Setting sights

‘Data integration is a key enabler for greater accuracy in drug development and patient safety’

Data integration is a key enabler for greater accuracy in drug development and patient safety. It achieves this by guaranteeing the precision and uniformity of data, establishing traceability, and offering a comprehensive data overview.

Ensuring that this data reaches the appropriate individuals is equally important. The pharmaceutical field encompasses diverse skill sets and personnel, making it essential to establish processes and frameworks that not only protect data from a security perspective, but allow it to be accessed by the right people and for the right purposes.

By utilising advanced platforms that can consolidate study data from various systems and sources, including the increasing number of electronic health records and wearable health tracking devices that have resulted from increased trial decentralisation, data scientists can create more robust data sets.

Platforms such as Domino provide a fast, cost-effective, unified AI platform that plays a pivotal role in bridging the gap between specialised IT systems and biomedical data. This, in turn, results in greater accuracy of insights sourced and developed from the data.

That said, integrating data from various sources still requires a comprehensive framework, especially to guarantee this insight accuracy. On top of using specialist data-gathering platforms, frameworks should be built around secure data-sharing protocols and interoperability standards.

By doing this proactively and risk assessing prior to implementation, pharmaceutical firms can better support the capacity of researchers to forecast potential issues, therefore reinforcing patient safety.

In such a fast-paced industry as pharmaceuticals, continuously pushing the boundaries of what’s possible with data is necessary to remain competitive. With integrated data comes greater data agility, and across a complex chain this will help to prevent knowledge gaps that impede strategic decision-making and the competitive climate.

As companies constantly seek this growth in their data systems, we are experiencing an exciting phase for the pharmaceutical industry with a high degree of promising innovation.

While the sector might currently be lacking structure in this space, we can expect upcoming frameworks to help support the full data life cycle – from development to post-market monitoring.

The introduction of ICH E6(R3), although still in the drafting phases, represents a pivotal shift in the credibility and integrity of the data generated in this new clinical trial landscape.

With the guidance being optional and non-mandatory, it’s important for firms to be aware of emerging regulatory standards, especially for compliance and data integrity.  Primarily, this innovation was driven by the limitations imposed by its predecessor, R2, on the adoption of novel trial designs and emerging technologies.

The updated guideline incorporates essential quality factors to prevent data errors that could harm trial results, while also assisting in randomisation, blinding and retention.

R3 also ties intorisk-based quality monitoring (RBQM), the full potential of which will be unlocked by the technical advances and novel trial designs encouraged by the guidelines. RBQM involves the identification, evaluation and management of potential risks that could impact trial participant safety, threats to data integrity and regulatory compliance.

Firms should embrace RBQM as a measure to assess and mitigate the risks inherent to clinical trials.

In simple terms, greater quality management lowers the likelihood of regulatory issues and accelerates development timelines by minimising the time needed to address common issues found in studies without a quality-by-design approach.

For post-market, consolidated data sets allow for real-time monitoring and support pharmacovigilance professionals in detecting potential safety signals through ongoing trend monitoring.

The adoption of RBQM alongside advanced technologies, like automation tools, will be crucial. Investing in automation tools and technologies to streamline processes and reduce manual errors in data handling can reduce the burden on patients and maintain a healthy study pipeline in compliance with regulatory standards.

Greater efficiency, compliance and the leveraging of emerging technologies are all areas firms are looking to improve but integrating the variety of organisational skill sets required for this can be challenging for effective data integration. Creating a cross-functional work environment that encourages greater collaboration can help to support this by harmonising all the diverse expertise available.

To drive innovation, organisations must continue to adopt new technologies and methodologies, not just to meet regulatory requirements, but also to compete in a demanding market.

Effective data integration is essential for managing the wealth of available patient information. Above all, it ensures that different sources can work in synchrony to provide insights for innovation, tailored patient care and regulatory compliance, while also preserving data integrity.