Data management
on the [drug development life cycle] timelines, in areas of data automation, data risk flagging, and content writing we’re seeing AI POCs ready to scale. AI will also begin to play a big role in synthetic control arms and digital twin simulations,” adds Qutab. Moreover, AI-powered analytics enable sponsors and biostatisticians to identify patterns and insights more quickly, improving the speed and quality of decision making. This capability is particularly critical in adaptive trials, where rapid adjustments based on interim results are essential. Breaking down silos requires more than technological solutions, however; it demands a cultural shift. Stakeholders must recognise the interdependence of their roles and commit to collaboration. Sponsors can take the lead by creating centralised Centers of Excellence (CoEs) for data management and biostatistics. These CoEs can function as hubs for best practices, fostering communication and shared objectives across teams. Additionally, sponsors must establish clear expectations and standards for external vendors. Insourcing key data functions, or requiring vendors to use sponsor systems, can improve data transparency and consistency. As Martin notes, sponsors increasingly seek to retain control of data, enabling their teams to work more efficiently and effectively. “We’re seeing sponsors have more and more of a driver’s seat at the table where they’re requiring vendors to conform to certain quality standards,” agrees Qutab.
The benefits of bridging silos The integration of data management, biostatistics, and external vendors offers far-reaching benefits for clinical trials and, ultimately, patients. By fostering collaboration and leveraging modern technology, the industry can significantly reduce trial timelines and costs. “At the end of the day, we’re doing it for the patients, right?” says Qutab. “The better quality, the more [we] speed up the data management life cycle, this means that the patients get therapies faster, they get results faster, the submissions happen faster.” Enhanced data quality and integrity, driven by real-time validation and centralised management, minimises errors and discrepancies, ensuring that data remains accurate and trustworthy. “We’re seeing a lot of sponsors focus on ‘how can we clean that data closer to when we actually acquire it versus at the end when we go to database lock’ and so that allows you to be ahead of the game and to get better quality data throughout the whole process,” explains Martin. Simultaneously, increased efficiency through automation and streamlined workflows eliminates redundancies,
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enabling teams to focus on strategic priorities and advancing trials at a faster pace. This acceleration not only enhances operational performance but also contributes to faster time-to-market for life- saving therapies, ensuring patients gain access to critical treatments more quickly. Moreover, improved decision-making is facilitated by access to integrated, high-quality data, empowering biostatisticians to conduct rigorous analyses that inform trial outcomes and guide future drug development strategies.
“Data standards take a pretty critical role now; of course, data standards also get vague as new data types come in, especially the digital data, synthetic data and simulated data.”
Bazgha Qutab
While the benefits are clear, achieving this vision is not without its challenges. Transitioning to modern platforms and processes requires significant investment and organisational change. Sponsors must balance the need for innovation with the realities of regulatory compliance and resource constraints. “I think the next two to three years are going to be very critical for data management transformation, with a big transformation wave towards data automation, technology modernisation and use of AI across the data life cycle,” says Qutab. Moreover, not all organisations will move at the same pace, explains Martin. Early adopters will set the stage, but the broader industry must follow suit to achieve widespread impact. Collaboration among stakeholders, including regulators, will be essential to standardise new practices and technologies.
The next few years will be pivotal for the transformation of data management in clinical trials. Advances in technology, coupled with a shift towards collaboration, promise to unlock new levels of efficiency and innovation. As Qutab also observes, the convergence of traditional data management with modern AI and digital platforms will initially be disruptive, but ultimately transformative.
Sponsors, vendors, and regulators must, strive to work together to build a cohesive ecosystem that effectively supports the complexities of modern trials. By breaking down silos and embracing innovation, the industry can then deliver on its ultimate goal: improving patient outcomes through faster, more effective drug development. ●
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