Efficacy and safety of dulaglutide versus sitagliptin after 52 weeks in type 2 diabetes in a randomized controlled trial (AWARD-5).

OBJECTIVE: To compare the efficacy and safety of two doses of once-weekly dulaglutide, a glucagon-like peptide 1 receptor agonist, to sitagliptin in uncontrolled, metformin-treated patients with type 2 diabetes. The primary objective was to compare (for noninferiority and then superiority) dulaglutide 1.5 mg versus sitagliptin in change from baseline in glycosylated hemoglobin A1c (HbA1c) at 52 weeks. RESEARCH DESIGN AND METHODS: This multicenter, adaptive, double-blind, parallel-arm study randomized patients (N = 1,098; mean baseline age 54 years; HbA1c 8.1% [65 mmol/mol]; weight 86.4 kg; diabetes duration 7 years) to dulaglutide 1.5 mg, dulaglutide 0.75 mg, sitagliptin 100 mg, or placebo (placebo-controlled period up to 26 weeks). The treatment period lasted 104 weeks, with 52-week primary end point data presented. RESULTS: The mean HbA1c changes to 52 weeks were (least squares mean ± SE): -1.10 ± 0.06% (-12.0 ± 0.7 mmol/mol), -0.87 ± 0.06% (9.5 ± 0.7 mmol/mol), and -0.39 ± 0.06% (4.3 ± 0.7 mmol/mol) for dulaglutide 1.5 mg, dulaglutide 0.75 mg, and sitagliptin, respectively. Both dulaglutide doses were superior to sitagliptin (P < 0.001, both comparisons). No events of severe hypoglycemia were reported. Mean weight changes to 52 weeks were greater with dulaglutide 1.5 mg (-3.03 ± 0.22 kg) and dulaglutide 0.75 mg (-2.60 ± 0.23 kg) compared with sitagliptin (-1.53 ± 0.22 kg) (P < 0.001, both comparisons). The most common gastrointestinal treatment-emergent adverse events in dulaglutide 1.5- and 0.75-mg arms were nausea, diarrhea, and vomiting. CONCLUSIONS: Both dulaglutide doses demonstrated superior glycemic control versus sitagliptin at 52 weeks with an acceptable tolerability and safety profile.

An adaptive, dose-finding, seamless phase 2/3 study of a long-acting glucagon-like peptide-1 analog (dulaglutide): trial design and baseline characteristics.

Dulaglutide (dula, LY2189265) is a once-weekly glucagon-like peptide-1 analog in development for the treatment of type 2 diabetes mellitus. An adaptive, dose-finding, inferentially seamless phase 2/3 study was designed to support the development of this novel diabetes therapeutic. The study is divided into two stages based on two randomization schemes: a Bayesian adaptive scheme (stage 1) and a fixed scheme (stage 2). Stage 1 of the trial employs an adaptive, dose-finding design to lead to a dula dose-selection decision or early study termination due to futility. If dose selection occurs, the study proceeds to stage 2 to allow continued evaluation of the selected dula doses. At completion, the entire study will serve as a confirmatory phase 3 trial. The final study design is discussed, along with specifics pertaining to the actual execution of this study and selected baseline characteristics of the participants.

Operational challenges and solutions with implementation of an adaptive seamless phase 2/3 study.

A wide variety of operational issues were encountered with the planning and implementation of an adaptive, dose-finding, seamless phase 2/3 trial for a diabetes therapeutic. Compared with a conventional design, significant upfront planning was required, as well as earlier, more integrated cross-functional coordination. The existing infrastructure necessitated greater flexibility to meet the needs of the adaptive design. Rapid data acquisition, analysis, and reporting were essential to support the successful implementation of the adaptive algorithm. Drug supply for nine treatment arms had to be carefully managed across many sites worldwide. Details regarding these key operational challenges and others will be discussed along with resolutions taken to enable successful implementation of this adaptive, seamless trial.

Application of adaptive design methodology in development of a long-acting glucagon-like peptide-1 analog (dulaglutide): statistical design and simulations.

BACKGROUND: Dulaglutide (dula, LY2189265), a long-acting glucagon-like peptide-1 analog, is being developed to treat type 2 diabetes mellitus. METHODS: To foster the development of dula, we designed a two-stage adaptive, dose-finding, inferentially seamless phase 2/3 study. The Bayesian theoretical framework is used to adaptively randomize patients in stage 1 to 7 dula doses and, at the decision point, to either stop for futility or to select up to 2 dula doses for stage 2. After dose selection, patients continue to be randomized to the selected dula doses or comparator arms. Data from patients assigned the selected doses will be pooled across both stages and analyzed with an analysis of covariance model, using baseline hemoglobin A1c and country as covariates. The operating characteristics of the trial were assessed by extensive simulation studies. RESULTS: Simulations demonstrated that the adaptive design would identify the correct doses 88% of the time, compared to as low as 6% for a fixed-dose design (the latter value based on frequentist decision rules analogous to the Bayesian decision rules for adaptive design). CONCLUSIONS: This article discusses the decision rules used to select the dula dose(s); the mathematical details of the adaptive algorithm-including a description of the clinical utility index used to mathematically quantify the desirability of a dose based on safety and efficacy measurements; and a description of the simulation process and results that quantify the operating characteristics of the design.

Linkage analysis with sequential imputation.

Multilocus calculations, using all available information on all pedigree members, are important for linkage analysis. Exact calculation methods in linkage analysis are limited in either the number of loci or the number of pedigree members they can handle. In this article, we propose a Monte Carlo method for linkage analysis based on sequential imputation. Unlike exact methods, sequential imputation can handle large pedigrees with a moderate number of loci in its current implementation. This Monte Carlo method is an application of importance sampling, in which we sequentially impute ordered genotypes locus by locus, and then impute inheritance vectors conditioned on these genotypes. The resulting inheritance vectors, together with the importance sampling weights, are used to derive a consistent estimator of any linkage statistic of interest. The linkage statistic can be parametric or nonparametric; we focus on nonparametric linkage statistics. We demonstrate that accurate estimates can be achieved within a reasonable computing time. A simulation study illustrates the potential gain in power using our method for multilocus linkage analysis with large pedigrees. We simulated data at six markers under three models. We analyzed them using both sequential imputation and GENEHUNTER. GENEHUNTER had to drop between 38-54% of pedigree members, whereas our method was able to use all pedigree members. The power gains of using all pedigree members were substantial under 2 of the 3 models. We implemented sequential imputation for multilocus linkage analysis in a user-friendly software package called SIMPLE.