An industry statistician's perspective on PHC drug development

Abstract In the past decade, the cost of drug development has increased significantly. The estimates vary widely but frequently quoted numbers are staggering—it takes 10–15 years and billions of dollars to bring a drug to patients [1] . To a large extent this is due to many long, expensive and ultimately unsuccessful drug trials. While one approach to combat the low yield on investment could be to continue searching for new blockbusters, an alternative method would lead us to focus on testing new targeted treatments that have a strong underlying scientific rationale and are more likely to provide enhanced clinical benefit in population subsets defined by molecular diagnostics. Development of these new treatments, however, cannot follow the usual established path; new strategies and approaches are required for the co-development of novel therapeutics and the diagnostic. In this paper we will review, from the point of view of industry, the approaches to, and challenges of drug development strategies incorporating predictive biomarkers into clinical programs. We will outline the basic concepts behind co-development with predictive biomarkers and summarize the current regulatory paradigm. We will present guiding principles of personalized health care (PHC) development and review the statistical, strategic, regulatory and operational challenges that statisticians regularly encounter on development programs with a PHC component. Some practical recommendations for team statisticians involved in PHC drug development are included. The majority of the examples and recommendations are drawn from oncology but broader concepts apply across all therapeutic areas.