Why It’s Time to Revisit FDA’s Quality by Design

Patients of Alzheimer’s disease, their families, and health care advocates recently encountered back-to-back disappointments when two clinical trials for Alzheimer’s drugs failed in quick succession. First, Eli Lilly & Co. stopped its late-stage clinical trial for solanezumab, then Merck & Co. ended its clinical trial for verubecestat. Both studies failed to show efficacy. Such failures—devastating for patients and costly for the sponsors—are not entirely surprising given that only 32 percent of clinical trials reach phase 3, according to a study published in Nature Biotech (1).Quality by design (QbD), as taught by Joseph M. Juran, is a systematic approach to close quality gaps, resolve quality issues, and prevent quality failures from the earliest phase—design and planning stage. Juran advocated quality planning, quality control, and quality improvement (2). The U.S. Food and Drug Administration (FDA) emphasizes the need to mitigate clinical-trial risks through its guidances and requirements pertaining to risk-based monitoring and quality-by-design approach in clinical trials.Given the recent high-profile clinical trials that failed, it’s time to revisit the concept of quality by design, FDA guidances related to the approach, and initiatives meant to help improve clinical trials.

Quality by Design & FDA

As part of the FDA’s effort to strengthen oversight of clinical trials, it partnered with Duke University in 2007 to establish the Clinical Trials Transformation Initiative (CTTI), which promotes the use of quality by design. What is QbD? In the revised Q8 (Pharmaceutical Development) guidance issued in 2009, the FDA defined quality by design as “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control based on sound science and quality risk management.” (3) The guidance said a QbD approach to product development should include these elements in addition to basic requirements:

• A systematic evaluation, understanding, and refining of the formulation and manufacturing process;
• A combination of the use of the enhanced product and process understanding with quality risk management to establish an appropriate control strategy.

In another document, “Guidance for Industry: Oversight of Clinical Investigations—A Risk-Based Approach to Monitoring,” the FDA recognizes the dramatic increase in the number of clinical trials and their great complexity. The 2013 guidance is meant to help ensure appropriate oversight through effective monitoring since it’s impossible for the agency to inspect every clinical trial (2).

Quality by Design & CTTI

In answer to questions I sent to CTTI, Annemarie Forrest, associate director of projects, acknowledged the concern about the current model for conducting clinical research. “The cost and complexity of trials have increased, and with that, the ability to generate reliable evidence essential for making decisions about clinical intervention benefits and harms is challenged,” she said.Adopting QbD as a strategy could increase data integrity and enhance the quality of clinical trials. “Improving protocol design, trial planning, and quality oversight can address inefficiencies,” explained Forrest. “Applying the CTTI QbD recommendations and principles can help organizations prioritize important determinants of a trial’s quality, identify activities that can be eliminated to streamline trial conduct and oversight, and make plans to define, avoid, mitigate, monitor, and address important errors.

The CTTI has anecdotal reports about increased use of QbD over the last few years, but it doesn’t have the statistics at present. While Forrest declined to comment on the recent spate of failed clinical trials, she encouraged investigators and research sponsors to move from concept to implementation and practice of quality by design (5). “Quality oversight can be improved by focusing on data and activities that are critical to trial participants’ safety and the reliability of trial results, rather than on monitoring the accuracy of each individual data point,” she said. “We like to say simply, ‘just think!’ That is, identify and mitigate the errors that matter.”