Alison Liddy, ICON
CWWeekly presents this feature as a spotlight on issues faced by executives in clinical research. This week we hear from Alison Liddy, Senior Vice President, Clinical Risk & Data Management, ICON Clinical Research Services.
Question: How are clinical trials evolving and what impact does this have on the complexity of data management?
Answer: The blurring of the lines between phase I and II trials, coupled with the rapid expansion of study cohorts based upon flexible study designs, have significantly decreased timelines for drug development in a number of therapeutic areas, including oncology. Multi-arm trials provide the flexibility of being able to reduce the number of study arms depending on the benefit shown or to add arms as new treatments become available. As the use of these types of adaptive studies has become more widespread, data management functions have had to become more adaptable and sensitive to adjusting timelines.
Risk-based monitoring directs the focus to the areas of greatest need, with particular attention to those activities that have the most potential to impact patient safety and data quality. This requires varying approaches for Source Data Verification (SDV). Source Data Review (SDR) must be managed and tracked by highly skilled, agile data analysts that understand the impact of these strategies in delivering quality data within given timelines.
Another way clinical trials are changing is the increased focus on personalized care. Pharmaceutical companies are showing interest in the “site-less” or direct-to-patient model and are building alliances with various likeminded companies such as “Science37”. Companies similar to “PatientsLikeMe,” which offers a real-time research platform, are starting to gain traction in the industry.
Clinical trials and clinical trial design have been getting more complex over the last five years with the introduction of approaches such as flexible study designs and risk-based monitoring. Despite the industry’s best efforts to improve protocol design, they are becoming increasingly more intricate, which result in an increase of amendments during the study.
The explosion of the amount and variety of data being generated has resulted in the increasingly complex, time-sensitive data collection process, which involves cleaning and delivery to clients combined with evolving innovation and trial design.
Question: What can CROs do to help sponsors with the increase in data complexity?
Answer: Integrated technology solutions: CROs are increasingly using integrated technologies and real-time access to data to improve data integration and exchange and achieve clean database lock more rapidly. Data cleaning strategies and data review tools using analytical software can enable data to be cleaned in keeping with the patient schedule and monitoring strategy and provide a more holistic view of data cleanliness across various sources. By using a unified integrated platform, CROs can deliver a more streamlined process for managing complex data, resulting in efficiencies and transparency for sponsors.
Protocol optimization: Full service CROs can provide multidiscipline specialists in protocol design, clinical development and regulatory, to help stress test the operational viability of the protocol to answer research questions. This service can help the sponsor to ensure the final protocol is both in line with research requirements and meets the quality of scientific data needed for better outcomes. This approach enables the collection of the right data for the right questions and provides the balance in data volume over value. Specialists in this area can also provide advice on collecting the most appropriate, optimum amount of data to leverage emerging biomarkers and imaging in the early stages of clinical development to enable better decision-making around the compound.
Question: Is technology improving the way data is managed in complex trials?
Answer: Many of the data management complexities are due to multiple data sources and the laboratory types involved. Skilled laboratory data experts use integrated technology that allows for the management of laboratory normal ranges. The unification of medical imaging and electronic data capture (EDC) platforms enables image data to be brought into the study, which reduces data reconciliations between imaging and clinical databases, therefore the images can be tracked in real time.
It has been a longstanding challenge to integrate patient data from electronic medical record systems (EMRs) with EDC systems for clinical studies. The value this could bring to data management and in the reduction of burden for sites and Sponsors creates a clear business case that drives ambition to resolve the issues. CROs and technology vendors are working to provide potential solutions to overcome this challenge in certain areas, for example, by interrogating EMR data to identify sites with suitable patient populations to conduct studies.
The use of artificial intelligence is also likely to have a role to play in future trials. There will be a need to build advanced analytics systems based on predictive modelling to handle the inflow of real time data and then manage the outflow of relevant, clean data.