Impact of the hepatoselective glucokinase activator TTP399 on ketoacidosis during insulin withdrawal in people with type 1 diabetes.

AIMS: To determine the effect of TTP399, a hepatoselective glucokinase activator, on the risk of ketoacidosis during insulin withdrawal in individuals with type 1 diabetes (T1D). MATERIALS AND METHODS: Twenty-three participants with T1D using insulin pump therapy were randomized to 800 mg TTP399 (n = 12) or placebo (n = 11) for 7 to 10 days. After the treatment period, an insulin withdrawal test (IWT) was performed, during which insulin pumps were removed to induce ketogenesis. The IWT was stopped after 10 hours or if blood glucose reached >399 mg/dL [22.1 mmol/L], if beta-hydroxybutyrate (BHB) was >3.0 mmol/L, or for patient discomfort. The primary endpoint was the proportion of participants who reached BHB concentrations of 1 mmol/L or greater. RESULTS: During the 7- to 10-day treatment period, mean fasting plasma glucose was significantly reduced ( -27.6 vs. -4.4 mg/dL [-1.5 vs. -0.2 mmol/L]; P = 0.03) and there were fewer adverse events, including hypoglycaemia, in the TTP399-treated arm. During the IWT, no differences were observed between TTP399 and placebo in mean serum BHB concentration, mean duration of IWT, or BHB at termination of IWT. However, serum bicarbonate was numerically higher and urine acetoacetate was quantitatively lower in the TTP399-treated participants. As a result of higher bicarbonate values, none of the TTP399-treated participants met the prespecified criteria for diabetic ketoacidosis (DKA), defined as BHB >3 mmol/L and serum bicarbonate <18 mEq/L, compared to 42% of placebo-treated participants. CONCLUSIONS: When used as an adjunctive therapy to insulin, TTP399 improves glycaemia without increasing hypoglycaemia in individuals with T1D. During acute insulin withdrawal, TTP399 did not increase BHB concentrations and decreased the incidence of DKA.

The SimpliciT1 Study: A Randomized, Double-Blind, Placebo-Controlled Phase 1b/2 Adaptive Study of TTP399, a Hepatoselective Glucokinase Activator, for Adjunctive Treatment of Type 1 Diabetes.

OBJECTIVE: 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 insulin-induced 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 HbA1c from baseline to week 12. RESULTS: The difference in change in HbA1c from baseline to week 12 between TTP399 and placebo was -0.7% (95% CI -1.3, -0.07) in part 1 and -0.21% (95% CI -0.39, -0.04) in part 2. Despite a greater decrease in HbA1c 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 ß-hydroxybutyrate and urinary ketones were lower during treatment with TTP399 than placebo. CONCLUSIONS: TTP399 lowers HbA1c 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.

Assessment of Azeliragon QTc Liability Through Integrated, Model-Based Concentration QTc Analysis.

Azeliragon is an inhibitor of the receptor for advanced glycation end products being developed for the treatment of Alzheimer's disease. The objective of the current analysis was to evaluate the relationship between plasma azeliragon concentrations and QT interval. Simultaneous QT values and plasma concentrations were available from 711 subjects (6236 records), pooled from 5 studies in healthy volunteers, 2 studies in patients with mild to moderate Alzheimer's disease, and 1 study in patients with type 2 diabetes and persistent albuminuria. Nonlinear mixed-effects modeling was conducted to describe azeliragon concentration-related changes in QT interval, after correcting for heart rate, using Fridericia's criteria (QTcF) and sex-related differences in baseline QTcF. Azeliragon-related changes in QTcF were predicted using 2 methods: simulation and bias-corrected 90% confidence interval approaches. A small positive relationship between azeliragon plasma concentration and QTcF was noted with a slope of 0.059 ms/ng/mL. Simulations predicted mean (90% prediction interval) changes in QTcF of 0.733 milliseconds (0.32-1.66 milliseconds) with the phase 3 dose (5 mg once daily steady state) and 4.32 milliseconds (1.7-8.74 milliseconds) at supratherapeutic doses (20 mg once daily steady state or 60 mg once daily × 6 days). Bias-corrected upper 90% confidence intervals for therapeutic and supratherapeutic doses were 0.88 and 5.01 milliseconds, respectively. Model-based analysis showed a small, nonclinically meaningful, positive relationship between azeliragon plasma concentration and QTcF with a slope close to zero. Neither the prediction interval nor the upper bound of the 90% confidence interval reached 10 milliseconds, demonstrating no clinically meaningful drug-related effect on QTcF at expected therapeutic and supratherapeutic doses of azeliragon.

Targeting hepatic glucokinase to treat diabetes with TTP399, a hepatoselective glucokinase activator.

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 ß 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.

Extended Risk-Based Monitoring Model, On-Demand Query-Driven Source Data Verification, and Their Economic Impact on Clinical Trial Operations.

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.

Risk-Based Monitoring: A Closer Statistical Look at Source Document Verification, Queries, Study Size Effects, and Data Quality.

BACKGROUND: Data quality within the clinical research enterprise can be defined as the absence of errors that matter and whether the data are fit for purpose. This concept, proposed by the Clinical Trials Transformation Initiative, resulted from a culmination of collaboration with industry, academia, patient advocates, and regulators, and it emphasizes the presence of a hierarchy of error types, resulting in a more efficient and modern data-cleaning paradigm. While source document verification (SDV) is commonly used as a quality control method in clinical research, it is disproportionately expensive and often leads to questionable benefits. Although the current literature suggests that there is a need to reduce the burden of SDV, there is no consensus on how to replace this "tried and true" practice. METHODS: This article proposes a practical risk-based monitoring approach based on published statistical evidence addressing the impact of database changes subsequent to SDV. RESULTS: The analysis clearly demonstrates minimal effects of errors and error corrections on study results and study conclusions, with diminishing effect as the study size increases, and it suggests that, on average, <8% SDV is adequate to ensure data quality, with perhaps higher SDV rates for smaller studies and virtually 0% SDV for large studies. CONCLUSIONS: 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.