2015 a Year in Review: The Year of Clinical Trial Innovation

The clinical trials industry has undergone quite a bit of change in 2015, and may arguably be deemed the year of the ramping of clinical trial innovation. In 2014, biopharmaceutical enterprises and CROs created innovations departments and infrastructures (that operate independently from clinical operations functions) that focus on evaluating and processing innovative pilots, and in 2015, many pilot programs were initiated and executed.  For example, Novartis initiated its internal RBM platform, TAPAS^TM, and ICON collaborated with IBM’s Watson to match oncology patients to clinical trials in new pilot programs. 

Moreover, the burst of new technology enterprises and innovative service providers that specialize in clinical research is a sign that the clinical research industry is starting to look into new ways to solving problems culminating from an antiquated system. This article will evaluate trends and offer future predictions in patient centricity, RBM, emerging fields, and cultural change in the clinical trials industry.

Patient Centricity, Enrollment and Engagement

The meaning of ‘patient centricity’ is honing in on discovering itself. A recent virtual panel on patient centricity discussed that patient centricity means empathizing with, and involving patients as stakeholders during the trial design phase.  Other concepts for patient centricity have emerged; for example, The Mount Sinai Health System launched its first patient centric clinical trial, where physicians are conducting advanced medical assessments remotely through telerobotics. 

On the other hand, others believe that patient centricity involves siteless models, where mHealth devices, such as FitBits, are used to collect and upload high frequency biometric data into clinical trial systems. Walgreens seems to have attempted to deliver convenience to patients’ homes by leveraging its pharmacies to be used at study sites.

On the enrollment front, innovative analytical models are emerging. TrialReach, for example, is focusing on bringing clinical trials directly to patients through machine learning and predictive algorithms, and IBM Watson uses cognitive learning to match oncology patients to trials.

On the patient engagement front, CROs, such as Quintiles, are using simple and effective mobile technologies, including text messaging and optimized engagement models, to effectively recruit patients into clinical trials, and emerging technology enterprises are able to analytically evaluate numerous dimensions of patient engagement through multi-platform integration, including Facebook, EMR, and mHealth devices.

Biopharmaceutical enterprises have also created new roles that involve patient advocacy in order to bridge the gap between patients, payers, researchers, and commercial teams.  Some biopharmaceutical enterprises have gone as far as appointing a Chief Patient Officer.

What Will Come in 2016? We will likely start seeing more implementation and results from patient centric models, particularly around the adoption of mobile technologies, which, if used effectively, can improve data quality and patient convenience. We will also likely see integrated technologies exploring additional data sources, and will generate breakthrough insights on how we perceive and predict patient engagement; this will likely find itself into new clinical trial applications, such as trial design optimization, and payer strategy enhancements.

RBM, Data Quality and Risk Management

The practice of RBM is becoming mainstream in the biopharmaceutical industry, as many enterprises are executing RBM, however, different companies exhibit and implement different interpretations of RBM. For example, as mentioned earlier, Novartis launched its TAPASTM platform in tandem with its adaptive approach to monitoring, whereas BMS implements a similar model, but, with different roles.

Certain metrics, such as KPIs/KRIs, are standardizing, and RBM technology systems are now capable of allowing study teams to better understand and diagnose trial risk issues, such as systemic factors impacting subject dropout, and predicting study site engagement in different countries. Further, Excel-based risk assessment tools are moving to cloud-based technology systems, which are allowing study teams to access standard KRIs, better visualizations and standardize risk interpretation.

Other technologies are also contributing towards streamlining data in real-time, mitigating risk, and improving data from where it matters most, at the study site level. To elaborate, data is showing evidence that eSource technology not only reduces study site workload, but, also seamlessly improves data quality, and eliminates redundant tasks, such as SDV.

With these advances in RBM come some pitfalls. According to an interview with FDA’s former compliance officer, some of those pitfalls include not differentiating between critical endpoints during protocol design and the study’s safety/efficacy endpoints; study endpoints should be simple and straightforward. Another pitfall includes monitors lacking data training, and misinterpreting results.

What’s in 2016 for RBM? Along with access to aggregated risk datasets, we will probably start seeing more instances and applications of what the risk means systemically and operationally. KPIs/KRIs will likely expand to address operational facets, and the use of technology systems in RBM will generate enough data to start molding predictive models, which can be used to mitigate future risk during trial design. The role of the on-site monitor will continue to change; SDV will start diminishing, and more on-site monitors will be guided by data-savvy centralized monitoring teams.

 

Emerging Fields

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