In many ways, clinical trials showcase the precision and care of modern medicine. However, they’re often hampered by key barriers such as an inability to find enough of the right subjects or accurately track them. In addition, physicians and researchers have difficulty discerning meaningful patterns in the data and predicting the patients most likely to benefit.
With increasing emphasis on outcomes-based data and the need to ensure that the cost and value of treatment align, how can we use technology to design smarter trials?
Apple’s asking that question, too, announcing this week its open source ResearchKit framework for iOS. The software is already being piloted by world-class institutions and brings trial monitoring to the iPhone with apps that can be pushed by trial coordinators and responded to by participants.
Shaping Smarter Trials
Clinical trials’ strength — their controlled nature — is also their weakness. To control for confounding variables, novel treatments must be tested on a relatively uniform group of patients. This can be problematic when the sample is not representative of real-world patients or when it’s unclear which patients are most likely to derive benefit. Trial success or failure is determined by statistical significance. If a treatment delivers only a small improvement over the standard of care, a larger sample size is necessary to detect that difference.
Can we curtail large, costly trials that result in drugs that meet statistical significance but offer little overall clinical benefit for patients? How can we tease out the few patients who saw vast improvement? The answer lies in smarter patient selection, aided by clinical data that is currently locked away in medical records. The answer also now lies in the most unlikely of places — the open source opportunities provided by the phone in your pocket, thanks to the promise of Apple’s ResearchKit.
Tomorrow’s Trials Through the Tech Lens
Not only should we be working toward greater open-sourcing of data, but also rethinking the clinical trial model itself. Tech companies are creating exponentially more sensitive, complex and inexpensive sensors at an exponential pace. The Apple iPhone — a commonplace smartphone owned by millions — is now deemed a ubiquitous clinical trial tool.
The old-fashioned trial collects a sample of the human population and periodically measures how accurately they respond to medication, whereas advances like ResearchKit allow real-time data from real-world patients in real-world situations anywhere. Advancing analysis capabilities allow us to parse this new, fast, huge quantity of data into easily digestible results. Ultimately, the clinical trial process will parallel prescription usage.
As an example, the American Society of Clinical Oncology initiated CancerLinQ to help physicians and scientists better learn from data collected in clinical practice. CancerLinQ advances the concept of a patient registry, creating a platform to discern meaningful patterns in data, match patients to the most appropriate treatments and aid in clinical decision-making. This real-world data can be validated in the controlled setting of clinical trials and can also help us refine trial design to streamline approval of drugs with big clinical impact. Trials can be more efficient when they are designed for, and enroll, the right patients.
Open Source Opportunities
From a patient with a wearable or a smartphone to a trial administrator with terabytes of data points and scans, everyone involved in pharma can appreciate the importance of data — and also the importance of protecting patient privacy and intellectual property while maximizing the potential of the tools available.
Open source has set a precedent in other security-concerned industries. And in pharma, open source can create enormous opportunities to further our knowledge and ability to treat disease states — indeed, to create a better world for humanity altogether.