Clinical Trial Innovation: What’s to Come in 2017

Dec 12, 2016

A lot has happened in the clinical trials industry in 2016, as biopharmaceutical enterprises are just starting to delve into clinical trial innovation. RBM is starting to expand into new concepts in Quality Risk Management; enterprises have aggregated their data to generate clinical trial predictive models; the definition of patient centricity is cementing; incorporating endpoint adjudication methodologies in trial design is talked about often; subject enrollment is increasing in efficiency and scalability through big data, and mHealth pilots are starting to demonstrate promising data.

RBM evolves into Quality Risk Management

The topic of RBM has been spoken about and implemented in many forms since FDA released its draft guidance document in 2011, however, as companies scale RBM, clinical quality departments are starting to voice how risk management should be executed.

While a data assessment has demonstrated that the Risk Assessment Categorization Tool (RACT) introduces subjectivity in risk analysis, TransCelerate launched several initiatives on quality risk management including the Site Qualification and Training (SQT), and Quality Management Systems (QMS) initiatives, and Abbvie’s Susan Callery-D’Amico elaborated on her perspective regarding the QMS initiative, suggesting that proper QMS frameworks must be defined and categorized continually throughout a study, risk needs to be triaged with resources, and expanded on the importance of using technology and analytics to continually measure and optimize study quality performance.

Boston Scientific’s Celeste Gonzalez went further by discussing how quality management diffuses into vendor oversight and performance, and how departments should work more collaboratively in order to develop comprehensive vendor oversight plans and analytics.

RBM is moving away from an overarching concept that defines risk management, and into a subcategory or activity under the quality risk management umbrella.

Data is aggregating, enabling predictive modeling

The biopharmaceutical industry is at a point where it is capable of aggregating data from numerous clinical systems. As a result, the industry has launched internal data sciences functions to analyze data for clinical operations. From a quality standpoint, Pfizer is leveraging aggregated data sets to generate predictive models used for foreseeing factors impacting GCP and study quality risk during study design. From an operational perspective, Clinical SCORE has generated enough data in its normative database with study sites to predict the impact of site issues with software on CRA relationships. On the toxicology front, BioCelerate was formed in order to promote toxicology data sharing, facilitate the discovery of new molecules, and enhance go/no-go decisions in clinical trials.

Endpoint adjudication is gaining importance

Many in the industry are starting to realize the importance of reducing data variability and enhancing data quality. Accordingly, involving independent endpoint adjudication committees—which is aimed at improving the quality of clinical decisions made by investigators—is gaining importance. However, while a survey on endpoint adjudication has shown that many biopharmaceutical development professionals recognize the importance and efficiencies of using eAdjudication technology, respondents in departments critical to operationalizing studies, such as clinical operations, are not even aware that such solutions exist. Moreover, no official guidance exists on endpoint adjudication, leaving the industry feeling a bit ambivalent.

Subject enrollment gains ground with big data

Tom Krohn from Antidote recently elaborated on how recruitment technologies are changing the way we engage and enroll patients. Specifically, these technologies are leveraging machine learning algorithms and structured eligibility questionnaires in order to enhance the qualification rate of recruited patients. This is done by delivering the most relevant studies to patients, and simplifying publicly listed studies to improve patient understanding. Moreover, the traditional recruitment model, which uses advertising to cast a large net, tends to be inefficient and expensive; the algorithmic model engages patients at the point of when they are most interested in learning about studies.

FDA defines patient centricity

In recent months, the FDA voiced its concerns regarding the way the industry is approaching patient centricity in clinical trials. According to some in the industry, patient-centric initiatives involve including the patient to design less burdensome studies, optimizing study protocols’ inclusion/exclusion criteria to enroll more patients, and creating a more engaging and convenient clinical trial environment for patients. However, the FDA has indicated that the industry does not fully grasp the concept of patient centricity, which includes involving the patient to design studies focused on generating outcomes that are clinically meaningful to patients, and leveraging validated research methodologies when defining clinical measures.

mHealth advances in clinical trials

There have been quite a few advances on the mHealth and wearables front. The FDA has suggested that its thinking on the topic of mHealth is aligned with a new global guidance on Software as a Medical Device (SaMD), and sponsors can use this guidance in order to establish their own feasibility criteria to evaluate wearables in clinical trials.

The Sleep Apnea Association is conducting its first mHealth study using Apple Research Kit to measure sleep outcomes in patients via a bring your own device (BYOD) model. Additionally, Clinical Ink published data on the impact of mHealth on patient engagement and subject dropout, offering a glimpse of the promise that mHealth and wearables offer in clinical trial settings.

What’s to come in 2017?

On the quality front, we will likely start seeing the initiation and implementation of new quality risk management infrastructures (but no real data on the impact these infrastructures are having on quality), and new sets of risk and performance indicators. On data aggregation, we will probably start seeing the emergence of case studies with data on predicting clinical operational outcomes. On endpoint adjudication, it is likely that we will see some form of guidance emerging from expert communities or non-profits on how to incorporate adjudication committees and adjudication charter templates. On subject enrollment, we hope to see data on patient behavioral outcomes in digital settings. In mHealth, we will probably see more case studies and data on how wearables are impacting clinical study and patient outcomes.

 

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