Super R&D – New Insights on R&D Productivity

Despite mounting pressure on pharma’s biggest revenue drivers, there is reason for optimism. After years of declining R&D productivity, the tide is turning. New technologies, fresh frameworks, and even breakthrough science are offering hope that drug development costs could plummet from billions to millions. Yet, one important aspect of R&D Productivity is only starting to be appreciated. Called R&D-P, the potential payback is surprising, as explored here.

The clock is ticking on many of pharma’s most profitable drugs. The resulting race to replenish pipelines has seen R&D spending rise to a staggering $300 billion annually. With a big splurge in terms of acquisitions, as well as a drive to optimize in-house development.

But, as activist investors warn, it’s a big gamble! After all, there are no guarantees in scientific innovation.

There is room for optimism, however. After decades of decline, R&D productivity is on the rise. There are new frameworks, new technologies, and even new science. Some even dream that the cost of drug development can be cut from billions to millions.

Those who have questioned the sustainability of the big pharma model are watching with interest. So too are activist investors and emerging Chinese players.

Rightly, people get excited about the potential of Ai as a game changer, especially in the analysis of trial data.

However, our research points to another powerful, yet relatively untapped driver of R&D Productivity. We call it R&D-P, that is the people-related aspects of developing new drugs and taking them to market.

Regardless of the research frameworks, technologies and tools being used, people still play a major role in pharma R&D. After all, they apply the frameworks, interpret the data and ultimately make or at least shape the decisions.

Yet, while everybody talks about the importance of talent to R&D success, few have measured or managed it with anything like the level of science or sophistication that is applied to other aspects of the R&D equation.

Determined to change this situation, we​ applied scientific principles and the power of big data to study 10 big pharma teams over 5 years, calculating the level of R&D-P at between 62% and 71%.

More important still, we developed and tested a framework and set of tools for optimizing these numbers. The economic rationale is both clear & compelling.

Even modest R&D-P gains can ​boost overall R&D ​productivity by between 2 and 6%. This has the potential to save any of pharma’s big players hundreds of millions of dollars and billions for the industry overall.

However, R&D-P is complex, and Pipeline Progression is not always the goal. Indeed, the ability of R&D teams to ‘call it’ early, when success is in doubt, is key to overall portfolio success.

Thus, R&D-P goes beyond limited traditional measures of productivity. It goes beyond organizational structure and culture, to something that leaders close to the action can manage and control, even within large bureaucratic organizations.

Thus, optimizing R&D-P requires a new operating model centered on cross-functional teams empowered to, and accountable for, making it happen. It optimizes ways of working, the quality of interactions, information sharing and decision-making within the R&D or product team.

Based on extensive research, Super Projects is a celebration of the new organizational heroes – those leaders who are bringing change and innovation to life.