simulate virtual patients and to evaluate the operating characteristics and probability of success of the trial.
Methods: Data from early phase studies were used to develop models of prospectively selected clinical endpoints for dose determination: a linked model of glucose-HbA1c, a weight loss model with placebo response and circadian rhythm models of blood pressure and heart rate. Published comparator’s longitudinal data were used to inform the timecourses of PD endpoints. Virtual patient populations (N=10,000) were simulated to match baseline demographic and disease characteristics of a typical Phase 3 study population for up to a year. A Bayesian theoretical framework was used to adaptively randomize virtual patients sampled from the dataset in Stage 1 to one of seven dula doses. At each interim analysis, a multi-attribute clinical utility function was applied to predefined dose selection criteria to support either stopping for futility or selecting up to 2 dula doses to advance to Stage 2.
Results: Dula drug-disease models predicted the most likely doses to demonstrate optimal and competitive glycemic efficacy and safety profiles. In simulated studies, the adaptive algorithm identified the correct dose 88% of the time, compared to as low as 6% for a fixed-dose design using frequentist decision rules.
Conclusions: Drug-disease models developed using limited Phase 1 and literature data are efficient tools to support the optimization of drug development. Model-based trial simulations allow systematic and robust evaluation of trial design and assessment of probability of trial success.
Reference: PAGE 22 () Abstr 2763 [www.page-meeting.org/?abstract=2763]
Poster: Study Design