Search

In the wake of the Global Financial Crisis (2007-2008) cheaper, softer money flooded the worldwide markets. Faced with historically low capital costs, the pharmaceutical industry chose to pay down debt through share buybacks rather than invest in research and development (R&D). Instead, the industry explored new R&D models for open innovation, such as open-sourcing, crowd-sourcing, public-private partnerships, innovation centres, Science Parks, and the wholesale outsourcing of pharmaceutical R&D. However, economic Greater Fool Theory suggests that outsourcing R&D was never likely to increase innovation. Ten years on, the period of cheaper and softer money is coming to an end. So how are things looking? And what happens next?

In the 1990s the pharmaceutical industry sought to increase R&D productivity by shifting development tasks into parallel to reduce development cycle times and increase development speed. This paper presents a simple model demonstrating that, when attrition rates are high as in pharmaceutical development, such development speed initiatives can increase the expected time for the first successful molecule to complete development. Increasing the development speed of successful molecules could actually reduce R&D productivity – the development speed paradox.

A Hogarth, or 'wicked', universe is an irregular environment generating data to support erroneous beliefs. Here, we argue that development scientists often work in such a universe. We demonstrate that exploring these multidimensional spaces using small experiments guided by scientific intuition alone, gives rise to an illusion of validity and a misplaced confidence in that scientific intuition. By contrast, design of experiments (DOE) permits the efficient mapping of such complex, multidimensional spaces. We describe simulation tools that enable research scientists to explore these spaces in relative safety.

By quickly clearing the development pipeline of failing or marginal products, fast-fail strategies release resources to focus on more promising molecules. The Quick-Kill model of drug development demonstrates that fast-fail strategies will: (1) reduce the expected time to market; (2) reduce expected RD costs; and (3) increase RD productivity. This paper outlines the model and demonstrates the impact of fast-fail strategies. The model is illustrated with costs and risks data from pharmaceutical and biopharmaceutical companies.