Proceedings: DIA Europe 2019

Modern Clinical Research and Paradigm Shift

Adam Istas
Science Writer

DIA Europe 2019

he long development cycle inherent to biopharmaceutical medicines often leads stakeholders across the healthcare ecosystem to wonder whether the entire process needs massive transformational change. Clinical trials are expensive. They take a long time. Enrolling patients in studies is challenging. Surely there must be a better way to generate the evidence necessary for regulatory, reimbursement and clinical decision making. Yet, rather than advocating for wholesale transformational change, biopharmaceutical stakeholders would be better served by adopting many of the tools and techniques that already exist—such elements simply need to be adopted more widely, shared more openly, and put into practice more frequently.

Key Takeaways

  • Development time, cost, and risk can be reduced in the current paradigm by better utilization of existing real world data sources and a greater focus on patient preferences and patient engagement.
  • Clinical development can be further streamlined by applying predictive analytics to existing real world data sources such as electronic health records and developing and integrating external platforms for new purposes.
  • Patient communities can be a powerful resource to help industry colleagues design relevant research protocols and to help inform regulatory decision making.
  • Innovative trial designs such as umbrella trials, basket trials, adaptive trials and multi-arm multi-stage trials, are already working in practice and are being used to support licensing decisions; however, many initiatives promoted by researchers and regulators remain unadopted and unshared with other stakeholders.
  • Collaboration across the system will result in greater adoption of innovative tools, technologies, and processes which will ultimately lead to higher quality clinical evidence.

Why this is important: “Competition is unhelpful and unproductive,” said Robert Hemmings, Statistics and Pharmacokinetics Unit Manager at the Medicines and Healthcare products Regulatory Agency (MHRA). “Instead of talking about big data, real world data or randomized controlled trial data, we should talk about how to plan studies, how to generate confirmatory evidence, how to find patients, how to best conduct signal generation in the post-authorization space and so on—then we will be able to identify the best research tool to do those things.”

Practical, Not Aspirational

Focused on simplifying and accelerating the R&D of innovative new therapies, TransCelerate Biopharma is helping to demonstrate better utilization of existing data sources through its Placebo/Standard of Care (PSoC) initiative. The PSoC program was established to enable the sharing of clinical data collected historically in the placebo and standard of care control arms of clinical trials. Participating companies can download and share historical clinical trial data to aid in disease modeling, optimize inclusion/exclusion criteria, and help to improve precision in sample size calculation.

Similarly, Electronic Health Records (EHR) can also be used to inform clinical development strategies. InSite, for example, is a private initiative that originated from the EHR4CR collaboration and now operates in 12 countries. Their platform—Europe’s largest live EHR data network providing real world data for clinical research—enhances study feasibility, informs protocol design, and facilitates more effective patient recruitment by providing access to a large pool of patients.

Better utilization of existing real world data sources is not only helping to inform clinical development decisions, but is also being used to support physician and provider treatment decisions. Applying predictive analytics to longitudinal health records, researchers from IBM Watson Health and Roche used IBM’s Explorys data set to assess the risk of chronic kidney disease in diabetes patients. The resulting data-based model outperformed published algorithms based on clinical study data—the same type of algorithms used to guide therapeutic recommendations and medical risk stratifications. From identifying potential drug interactions to monitoring medication adherence, there is nearly unlimited potential to apply predictive analytics to real world data sources to inform decisions along the entire healthcare continuum.

Patient Involvement and Initiatives

Perhaps the most encouraging efforts to improve clinical research are those that include the end users: patients. One such initiative is the Patient Centricity project launched in 2018 by the UK’s NIHR Clinical Research Network. Tasked with developing a range of patient involvement services to the life sciences industry, the project leverages clinicians across 30 therapeutic areas in 15 Local Clinical Research Networks to provide a standardized, industry-wide approach to engaging patients earlier in the clinical development process. A pilot case study was conducted between the NIHR and a global pharmaceutical company to develop network connecting industry with willing to contribute during the protocol design stage. The project hopes to yield a greater understanding of patient needs and requirements, and thus an increase in patient retention and protocol adherence.

Paradigm Shift? Or a Shift in Perception?

Transformational change across the biopharmaceutical development landscape may indeed sound like a worthy goal, but focused approaches to better use what already exists within the current paradigm is already helping to advance innovation. Smarter, more connected use of real world data sources combined with an increased emphasis on patients is already producing quantifiable benefits.