Abstract

A large majority of Phase III, large scale, clinical trials will fail, including gene therapy trials. This paper attempts to address some of the causes that may have inadvertently led to such a high failure rate. After briefly reviewing the detailed and high quality work that goes both into the preparation and conduct of such large Phase III clinical trials, and the preclinical science that is used to support and originate such trials, this paper proposes a novel approach to translational medicine which would increase the predictability of success of Phase III clinical trials. We propose that a likely cause of such failures is the lack of “robustness” in the preclinical science underpinning the Phase I/II and III clinical trials. Robustness is defined as stability/reproducibility in the face of challenges. Many times preclinical experiments are tested in a very narrow set of experimental conditions. Thus, when such approaches are finally tested in the context of human disease, the challenge provided by the varied age of patients, the complex genetic makeup of human populations, and the complexities of the diseases to be treated provide challenges which were never tested or modeled. We believe that the introduction of revised approaches to preclinical science, including the use of the latest developments in statistical, scientific, mathematical, and biological models, ought to lead to more robust preclinical experimentation with its subsequent translation, to more robust Phase III clinical trials.

Keywords: Glioblastoma multiforme, phase III clinical trials, bayesian statistics, effect size, gene therapy, cancer