The therapeutic success of interventions targeting glucokinase (GK) activation for the treatment of type 2 diabetes has been limited by hypoglycemia, steatohepatitis, and loss of efficacy over time. The clinical characteristics of patients with GK-activating mutations or GK regulatory protein (GKRP) loss-of-function mutations suggest that a hepatoselective GK activator (GKA) that does not activate GK in β cells or affect the GK-GKRP interaction may reduce hyperglycemia in patients with type 2 diabetes while limiting hypoglycemia and liver-associated adverse effects. Here, we review the rationale for TTP399, an oral hepatoselective GKA, and its progression from preclinical to clinical development, with an emphasis on the results of a randomized, double-blind, placebo- and active-controlled phase 2 study of TTP399 in patients with type 2 diabetes. In this 6-month study, TTP399 (800 mg/day) was associated with a clinically significant and sustained reduction in glycated hemoglobin, with a placebo-subtracted least squares mean HbA1c change from baseline of −0.9% (P < 0.01). Compared to placebo, TTP399 (800 mg/day) also increased high-density lipoprotein cholesterol (3.2 mg/dl; P < 0.05), decreased fasting plasma glucagon (−20 pg/ml; P < 0.05), and decreased weight in patients weighing ≥100 kg (−3.4 kg; P < 0.05). TTP399 did not cause hypoglycemia, had no detrimental effect on plasma lipids or liver enzymes, and did not increase blood pressure, highlighting the importance of tissue selectivity and preservation of physiological regulation when targeting key metabolic regulators such as GK.

Increasing evidence supports the role of the Receptor for Advanced Glycation Endproducts (RAGE) in the pathology of Alzheimer’s disease. Azeliragon (TTP488) is an orally bioavailable small molecule inhibitor of RAGE in Phase 3 development as a potential treatment to slow disease progression in patients mild AD. Preclinical studies in animal models of AD (tgAPPSwedish/London) have shown azeliragon to decrease Aβ plaque deposition; reduce total Aβ brain concentration while increasing plasma Aβ levels; decreases sAPPβ while increasing sAPPα; reduce levels of inflammatory cytokines; and slow cognitive decline and improve cerebral blood flow. In the Phase 2b study, 18-months treatment in patients with mild-to-moderate AD indicated a baseline to endpoint change in ADAS-cog of 3.1 points in favor of drug. A greater magnitude of effect was evident in the sub-group of patients with mild AD (MMSE 21-26) with a baseline to endpoint change of 4 points on the ADAS-cog in favor of azeliragon and a 1 point change in CDR-sb in favor of drug. Azeliragon 5 mg/day delayed time to cognitive deterioration (7-point change in ADAS-cog from baseline, logrank p=0.0149). Based on promising results from the Phase 2b study, a Phase 3 registration program (STEADFAST) is being conducted under a Special Protocol Assessment from FDA. The ongoing Phase 3 program, if successful may demonstrate azeliragon can slow cognitive decline in mild AD patients.

It is recommended that SDV, rather than just focusing on key primary efficacy and safety outcomes, focus on data clarification queries as highly discrepant (and the riskiest) data.

Despite advances in exogenous insulin therapy, many patients with type 1 diabetes do not achieve acceptable glycemic control and remain at risk for ketosis and insulininduced hypoglycemia. We conducted a randomized controlled trial to determine whether TTP399, a novel hepatoselective glucokinase activator, improved glycemic control in people with type 1 diabetes without increasing hypoglycemia or ketosis. RESEARCH DESIGN AND METHODS SimpliciT1 was a phase 1b/2 adaptive study. Phase 2 activities were conducted in two parts. Part 1 randomly assigned 20 participants using continuous glucose monitors and continuous subcutaneous insulin infusion (CSII). Part 2 randomly assigned 85 participants receiving multiple daily injections of insulin or CSII. In both parts 1 and 2, participants were randomly assigned to 800 mg TTP399 or matched placebo (fully blinded) and treated for 12 weeks. The primary end point was change in HbA 1c from baseline to week 12. RESULTS The difference in change in HbA 1c from baseline to week 12 between TTP399 and placebo was 20.7% (95% CI 21.3, 20.07) in part 1 and 20.21% (95% CI 20.39, 20.04) in part 2. Despite a greater decrease in HbA 1c with TTP399, the frequency of severe or symptomatic hypoglycemia decreased by 40% relative to placebo in part 2. In both parts 1 and 2, plasma b-hydroxybutyrate and urinary ketones were lower during treatment with TTP399 than placebo. CONCLUSIONS TTP399 lowers HbA 1c and reduces hypoglycemia without increasing the risk of ketosis and should be further evaluated as an adjunctive therapy for the treatment of type 1 diabetes.

Background: Computer-aided data validation enhanced by centralized monitoring algorithms is a more powerful tool for data cleaning compared to manual source document verification (SDV). This fact led to the growing popularity of risk-based monitoring (RBM) coupled with reduced SDV and centralized statistical surveillance. Since RBM models are new and immature, there is a lack of consensus on practical implementation. Existing RBM models’ weaknesses include (1) mixing data monitoring and site process monitoring (ie, micro vs macro level), making it more complex, obscure, and less practical; and (2) artificial separation of RBM from data cleaning leading to resource overutilization. The authors view SDV as an essential part (and extension) of the data-validation process. Methods: This report offers an efficient and scientifically grounded model for SDV. The innovative component of this model is in making SDV ultimately a part of the query management process. Cost savings from reduced SDV are estimated using a proprietary budget simulation tool with percent cost reductions presented for four study sizes in four therapeutic areas. Results: It has been shown that an “on-demand” (query-driven) SDV model implemented in clinical trial monitoring could result in cost savings from 3% to 14% for smaller studies to 25% to 35% or more for large studies. Conclusions: (1) High-risk sites (identified via analytics) do not necessarily require a higher percent SDV. While high-risk sites require additional resources to assess and mitigate risks, in many cases these resources are likely to be allocated to non-SDV activities such as GCP, training, etc. (2) It is not necessary to combine SDV with the GCP compliance monitoring. Data validation and query management must be at the heart of SDV as it makes the RBM system more effective and efficient. Thus, focusing SDV effort on queries is a promising strategy. (3) Study size effect must be considered in designing the monitoring plan since the law of diminishing returns dictates focusing SDV on “high-value” data points. Relatively lower impact of individual errors on the study results leads to realization that larger studies require less data cleaning, and most data (including most critical data points) do not require SDV. Subsequently, the most significant economy is expected in larger studies.