Posted: 22 Jan. 2019 6 min. read

Five strategies that could help biopharma firms get more ROI bang for their R&D buckTitle

By Greg Reh, vice chairman, US and Global Life Sciences leader, Deloitte LLP

The US Food and Drug Administration (FDA) approved 59 new drugs in 2018—the most approvals in more than a decade. Almost one-quarter of these drugs received “Breakthrough Therapy” designation, and 41 percent received “Fast Track” designation, according to data the agency released in January.1

Despite the number of approvals last year, biopharmaceutical firms are finding it increasingly difficult to replenish late-stage pipelines. Moreover, while clinical cycle times and research and development (R&D) spending have been increasing, returns over the past eight years have steadily declined, according Deloitte data based on information from 12 large biopharmaceutical firms. During the same period, clinical cycle times (the time it takes to bring a drug through clinical development) have increased.

In 2018, the average cost of developing a new drug and bringing it to market topped $2.1 billion—up $362 million from 2017, and almost double the average cost in 2010, according to our most recent research. While the 12 companies we track have increased R&D spending by an average of 15 percent, their return on investment (ROI) fell from 10.1 percent in 2010 percent to 1.9 percent in 2018—the lowest percentage we have seen since we started tracking it. In addition, peak sales per asset totaled $407 million in 2018—about half what it was in 2010, and these companies also have fewer late-stage assets in their pipelines. Since we began our series, the number of late-stage pipeline assets has fallen 23 percent—from 206 in 2010 to 159 in 2018.

Can companies reverse this trajectory? Balancing the risk-reward equation will likely require a multi-faceted approach, including new development paradigms, evidence-generation techniques, and productivity-enhancing technologies. Technologies such as artificial intelligence (AI) and machine learning algorithms can streamline processes, sift through the mountains of data generated during the R&D process, and enable much more efficient drug discovery and development. Our recent report, Unlocking R&D Productivity, discusses some strategies for reversing the declining ROI.

More biopharma firms are focusing on oncology

In response to shrinking revenues from other segments, as well as opportunities from advancing science, many biopharmaceutical manufacturers have shifted their focus to oncology. In 2010, oncology made up 18 percent of late-stage pipelines among the 12 large biopharma companies we track. That percentage climbed to nearly 40 percent by 2018. This shift toward oncology has led to an increasingly crowded clinical research space and longer development timelines, which has lowered returns. According to the Cancer Research Institute, more than 2,000 immunotherapies were being marketed or were in development in 2017.

Clinical development of oncology therapies is complex and typically targets small numbers of patients. If multiple companies target the same patients, or the same types of tumors, they could find it more difficult to find and enroll participants in clinical trials. This can delay trials and increase costs. Case in point: Clinical cycle times for cancer therapies have increased from an average of 6.16 years in 2015 to 6.61 years in 2018, according to our research. Moreover, biopharma companies are taking longer than ever to develop drugs—particularly cancer therapies. One reason for this could be an insufficient number of patients who are eligible to register in clinical trials.

How can biopharma companies improve their R&D ROI?

As long as the numerator (the value of assets) decreases, while the denominator (R&D costs) increases, biopharma companies will see declining returns. To reverse this trend, biopharma companies should consider the following five strategies to create a transformational change in R&D productivity:

  • Embrace technology: Technologies such as robotic process automation, natural language processing, and natural language generation can help automate tasks so that they can be done faster, cheaper, and more accurately. Incorporating these technologies can lead to a more productive and cost-effective workforce. Other technologies, such as machine learning, can be used to support and improve R&D decision-making and clinical-trial design, and could transform drug discovery and business development. Drug development is a heavily regulated process, and companies must file documents throughout the R&D process. Some of this paperwork could be automated, which would free up employee capacity. R&D leaders should consider how other work processes could be automated. The process to deliver the clinical supply for CAR-T therapies, for example, is fundamentally different from processes used to develop traditional small-molecule drugs and biologics. By using technology to automate repetitive and administrative tasks, R&D teams could spend more time on higher-value activities. Moreover, applying machine learning to data gathered from electronic health records (EHRs), for example, could help biopharma companies design more realistic inclusion/exclusion criteria for clinical trials.
  • Recruit people who have a background in new technology: Increasingly complex R&D processes, combined with sophisticated technologies, require biopharma companies to improve their technical and analytical skills. For companies to tap the potential of big data, they will need employees with data science skills who can clean and analyze data. These employees also need to be able to frame the right questions, identify correct hypotheses, and accurately interpret the strategic and clinical significance of the results.
  • Invest in innovation hubs: Certain places can be important sources of both innovation and talent. Some biopharma companies that invested in innovation hubs are expanding that footprint to access people who can work on digital innovation. Relationships with local universities, academic medical centers, and biotech and digital health start-ups can support two-way learning and faster innovation cycles.
  • Collaborate with external stakeholders: A collaborative approach to drug development—known as master protocols—could allow non-profits, academia, drug-makers, and other stakeholders to share infrastructure (including analytical capabilities and costs) and test clinical hypotheses in parallel. A recent paper from the Deloitte Center for Health Solutions estimates that such collaborative clinical studies could reduce oncology trial costs by as much as 15 percent and cut study time by between 15 and 21 weeks. For collaborations like master protocols to succeed, however, participating companies should define new operating models and governance structures.
  • Consider new sources of talent: R&D leaders should consider new sources of talent outside of their four walls. For example, companies might be able to gain more insight from patients who are treated as collaborators, rather than subjects, in the research process. Patients can participate on advisory boards, study pilots, surveys, focus groups, and can offer input through crowdsourcing. Company leaders might want to consider crowdsourcing input on other aspects of drug development from external stakeholders or leveraging gig workers to help manage capacity constraints.

We believe it is imperative that biopharmaceutical companies develop a strategy to reverse the trend of declining returns. They should find ways to collaborate—with other companies and with stakeholders, including patient advocacy groups and academic entities. Company leaders also should ensure they are connected to technology companies that are working on products and services that could help improve their R&D. But they should begin with a clear vision for how digital transformation can improve returns. Digital transformation can be a multi-year process, and people who possess backgrounds in technology and data analytics are in high demand across all industries. Biopharma companies have a real opportunity to streamline R&D processes, reverse declining returns, and focus on developing new cures.

1. 2018 New Drug Therapy Approvals, FDA Center for Drug Evaluation and Research, January 2019 (


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Greg Reh

Greg Reh

DTTL Global Life Sciences Sector Leader

Greg is the Deloitte Global Life Sciences & Health Care Industry leader. In this role, he guides and advises the Life Sciences & Health Care leaders of Deloitte’s global member firm network. He has more than 25 years of experience working with multinational pharmaceutical, biotechnology, and chemical manufacturing organizations. In his role, Greg advises on the delivery of services and solutions for life sciences clients, practice operations, and the development of methods & research related to the transformation of life sciences companies. Throughout his career, he has served clients that include multinational pharmaceutical, biotechnology, and chemical manufacturing organizations.