Applied Clinical Trials
In recent documents and presentations, officials in the OSI in the CDER are highlighting the value of building quality into the design and operation of clinical trials to gain more efficient and effective monitoring and data verification systems.
A main regulatory thrust at FDA is to encourage sponsors to adopt a risk-based approach to managing clinical research. In recent documents and presentations, officials in the Office of Scientific Investigations (OSI) in the Center for Drug Evaluation and Research (CDER) are highlighting the value of building quality into the design and operation of clinical trials to gain more efficient and effective monitoring and data verification systems.
Tight FDA funding emphasizes FDA's need for a more efficient approach to GCP enforcement and research oversight. In 2012, OSI conducted fewer clinical site inspections, compared to three or four years ago, except for a slight rise in foreign inspections. Under a proactive approach based on quality by design (QbD) and quality risk management (QRM) methods, FDA expects a continued drop in site inspections, while gaining greater assurance of data quality and the safety of study participants.
This QbD approach for clinical trials begins with design of a research protocol that provides a blueprint for investigator monitoring, communications, and data handling, explained Jean Mulinde, Senior Advisor at OSI's Division of Good Clinical Practice Compliance at the January 2013 conference on CAPAs in the GCP environment sponsored by ExLPharma. A clear risk evaluation process would identify what could go wrong, the likelihood it will go wrong, and the consequences of such events. Critical risks include those specific to the study, such as an inadequate consent process, faulty randomization, unclear endpoints, serious adverse events, and inadequate CRO oversight. There also could be risks related to the test drug itself, such as a significant toxicity problem, narrow therapeutic window, or unreliable supply.
Once risks are clarified, the next step is to establish mitigation strategies for controlling and addressing the most likely and serious problems. This could involve centralized or on-site monitoring, a schedule for data auditing, and clear metrics for regular tracking. FDA wants sponsors to have a well-designed corrective and preventive action plan that can identify and document how and why an error occurred, who was responsible, how widespread the problem is, and what corrective actions were taken.
Adopting QbD and risk approaches for clinical trials build on concepts increasingly used by biopharmaceutical companies to ensure consistent quality in drug production. Here, manufacturers establish systematic processes for assessing and reviewing the risks to quality across the product lifecycle—development through commercial scale-up and post-marketing. A series of standards adopted by the International Conference on Harmonization (ICH) provides guidance on establishing QbD systems and risk evaluation approaches that can replace checking drug quality at the end of the manufacturing process, when it's costly to correct errors and generates considerable waste. Detecting and correcting problems in real time can save resources and avoid delays in bringing important new therapies to market.
One aspect of this risk-based model was outlined in a draft guidance published in 2011 describing how the approach could apply to monitoring clinical investigators. Instead of visiting every site and verifying every piece of data, FDA encourages sponsors to develop monitoring plans tailored to the specific risks of the study. FDA also encourages sponsors to utilize centralized monitoring methods "where appropriate," instead of visiting every site regularly.
While the agency weighs comments for finalizing this site monitoring program, OSI is encouraging sponsors to discuss prospective monitoring plans with their staff. OSI is conducting pilots with new drug review divisions on the value and feasibility of approving such monitoring methods, and the agency believes that dialogue with sponsors will help evaluate this model and the processes and resources involved.
FDA and industry are exploring these issues further through the Clinical Trial Transformation Initiative (CTTI), a public-private partnership led by FDA and Duke University which is developing QbD models for ensuring the quality of data and integrity of clinical trials. It may take more time for industry to agree with FDA on just how to design and implement risk-based approaches to clinical trial oversight. Pharma and biotech companies have raised strong objections to a recent FDA guidance for submitting information on the characteristics and outcomes of trials to help the agency select sites for inspection during the application review process. In comments to the December 2012 guidance on "Providing Submissions in Electronic Format–Summary Level Clinical Site Data for CDER's Inspection Planning," the Pharmaceutical Research and Manufacturers of America complained that the information requested is duplicative and that the process thus is likely to delay inspections and product approvals. The Biotechnology Industry Organization requested that such data requests fit the standards and timelines laid out in the recent user fee agreement, and that OSI should clarify the broader range of information requests it makes to sponsors during pre-submission meetings. FDA claims that compliance with the guidance is "voluntary," but that's not how sponsors and investigators see it.
—Jill Wechsler
Driving Diversity with the Integrated Research Model
October 16th 2024Ashley Moultrie, CCRP, senior director, DEI & community engagement, Javara discusses current trends and challenges with achieving greater diversity in clinical trials, how integrated research organizations are bringing care directly to patients, and more.
AI in Clinical Trials: A Long, But Promising Road Ahead
May 29th 2024Stephen Pyke, chief clinical data and digital officer, Parexel, discusses how AI can be used in clinical trials to streamline operational processes, the importance of collaboration and data sharing in advancing the use of technology, and more.
The Rise of Predictive Engagement Tools in Clinical Trials
November 22nd 2024Patient attrition can be a significant barrier to the success of a randomized controlled trial (RCT). Today, with the help of AI-powered predictive engagement tools, clinical study managers are finding ways to proactively reduce attrition rates in RCTs, and increase the effectiveness of their trial. In this guide, we look at the role AI-powered patient engagement tools play in clinical research, from the problems they’re being used to solve to the areas and indications in which they’re being deployed.