Combined assessment of early and late-phase outcomes in orphan drug development

Abstract

In drug development programs, proof-of-concept Phase II clinical trials typically have a biomarker as a primary outcome, or an outcome that can be observed with relatively short follow-up. Subsequently, the Phase III clinical trials aim to demonstrate the treatment effect based on a clinical outcome that often needs a longer follow-up to be assessed. Early-phase outcomes or biomarkers are typically associated with late-phase outcomes and they are often included in Phase III trials. The decision to proceed to Phase III development is based on analysis of the early-Phase II outcome data. In rare diseases, it is likely that only one Phase II trial and one Phase III trial are available. In such cases and before drug marketing authorization requests, positive results of the early-phase outcome of Phase II trials are then likely seen as supporting (or even replicating) positive Phase III results on the late-phase outcome, without a formal retrospective combined assessment and without accounting for between-study differences. We used double-regression modeling applied to the Phase II and Phase III results to numerically mimic this informal retrospective assessment. We provide an analytical solution for the bias and mean square error of the overall effect that leads to a corrected double-regression. We further propose a flexible Bayesian double-regression approach that minimizes the bias by accounting for between-study differences via discounting the Phase II early-phase outcome when they are not in line with the Phase III biomarker outcome results. We illustrate all methods with an orphan drug example for Fabry disease.

Keywords: Bayesian; bias correction; biomarker; borrowing strength; decision-induced bias; rare diseases; surrogate endpoint; trial combination.