By Ben McGraw
August 27, 2010 | Expert Commentary | When researchers at the Cancer Vaccine Center (CVC) at Dana-Farber Cancer Institute (DFCI) began to study the cancer vaccine field, they gathered data from more than 645 vaccine trials, including cancer types, incidence, and survival rates, in order to identify relationships, trends and patterns.
The daunting challenge facing the CVC researchers was to glean insights from their disparate data sources, which illustrated the complexity of data analysis in today’s research environment. A single late phase clinical study involving thousands of safety and efficacy measurements can turn into a major project that significantly slows research. Often analysts find themselves trying to rework cycles that can’t produce results fast enough for clinical development stakeholders, who, in turn, complain the static reports and listings they receive make it difficult for them to interrogate the data—comparing patients with the same condition, combining various clinical data, or discerning root causes regarding disproportionate queries, protocol non-adherence, and missing data.
The reality is that most clinical development teams today are making decisions based on canned business intelligence reports that contain stale data pulled from disparate clinical systems via hardcoded point-to-point integrations. This approach to clinical and operational data analysis isn’t scalable and slows the ability to explore and ask questions that can reveal key insights that could affect the trial outcome. In this model, Clinical IT is also burdened with maintaining numerous, inflexible point-to-point integrations.
Fortunately for the CVC researchers, advances in interactive data analysis and high-impact data visualization provided novel insights into the cancer vaccine trial data. Using analytics technology, clinical development teams can be empowered to perform faster medical review, expediting early-phase proof of concepts and late-phase confirmatory trials. As a whole, the technology will allow these teams to quickly visualize and interact with enormous volumes of operational, clinical and safety data in a single interface to support forward-looking decisions, conduct advanced what-if scenarios, and run sophisticated analysis to gain key insights into the data earlier into the clinical development process.
Networked Model
The advances that have been made in empowering clinical development teams with advanced analytics constitute an important component to the new “networked life science model” that is driving therapeutic development today. In this model, each organization focuses on their core competencies and collaborates with contract research organizations, academia, and other companies that serve as R&D partners.
An example is Merck’s announcement of the Global Trial Network For Cancer-Drug Creation, focused on the development of cancer drugs and vaccines. The reasons cited for the formation of the network was an Institute of Medicine report that about half of all cancer studies are never completed due to cumbersome procedures, bureaucracy and poor coordination.
In this networked life science model, close and constant collaboration plays a key role. Having a secure, collaborative, online environment for all parties to interactively explore the clinical data is crucial. For the Global Trial Network to succeed, a single federated R&D environment for analysis and reporting of research, development and health care data will be essential for the assessment of safety and efficacy measurements.
Using advanced interactive analysis and high-impact visualization technologies in combination with collaboration platforms and R&D data warehouses, clinical development organizations can streamline data management and analysis processes by getting meaningful data views to medical monitors; clinicians and safety officers for in stream review and safety analysis; and to trial managers and research associates for protocol adherence and operations metrics.
Given that presentations, journal articles, clinical study reports and submission documents will be created and presented on behalf of all parties in the Global Trial Network, use of the latest visual analytics tools will be essential to support the necessary collaboration in the networked life science model. By providing a standardized approach for analyzing, reviewing and presenting data—as suggested by FDA and PhRMA—life sciences organizations will be able remove the data collection and analysis obstacles that have slowed their clinical development processes to date.
As the Global Trial Network evolves, these analytics tools, which support the networked life science model, can be leveraged to empower life sciences companies to collaborate and work closely with regulators to accelerate the time it takes to bring a new therapy to market. Collaboration platforms, visualization technology, and federated research and development data warehouses will enable them to do this with the confidence that they have been able to access all the data, from all sources—including previous research work, epidemiological data, disease knowledge, data from clinical usage from similar products, and proof of value requirements—to ensure more accurate clinical data results resulting in a dramatic reduction in the typical time in which a new medication reaches the market.
Once the therapy is on the market, the sharing of data in existing health care databases to identify and evaluate the safety and benefit issues of approved therapies is another area that will benefit from the latest advances in collaboration and visualization.
Ben McGraw is the Director of Life Sciences Industry Solutions for Spotfire, TIBCO Software, Inc. He can be reached at bmcgraw@tibco.com