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Moving to a Standards-Based, Agile Clinical Development Lifecycle

As the BioPharma industry migrates from paper-based to technology-based clinical trials, it has the opportunity to make better use of the data it collects. This article describes a new agile Approach to the clinical development lifecycle that maximizes use of content using Nurocor MDR™.


Data Sheet

Nurocor MDR for BioPharma

Nurocor MDR is an ISO 11179 compliant data governance solution built on the Nurocor industry leading metadata repository and governance platform, pre-populated with CDISC and BRIDG standards.


CDISCIn the highly regulated BioPharma industry the efficient use of data standards plays a crucial role in clinical development and regulatory review. CDISC data standards are now required by Federal Drug Administration (FDA) and Pharmaceuticals and Medical Devices Agency (PMDA) for the regulatory approval of new medical products (drugs, biologics, devices). The complexities of the healthcare and research world makes the use of other data standards a necessity too. Deciding where and when to implement data standards in the clinical development lifecycle, a decision that all BioPharma companies are required to make, can have a large impact on the cost, cycle time, and quality as it relates to data acquisition, processing, evaluation and analysis. One thing is certain, to bring any measure of efficiency to the data processes, reuse of tightly governed and versioned data definitions and ontologies is essential.

Data is the primary asset of a clinical trial and when the costs of running a trial are added up, even simple phase I trials, it is indeed an incredibly valuable asset. Data standards play a crucial role in unlocking the potential of those data assets. Data standards ensure consistency of data across trials, and that consistency means that data can be aggregated or combined more easily for meta analyses or integrated summaries or answering regulatory questions, or, to support hypothesis testing and clinical trial simulations. Using consistent data standards across studies means that these secondary uses of data can realized with less overhead, fewer programmers, and in shorter time.

Using industry developed standards, as opposed to proprietary company created standards, means that sharing data with partners becomes easier, common data definitions are clearly understood by both sides without the need for translation or having to make assumptions.

BioPharma organizations are collecting increasingly more complex data for regulatory purposes, and are gathering data through various novel sources: apps, social media, and other methods for marketing purposes. The transformation of these different types of clinical data to knowledge requires a deep understanding of the data, as well as the drug development process.

It would be beneficial if the same definitions were used across the clinical data lifecycle, allowing for different implementations in the necessary systems. The ISO 11179 registry metamodel provides an excellent platform for managing concepts and their domains, including their associated implementations.

By binding systems to a central repository with robust common domain definitions and terminologies, and managing the metadata components to maximize data reuse, industry can implement more efficient end-to-end clinical lifecycle processes and generate reusable data that feeds the next wave of discovery and innovation efforts.

With a centralized metadata repository (MDR) at the heart of the R&D data infrastructure, data standards and standardized metadata can be reused more reliably and consistently in systems across the entire R&D enterprise. This helps to streamline processes and reduce risk by minimizing unnecessary data transformations along the chain of custody. Metadata is defined once, and then reused across all systems or artifacts, thereby removing silos created by functional area specific standards.

A metadata management tool should support the management of semantics and offer a flexible configuration that can manage regulatory and industry data standards. In addition, a metadata management tool should support enterprise specific standards and metadata that may be defined at different levels of granularity. As becomes apparent, the management of standards is multi-axial and hierarchical and each level of the hierarchy needs to be versioned. Standards change over time and clinical study programs take many years to complete meaning that multiple versions of standards will be used concurrently within an organization. An MDR will provide the capability to manage the additional intricacies that versioning of standards requires. A metadata management tool must provide automated governance workflow capabilities to manage utilization and uphold the integrity of standards.

Data standards-based semantics can be used to structure data entry and streamline data flows, minimizing data quality loss due to entry errors or incorrect data processing. Applied consistently, standards facilitate data interoperability to ensure correct information exchange and interpretation. The last point on interoperability is important as companies increasingly rely on CRO partners and specialist data providers, where the communication of unambiguous data definitions is essential.

The figure below provides a simple example to illustrate the benefits of an MDR. A central MDR contains pre-defined CRF and EDC standards, based on CDISC and company requirements, that are utilized by a Clinical Data Manager. Rather than manually interpreting the study protocol, data collection standards and prior study CRFs the Clinical Data Manager uses existing well-defined metadata content from the MDR to create a new set of metadata to represent a study specific eCRF and edit check specifications. Therefore, all the data manager is required to do is select existing components, from the appropriate standard version, to build an eCRF. The same process applies for the definition of edit checks.

The Nurocor MDR provides the core platform for CDISC SHARE.