By Maxine Bookbinder

September 7, 2016 | A clinical immuno-oncology team at AstraZeneca’s Global Biologics R&D Unit—MedImmune—has created a trial feasibility informatics program using a “data-driven decision-making model” designed to identify appropriate prospective patients, sites, and expert physicians; eliminate guess work; and reduce cycle time of site and patient analysis from weeks to just hours.

The program, Trial Feasibility Informatics, launched last year and “is a whole paradigm shift,” says Jill Loftiss, MedImmune Clinical Oncology Operations Head. “Before, it was everyone’s own ad hoc experience.” Loftiss and Jane Fang, MD, Head of AZ/MedImmune R&D Information & Analytics for Clinical Biologics, developed the evidence-based program to quickly gather data and pinpoint specific patients and suitable clinical trial sites.

“This serves the patients better,” says Loftiss, “because we know where they are.” It gives study teams a comprehensive view of the clinical trial landscape, providing evidence-based insight and producing faster, smarter, and more efficient site and patient recruitment analysis. “A data, science, and analytics approach gives us an intelligent way to do science and business,” says Fang.

The program has five components: Trial Competitive Landscape; Protocol Design, Patient Population & Disease Incident; Recruitment Rate & Trial Enrollment Projection; Country Selection Strategy; and Site Selection Strategy.

Trial Feasibility Informatics conforms to the growing industry trend of peer collaboration and transparency between patients and healthcare providers. Fang describes the program, which interlaces big data, analytics, site optimization, and perfected protocol design with old-fashioned human scrutiny as “cutting-edge.” In the past, pinpointing the right patients and the right trial sites could take weeks or months, Fang says. “Now it takes about 5 hours. We look at relevant information for the right patients, the right hospitals, and the right physicians.”

The project allows AstraZeneca’s MedImmune team to choose a site—whether domestic or global—based on clinical research protocol requirements, recruitment needs, patient demographics, and site requirements empowered by evidence-based analytics rather than previous experience or personal preference. “We link the internal and external information together through a portal,” says Fang. “We can quickly get a landscape and see where the internal and external trials are conducted and where the patient populations are. Before, we couldn’t link this information. Now, it is at our finger tips.”

The Trial Feasibility Informatics program gives a visual map of which countries and regions have ongoing trials for specific illnesses, such as colorectal cancer. This helps their clinical trial research teams understand the regions with higher or lower numbers of studies for that particular illness, even those not listed in ClinicalTrials.gov. “We can identify potential patients who would best benefit from treatment,” says Fang. “We look at the right patients and the right sites, not just what worked previously.”

Real world evidence data sources used for analytics include healthcare claims records from health insurance companies and electronic health records which can be combined in one view to quickly show specific patient or illness demographics. The team also uses MDCPartners’ ta-Scan, a clinical trials and research information platform that can put evidence-based data into dashboards, tables, or summary reports and forward to other team members. It also includes global trial registry information not listed in ClinicalTrials.gov.

Trial Feasibility Informatics allows study teams to communicate in real time. In one bladder cancer trial, the patient recruitment team needed patient referrals from physicians to increase participant enrollment. Using claims records and EHRs, the team identified patients diagnosed with bladder cancers and their physicians in close proximity to the study sites. “More than 4,000 physicians with bladder cancer treatment experience were identified in the U.S. in less than two weeks for potential patient referral,” says Fang.

In another recent example, an oncology study team wanted sites and physicians with experience for a global Phase II trial for an immuno-oncology therapy. However, its only resource was personal experience. After the Trial Feasibility Informatics program launched, the team implemented its search option which, among other trial intelligence tools, gave, “results at a few clicks within a few seconds,” says Fang. “A most recent landscape of the sites and physicians with the right experience will be presented to the study team to support study site selections.”

There are other benefits; for example, better trial planning allows different tumor types and diseases that previously were treated exclusively at large academic centers to now routinely be treated in community settings, says Loftiss. “This is happening on a really fast scale.” She adds that the immuno-oncology therapy has produced “a larger influx of studies,” including trials for cancers that are rarer or harder to treat. Small towns are hosting trials that used to only run in large research centers. With the Trial Feasibility Informatics program, study teams can also research, in addition to their own company’s sponsored trials, how many other trials for a specific disease are currently running, by whom, and which sites have more capacity.

Despite their reverence for evolving technological innovation, Loftiss and Fang remain mindful of their primary purpose: better patient care, giving back to communities, faster therapies to market, and more lives saved more quickly. “People are waiting for these new immuno-oncology drugs to come to the market quicker to help millions of cancer patients and we have to find innovative ways to conduct clinical trials better and faster,” says Loftiss.