Five Potentially Transformative Strategies for Accelerating Drug-Development | Deloitte US has been saved
By Amy Cheung, principal, Deloitte Consulting, LLP
The average cost of bringing a new drug to market was over $2 billion in 2020; and returns on research and development (R&D) costs have been declining for much of the past decade, according to new Deloitte research. Moreover, clinical trials increasingly need to generate a greater body of evidence to meet the demands of health plans, clinicians, regulators, and patients.
I recently led a panel of industry leaders who explored approaches that could reshape the way biopharmaceuticals are developed and tested. Here’s a look at five potentially transformative strategies that our panel suggested to boost R&D returns and generate more insightful data:
- Identify and design trials for subpopulations: It is important to understand the progression of the disease itself, as well as the way it presents in patients who have the disease, explained Greg Friberg, M.D., vice president and head of Oncology at Amgen. While several patients might have the same disease, it might affect each of them a little differently. Similarly, they might not all respond the same to therapies. For example, a researcher who looks at bone-marrow cells under a microscope might conclude the therapy was effective in treating leukemia in all participants in a clinical trial. However, a deeper look at the cells—using new technologies—might identify a few patients who still have residual traces of the disease and could need additional treatment. In addition, genetic sequencing could be used to identify various molecular switches that could be turned on and off in certain patients, he said.
- Bring trials to the community to improve diversity: The ethnic makeup of clinical trials should reflect the real world. If a disease is more prevalent among Black people, the trial should have more Black participants, said Najat Khan, Ph.D., chief data science officer and global head of strategy and operations at Janssen. Murray Abramson, M.D., senior vice president of clinical innovation at Tempus Labs, Inc., agreed and suggested that bringing trials to diverse communities will lead to a more diverse patient population in clinical trials. Different community health sites may have more diverse patient populations than academic medical centers. Such clinics might also be more accessible to some patients. Moreover, community health sites are often willing to participate in meaningful and important research, but they might not have the training or staff. “Heterogeneity is both your friend and your enemy in clinical trials,” he cautioned. Companies should work to ensure that clinical trials reflect the diversity of the intended patient population. At the same time, trials should be statistically powered in order to identify any nuances in how different populations respond to therapy.
- Consider just-in-time clinical trials: Just-in-time (JIT) manufacturing is an inventory-management strategy that emerged in Japan during the 1960s and 1970s. The concept—which ensures that materials become available as they are needed—helped to revolutionize automobile manufacturing. Could this same concept revolutionize clinical trials? Murray explained that it can be difficult to recruit a diverse patient population for a clinical trial, particularly when trials take place at a large teaching hospital. Creating flexible trials that can take place in communities could lead to a more diverse patient population. This JIT manufacturing concept makes it possible to target specific types of patients for smaller, but more targeted research. Tempus developed a network of clinical-trial sites that can be activated in a matter of days. The JIT concept “is one way to equalize the playing field for everyone who has a disease and needs specialized treatment,” Murray said. Najat noted that Janssen is using JIT and targeting community clinics for some of its clinical trials.
- Leverage real-world data and data science: Disease-specific datasets, lab results, clinical genomics data, and other real-world data (RWD) can be combined with advanced analytics and artificial intelligence to reduce cycle times. This can help biopharma companies make better-informed decisions about product profiles, identify high-responder sub-populations, simulate clinical trial enrollment, and support regulatory submissions, according to Deloitte's 2020 report on transforming clinical development. Combining RWD with data science techniques can also help identify predictive risk factors that can be used to design the clinical trial protocol, Najat said. “If you can select the right patients, you can accelerate the timelines and address high unmet needs sooner,” she explained. As COVID-19 vaccines were being developed, Najat said her team used data analytics to identify future hot spots where clinical trials could be established.
- Consider patient centric approaches to clinical trials: Bringing trials to the patients—and making it easy for them to participate—has to go into the design of the trial, said David Scholfield, head of clinical development and operations at Pfizer. It can be difficult to recruit patients who can’t afford to take time off of work to have bloodwork done. Clinical trials need to have infrastructure that supports patient participation, he said. When the COVID-19 pandemic hit, health care systems were stressed, and many patients were afraid to enter a hospital. It was important that patients felt safe continuing in a trial, and that the continuity of care continued. “We also wanted to reduce the burden on clinical sites,” he recalled.
Individual sponsor-run trials competing in the same therapeutic area are also not ideal from a patient’s perspective. How do patients identify the trial that’s best for them, and the most likely to help treat their particular disease subtype? From the sponsor’s perspective, there might not be enough patients with a specific subtype of a disease. A master protocol combines several sub-trials, each with its own research objectives, into a single clinical trial, according to Deloitte's report on master protocols. Master protocols can also be used to look at how certain drugs affect a specific mutation in tumors, Greg added. These types of trials are patient-centric because they match patients to the trial arm where they are most likely to see benefit. And the chances of being enrolled in a control arm are minimized (since the control arm is shared across all sponsors).
Deloitte’s research on returns on pharmaceutical investments found that companies are taking longer than ever to bring new drugs to market. This is driven by increasingly complex protocols, the need to generate endpoints to satisfy multiple stakeholders, antiquated processes and technologies to collect and report data, and increasing competition for the still limited pool of clinical trial participants. The five transformative strategies outlined during the webinar could have a significant impact on the future of new therapies. Biopharmaceutical companies might start by piloting the approaches in oncology and rare disease. They can then scale them in those areas and eventually expand to applicable disease areas in their broader portfolio. They should ask themselves where these approaches can be applied, who are the key stakeholders that need to be engaged, and what investments need to be made.
