Big Data in Adaptive Trials – Visualizing Improved Safety, Speed & Economics
The concept of big data in clinical trials has been discussed for the past 5 or 6 years.
Sponsors have huge streams of disparate data and associated endpoints across all sites related to the design of their trial. With the appropriate infrastructure, a sponsor can couple massive amounts of statistical data within an adaptive trial to perform interim analysis in real time.
This represents a huge environmental shift that can enable sponsors to make more informed decisions about whether to continue investing in a trial, change or add additional treatment arms, or make other necessary adjustments along the study lifecycle.
Knowing how to leverage these enormous data streams from different points across a trial has been a challenge for many sponsors over the last few years, in what is still a growing body of work. eClinical technology providers like Cenduit are building paths to help sponsors pilot this new paradigm, offering guidance on how to optimize and create better ROI around big data and the various endpoints associated with clinical studies.
Leveraging Real-Time Data Throughout the Life of the Trial
As a global eClinical technology provider, we recognize that a tremendous amount of data can be mined through the Cenduit IRT platform. We have the tools and computing power to use data visualization to help clients solve problems and recognize emerging trends, whether on the recruitment or supply chain side. This is especially true today for trials with connected devices, where we are receiving a large number of data points that are being analyzed and optimized to provide concise feedback and visualizations to sites and sponsors.
Often in Phase II trials, sponsors and site personnel are researching the optimal dosage for efficacy and safety, analyzing data and reviewing different treatment arms. With just-in-time data visualization, site personnel and sponsors can begin to discern if there is meaningful separation between treatment arms. Sponsors might open another arm to learn if a subject can tolerate a higher dose, and if this has added efficacy.
Similarly, in today’s multi-phase, complex oncology studies, if interim results demonstrate that the sponsor is exceeding endpoint goals and they have effectively discovered a medicine better suited than anything else on the market, they can pull subjects off the placebo, go open label, and commercialize the medicine as soon as possible. On the reverse side, a sponsor can stop a trial faster if they determine that the therapy is not more efficacious than a comparator drug or placebo.
There are interesting applications of big data on both ends of the process – by high-value startups focused on drug discovery, and in post approval studies. From a supply chain perspective, the use of big data and visualization in adaptive trials is gaining ground slowly, but it’s a significant opportunity to evolve the dynamics of trial conduct.
Sharing the Risk to Improve Quality of Life
We’re in an extremely risk-averse industry, where there’s very little incentive to innovate clinical trials. But there’s no real excuse not to use these applications.
A Call to Action: Big pharma needs to take on some additional risk and obtain proof points for regulators and the rest of the industry. Many have innovation groups where experimentation already happens. For example, 10 years ago Pfizer ran a landmark study that was completely virtual, solely for exploratory and educational purposes. These innovation groups represent a small percentage of big pharma’s R&D, but we expect continued progress.
Regulatory bodies also can help to drive changes, as we’ve seen with adaptive trials. Regulators can offer guidance to give sponsors more comfort with the risks of big data to their trials, as they are doing around machine learning and AI. Regulators need to emphasize the allowable, or expand the bounds within trials to help shape and encourage adoption.
Cenduit’s expert system design and R&D teams are working closely with our clients to determine how to best leverage big data and the adaptive trial model to innovate and deliver quality studies that push boundaries in clinical research. Beyond IRT, there’s an opportunity for cross-study data to help clients make better decisions. We’re ready, willing and able to support these innovations with sophisticated analysis and proof points from an integration and enterprise architecture perspective.
It’s inevitable that clinical trials will continue evolving through the use of big data so our industry can open new avenues to meet trial volunteers where they are, and improve their quality of life while also improving trial conduct. We’d welcome the opportunity to speak with you about how we can help your organization optimize this important new model.
Posted by Chris Dailey, Vice President of Global Technology; and Chris Driver, Enterprise Architect
