Over the last 10 years, advances in technology have revolutionized how we communicate, collaborate, and transact business, whether it be paying for groceries, video conferencing with worldwide colleagues, or analyzing data… all from the palm of our hand.
So, why has the pharmaceutical industry been slow to adopt these new advances, especially at a time when clinical trial performance benchmarks reveal an industry that is performing worse overall than a decade ago?
Industry Performance Metrics Reveal a Broken Model
Between 2005-2015, the average number of countries involved in a phase III trial doubled and the number of investigative sites required for an average trial increased by 63%. During this time, the mean number of patients declined 18%, adding complexity to study startup processes.
Additionally, well over 50% of sites fail to meet enrollment expectations. As a result, enrollment periods are extended, prolonging studies and leading to a near-universal inability to maintain study budgets and meet key milestones.
While it must be acknowledged that trial sponsors are facing new challenges, some of their issues are also self-created.
Higher Protocol Complexity = Lower Performance
Between 2005-2015, the average number of endpoints in trials increased by 71%. The primary culprit here is the tendency for sponsors to get as much data as they can out of enrolled patients ─ via secondary supplementary, tertiary, and exploratory endpoints ─ in the hopes that even if the trial fails, they’ll have collected enough data to improve their ROI potential, such as exploring whether the compound has promise in another indication.
While this may make sense in a vacuum, the burden it has put on sites and patients has contributed to increasing difficulties in patient retention. 30% of patients drop out of clinical trials, which pushes timelines back further as more patients are needed to collect the data required to meet the study’s primary endpoint.
Risk-Based Monitoring & Beyond
Since 2013, regulatory agencies and industry consortiums have encouraged the use of risk-based monitoring (RBM) and quality by design processes for the planning, monitoring, and management of clinical trials. These efforts are intended to:
- Shift the industry paradigm from retrospective to active
- Focus on the critical data required for the protocol
- Identify the most critical data elements for quality oversight
So far, the industry’s response has been limited. A 2017 study by the Avoca Group revealed that consistent and standardized risk assessment processes are rarely followed. The culture change required to shift from identifying issues after the fact (retrospective) to using analytics to identify and mitigate risks before they become issues (active) has been the largest hurdle to adoption.3
The ICH E6 R2 Addendum to GCP
The 2016 ICH E6 (R2) Addendum to Good Clinical Practice calls for sponsors and CROs to develop processes and technology that support a more active risk-based study oversight approach, specifically to “…identify those processes and data that are critical to assure human subject protection and the reliability of study results.” This creates an opportunity, and a necessity, for sponsors and CROs to rethink their risk management strategy as a way to improve data quality, increase patient safety, and achieve trial outcomes more quickly — a competitive advantage any organization would sign up for.
However, sponsors and CROs have been slow to embrace this change, in part because they aren’t sure where to begin. One study found a third of sponsors said they still lack a good understanding of best practices for risk-based approaches.1
Click here to learn the 4 steps for implementing effective risk-based quality management in clinical development.
From RBM to RBQM
Taking a risk-based approach to trial oversight and management and focusing on quality by design throughout clinical trials really translates into Risk-Based Quality Management (RBQM). Organizations who embrace RBQM can dramatically reduce site and data issues as well as faulty patient enrollment projections and poor performing sites by:
- Designing more focused protocols
- Using data analytics to model site performance
- Leveraging detection and alerts that centralized monitoring teams act on in real-time,
However, this calls for a significant shift from today’s resource-driven, reactionary mindset, to an active, data-driven mindset. While easier said than done, having the right technology in place can be the catalyst for an organization to make this change.
An effective RBQM strategy must integrate data from multiple sources into an easy to access platform that supports real-time decisions. A technology platform that centralizes data, information, and related action items, allows study teams access to the data needed to carry out risk management activities with confidence and transparency.
Regulators are on Board
For years, leading industry voices have pushed for changes to investigator oversight processes that better ensure patient safety and data integrity. FDA’s 2013 “Oversight of Clinical Investigations — A Risk-Based Approach to Monitoring” stated, “FDA considers monitoring to be just one component of a multi-factor approach to ensuring the quality of clinical investigations.”
And, industry consortiums such as TransCelerate BioPharma are publishing tools for companies to integrate risk assessment and measurement into their oversight processes. The discussion has moved from if to how soon companies will begin implementing these changes.
The Time has come
Sponsors who comply with ICH and adopt RBQM can achieve significant cost savings and study performance improvements. By actively managing risks to ensure they don’t become costly problems and ensuring the best quality data is captured consistently throughout the trial lifecycle, an RBQM approach to trial oversight and management can make the difference between success or failure in achieving trial outcomes and being first to market.
To learn more about how technology can support this transformation Click Here.
Robert Bolduc is the Director of Product Management at ERT
- Tufts CSDD Impact Report July/August 2018, Vol. 20 No.4
- Tufts CSDD Impact Report March/April 2018, Vol. 20 No.2