Clinical Trial Innovation Update

Innovation in clinical trials is always a hot topic, and a variety of novel concepts continue to emerge. This year at Cambridge Health Institute’s Clinical Trial Innovation Summit, there was plenty of discussion on innovation ranging from enhancing information exchange between patients and clinical trials, to risk-based management, to big data analytics. This article will summarize some of these discussions.

Information Exchange: A Much-Needed Innovation

Christine Crandall, Head of Strategic Clinical Planning at GSK, elaborated on TransCelerate’s initiatives with creating a registry for clinical trial information exchange to facilitate clinical trial information between patients and sites. Crandall explained that the biggest challenge involves matching patients with clinical trials, as (a) sites often don’t have the right clinical trial information when speaking to patients about study opportunities, (b) patients do not have access to effective tools in order to search for clinical trials, (c) clinical trial registries contain a lot of medical jargon that patients do not understand, and lastly (d) patients have poor access to simple and relevant information about clinical trials. Opportunities, however, exist, as 80% of respondents to CISCRP’s Perceptions and Insights Study indicated they were somewhat-very willing to participate in a clinical trial, and willingness to participate ranged from 93%-96%. Crandall believes the industry can address this gap with better registry design that is accessible (i.e., easy to navigate, personalized user experience, and ability to easily share information with others), informative (i.e., provide patients with additional information on study participation, using simple language in order to enhance comprehension, logically present information, and offer actionable next steps), and is trustworthy (i.e., demonstrate credibility, and add context to information). TransCelerate is planning the creation of a clinical trial registry that contains key functionality for patients, such as bookmarking clinical trials, hiding irrelevant studies, enabling notes, and color indications of new studies. Additional functionality includes study comparison, account registration and alerts, and guided search maps.

RBM: Business as Usual

As the RBM process cementsin the industry, biopharmaceutical enterprises are efficiently applying RBM to more studies. Teresa Ancukiewicz, Senior Manager of Clinical Data Management at Boston Scientific (BSC), elaborated on BSC’s central analysis and review process. At BSC, there is a centralized monitoring lead who interprets signals, identifies trends, tracks decisions, and ensures completion of outstanding actions. This role interacts with project management (who make decisions, set expectations, and ensure smooth process flow), functional representatives (who review thresholds and signals, conduct root cause analyses, and offer recommendations), and RBM champions (who offer expertise and provide tools across studies). Ancukiewicz indicated that it is important for study teams to determine what key risk indicators to focus on, understand how to interpret signals, and derive actions from those signals. Accordingly, BSC has developed a risk review process, where they document signals in the RBM system, data managers escalate relevant risks, they evaluate these risks in compliance meetings, and they determine actions. Essentially, this process establishes traceability from signal to action to resolution.

Esther Huffman, Associate Director of Monitoring Excellence at Bristol-Myers Squibb (BMS), showed that BMS is now ramping up RBM application on most studies, deeming the practice business as usual; in 2012 RBM was in pilot phase with less than tenstudies, and in 2018, BMS implements RBMin around 120 studies.  Huffman elaborated on several key risk indicators including quality indicators (i.e., major/critical audit findings per audited site, percentage of correctly reported and confirmed SAEs, and number of significant protocol deviations/site), efficiency indicators (i.e., average monitoring cost per site, average interval between on-site monitoring visits per site), and cycle time indicators (i.e., median number of days from patient visit to eCRF data entry, and median number of days from query open to close).

Big Data Analytics

There were numerous discussions about the use of big data analytics in clinical trials. Margaret McDonald, Senior Director of Real-World Data & Analytics, Patient & Health Impact at Pfizer, discussed how real-world evidence (RWE) is impacting the way biopharmaceutical enterprises are addressing unmet medical needs. McDonald indicated that the continual rise in data collection would empower healthcare research; for example, digital mobile engagement amongst patients, life science enterprises, and healthcareproviders will increase by 50% in 2019. Additionally, by 2019, more than 50% of life science enterprises will have dedicated resources to support RWE analysis, and by 2020, 25% of healthcare data will be collected by patients and shared with health systems. McDonald suggested that big data includes mobile wearables/sensors, genomic data, imaging data, unstructured notes from EMR, and claims data. McDonald emphasized that RWE will be used to optimize study design (i.e., understanding effect size for powering a study, foreseeing enrollment probabilities, and evaluating dropout rates), identifying study sites, and operationalizing studies.

Moe Alsumidaie, MBA, MSF is Chief Data Scientist at Annex Clinical, and Editorial Advisory Board member for and regular contributor to Applied Clinical Trials.