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Multiple Choice

Finding the right investigative sites is a critical early step in ensuring the success of a clinical trial, but this task can be particularly complex in large global studies. With 11% of investigative sites failing to enrol a single patient, at a cost of $30,000 to initiate a site, and an additional 37% of sites under-enrolling for a protocol, it is no wonder that some $110 billion is spent each year on global R&D – 60% of which is devoted to clinical trials (1-3).

Pharmaceutical companies and CROs have access to a variety of data sources to help find and evaluate trial sites, but these data are often spread over multiple internal and external systems. Because the time and complexity required to integrate across such data sources is significant, companies often make suboptimal decisions related to study planning, feasibility and site selection.

Best practice for these activities involves visualising multiple sources of investigator, site and protocol data through one single global technology platform. At the same time, innovative companies are taking this integration of big data one step further – shifting their focus away from competition on information and towards data sharing in order to drive further efficiencies for researchers, as well as decreasing the burden for investigators.

Lots of Data, Little Information

Today, most pharma companies and CROs rely on two types of data to identify investigators for a clinical trial:
  • Internal database(s) of investigators who have participated in previous studies
  • Publicly available data from regulatory agencies (for example, information on FDA 1572 – Statement of Investigator forms) and clinical trial registries (such as clinicaltrials.gov), most frequently purchased through a third-party subscription provider
However, it is not availability of data to identify investigators that is the real problem. Rather, the challenge is two-fold: integrating information across data sources, and selecting successful investigator sites.

Hosting Platforms

Because information on investigators is typically spread over multiple sources – pharma company and CRO databases, third-party subscription data, etc – companies either use these sources individually and sequentially to identify potential sites, or expend significant labour in manually matching across sources.

This issue can be further complicated for those companies that have switched clinical trial management system (CTMS) providers and/or grown through acquisition. In these cases, researchers may also need to integrate within their own internal systems, as well as across the various data sources.

Innovative technology companies have developed fit-for-purpose platforms to address this data integration issue. Specifically, these platforms host data from various sources in virtual private databases; append a unique identifier for investigators and facilities; standardise the information from each source to a common terminology; and provide a query tool to enable viewing of information across sources.

Technically, what this means is that pharma companies and their CRO partners are able to see a combined view of investigators and sites. However, whether or not this is achievable for an individual company relates to business strategy, and relies on all parties being willing to share – and having the rights to share – the required performance data.

Site Selection


When it comes to investigator site selection, the published literature identifies four factors as promising for predicting successful site performance (4):
  • Investigative site focus
  • Experienc
  • Past performance
  • Speed to randomise first subject
Public data sources like FDA 1572 forms and clinical trial registries such as clinicaltrials.gov provide some information on whether physicians have an investigative site focus and give information on their study experience, whereas a pharma company CTMS provides data on all four factors. However, all of these sources have limitations:
  • FDA 1572 information only covers investigators working on studies that will ultimately be submitted to the FDA for approval
  • Clinical trial registries are increasingly becoming more comprehensive in terms of study inclusion, but often lack data on certain types of studies (for example, observational). Furthermore, some of these registries hide actual investigator contact details and instead display generic information (for instance, 'investigator site, Phoenix, AZ, USA')
  • A CTMS is the most comprehensive data source, offering information on all four factors – although this is limited to what is available within each company’s data systems
Third-party subscription providers try to address the restrictions of publicly available data from the FDA and registries by combining both sources and estimating metrics, such as past performance and speed to randomise first subject. Yet, unpublished analyses that compare actual to projected site performance suggest that the projections typically over-inflate the performance of most sites, resulting in inaccurate planning and the need for rescue sites to meet study enrolment levels and deadlines (5). In addition, these projections are unable to identify non-enrolling sites, which perpetuates cost and time inefficiencies associated with non-performing sites.

Sharing Information

While CTMS data enable access to actual site-level data (not projected) on all four factors, the primary limitation described above is that the data only exist for studies conducted by a single company. Collaborations like the Investigator Databank are addressing this constraint by sharing information across companies with investigator consent. As more and more companies participate in data sharing, the need for publicly available information to supplement data outside of the collaboration will diminish.

Data integration and sharing offers the opportunity to derive both time and cost efficiencies through better planning, increased access to investigators, and more predictable recruitment.

Researchers looking for ways to reduce non-performing sites should consider investing in technology that enables integration of multiple sources of investigator, experience and performance data. Integrating a view across these sources is likely to result in more rapid recruitment, as well as fewer zero-enrolling sites.

For organisations willing and able to share information, access to more site-level performance data through collaboration generates even more efficiencies than data integration alone. And the more organisations that agree to share data and collaborate on investigator-facing activities, the simpler clinical trial operations will be for investigators. This, in turn, has the potential to address another critical issue faced by the industry today – investigator turnover.

References
1. Stone J, Conducting clinical trial research, Chapter 2: Scrounging your first study. Visit: http://conductingclinicalresearch.com/sample_chapter.php
2. Tufts Center for the Study of Drug Development Impact Report 15(1), January/February 2013
3. Restoring innovation as global pharma’s center of growth, IMS Health
4. Getz K, Predicting successful site performance, Applied Clinical Trials, November 2011
5. Cascade E, Investigator Databank Collaboration: A global platform for pre-competitive data sharing, presented at the DIA 54th Annual Meeting, June 2014

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Claire Sears is Director, Investigator Engagement, at DrugDev, and also acts as scientific consultant to the company's site services team. She emphasises keeping an open line of communication with doctors, firmly believing that closer relationships between investigators, sites and industry can bring great benefits to the clinical trial process. After gaining her PhD in Cardiovascular Physiology, Claire spent eight years at AstraZeneca in scientific communications and medical affairs roles. She also had a stint as a Royal Society Dorothy Hodgkin Research Fellow in the Department of Cardiovascular Medicine at the University of Oxford.

Elisa Cascade is President, Data Solutions, at DrugDev, where she manages the company's platform solutions, including Site Cloud and the Investigator Databank. Elisa works to address the time and complexity required to integrate data sources to aid clinical study planning, feasibility and site selection. She has over 20 years of experience in pharmaceutical clinical research, market research, brand strategy, salesforce deployment, health economics and pricing. Prior to joining DrugDev, Elisa ran the global operations for Quintiles' Digital Patient Unit. She has an MBA from The Wharton School and a BS from the University of Michigan.
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