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.

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.