The costs and risks of drug development have never been higher in both financial and human terms.
These inefficiencies largely result from an outdated clinical trial model. Nearly $1.5 billion per approved new drug is attributed to clinical development, the majority of which is for clinical studies. The traditional approach of three discrete, fixed trial phases designed for testing mass-market drugs often is not viable in today’s increasingly competitive, value-based therapeutic markets. It lacks the flexibility, analytic power and speed required to develop complex new therapies targeting smaller and often heterogeneous patient populations.2
With development cycles becoming too long, trial complexity increasing, and greater scrutiny of the economic value of new treatments, pharma R&D business models are under significant pressures to improve R&D efficiency.
In an industry survey of pharmaceutical executives and professionals by ICON and Pharma Intelligence the challenges most frequently cited are:
- Patient enrollment—56% of survey respondents;
- Site start-up—43% of respondents; and
- Regulatory approval delays and changes—43% of respondents.
Elevating efficiency & enhancing trial savings
Patient identification and recruitment and risk-based approaches to study monitoring are expected to have the most impact in transforming the efficiency, speed, and productivity of clinical development.
In our survey, the top five key areas identified by industry experts as having the most potential for generating savings and improving trial efficiency were:
- Improving protocol development —38% of respondents;
- Study start-up activities —37% of respondents;
- Patient recruitment and retention —37% of respondents;
- Vendor selection and management —32% of respondents; and
- Study monitoring —25% of respondents.
Some of these market changes are addressed below.
Smaller Targets. The traditional three-stage randomized clinical trial structure was built to study drugs intended to treat large populations. The numbers involved made it relatively easy to recruit for very large trials, while mass market potential made it economically feasible to conduct them. Today’s markets are different. Driven by scientific advances in areas including biochemistry, genomics and biomarkers, the market for new therapies has moved toward targeted therapies and orphan indications, with big gains in approvals for neoplastic therapies and declines in cardiovascular and broad spectrum anti-infectives since 1980.3
The smaller potential markets mean the R&D enterprise—and clinical trial designs and procedures—must be tightly focused on patient needs, relevant clinical and research expertise, and maximizing efficiency in demonstrating safety and efficacy.
Personalized Medicine. Taking the smaller target trend to its logical conclusion, the market for personalized medicine is growing exponentially. New product offerings target specific biomarkers, such as biologic chemotherapy agents, or even individual patients, such as CAR-T immunotherapy. Similarly, therapies that combine mobile sensors and devices with drugs and delivery devices, such as an artificial pancreas or apps assessing the daily effects of Parkinson’s or other mobility-restricting conditions, require evidence of real-world efficacy and safety that cannot be generated in a controlled environment.
Value-Based Care. Rising healthcare costs as a percentage of GDP is driving greater scrutiny of the economic value of new treatments by government and private payers. In addition to efficacy and safety, clinical trials increasingly must demonstrate a meaningful impact on patients’ lives. This is particularly true for high-cost therapies targeting smaller patient groups, many of which struggle to be covered by national health systems and private insurers. Screening patients to identify potentially better responders and linking payments to individual patient outcomes are among the measures payers are negotiating with sponsors to ensure they are getting value for the money they spend.
Approaches to adopt
Adaptive Clinical Trials. Broader use of trials that modify study protocols in predetermined ways based on interim patient data have the potential to eliminate many unanticipated risks that undermine efficacious drugs and unnecessarily extend development timelines.
For example, adaptive approaches often can deliver in a single two-year period combined Phase II/III trial information that otherwise might require three or more consecutive conventional trials over three or more years. These seamless trials reduce the total sample size needed by combining data from patients studied in both phases of the trial. We estimate that optimal use of adaptive trials across a portfolio, which is encouraged by regulatory agencies in Europe and the US, could reduce trial costs, by 25%.
Automated Data Collection and Analytics. Basing trial inclusion criteria on actual patient data, automatically identified from electronic medical records (EMR), reduces the risk of extra cost and delays when unrealistic recruitment protocols need to be revised. Accessing EMR data can also cut recruitment costs, while automated site support and monitoring greatly reduce start-up and site management costs while ensuring that data are properly collected and validated. Remote data links enable data collection directly from patients at home, reducing the number of costly site visits required for a trial. EMR data allow automated post-market surveillance in Phase IV trials.
Real World Data. The traditional three-stage randomized control trial (RCT) model is not designed to collect information that meets emerging market needs. In some cases, the populations involved are too small to conduct randomized trials. While RCTs are likely to remain the gold standard for validating the safety and efficacy of new compounds for initial registration, innovative trials using real-world data are likely to play an increasing role in defining new, patient-centered endpoints and expanding and refining indications.
Collecting real world data (RWD) to expand label indications, or to truly personalize therapies, cannot be done in a strictly controlled trial structure. Demonstrating value also requires collecting real-world clinical information, as well as non-clinical data on costs.
Patient Centricity. Improving patients’ lives is the ultimate goal of drug development. It means everything from defining outcomes that make the most difference in patient’s lives, to offering trials to patients identified through EMRs in their physicians’ office, to minimizing control arms using advanced statistical methods and providing study results as soon as they are available.
Patients want to have more input into research and treatment of their condition and its impact on their lives. This can benefit clinical research in many ways. Focused patient advocacy groups can help define new therapeutic targets, plan and recruit patients to trials, and demonstrate the value that new therapies bring to their lives.
Making clinical trials available as a treatment option when patients present for primary care, secondary care, and at the consultant level could greatly expand participation. Organizations such as PMG Research address this gap by partnering with physician networks to provide sophisticated trial support in community settings.
The impact of digital disruption
Emerging technology capabilities are expected to play a vital role in transforming clinical trials—including leveraging big data and predictive analytics—which can enable the efficient identification of promising study subjects and sites, as well as risk-based monitoring of trial performance in real time. Integrating study and electronic health records (EHR) may increase data collection reach and efficiency, and help better integrate trials into clinical practice.
Patient-focused technologies, including mobile sensors, smartphone apps and telemedicine, are seen as ways to collect richer patient data, develop new endpoints and help design novel kinds of trials that may better demonstrate real-world clinical and functional value.
Big Data. The world’s capacity for producing data is expanding exponentially, and that data, from medical and non-medical sources, has the potential to greatly increase the efficiency of clinical R&D.
However, establishing and structuring data sourced from diverse systems is required, and this can be technically daunting. Outcomes based on such data also must be modelled and validated, and this, too, requires significant expertise.
Wearables & Mobile Devices. Within trials, mobile apps and sensors can measure key symptoms and signs, such as tremor, blood pressure, blood sugar or activity level, while diagnostic apps, reminders and telemedicine can help keep patients engaged and on protocol. Sensors also may support development and measurement of new endpoints that are more relevant to patient needs.
As with big data, though, new outcome measures and endpoints using mobile devices must be rigorously validated, as must the use of any mobile device supplied by patients to support any apps and sensors used in a clinical trial.
Statistical Analysis & Artificial Intelligence (AI). AI is another area evolving alongside the data explosion—AI-enabled measures include data integration, data management and interpretation. These can improve trial performance at every level, from enabling risk-based trial monitoring to modelling investment return at the portfolio level.
Powerful statistical approaches, including Bayesian statistics for guiding trial design based on accumulating evidence and MCP-Mod for dose-finding, can greatly increase the efficiency of trials, making smaller trials possible by achieving adequate statistical power with fewer subjects. Regulators increasingly are embracing these advanced features, as evidenced by the FDA’s designation of MCP-Mod as ‘fit for purpose’ to improve dose finding efficiency, and are being incorporated into powerful trial design software packages such as ADDPLAN.
However, applying AI and advanced statistical methods requires effort, often including extensive process modelling, and a high degree of specialized skill to achieve results useful for development and acceptable to regulators.
While the technical challenges of applying these new technologies to clinical trials are significant, their value already has been confirmed in many studies, saving millions in development costs. They make possible innovations that are fundamental for transforming clinical trials, such as seamlessly combining phase I and II of clinical trials, developing novel patient-centered endpoints, and collecting and analyzing real world data.
A holistic, integrated approach to transformation
The cost pressures on drug development are driving the search for savings. Whilst large-scale operational efficiencies are being instituted in many pharmaceutical organizations, efforts need to be integrated if they are to be effective.
There is a growing understanding that improving R&D efficiency and return on investment will take more than gradually adopting a number of new technologies. It will require a holistic approach to transforming trials, rethinking and redesigning the trial product itself and the enterprise that supports trials from the ground up.
Transforming clinical trials is essential. A comprehensive rethinking of the entire clinical trials process that uses new approaches coupled with existing, tested technologies to substantially reduce the risk and cost of clinical drug development, is the way of the future.
- DiMasi et al. Cost of Developing a New Drug. Tufts Center for the Study of Drug Development, 2014.
- Jones DS et al. The Burden of Disease and the Changing Task of Medicine.
- N Engl J Med 2012; 366:2333-2338 June 21 2012, DOI: 10.1056/NEJMp1113569.
- Tufts Center for the Study of Drug Development – Outlook 2016.