UK: A Personal Take On Measuring Pharma R&D Returns

Last Updated: 30 December 2015
Article by Oliver Whight

You may have seen the headlines this week that pharma R&D returns are at their lowest level for at least 5 years. This comes from our 6th ' Measuring the returns from pharmaceutical R&D' report – an annual study which I have worked on for the last three years. I thought I'd take this opportunity to reflect on the origins of this report and its contribution to the debate on pharma R&D productivity.

Back in 2010 (before I came on board) our healthcare and life sciences' R&D teams at Deloitte were becoming increasingly aware that there was no well-understood industry benchmark for measuring R&D returns across companies. Furthermore, our clients often asked us if we had a view on how to improve R&D returns and drive efficiencies, and what strategies should be used to improve returns and to maintain growth in pipeline value.

While we were aware that some companies and external groups had attempted to measure R&D returns, typically pharma companies had different methodologies for doing this and generated data in different ways leading to an inability to benchmark across peers. The data was simply not comparable. External groups on the other hand (consulting companies, industry bodies and academia) tended to use methodologies that were overly complex and therefore poorly understood, and/or the results perceived as over-inflated. All of which typically led to the results being dismissed.

This is where the idea for our research was born – we believed that we were in an ideal position to develop a set of tangible and transparent benchmarks for the industry by combining expertise from our life science R&D, modelling and accounting teams. We also understood the complexity of calculating R&D returns but felt it was important to avoid a complex, 'black box' methodology, to avoid the results being poorly understood and/or dismissed.

We therefore set out to develop analytics that are grounded in data that is either publically available (from pharmaceutical company annual reports) or readily accessible (21 year sales forecast data that can be purchased from third party data providers). We also tried to avoid the use of overly technical language when presenting the results, as we wanted this report to be accessible to a broader audience given the significance of the issues at hand.

Our first report, published in 2010, calculated the annual R&D returns for the 12 leading, global pharmaceutical companies (by revenue). Little did we know, at that juncture, that the returns would, as our latest report shows, turn out to be the highest annual return enjoyed by the combined cohort...

Each year since 2010 we've mobilised an increasingly global set of experts within Deloitte to generate insights, develop our ideas and evolve the report. The real value in the report however is in enabling our clients to review, in a consistent and comparable way, their own performance relative to peers to understand:

  • what is best in class performance
  • what strategies are linked to best in class performance
  • what are the levers of performance and how does each impact return on investment?

As the report has matured the dataset we work with has become richer, new analysis has been possible and client discussions generated by the report have shifted in focus. In the early years of the research (2010 – 2013) discussions often centred on the methodology and snapshots of year on year performance. Our most recent reports have focused much more on teasing out insight on the characteristics of leaders in R&D returns as well as lessons that can be learned from an extension cohort of mid-to large-cap biotechnology companies. Look out for next week's blog where we will discuss the key findings and our opinions on them.

Our goal with this latest report is to continue to develop evidence based strategies that could be used to drive better productivity and efficiency in R&D and ensure patients continue to benefit from life changing medicines in the future.

Footnote

1 US Department of Energy, O&M Best Practices Guide, Release 3.0, Chapter 6: Predictive Maintenance Technologies," 2013

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