Right Drug, Right Patient: Streamlining Clinical Trials to Speed Drug Development

Advances in basic molecular science are paving the way for increasingly targeted therapies that address the unique biological mechanisms involved in a patient’s illness. Ultimately, medicines may become truly “personalized,” allowing for a fully customized approach to health care that takes into account an individual patient’s medical history, disease risk, and pathology, and selects the most promising course of treatment based on these and other characteristics. This vision for the future may be a long way off, since translating scientific advances into targeted therapies has not proven to be quick or easy. However, taking advantage of innovative clinical trial designs could lead to more efficient clinical trials that do a better job of matching treatments to specific patient populations and speed the development of targeted therapies.

The Role of Clinical Trials in Biomedical Innovation

A major barrier to this type of innovation is that our current clinical trial and drug regulatory process – the formal system by which novel medicines are evaluated and approved by the U.S. Food and Drug Administration (FDA) – has lagged behind advances in scientific research. Regulatory approval decisions are typically based on evidence of efficacy and safety gathered from lengthy Phase III pivotal trials that enroll a large, diverse patient population. When the study population is heterogeneous, larger trials are often necessary to have adequate statistical power for obtaining statistical significance and identifying rare side effects that might not be detected in a small group. But most agree that the conventional approach to drug development constitutes a blunt tool where more elegant experiments could suffice, particularly to evaluate more targeted products for which a one-size-fits-all approach would be ineffective and wasteful.

In addition, the industry has been plagued by rising costs in recent decades. The cost of bringing a single product to market has been estimated in recent years at between $1.2 and 1.8 billion and unfortunately, these ballooning costs have not corresponded with an increase in successful product approvals. In fact the opposite is true – new data indicate the number of drugs invented per billion dollars invested in R&D has been nearly cut in half every nine years for the last fifty. Because large Phase III trials continue to be a major cost driver associated with drug development, there is much to be gained from greater efficiency at this stage. However, regulatory uncertainty, tight margins and high risk of failure have precluded much experimentation in this space.

Many have suggested that novel clinical trial designs could capitalize on our growing knowledge of patient subpopulations for which a therapy may be more effective without compromising FDA’s rigorous safety standards. Many are optimistic that these designs could also improve regulatory success rates and ensure the more rapid and cost-effective delivery of innovative products to patients who are predisposed to respond favorably.

One of the most promising areas for investigation is oncology – we now recognize cancer to be an incredibly diverse group of diseases that almost defy a single label. Even within breast cancer, for instance, we know of a number of tumor biomarkers that are highly deterministic of disease severity, survival, and optimal treatment regimen. For these diseases, and other conditions like them, the traditional design of clinical trials is not always the most effective or efficient way to evaluate novel therapies. A future of increasingly personalized medicines will demand a rethinking of how we weigh treatments’ benefits and risks for individual patients and how we can develop sufficient evidence of safety in an economically viable manner.

Adaptive Clinical Trials: Encouraging Flexibility and Efficiency

One promising approach for modernizing clinical trials and maximizing their efficiency is using data accumulated during the trial to inform their design. While traditional trials have fixed parameters that are determined in advance and held constant throughout the trial, “adaptive” trials allow for certain parameters – such as treatment regimen, study population, and sample size – to be modified based on interim results. These preliminary analyses conducted during an ongoing trial can also be used to stop a trial early if the product is unlikely to meet its target endpoint. Or, to drop certain treatment arms that appear less effective, which helps to avert failure, additional costs, and unnecessary risk to patients further down the line.

Adaptive designs could have particular value in enhancing the development of personalized therapies. They allow investigators to learn much more about products during development and to identify the most responsive patient subpopulations and the best drugs for these individuals. In oncology trials, for example, investigators are using tumor biomarkers to match patients with the most likely effective treatment. These designs could help increase the odds of success, and of delivering more valuable products to patients.

However, there is some hesitation on behalf of FDA and the pharmaceutical industry to use adaptive designs for Phase III pivotal trials. Because adaptive trials rely on preliminary data analysis, many fear that the results could influence the behavior of investigators, patients, or investors, They could also potentially introduce bias. Adaptive trials are also more likely to employ Bayesian statistical approaches to assess the trial evidence as it accumulates and to demonstrate the efficacy of a treatment. The application of these statistical methods to drug development is still fairly novel for most sponsors and reviewers, and their use introduces new challenges related to estimation and error control that might be particularly concerning in trials submitted for regulatory approval. For these reasons, complex adaptive designs and the use of Bayesian statistics remain mostly uncharted territory in Phase III designs, despite the pressing need to build efficiency and learning opportunities into this most expensive phase of development.

A number of factors could contribute to the success of adaptive trials:

Experience. Industry and regulators have significant experience with traditional clinical development paradigms, but at one point, even these were novel. Ongoing exploration and experience using adaptive designs is necessary to develop the relevant operational and statistical expertise to ensure that products are safe and effective for the American public. This will also help product developers and the FDA reviewers become more confident in using the results for regulatory decision making.

Transparency. Recent comments from FDA have indicated that one of the biggest obstacles for regulators is the need for data transparency when drug sponsors employ novel statistical approaches. Effective communication between drug sponsors and FDA regarding their analyses and willingness to share lessons learned can facilitate acceptance of adaptive designs on both sides.

A balance of clarity and flexibility. Clear standards for approval can help drug sponsors feel more secure and willing to make the decision to use adaptive designs. To this end, FDA is currently working to finalize its draft guidance for industry on adaptive designs. That said, in these early days, flexibility and openness on the part of both drug sponsors and regulators is needed to capitalize on the opportunities to learn from the implementation of adaptive trial designs.

Exploring adaptive trial designs and their potential to support timely and value-driven drug development and regulatory review will be important as FDA, industry, and other stakeholders continue their work to modernize the biomedical innovation process in the United States. A number of ongoing initiatives could be supported by adaptive methodologies and other streamlined approaches, including FDA’s Breakthrough Therapy Designation, proposed regulatory pathways such as Special Medical Use, or any number of other efforts that may result from the House Energy & Commerce Committee’s 21^st Century Cures initiative.

For more information on adaptive trials, please visit the event page for the Brookings/FDA expert workshop, “Pioneering Statistical Approaches to Accelerate Drug Development through Adaptive Trial Designs.”