Industry Insight

Dr Werner Engelbrecht at Veeva Systems discusses how compliance can be turned into a competitive advantage with connected platforms and data, AI-driven efficiencies and new patient-centric models

Regulatory momentum is building. In recent years, the European landscape for clinical trials has undergone a significant transformation with new frameworks such as the EU General Data Protection Regulation (GDPR), the Artificial Intelligence (AI) Act, Accelerating Clinical Trials (ACT EU) and the upcoming European biotechnology law. Biopharma companies must navigate both opportunities and challenges as they adapt to stricter compliance requirements, increased data transparency and evolving technological capabilities.

In 2024, the European Commission President reaffirmed the EU’s commitment to advancing Europe’s life sciences industry, emphasising innovation-friendly policies and regulatory harmonisation. As these regulatory changes take effect, biopharma companies can turn compliance into a competitive advantage by embracing connected platforms and data, AI-driven efficiencies and new patient-centric models.

Regulatory shifts continue to redefine 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 silos and create real-time data visibility will be a key differentiator, enabling successful companies to accelerate drug development in support of better patient outcomes.

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.

Meanwhile, European regulatory authorities are 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 can be shown to be both effective and compliant.

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 data sets 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 that proactively develop data governance frameworks can mitigate compliance risks before they arise. 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.

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 drop-out 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 levels of patient engagement throughout the study duration.

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.

The regulatory changes coming in 2025 present both challenges and opportunities for biopharma companies. Those who embrace AI-driven efficiencies, implement transparent compliance frameworks and optimise patient engagement strategies will emerge as clinical research leaders.

Organisations that prepare now by investing in technology to connect clinical functions will not only meet regulatory requirements but will also be able to leverage new technologies, like AI, to drive long-term success. Rather than viewing regulations as an obstacle, companies should see them as a catalyst for innovation – an opportunity to modernise operations, enhance patient-centric approaches and ultimately bring new therapies to market faster and more efficiently.