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Blog: The Value of Interconnected Data Across the Drug Development Lifecycle

The management of data within organizations has far-reaching effects, as interconnected data provides valuable insights that can yield positive outcomes across various domains. However, drug development takes 8-15 years and incurs costs up to $11 billion. It relies on costly and often inefficient clinical trials, which lack insights into outcomes and side effects. To improve the process and reduce expenses, life science companies can collaborate with healthcare transformation firms to leverage extended real-world data (RWD) and real-world evidence (RWE), which yields a better understanding of the populations using their drugs and their associated outcomes. Several knowledge areas become crucial for establishing an outcomes-driven healthcare system:  

  • understanding real-world patient care variability within and between health systems 
  • real-time measurement of outcomes across diverse provider types and locations
  • comprehension of the clinical processes driving these outcomes 

Isolated data and limited access to it significantly impede drug discovery, hinder drug development, and burden organizations’ return on investment (ROI). Not having interconnected data poses significant dangers, particularly in terms of risk and regulatory compliance. A recent incident serves as a prime example: when a study coordinator called out sick, a backup coordinator stepped in to enroll a subject, unknowingly using an outdated version of the protocol. The result? A chaotic situation with data and documentation discrepancies, as the subject was enrolled incorrectly, failing to meet the inclusion criteria. The root cause was the failure to update the protocol on the clinical database website, resulting in widespread reliance on an incorrect version, despite receiving it via email. The aftermath affected risk analysis and rippled through the site, enrollment process, and the Institutional Review Board (IRB). Unfortunately, such incidents are not isolated occurrences making it essential to have a centralized location for information. The absence of interconnected data creates far-reaching and disruptive consequences.

 

Data Silos in Organizations 

Data silos continue to persist in companies. In fact, a study conducted by Aspen Technology1 revealed that nearly half of the 400 surveyed global pharma organizations reported having data silos, which was further confirmed by the panelists themselves and those in the live audience. For some, those silos become greater impediments over time. Regulations hinder the operational efficiency of data management. The lack of interconnectivity and data standardization between multiple systems (and vendors) within departments further compounds the problem. This fragmentation is particularly evident in regulatory processes, where duplication of documents and data is common.

Why is this issue more prominent now? The industry has undergone significant changes with the shift from small molecules to large molecules and more data-rich innovations like biologics and cell and gene therapy. The need for data to fulfill both regulations and discover key insights drives the need for more information sharing and collaboration with different stakeholders. But data inefficiencies limit a company’s discovery potential and any subsequent development cycles. The goal is to accelerate products to market for patients by leveraging data with a focus on specific patient populations and diseases. Making data available to the right people at the right time through role-based permissions and centralized management processes reduces human error and increases efficiency.

Overcoming data silos is crucial for optimizing the return on investment in drug discovery and development. Interoperability and breaking down silos facilitate the use of data for scientific, operational, and visibility purposes across the value chain. Integration of platforms with the primary database, such as the electronic data capture (EDC) system, is essential.

Trends indicate a shift toward data-centric regulations where information exchange replaces document-based submissions. Initiatives like the European Union (EU) Clinical Trials Regulation (CTR) implementation highlight the regulator’s role in the data revolution with progress being made in developing systems and data-centric processes aligned with standards like Identification of Medicinal Products (IDMP). Several platforms have recently released solutions such as product lifecycle management (PLM) with enterprise architecture framework (EAF), enterprise performance improvement (EPI), clinical documentation improvement specialist (CDIS), and Iris to help organizations establish data-centric processes. 

 

Regulatory Push for Transformation 

A recognition of the struggles within life sciences and the increased emphasis on the significance of regulatory compliance drive this transformation. This is an opportune time to discuss data-driven content and explore relevant technologies while taking into consideration the complexities of managing structured, semi-structured, and unstructured data. The advancements in telehealth and a focus on patient centricity are leading to the emergence of data in different formats and media, such as audio, visual, genomic structures, and digital imaging and communications in messaging (DICOM) images. The ability to collaborate and share data within trusted environments, such as research and clinical settings, is also a significant requirement.

In the realm of regulatory affairs, duplications and disconnectedness between documents and data are common. As an example, in Europe, when submitting extended eudravigilance medicinal product dictionary (xEVMPD) data, there is a reliance on data points from product labeling and SMPC. However, these valuable sources of information are often maintained separately in different systems, creating the constant need for realignment.

Another regulatory aspect that exemplifies this challenge is the management of transactional data, particularly submission and approval dates. Typically, these dates are extracted from the publishing system. Yet, it is common to encounter a separation between the publishing system and the regulatory information management (RIM) system responsible for tracking product registrations. Consequently, data handoffs become necessary, with additional systems required for change control and pharmacovigilance.

It is important to reuse information across procedures to achieve global impact by aligning with different regulators and regions for harmonized implementation of integrated data packages. Interconnected data has the great potential to provide better insights, which allow for informed decision-making while accelerating progress in areas including genomics, safety studies, and real-world evidence through artificial intelligence and machine learning.

Efficient management of data insights enhances the potential for improved patient outcomes. Typically, each sponsor maintains its own separate database for these purposes. It is important to differentiate between a sponsor’s internal systems, which often lack integration between regulatory, clinical, safety, and other components, and the various external systems that can be utilized through a partner, such as a contract research organization (CRO). These external systems enable functions like storing a trial master file (TMF) and tracking clinical trial enrollment.

While there are promising individual systems available, the problem lies in their underutilization as a cohesive whole. Integrating these systems often proves costly and burdensome, as they originate from different vendors and lack standardized data formats.

 

Data Governance and Stewardship  

Data governance encompasses supervision of the quality of data from its entry into a company and throughout its utilization across the entire organization. A recent paper published by Snowflake entitled 5 Critical Components of Successful Data Governance stresses that, “data stewards need to be able to identify when data is corrupt, inaccurate, or outdated, or when it’s being analyzed out of context.”

There is a recognized need for technologies that allow interconnectivity and collaboration among data sources without introducing yet another platform. Two key challenges are data governance and stewardship and the transition from the entrenched culture of siloed data to a new open mindset demonstrating a willingness to share data. There are difficulties in managing these challenges, which demands the rethinking of data governance and the adjustment of processes.

As for governance in platforms, many already have permissioning systems in place with either role-based or attribute-based governance. Therefore, it is important to work with partners who excel in governance and change management and to explain the cost efficiencies to decision-makers. The timeline for implementing new platforms or integrating data can vary, so it is crucial to have a clear vision, start with small pilots, and learn from them.

Interconnected data enables organizations to facilitate change and unlock business benefits. While it certainly contributes to regulatory compliance, its advantages go beyond meeting compliance obligations.

 To watch the panel discussion in its entirety, click here.  

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