Model-based meta-analysis of the effects of non-selective and α1-selective GABAA receptor agonists in healthy volunteers

Abstract

The integral PK/PD models of GABAA agonists were established in healthy volunteers. SEM was identified as the most sensitive biomarker in differentiating GABAA receptor α1 subtype selective compounds. The exploratory analysis implied that different relationships existed between the drug effects on biomarkers and the adverse event profiles in healthy volunteers.

Applications of Model-Based Meta-Analysis in Drug Development

“…Additional collaborations are also possible with the health economics and outcomes research (HEOR) function, with which outputs from the MBMA could be integrated into other, non-clinically focused models, such as cost-effectiveness models or financial forecasting models, potentially leading to improved commercial and financial strategies [45]. Therefore, continued education and communication among project stakeholders, team collaborators, and [19,[107][108][109][110] Correlation between early and late endpoints Allow the use of a biomarker or an early clinical efficacy time point to detect a signal of the treatment effect [38,42,63,111] Pharmacokinetic and pharmacodynamic relationship Establishing the relationship between exposure and an efficacy or safety biomarker, possibly a less frequently reported one and would need data from a large population to be detected [41,[112][113][114][115][116][117] Simulation of established MBMA models Simulate various scenarios using established MBMA models to optimize clinical trial design [62,118] Pharmacoeconomics Incorporate cost-effectiveness into a MBMA model [119] other pharmacometric specialty experts regarding MBMA and MIDD approaches in general are warranted [1]. Another important ally of MIDD approaches is from statistics.…”

Model‐Based Meta‐Analysis: Optimizing Research, Development, and Utilization of Therapeutics Using the Totality of Evidence

“…Nevertheless, pharmacologically inspired cross‐compound meta‐analytical exposure‐response models represent a powerful framework to inform compound selection in drug discovery and to define exposure‐based or biomarker modulation‐based criteria for assessment of viability in translational (e.g., phase 0, phase I) development. They can be valuable in establishing the relationships between the molecular pharmacologic profile of drug‐target interactions and clinical effect phenotypes, as illustrated in an MBMA of GABA A ‐receptor agonists relating receptor subtype selectivity to electroencephalographic and psychomotor effects …”

“…They can be valuable in establishing the relationships between the molecular pharmacologic profile of drug-target interactions and clinical effect phenotypes, as illustrated in an MBMA of GABA A -receptor agonists relating receptor subtype selectivity to electroencephalographic and psychomotor effects. 19 One recent application of translational MBMA in drug discovery is in antiretroviral drug research. 20 Xu and colleagues conducted a translational MBMA relating the dynamics of clinical viral load suppression by antiretroviral agents sharing a common mechanism of action to their respective in vitro potency-normalized clinical exposures.…”

Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4–5 December 2014)

“…[54][55][56] Model-based meta-analysis (MBMA) has also been proposed when different (competing) models are available to describe the data. 57 Quantitative and systems pharmacology models. Quantitative and systems pharmacology (QSP) models are mechanistic disease progression, PK/PD, and physiologically based (PB) PK models that focus on describing in a quantitative manner the interactions between the (healthy and diseased) organism (so-called system) and different drugs or drug candidates, starting from the characterization and quantification of a network of biological/molecular mechanistic pathways.…”