Assessing patient PREFERence between the dulaglutide pen and the semaglutide pen: A crossover study (PREFER).

AIM: When selecting treatments for type 2 diabetes (T2D), it is important to consider not only efficacy and safety, but also other treatment attributes that have an impact on patient preference. The objective of this study was to examine preference between injection devices used for two weekly GLP-1 receptor agonists. MATERIALS AND METHODS: The PREFER study was an open-label, multicentre, randomized, crossover study assessing patient preference for dulaglutide and semaglutide injection devices among injection-naïve patients receiving oral medication for type 2 diabetes. After being trained to use each device, participants performed all steps of injection preparation and administered mock injections into an injection pad. Time-to-train (TTT) for each device was assessed in a subset. RESULTS: There were 310 evaluable participants (48.4% female; mean age, 60.0 years; 78 participants in the TTT subgroup). More participants preferred the dulaglutide device than the semaglutide device (84.2% vs. 12.3%; P < 0.0001). More participants perceived the dulaglutide device to have greater ease of use (86.8% vs. 6.8%; P < 0.0001). After preparing and using the devices, more participants were willing to use the dulaglutide device (93.5%) than the semaglutide device (45.8%). Training participants to use the dulaglutide device required less time than the semaglutide device (3.38 vs. 8.14 minutes; P < 0.0001). CONCLUSIONS: Participants with type 2 diabetes preferred the dulaglutide injection device to the semaglutide injection device. If patients prefer a device, they may be more willing to use the medication, which could result in better health outcomes. Furthermore, a shorter training time for injection devices may be helpful in busy clinical practice settings.

Adaptation of the robust method to large distributions of reference values: program modifications and comparison of alternative computational methods.

The objective of this research was to compute reference limits using reference values from patients entering pharmaceutical development clinical trials by the nonparametric method and the robust method of Horn and Pesce, with and without outlier exclusion, and compare the methods with respect to influence on the limits. Reference limits were computed for 38 analytes with over 130,000 subjects contributing reference values. Subjects were partitioned into 10 demographic strata for limit computation. Limits were computed for both 95- and 98-percentile reference intervals by both methods. For each reference interval and method, the limits were calculated with and without outliers. Outliers were excluded by the Horn algorithm. Irrespective of method, reference limits were expanded with the 98-percentile interval, but some expansions were small. Outlier exclusion contracted limits with more influence on the upper limit. The robust method contracted the upper limit to a meaningful degree and slightly expanded the lower limit for many analytes. Outlier exclusion and computation by the robust method have an increasing influence on analytes with right-skewed distributions of reference values from large populations not screened to exclude common, stable diseases and environmental factors that might affect analyte variability. The method has advantages for computation of reference limits used in clinical trial analyses.

On the (un)predictability of a large intragenic fitness landscape.

The study of fitness landscapes, which aims at mapping genotypes to fitness, is receiving ever-increasing attention. Novel experimental approaches combined with next-generation sequencing (NGS) methods enable accurate and extensive studies of the fitness effects of mutations, allowing us to test theoretical predictions and improve our understanding of the shape of the true underlying fitness landscape and its implications for the predictability and repeatability of evolution. Here, we present a uniquely large multiallelic fitness landscape comprising 640 engineered mutants that represent all possible combinations of 13 amino acid-changing mutations at 6 sites in the heat-shock protein Hsp90 in Saccharomyces cerevisiae under elevated salinity. Despite a prevalent pattern of negative epistasis in the landscape, we find that the global fitness peak is reached via four positively epistatic mutations. Combining traditional and extending recently proposed theoretical and statistical approaches, we quantify features of the global multiallelic fitness landscape. Using subsets of the data, we demonstrate that extrapolation beyond a known part of the landscape is difficult owing to both local ruggedness and amino acid-specific epistatic hotspots and that inference is additionally confounded by the nonrandom choice of mutations for experimental fitness landscapes.

A systematic survey of an intragenic epistatic landscape.

Mutations are the source of evolutionary variation. The interactions of multiple mutations can have important effects on fitness and evolutionary trajectories. We have recently described the distribution of fitness effects of all single mutations for a nine-amino-acid region of yeast Hsp90 (Hsp82) implicated in substrate binding. Here, we report and discuss the distribution of intragenic epistatic effects within this region in seven Hsp90 point mutant backgrounds of neutral to slightly deleterious effect, resulting in an analysis of more than 1,000 double mutants. We find negative epistasis between substitutions to be common, and positive epistasis to be rare--resulting in a pattern that indicates a drastic change in the distribution of fitness effects one step away from the wild type. This can be well explained by a concave relationship between phenotype and genotype (i.e., a concave shape of the local fitness landscape), suggesting mutational robustness intrinsic to the local sequence space. Structural analyses indicate that, in this region, epistatic effects are most pronounced when a solvent-inaccessible position is involved in the interaction. In contrast, all 18 observations of positive epistasis involved at least one mutation at a solvent-exposed position. By combining the analysis of evolutionary and biophysical properties of an epistatic landscape, these results contribute to a more detailed understanding of the complexity of protein evolution.

Efficacy and safety of dulaglutide in the treatment of type 2 diabetes: a comprehensive review of the dulaglutide clinical data focusing on the AWARD phase 3 clinical trial program.

Dulaglutide (DU) is a once weekly glucagon-like peptide-1 receptor agonist (GLP-1 RA) approved for the treatment of type 2 diabetes mellitus (T2DM). Glycaemic efficacy and safety characteristics of dulaglutide have been assessed in six Phase 3 studies in the AWARD program. The objective of this review article is to summarize these results from the six completed AWARD studies. At the primary endpoint, in five of the six studies, once weekly dulaglutide 1.5 mg was superior to the active comparator [exenatide, insulin glargine (two studies), metformin, and sitagliptin], with a greater proportion of patients reaching glycated hemoglobin A1c (HbA1c) targets of <7.0% (53.0 mmol/mol) and ≤6.5% (47.5 mmol/mol). Dulaglutide 1.5 mg was non-inferior to liraglutide in AWARD-6. Once weekly dulaglutide 0.75 mg was evaluated in five of these trials and demonstrated superiority to the active comparator in four of five AWARD studies (exenatide, glargine, metformin, and sitagliptin), and non-inferiority to glargine in the AWARD-2 study. Similar to other GLP-1 receptor agonists, treatment with dulaglutide was associated with weight loss or attenuation of weight gain and low rates of hypoglycaemia when used alone or with non-insulin-secretagogue therapy. The most frequently reported adverse events were gastrointestinal, including nausea, vomiting, and diarrhea. The incidence of dulaglutide antidrug antibody formation was 1-2.8% with rare injection site reactions. In conclusion, dulaglutide is an effective treatment for T2DM and has an acceptable tolerability and safety profile. Copyright © 2016 John Wiley & Sons, Ltd.

Latent effects of Hsp90 mutants revealed at reduced expression levels.

In natural systems, selection acts on both protein sequence and expression level, but it is unclear how selection integrates over these two dimensions. We recently developed the EMPIRIC approach to systematically determine the fitness effects of all possible point mutants for important regions of essential genes in yeast. Here, we systematically investigated the fitness effects of point mutations in a putative substrate binding loop of yeast Hsp90 (Hsp82) over a broad range of expression strengths. Negative epistasis between reduced expression strength and amino acid substitutions was common, and the endogenous expression strength frequently obscured mutant defects. By analyzing fitness effects at varied expression strengths, we were able to uncover all mutant effects on function. The majority of mutants caused partial functional defects, consistent with this region of Hsp90 contributing to a mutation sensitive and critical process. These results demonstrate that important functional regions of proteins can tolerate mutational defects without experimentally observable impacts on fitness.

Experimental illumination of a fitness landscape.

The genes of all organisms have been shaped by selective pressures. The relationship between gene sequence and fitness has tremendous implications for understanding both evolutionary processes and functional constraints on the encoded proteins. Here, we have exploited deep sequencing technology to experimentally determine the fitness of all possible individual point mutants under controlled conditions for a nine-amino acid region of Hsp90. Over the past five decades, limited glimpses into the relationship between gene sequence and function have sparked a long debate regarding the distribution, relative proportion, and evolutionary significance of deleterious, neutral, and advantageous mutations. Our systematic experimental measurement of fitness effects of Hsp90 mutants in yeast, evaluated in the light of existing population genetic theory, are remarkably consistent with a nearly neutral model of molecular evolution.

Reference Limits for Outlier Analyses in Randomized Clinical Trials.

BACKGROUND: Reference limits used in clinical medicine to screen and manage patients are typically developed nonparametrically using reference values from a limited number of healthy subjects using a 95th percentile reference interval. We have evaluated alternative methods of computation and the resulting limits for use in the analyses of treatment-emergent outliers in clinical trials. METHODS: We developed a set of alternative reference limits for 38 laboratory analytes based on alternative statistical methods and assessed their relative performance in clinical trial analysis. Performance assessment was based on the clinical credibility of the limits, inferential statistical performance, consideration of incidences for the test drug and control (placebo) in cases where the drug was reasonably believed to be associated with a change in an analyte (positive cases), and in cases where prior analyses failed to demonstrate a change associated with the drug (negative cases). RESULTS: Based on consideration of these cases, no single method resulted in optimal limits for all cases considered. However, with the limits developed using clinical trial subjects' values at baseline as reference values, excluding outliers, the robust method and the 98th percentile interval appeared to produce optimal limits across the greatest number of cases considered. CONCLUSION: Although no single method of limit computation will result in optimal limits for all outlier analyses for all analytes across all clinical trials, the 98th percentile reference interval robust limits based on clinical trial reference values appeared superior to multiple alternatives considered for such analyses.

Dulaglutide in the treatment of adult type 2 diabetes: a perspective for primary care providers.

Approximately 90% of T2D patients in the US are diagnosed and treated in the primary care setting, and the majority of the burden of disease management falls to primary care providers. Here, we discuss the clinical data for once weekly dulaglutide, e.g. the results of seven completed Phase 3 trials, patient preference studies, patient reported outcomes (PRO), and clinical data surrounding the dulaglutide administration device. Dulaglutide 1.5 mg once weekly demonstrated superiority to placebo, metformin, sitagliptin, exenatide BID, and insulin glargine (in 2 trials), and non-inferiority to liraglutide in reduction of HbA1c from baseline, with an acceptable safety profile. Dulaglutide-treated patients achieved the composite endpoint of an HbA1c <7.0% with no hypoglycemia, no severe hypoglycemia, and no weight gain significantly more than metformin, sitagliptin, exenatide BID or insulin glargine treated patients. Dulaglutide consistently showed an early onset of glycemic control, lasting up to 104 weeks. Additionally, PRO and patient preference data support the benefit of once weekly dulaglutide for the treatment of T2D.

A bayesian MCMC approach to assess the complete distribution of fitness effects of new mutations: uncovering the potential for adaptive walks in challenging environments.

The role of adaptation in the evolutionary process has been contentious for decades. At the heart of the century-old debate between neutralists and selectionists lies the distribution of fitness effects (DFE)--that is, the selective effect of all mutations. Attempts to describe the DFE have been varied, occupying theoreticians and experimentalists alike. New high-throughput techniques stand to make important contributions to empirical efforts to characterize the DFE, but the usefulness of such approaches depends on the availability of robust statistical methods for their interpretation. We here present and discuss a Bayesian MCMC approach to estimate fitness from deep sequencing data and use it to assess the DFE for the same 560 point mutations in a coding region of Hsp90 in Saccharomyces cerevisiae across six different environmental conditions. Using these estimates, we compare the differences in the DFEs resulting from mutations covering one-, two-, and three-nucleotide steps from the wild type--showing that multiple-step mutations harbor more potential for adaptation in challenging environments, but also tend to be more deleterious in the standard environment. All observations are discussed in the light of expectations arising from Fisher's geometric model.

Shifting fitness landscapes in response to altered environments.

The role of adaptation in molecular evolution has been contentious for decades. Here, we shed light on the adaptive potential in Saccharomyces cerevisiae by presenting systematic fitness measurements for all possible point mutations in a region of Hsp90 under four environmental conditions. Under elevated salinity, we observe numerous beneficial mutations with growth advantages up to 7% relative to the wild type. All of these beneficial mutations were observed to be associated with high costs of adaptation. We thus demonstrate that an essential protein can harbor adaptive potential upon an environmental challenge, and report a remarkable fit of the data to a version of Fisher's geometric model that focuses on the fitness trade-offs between mutations in different environments.