Comparisons of Analysis Methods for Proof‐of‐Concept Trials

Abstract

Drug development struggles with high costs and time consuming processes. Hence, a need for new strategies has been accentuated by many stakeholders in drug development. This study proposes the use of pharmacometric models to rationalize drug development. Two simulated examples, within the therapeutic areas of acute stroke and type 2 diabetes, are utilized to compare a pharmacometric model–based analysis to a t-test with respect to study power of proof-of-concept (POC) trials. In all investigated examples and scenarios, the conventional statistical analysis resulted in several fold larger study sizes to achieve 80% power. For a scenario with a parallel design of one placebo group and one active dose arm, the difference between the conventional and pharmacometric approach was 4.3- and 8.4-fold, for the stroke and diabetes example, respectively. Although the model-based power depend on the model assumptions, in these scenarios, the pharmacometric model–based approach was demonstrated to permit drastic streamlining of POC trials.

Study Design Selection in Early Clinical Anti‐Hyperglycemic Drug Development: A Simulation Study of Glucose Tolerance Tests

“…It had been shown that pharmacometric analysis made use of all available data, increasing the overall information content of the trial, and providing an accurate determination of its main aspects as study power and parameter uncertainty . Furthermore, it can be implemented as a decision‐making tool in drug development throughout all the different phases of drug development …”

Power Determination During Drug Development: Is Optimizing the Sample Size Based on Exposure‐Response Analyses Underutilized?

“…With this type exposure‐response it was demonstrated that an exposure‐response powering methodology can be used to help guide the planning of clinical trials and substantially increase the power of clinical trials and/or decrease the number of subjects required within a trial. Although such concepts have been presented before within the context of MBDD, the use may have been limited by previously perceived complexity in its use. The primary advantage of MBDD analysis for guiding the planning of clinical trials is the reduction in sample size when utilizing exposure‐response powering methodology.…”

“…Hence, it is important to understand the exposure‐response relationship as early as possible. Further increases in power can be achieved if modeling longitudinal end points are considered . However, the increased model complexity involved in longitudinal end points limits the use of such methodology due to the increased time it takes to implement and greater difficulty in being able to communicate exactly what (e.g., model parameter) you are powering the study on.…”

Is It Time for Going Beyond the P‐Value Paradigm With the Estimation of the Probability of Clinical Benefit as a Criterion for Assessing the Outcomes of a Clinical Trial? A Case Study in Patients With Major Depressive Disorder

“…An example of the implementation of the proposed framework is presented to provide the clinician with a quantitative measure of the expected clinical benefit associated with the treatment of major depressive disorders. The methodology is implemented using an integrated drug‐disease model and pharmacometric methodologies …”