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Projected returns from innovation have improved this year

Our analysis over the past 14 years has shown a steady decline in productivity between 2010 and 2019, a short-lived improvement due to the impact of the COVID-19 assets in 2020 and 2021, followed by a dip in 2022. In the 2023 cycle, we are beginning to see signs of some improvement. This year’s modeling, based on a dataset that includes an expanded scope of assets and line extensions, calculates that the internal rate of return (IRR) has risen to 4.1%, from 1.2% last year, which was the lowest point for the cohort since our analysis began.

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IRR depends on both efficiency and value creation

Total reported R&D spend by our cohort has increased from $139.2 billion in 2022 to $145.5 billion in 2023, an increase of 4.5%. The average R&D cost to progress an asset from discovery to launch has remained flat for 2022–2023 at $2.3 billion per asset, reflecting an expanded range of assets and line extensions in the analysis this year.

The cohort’s average forecast peak sales per pipeline asset fell from $389 million in 2022 to $362 million in 2023. This continues the decline from the 2021 peak ($500 million) that was driven by high-value COVID-19 assets. Reflecting the successful approval of high-value assets that we have observed year over year, the total revenue for our cohort with reported top 20 pharma R&D sales increases by 9.6% in 2023.

Opportunities to tackle the drivers of IRR and improve productivity

This year, regulatory changes, the impending and unprecedented scale of the loss of exclusivity of high-value assets for many companies in our cohort, inflationary pressures, the rapid pace of scientific and technological advances, and rising protocol design complexity are all placing significant pressures on the current R&D operating model, but they are also creating new opportunities to improve R&D productivity.

AI-enabled digital transformation is fast becoming a strategic imperative for leaders in life sciences. The biopharma industry is on the brink of large-scale disruption driven by interoperable data, advances in AI and analytics, open and secure platforms, and patient-centric care, which have the potential to deliver less costly and more productive drug development. If biopharma succeeds in capitalizing on AI’s potential, the internal and external productivity challenges driving the decline in the IRR of biopharma innovation could be reversed and enable the industry to thrive.