By Ashwin Mundra
Given the rapidly growing cost of drug development over the past decade, clinical R&D organizations have come under increasing pressure to find more cost-efficient methods to conduct their clinical trials. A high-level review of clinical operations budgets reveals that site monitoring comprises well over 30% of the total trial cost, and up to 50% of that can be attributed to source document verification (SDV). Adding to trial complexity and costs is the industry trend toward trials with increasing numbers of sites and smaller numbers of subjects participating at each site. This further exacerbates both the economic and logistical burden associated with a traditional site monitoring approach. As a result, a new risk-based monitoring approach using partial SDV is emerging.
One of the major functions of site monitoring is SDV, and for many years the industry has favored full SDV coverage even though it is not specifically mandated within good clinical practice (GCP) regulations. Rather than ensuring the highest quality data from each site, full SDV coverage could instead divert attention away from the most value-added monitoring activities, such as focusing on SDV review of the most critical efficacy and safety data and assessing site protocol compliance and understanding. The new alternative, in which monitoring attention is targeted in a risk-based fashion to the most critical review activities and to those sites requiring the highest level of attention, promises to yield not only a more efficient use of monitoring resources, but also a clinical trial database of equal if not higher quality.
What Should Companies Do To Implement This Approach?
Some considerations include:
A partial SDV plan should generally focus a more significant percentage of the overall SDV coverage on the most critical data, such as primary endpoint data and key safety data, including adverse events.
Study teams may want to require higher SDV coverage on the first one or two subjects enrolled at each site in order to establish an early data quality “yardstick” for each site.
As with subject randomization techniques, study teams may vary the SDV requirements from subject to subject at each site in order to effectively “blind” the site to the actual subjects and data targeted for SDV. This can help to prevent potential bias in a site’s attentiveness to capture subject data.
Study teams should be able to quickly and dynamically revise the SDV plan during the study, either for the whole study or for individual sites and/or subjects. Sites indicating initial data quality issues, for example, may warrant an increased level of SDV scrutiny or even full SDV.
As the discussion around risk-based site and data quality monitoring gathers momentum, some in the industry remain cautious. This is understandable given the high stakes associated with protecting patient safety and ensuring accurate trial results. There is no public evidence to unequivocally establish the positive or negative impact that a risk-based monitoring approach using partial SDV has on data quality and integrity. An absolute baseline level of data quality, in terms of expected rate of data sampling errors, has also not yet been established to justify the traditional monitoring approach. Experiments can be conceived to measure the difference in data error rates between groups of subjects or studies with and without SDV, but even here it will take time and require proper controls to remove process variations between the groups. In the meantime, and given the flexibility afforded by GCP/ICH guidelines regarding SDV coverage, the arguments in favor of the new risk-based approach are compelling, and the industry will continue to move steadily in this direction. Is your organization prepared to lead the charge into the new paradigm?
Used with permission from Life Science Leader magazine.