Rd

In December, we published our eighth annual report on Measuring the return from pharmaceutical innovation. This series of reports tracks the annual return on investment (ROI) that 12 leading biopharma companies (by 2009 R&D spend) are projected to achieve from their late-stage pipelines. For the third consecutive year, we also tracked the performance of an extension cohort of four mid-to-large cap biopharma companies in order to compare their performance with our original cohort. This week’s blog briefly summarises the performance of our two cohorts and then explores some emerging technologies that we anticipate should increase the productivity and efficiency by which drugs are discovered, developed and brought to patients.

This year’s analysis of biopharmaceutical ROI demonstrates just how challenging R&D has become. Since 2010, the internal rate of return calculated for our original cohort of 12 leading biopharma companies has declined from 10.1 per cent to a new low of 3.2 per cent in 2017, a decrease of 0.5 percentage points from 2016 (see Figure 1). While variation in the returns for individual companies within the original cohort remains, the range in values between the top and bottom performer is at its lowest point ever.

Figure 1. Return on late-stage portfolio, 2010-17 - original and extension cohorts

Graph

The ROI calculation is driven by two main factors – the cost to bring assets to market and the projected peak sales these assets are expected to generate. For our original cohort, the average cost to bring an asset to market in 2017 rose to $1.992 billion – up from $1.118 billion in 2010. Meanwhile, the average projected peak sales per asset increased slightly from 2016 levels from $394 million to $465 million, although this was significantly lower than the $816 million we calculated for 2010.

In contrast, we calculated a much higher rate of return for our extension cohort, at 11.9 per cent. The extension cohort’s average cost to bring an asset to market was $2.173 billion, while projected peak sales rose to $1.128 billion, up from $801 million in 2015. The stark contrast in projected peak sales between the two cohorts suggests that the extension cohort has been much more successful bringing high-value assets into their late-stage pipeline. The extension cohort also managed to replenish their pipelines, whereas the original cohort saw a sharp decrease in the number of late stage pipeline assets in the past year.

Our results are a stark reminder that investing in biopharma is risky and returns are by no means guaranteed. While the focus of the series of reports has always been on projected financial returns this is not the only measure of the industry’s ability to innovate, with numerous examples of innovation that demonstrate biopharma’s resilience and optimism about the future. In the coming years, the biopharma operating model will necessarily become leaner, as the future of work becomes a reality. In the near future, we anticipate that emerging technologies (see Figure 2) will bring transformational changes to how the biopharma industry functions. Biopharma companies are just starting to experiment with these technologies, and we predict that early adopters will reap the rewards of a much more efficient R&D process, improving both the quality of assets and the time and cost it takes to bring them to market.

Figure 2. Technologies that could improve biopharma R&D productivity

Graph2

Artificial intelligence

Without the expertise in-house, a number of biopharma companies are forming partnerships with technology companies and start-ups in order to utilise artificial intelligence (AI) in their drug discovery efforts.1,2,3 AI algorithms can recognise patterns and trends and develop hypotheses at a much faster rate than researchers on their own. Utilising these characteristics could make the process of screening new drugs faster, cheaper and more efficient. In addition, AI has the potential to improve study design and decision-making in clinical trials by: 

  • effectively tracking clinical trial recruitment and enrolment
  • identifying drivers of value in patient engagement
  • improving adherence in clinical trials
  • centralising and monitoring clinical trials in real-time.

Real-world evidence

Real-world evidence (RWE) is helping to revolutionise the way biopharma companies evaluate new therapies for safety and effectiveness and could also reduce the time it takes to recruit patients, identify subpopulations and conduct research. In many cases, RWE could make drug development and approvals more efficient and help biopharma understand rare diseases, serve as a control arm in clinical trials, support label expansion, expedite the development of life-saving treatments and expedite patient enrolment. Embracing RWE could also demonstrate effectiveness beyond clinical trials and help biopharma companies provide evidence on improved patient outcomes and improve health care system efficiencies. 

Robotic and cognitive automation

Robotic and cognitive automation (RCA) can enable cost efficiency, productivity gains and quality/compliance improvements across the clinical trial value chain. This can free up programme teams to focus on critical path activities or accomplish tasks that were previously considered too time consuming or costly. Beyond automation, some cognitive technologies can provide insight and expedite report writing. One such technology, Natural Language Generation, can expedite the creation of dossier submissions by automating the safety and efficacy sections and improving consistency of communication, reducing compliance risk and ultimately reducing time to market.

Other digital technologies

A number of other digital technologies can help drive patient engagement and improve data quality in clinical trials while improving patient experience (see Figure 3).

Figure 3. Technologies that can benefit patient engagement and clinical trial productivity

Graph3

Drug development continues to be challenging, complex, costly and time-consuming, as evidenced by our 2017 report. However, we maintain an optimistic view of the future of drug development as emerging technologies are applied across R&D that can impact process efficiency and success rates. We believe that realising the full potential of these technologies could lead to a vibrant and sustainable biopharma industry focused on high-value outcomes – an objective that is vital to the future of global public health.

Mark_Steedman

Dr Mark Steedman (PhD)- Research Manager, Deloitte UK Centre for Health Solutions

Mark is the Research Manager for the Deloitte UK Centre for Health Solutions. Until November 2016, he was the Institute Manager and a Policy Fellow at the Institute of Global Health Innovation at Imperial College London, where he supported research on palliative and end-of-life care, maternal and child health, design, philanthropy and electronic health records. Mark has a PhD from the UC Berkeley - UCSF Graduate Programme in Bioengineering, where he worked with Professor Tejal Desai on retinal tissue engineering and drug delivery. He also completed a Whitaker International Postdoctoral Fellowship with Professor Molly Stevens in the Departments of Materials and Bioengineering at Imperial College London.

Email | LinkedIn

____________________________________________________________________________

1 Sennaar K. AI in pharma and biomedicine – analysis of the top 5 global drug companies. Tech Emergence, 11 January 2018. See also: https://www.techemergence.com/ai-in-pharma-and-biomedicine/
2 Smith S. 16 pharma companies using artificial intelligence in drug discovery. BenchSci, 13 December 2017. See also: https://blog.benchsci.com/pharma-companies-using-artificial-intelligence-in-drug-discovery
3 Hurschler B. Big pharma turns to AI to speed drug discovery, GSK signs deal. Reuters, 2 July 2017. See also: https://uk.reuters.com/article/us-pharmaceuticals-ai-gsk/big-pharma-turns-to-ai-to-speed-drug-discovery-gsk-signs-deal-idUKKBN19N003

Comments

Verify your Comment

Previewing your Comment

This is only a preview. Your comment has not yet been posted.

Working...
Your comment could not be posted. Error type:
Your comment has been saved. Comments are moderated and will not appear until approved by the author. Post another comment

The letters and numbers you entered did not match the image. Please try again.

As a final step before posting your comment, enter the letters and numbers you see in the image below. This prevents automated programs from posting comments.

Having trouble reading this image? View an alternate.

Working...

Post a comment

Comments are moderated, and will not appear until the author has approved them.