LONDON—Projected returns on investment in research and development (R&D) for the top 12 biopharmaceutical companies have fallen to 1.9 percent, according to a study by Deloitte’s Centre for Health Solutions leveraging revenue forecasts and industry benchmarks generated by GlobalData, a leading data and analytics company.
The study, entitled “Unlocking R&D productivity: Measuring the return from pharmaceutical innovation 2018” and published by Deloitte, reveals that returns are down 1.8 percentage points from 3.7 percent in 2017, and forecast average peak sales are at $408 million, making 2018 the lowest level since Deloitte’s first R&D report in 2010. In the rapidly changing biopharma landscape, R&D returns have dropped by 8.2 percentage points from 10.1 percent in 2010.
The increase in average cost of development of biopharmaceutical drugs is a driver of this declining return. Average costs of development before regulatory approval for commercialization have increased in six out of the last eight years, with the average cost now at $2.18 billion, compared to just under $1.19 billion in 2010.
The study also confirms that a systemic approach to productivity improvement, driven by innovative streamlining approaches, is needed to lessen development costs and timeline, ultimately increasing R&D returns. Companies need to act now and embrace new ways of working, embed new technologies, such as artificial intelligence and robotic process automation, and seek out talent with the right skill sets to optimize their return on investment in pharmaceutical innovation, say Deloitte and GlobalData.
“The good news is that advances in these technologies are already starting to have an impact in R&D. Companies will increasingly use AI, in particular machine-learning algorithms, to reduce R&D cycle time, costs and ultimately build a strong and sustainable drug pipeline,” said Dr. Bonnie Bain, global head of pharma at GlobalData. “We will also continue to see use of AI extend beyond drug discovery and lead optimization to playing an important role in clinical trials—not only for analyzing the large amounts of data being generated from clinical studies, but also for trial recruitment and management.”