Contributed Commentary By Rebecca Daniels Kush and Karim Damji,

November 15, 2018 | In the almost two years since the FDA mandated compliance with the Clinical Data Interchange Standards Consortium’s (CDISC) data standards, life sciences organizations have striven to adhere to these newly established guidelines for all clinical and non-clinical studies initiated after December 2016. As early as 2004, the FDA encouraged the use of CDISC standards, and while some pharma and biotech implemented quality principles early-on, many companies deferred until it was clear that compliance would be required. Of course, conforming to submission standards without implementing them at the start of a clinical research study posed a number of not-insignificant obstacles. Data in non-standard or proprietary standard formats needed to be migrated/mapped to the standards, internal teams required training and implementation planning, compliance procedures and governance processes had to be instituted, and resistance to discarding legacy procedures needed to be surmounted. In short, mandated compliance cost money, took time, and likely created some frustrations.

But, sometimes, the things we are required to do wind up working to our advantage.

When one looks beyond these initial challenges, the very real—and not inconsequential—operational and commercial benefits that clinical data standards offer the life sciences industry become apparent. These opportunities can be leveraged to maximize business outcomes for pharma,  biotech, and academia in a number of areas, both in the short-term and long run.

Improved Data Quality

A standards-driven approach to clinical development enables more rapid study start-up and optimal data capture, facilitating the consolidation and aggregation of patient-centric datasets, interpretation, and integration. Data loss is minimized, and a clear path demonstrating data traceability is established. An opportunity is created to have a cleansed and harmonized dataset for FDA review, dramatically reducing process time from data acquisition to submission since the data have been integrity-checked throughout. Additionally, researchers are empowered by real-time clinical data exchange and collaboration, as well as cross-study analyses and end-to-end data flow. For instance, through the Coalition for Accelerating Standards and Therapies and working with experts from OneMind, a CDISC standard for Traumatic Brain Injury (TBI) was developed. This standard helps drive awareness of and consistency in the types of information, including clinical measures and biomarkers, that may be relevant to capture in TBI studies. This standards development project also stimulated discussions among experts with respect to the status quo for assessing TBI.

Looking to the future, the uniform translation of Electronic Health Records (EHR) data for research into CDISC data standards could eliminate the need for duplicate entry of medical record data into clinical research systems, which is the current practice. Inconsistencies in EHR formatting and data capture could be eradicated, making this “Real World Data (RWD)” more accessible, actionable, and valuable to life sciences companies than ever before, not to mention the value this would have to patients.

Reduced Submission Timelines

Clinical data standards may help speed time to market for new therapeutics. Optimizing data collection, processing, and analysis translates to more efficient clinical development timelines. The CDISC Business Case reveals that, “use of CDISC standards at study initiation can save 70% - 90% of time and resources spent prior to first patient enrolled and approximately 75% of the non-patient participation time during the Study Conduct and Analysis stages.” Additionally, schedules for potential new compounds to be approved are accelerated, which means that FDA submission dates can be realized earlier, regulatory reviews are of higher quality with fewer questions returned to sponsors, and commercialization timetables are advanced.

Optimal Organizational Performance

Optimizing clinical data, enhancing safety, and expediting timetables should translate to reduced drug development costs for pharma and biotech. Conversely, mapping data to standards at the time of a submission adds significant time and cost to the project and can negatively impact data integrity and traceability. A standards-driven approach enables benchmarking activity that quantifies and guides enterprise-wide and process-specific performance improvements, as well as ensures organizational transparency. Time savings are realized in terms of a productivity focus on core vs context, which is crucial from outcomes perspective.

Sharing Across Industry

When individual companies realize the benefits from implementing clinical data standards, the performance of and bar for the entire industry is elevated. Data can be combined from multiple disparate sources industry-wide for analyses that inform optimal planning of new studies, allowing the industry to communicate and share data between organizations. Overall medical and scientific insights and knowledge are enhanced, thus advancing the industry’s ability to safely and effectively manage, mitigate, and cure disease on a global level.

As an example, the Coalition Against Major Diseases (CAMD) formed by the Critical Path Institute was instrumental in the development of an Alzheimer’s Disease standard, which was then leveraged  to create a database that facilitates pooling of data from different sources into an Online Data Repository for Alzheimer’s Disease. This repository now benefits researchers who continue the search for a cure for Alzheimer’s Disease. Furthermore, clinical data standards themselves will benefit from ongoing refinement as the industry learns what works, identifies best practices, and shares these experiences with one another through consensus-based standards development processes.  

Enhanced Care 

CDSIC standards can contribute to improved care for patients, including those who are most vulnerable. The Worldwide Antimalarial Resistance Network (WWARN), University of Oxford, and Infectious Diseases Data Observatory (IDDO) teamed up to apply CDISC standards in field research data collection and aggregation. This effort resulted in new treatment recommendations for low-weight children suffering from malaria in sub-Saharan Africa, including increased dosing that resulted in a reduction of mortality.

The application of CDISC standards also has the potential to surface critical existing but previously obscured data that can lead to transformative enhancements in research and patient care. According to the Polycystic Kidney Disease (PKD) Foundation, harnessing CDISC standards contributed to the discovery of clinical research biomarkers for PKD. The evidence for these biomarkers had been hidden in non-standardized datasets for years, but until they were  standardized, aggregated, and analyzed they remained unknown. These biomarkers now enable researchers to identify kidney diseases early, making intervention more viable.

Using CDISC standards to transform clinical research can serve to accelerate the learning health cycles that in turn contribute to the positive transformation of patient care, thus realizing Learning Health Systems (LHS). All stakeholders in clinical research and healthcare have the opportunity to contribute to improving processes and health outcomes through strategic forums like the Bridging Clinical Research & Clinical Health Care Collaborative. The Collaborative brings together leaders from academia, pharma, biotech, CROs, health care organizations, patient advocacy groups, regulators, and others to stimulate collaborative discussions and solutions to the multifaceted challenges of improving healthcare, increasing patient and physician participation in clinical research and making clinical research a clinical care option, and furthering the LHS values and consensus action plan of the Learning Health Community. 

Clearly, the ongoing operational, commercial, and patient benefits of clinical data standards outweigh the initial financial, time, and productivity investments needed for life science organizations to align with the new requirements. To realize and maximize the true value of clinical data standards, life science companies need to shift from viewing them through a compliance-mandated lens to seeing them as an engine for growth.

Standards need to become an integral part of the early planning process of a research program, rather than an afterthought. In fact, the Elligo Health Research model of ensuring that source data are collected in CDISC format from the start is directly aligned with the FDA’s Critical Path Initiative, citing the importance of making it easier for investigators to participate in clinical research by standardizing data collection for the site personnel, in addition to providing data in a format already aligned with the requirements for regulatory submissions.

Revolutionary new, end-to-end data management solutions can facilitate the transition and implementation of standards. Unified, AI-powered clinical data analytics platforms (like Saama’s Life Science Analytics Cloud, LSAC) leverage machine learning capabilities to quickly align data to CDISC standards. When clinical data is not initially collected in CDISC format, tools can provide comprehensive, cloud-based technology to deliver cleansed, CDISC-standardized, aggregated operational and clinical data for more cost-effective management of clinical trials. Comprehensive, standards-aligned ecosystems inform and advance data development from capture in clinical trials to regulatory filing and beyond.

Such systems, and by extension clinical development standards overall, cannot be effectively implemented or leveraged without the support of company leadership. To optimize the positive impact clinical development standards can have on an organization’s bottom line, the C-suite needs to be all-in; achieving enterprise-wide understanding of and commitment to clinical development standards as a resource to fuel company growth needs to be a top-down process. To optimize the business impact of clinical data standards, leadership must proactively carry the torch. Data management must be viewed as a key role within the development process, especially since data are the core resource of a pharma or biotech company. 

Industry history shows that it’s possible to conduct clinical development analytics without standards—it was done for decades. However, the rationale for employing standards end-to-end, other than the fact that they are now required for submissions, is that standards have been shown to stimulate innovation and positively influence both an organization’s bottom line and human health by making the clinical development process faster, easier, more informative and more productive. That’s an outcome the entire industry can get behind.

Dr. Kush is President of Catalysis, Inc. a consulting company founded in 1997 with a vision to apply enterprising solutions to transform clinical research and accelerate learning health systems. She is the Chief Innovation Officer for Elligo Health Research and  the Founder of Clinical Data Interchange Standards Consortium (CDISC), where she served as President and CEO for 20 years. She can be reached at rkush@catalysisresearch.com.

Mr. Damji plays a pivotal role in helping Saama realize its audacious goal of enabling pharmaceutical and biotechnology companies to accelerate the development of life-saving and life-altering therapies. He is responsible for driving product strategy for Saama’s AI-enabled data analytics solutions and identifying opportunities that lead to successful market share growth. He can be reached at Karim.damji@saama.com.