A population pharmacodynamic Markov mixed-effects model for determining remimazolam-induced sedation when co-administered with fentanyl in procedural sedation.

The clinical effects of remimazolam (an investigational, ultra-short acting benzodiazepine being studied in procedural sedation) were measured using the Modified Observer's Assessment of Awareness/Sedation Scale (MOAA/S). The objective of this analysis was to develop a population pharmacokinetic/pharmacodynamic model to describe remimazolam-induced sedation with fentanyl over time in procedural sedation. MOAA/S from 10 clinical phase I-III trials were pooled for analysis, where data were collected after administration of placebo or remimazolam with or without concomitant fentanyl. A Markov model described transition states for 35,356 MOAA/S-time observations from 1071 subjects. Effect-compartment models of remimazolam and fentanyl linked plasma concentrations to the Markov model, and drug effects were described using a synergistic maximum effect (Emax ) model. Simulations were performed to identify the optimal remimazolam-fentanyl combination doses in procedural sedation. Fentanyl showed synergistic effects with remimazolam in sedation. Increasing age was related to longer recovery from sedation. Patients with body mass index greater than 25 kg/m2 had ~30% higher rates of distribution from plasma to the effect site (keo), indicating a slightly faster onset of sedation. Simulations showed that remimazolam 5 mg was more appropriate than 4 or 6 mg when administered with fentanyl 50 µg. The model and simulations support that a combination of remimazolam 5 mg with fentanyl 50 µg is an appropriate dosing regimen and the dose of remimazolam does not need to be changed in elderly patients, but some elderly patients may have a longer duration of sedation.

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.

Pharmacogenetics and pharmacogenomics in drug development and regulatory decision making: report of the first FDA-PWG-PhRMA-DruSafe Workshop.

The use of pharmacogenetics and pharmacogenomics in the drug development process, and in the assessment of such data submitted to regulatory agencies by industry, has generated significant enthusiasm as well as important reservations within the scientific and medical communities. This situation has arisen because of the increasing number of exploratory and confirmatory investigations into variations in RNA expression patterns and DNA sequences being conducted in the preclinical and clinical phases of drug development, and the uncertainty surrounding the acceptance of these data by regulatory agencies. This report summarizes the outcome of a workshop cosponsored by the Food and Drug Administration (FDA), the Pharmacogenetics Working Group (PWG), the Pharmaceutical Research and Manufacturers of America (PhRMA), and the PhRMA Preclinical Safety Committee (DruSafe). The specific aim of the workshop was to identify key issues associated with the application of pharmacogenetics and pharmacogenomics, including the feasibility of a regulatory "safe harbor" for exploratory genome-based data, and to provide a forum for industry-regulatory agency dialogue on these important issues.