By Maria João Cruz, Research Analyst, Centre for Health Solutions

Pharma

This week, we published the tenth in our series of annual reports, Measuring the return from pharmaceutical innovation. For the past decade we have used a consistent and objective methodology to provide analyses and insights on the R&D productivity of the top 12 biopharma companies (original cohort), and the returns they can expect to achieve from their late stage pipelines. For five years, we have contrasted our original cohort’s performance with the performance of four smaller, more specialised companies, which we tracked back to 2013. While our findings have demonstrated a systemic, cross-company, decade-long decline in the productivity of R&D, we have also witnessed significant advances in science and the development of innovative therapies delivering impressive improvements in health outcomes.

This, our 10th anniversary report, is my first involvement in the research series, having joined the Centre as a research analyst in October. Before joining the team, I studied bioengineering and regenerative medicine and was working as a research scientist. I was delighted, therefore, to use this week’s blog to provide my take on our 2019 report findings, the key lessons we have identified some of the solutions that could help ensure a sustainable future for biopharma R&D.

Measuring the return from pharmaceutical innovation – a decade of decline
Our original cohort has seen their projected IRR decline from 10.1 per cent in 2010 to 1.8 per cent in 2019, down 0.1 percentage points from 2018 and 8.3 percentage points overall (Figure 1). However, in 2019, while eight of the 12 companies in our original cohort improved their returns compared to 2018, only one company achieved returns above five per cent, and the range in values between the top and bottom performer has narrowed to 7.1 per cent, its lowest value yet. Similarly, in 2019 the returns for our extension cohort also declined to a low of 6.2 per cent, from 9.3 per cent in 2018 and 17.1 in 2015, mainly due to asset terminations. However, the companies in this extension cohort are still outperforming their larger original cohort peers.

Figure 1. Return on late-stage pipeline, 2010-19 – original and extension cohorts

Figure1

On measuring the internal rate of return (IRR) we factor in the average cost to develop the assets in each company’s pipeline and the forecast average peak sales from these assets once approved for marketing. A notable feature is the significant increase in drug development costs, specifically:

  • for our original cohort, the average cost per asset in 2010 was $1,188 million, in 2019 it had increased to $1,981 million (which was a slight decrease from the 2018 high of $2,168 million). This was mainly due to a successful replenishment of assets in the cohort’s pipelines including in-licensing deals. Overall, the number of late-stage pipeline assets in the original cohort’s portfolio increased from 159 to 183, a three-year high and very close to the ten-year average of 186.5
  • our extension cohort’s average cost of asset development doubled from $1,260 million in 2015 to $2,422 million in 2019 (2018 also recorded the highest average cost, at $2,805 million, again due to having fewer assets in their pipeline in 2018 compared to 2019). The extension cohort’s portfolio increased from 23 to 30 late-stage assets.

On forecast peak sales per asset, we have also seen large reductions for both cohorts. In 2019, the original cohort’s average peak sales fell to $376 million, down from $407 million in 2018 and $816 million in 2010. For our extension cohort, while forecast peak sales increased from $1,113 million in 2015 to $1,165 million in 2018, they decreased significantly in 2019, to $658 million. This decrease in forecast peak sales has, and continues to be, a key reason for decline in returns.

The key drivers of the changing R&D model
Our research shows that the main drivers of R&D performance are the change in the portfolio of drug modalities in the pipeline, the increase in development (or cycle) times, and sources of innovation. In recent years there has been an increased focus on biologics, which now account for 57 per cent of assets in the pipeline. This shift in drug development towards biologics has led to a more diverse pipeline, with antibody therapies the biggest proportion (37 per cent in 2019, up from 15 per cent in 2010) (Figure 2).

In addition, the proportion of other modalities within biologics, including cell and gene therapies, has remained relatively steady over recent years. Nevertheless, we believe that ‘next gen’ modalities will drive biopharma innovation over the coming years and will significantly affect the biopharma business model by posing a number of manufacturing and regulatory challenges. Indeed, while pharma can manufacture small molecules relatively easily, using well-defined processes that target large populations of patients; biologics have a more costly and complex production process and target smaller populations with higher specificity. ‘Next gen’ therapeutics, particularly cell and gene therapies, which target even smaller groups, even individual patients, take these challenges a step further, including increasing regulatory scrutiny.

Figure 2. Pipeline focus by modality, 2010-19 – original cohort

Figure2

Although regulators are increasingly looking to introduce initiatives to help accelerate drug development and approvals, the growth in more scientifically complex modalities and therapy areas means clinical cycle times are increasing (Figure 3). One of the main reasons for this is the increasing focus on oncology, a therapy area that typically has lengthier clinical development times. Consequently, the industry needs to find and implement strategies to optimise the clinical trial process, specifically, by capitalising on digital technologies, including the use of artificial intelligence (AI) tools. Examples, which we are exploring further in a series of report on the impact of AI across the biopharma value chain, include:

  • using AI technologies to analyse and interpret both structured and unstructured clinical data
  • mining electronic health records to match patients to trials
  • analysing data on location and site activity to inform feasibility and start-up decisions
  • automating processes and digitalising clinical assessments to improve investigator productivity
  • using AI tools to enhance patient engagement to ensure medication adherence and retention
  • using wearable technologies to collect digital endpoints on disease progression and quality of life indicators.

Figure 3. Average clinical cycle times, 2014-19 – original and extension cohorts (combined)

Figure3

Furthermore, while the proportion of sources of innovation for biopharma has fluctuated over the last 10 years, over half of late-stage pipelines were sourced externally in the last two years (Figure 4). Markedly, in 2019, the original cohort has relied increasingly on M&A as a source of innovation, with 33 per cent of forecast sales coming from acquisitions. Meanwhile, companies in the extension cohort are increasingly partnering to access both capability and innovation.

Figure 4. Proportion of late-stage pipeline revenue from internal and external sources, 2010-19 - original and extension cohorts

Figure4

Shaping the future of biopharma innovation
The decline in expected returns over the past decade shows quite clearly that the current high-risk, high-cost R&D model is unsustainable, and requires a fundamental shift. Adopting a digital mind-set is now a business imperative. Specifically, biopharma companies need to take full advantage of the growing number of rich datasets, as well as scientific and technological advancements. This is an opportunity for every biopharma company to decide what type of R&D model will be more appropriate for a sustainable future. Based on our global ‘Future of Health’ research, we consider that data conveners, science and insight engines, and data and platform infrastructure builders are the three main business archetypes likely to drive the future of health for biopharma (Figure 5).

Figure 5. The three main business archetypes that are likely to apply to biopharma in the Future of Health.

Figure5

While we maintain the ‘tempered optimism’ from our first report in this series, we believe that biopharma companies can reverse this decade-long decline in R&D productivity. Scientific breakthroughs will increase at an exponential pace, supported by radically interoperable data. Innovative digital technologies will continue to provide biopharma companies with opportunities to improve how they conduct clinical development and engage with trial participants and regulators. Biopharma’s ability to overcome the current challenges heavily relies on learning the lessons of the past and capitalising on the current wave of innovative technologies. The capacity to adapt will determine the success of the industry.

MJCphoto-deloitte

Maria João Cruz - Research Analyst, Centre for Health Solutions

Maria João is a Research Analyst for The Centre for Health Solutions, the independent research hub of the Healthcare and Life Sciences team. At the Centre she conducts rigorous analysis and research to generate insights that support the practice across Life Sciences and Healthcare. She loves a good challenge and is always passionately curious. Before joining Deloitte, Maria João was a postgraduate researcher in Bioengineering at Imperial College London, jointly working with Instituto Superior Técnico, University of Lisbon. She holds a BSc and MSc in Biological Engineering from IST, Lisbon.

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