The clinical trial innovation surge

Dr. Werner Engelbrecht, senior director strategy, Veeva Systems explores how this year could see clinical trial innovation surge as EU regulation evolves.

Europe’s clinical trial regulations are evolving fast. Policies including the EU General Data Protection Regulation (GDPR), the AI Act, ACT EU and the soon-to-launch European Biotechnology law are redefining clinical trial standards. Biopharma companies must now balance compliance with innovation, navigating a landscape that demands both transparency and technological agility. 

In a 2024 address, the European Commission President reinforced the EU’s ambition to lead in life sciences, advocating for more harmonised and innovation-ready regulation. As these reforms unfold, biopharma firms can gain a competitive edge by using AI, connected data and patient-centric models to align compliance with progress. 

The clinical trial landscape is being reshaped by regulatory change 

The regulatory shift is redefining how clinical trials operate. Since 2022, the EU has moved toward greater harmonisation of regulatory requirements, aiming to reduce administrative burdens while maintaining high ethical and scientific standards. The Clinical Trials Regulation (CTR) has already standardised submission and approval processes across EU member states. Now, data and AI regulations are set to reshape compliance frameworks and digital capabilities. 

These regulations are paving the way for a more connected, efficient, and transparent clinical trial ecosystem. Companies are rethinking their approach to technology, data governance, AI integration, and compliance strategies to advance with agility in the evolving regulatory environment. A critical component of this shift is establishing a unified platform approach that facilitates the exchange of data from clinical, regulatory, safety, and quality functions and lays the groundwork for useful applications of AI. Many organisations still operate in silos, where compliance is treated as a separate function rather than an integrated part of the development process. The ability to break down these silos and create real-time data visibility will be a key differentiator by accelerating drug development to support patient outcomes.  

Turning AI promise into action in clinical research 

AI in clinical trials is often discussed in broad, futuristic terms, but its practical applications are already making an impact. Rather than focusing on theoretical advancements, biopharma companies can identify where AI could add real value. For example, AI could help enhance data quality by detecting anomalies and inconsistencies, ensuring more reliable clinical outcomes. Predictive analytics could help transform patient recruitment, identifying eligible participants faster and improving trial diversity. 

Regulatory agencies are now considering how AI should be validated within clinical settings. The AI Act, for instance, proposes specific requirements for high-risk AI applications, including transparency, robustness, and human oversight. However, the effectiveness of AI in these areas is only as strong as the underlying data. A well-integrated, high-quality, clean data foundation is essential for AI-driven insights to deliver real value. Moving forward, organisations should continue balancing innovation with accountability, embedding AI into clinical workflows in a way that is both effective and compliant. 

Elevating compliance through trustworthy, transparent data 

As data transparency becomes a central theme in EU regulations and the updated ICH E6(R3) guideline, companies are shifting toward a more structured approach to data governance. The industry is moving beyond simply collecting large datasets to ensure data integrity, auditability, and regulatory compliance remain at the forefront. The latest EU regulations demand end-to-end visibility of clinical trial data so that all study records are traceable and compliant. Companies proactively developing data governance frameworks can mitigate compliance risks before they arise.  

The accuracy and integrity of clinical trial data is not just a regulatory requirement—it is critical to patient well-being. Providing regulators with real-time access to high-quality data will be a key factor in securing faster approvals and reducing the risk of compliance-related delays. With the increasing use of remote monitoring and decentralised trials, unifying data sources will be critical for regulatory adherence. 

Using AI to improve patient engagement and outcomes 

One of the most promising aspects of AI in clinical research is its potential to enhance patient recruitment and monitoring. Historically, patient enrolment has been a major bottleneck in drug development. Sponsors with the right data can now use AI to identify patients and engage with them faster. Digital biomarkers and remote monitoring could allow real-world data collection without requiring frequent site visits. Personalised patient engagement strategies improve retention and study adherence by reducing the burden on patients, which ultimately increases trial efficiency and diversity. Leveraging AI-driven patient insights can also enhance trial design by identifying potential dropout risks early so study teams can make real-time adjustments. This level of adaptability will be crucial in efficient recruitment for trials that also maintain high patient engagement throughout the study duration. 

Making clinical AI work within regulatory boundaries 

While connected technologies and AI offer improved efficiencies, they also introduce new regulatory complexities. Biopharma companies should strike the right balance between automation and compliance, ensuring that processes meet EU ethical guidelines and transparency requirements. One emerging challenge is disclosure management. Under GDPR and upcoming EU transparency requirements, companies must ensure that sensitive clinical trial data is shared responsibly. Connected technologies can help streamline compliance reporting, enhance regulatory filings, and manage public disclosures. 

Furthermore, EU regulators increasingly emphasise the need for verifiable AI models in study documentation, adverse event detection, and protocol optimisation. Companies should proactively integrate validated AI workflows into their clinical operations, ensuring they remain both compliant and competitive. This makes a unified clinical data foundation even more vital to ensure regulatory readiness and maximise the impact of AI.  

Converting regulatory demands into strategic advantage 

The regulatory reforms coming in 2025 will test the adaptability of biopharma companies. Biopharmas that respond with the implementation of AI, robust compliance practices and stronger patient engagement will be best placed to lead the field. By investing now in technology that connects clinical functions, organisations can stay ahead of regulatory demands and create a strong foundation for AI-driven innovation. Regulations should be seen not as blockers but as opportunities to modernise, become more patient-centric and ultimately, speed up the path to market.