Measuring the return from pharmaceutical innovation——Unleash AI’s potential

Unleash AI’s potential, Deloitte’s 14th annual report in Measuring the return from pharmaceutical innovation series, explores the performance of the biopharmaceutical (biopharma) industry and its ability to generate returns from investment in innovative products in the development pipeline. The current biopharma R&D operating model faces several serious challenges, including ongoing regulatory changes, loss of exclusivity of an unprecedented number of high-value assets, and the rapid pace of scientific and technological advancements. However, advances in digitalization and artificial intelligence (AI) present new opportunities to improve R&D productivity, paving the way for a new era of innovation and accelerating patient access to new therapies.

Insights from our year-on-year analysis have demonstrated that transformational change in R&D productivity is essential if improvements in projected returns across the biopharma industry are to be sustained and grow. Our analysis this year shows that this conclusion is as relevant as ever given R&D projected returns remain below the cost of capital which will make R&D leaders’ funding requests continue to be challenging.

The following are the key findings from the report:

1. Measuring the return from pharmaceutical innovation

Our annual report series Measuring the return from pharmaceutical innovation analyses the projected IRR that biopharma companies can expect to earn from their late-stage pipelines. The past 14 years have demonstrated that transformational change in R&D productivity is required to reverse the declining trends in returns across the biopharma industry while continuing to deliver innovation to patients.

• 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, and in the 2023 cycle, we are beginning to see signs of some improvement.
• IRR depends on both efficiency (cycle times and costs) and value creation (risk-adjusted forecast sales), each of which has multiple parameters that can improve outcomes. It is therefore important to understand both the trends in costs to develop an asset from discovery to launch and also the risk-adjusted forecast revenue of the assets in the pipeline.

Figure - Opportunities to tackle the drivers of IRR and improve productivity

2. Realising efficiency opportunities

While R&D executives prioritise expediting the time to market for drugs targeting unmet needs, they also have pressing concerns about the consistently high expenditure and rising costs of R&D. By scaling end-to-end digital transformation and the use of AI and other technology tools, companies have the potential to increase drug development efficiencies dramatically. However, investment in data infrastructure and AI capabilities needs to recognise the importance of maintaining ‘the human in the loop’ in realising value and efficiency gains.

• This rise in R&D costs can be attributed to several factors, including more complex trial requirements, regulatory changes, the impact of inflation, and continuing to operate in functional silos. The primary driver is to develop a successful product that benefits the intended patient population.
• Long development cycle times have been a challenge for the industry for many years, developing more flexible and adaptable clinical trial processes can improve productivity and help companies respond more effectively to the rapidly evolving regulatory and commercial landscape, and in turn reduce costs, while bringing products to market more quickly and effectively.
• Regulatory compliance can be either a barrier to or an enabler of productivity in the highly regulated biopharma industry. Interpreting the new and evolving regulatory expectations and implementing any necessary changes in a coordinated, cost-efficient and timely manner, across a number of business functions, is a significant challenge for the industry.
• The amount of data produced during clinical trials is growing exponentially; but the benefits can be obtained only if the data is managed, processed and utilised to gain actionable insights. Given the pace of technology innovation, and increasing use of AIenabled technologies, the time is ripe for the industry to scale the use of digital technology to obtain enduring value.

3. Optimising the value of pipelines

As biopharma companies work to sustain a profitable R&D pipeline and bring new therapeutics to market, they navigate a complex landscape of regulations, looming patent expiries, technological advancements, and competitive pressures. Today the IRA, EU patent laws and the rapid advent of AI across the industry are demanding fast-paced, flexible and collaborative R&D operating models to stay ahead of the curve.

• We consider that the IRA in the US is likely to be an ongoing catalyst for change, rather than a one-time event. Despite the IRA being US legislation, there is and will continue to be a global ripple effect on biopharma strategies due to the international nature of the industry. EU patent law revisions will also impact development and launch strategies across multiple geographies.
• With the greater incentives and penalties imposed by regulations, companies should map their commercial strategy for assets as early as possible in the development process. There is also a need to embed flexibility and a dynamic balance of internal and external sourcing in pipeline strategy.

4. Strategies to improve productivity

Since 2010, our cohort of companies have struggled to replenish their R&D pipeline with new assets at the same pace, and to the same value, as the assets leaving the pipeline due to successful regulatory approval or late-stage termination. With rising costs, long cycle times, looming patent expiries, a complex M&A landscape and changing regulations, biopharma is nearing the point where the commercial portfolio is unable to sustain innovative R&D and support long-term growth.

• AI-enabled digital transformation is fast becoming a strategic imperative for leaders in life sciences.
• Competitive intensity, scientific breakthroughs and regulatory incentives have skewed R&D spending toward certain areas, particularly oncology and rare diseases. As competition in over-concentrated therapeutic areas heats up and the focus of payers on the equitable allocation of health care spending rises, the current dynamic could change.
• Ultimately, transforming the productivity of R&D will require companies to work entirely differently, drawing on change management skills, as well as partnerships and collaborations.