In 2024 the life sciences sector experienced high levels of cost pressure that drove process transformation in regulatory functions and smarter use of data and human resources. At the same time, the potential of artificial intelligence (AI) began to be widely realised, with new use cases emerging to address the multiple challenges facing the sector. In this article, expert commentators review the developments of the past year and look forward to 2025.
The life sciences sector has seen the beginning of a major transformation in 2024, driven by AI and data science technology that has underpinned process change and efficiencies in areas ranging from regulatory through to communications strategies.
Cost reductions and attempts to leverage AI to save cost or become more efficient were overriding themes for 2024, according to Peter Muller, Director, Americas at Schlafender Hase: “This looks set to continue into 2025. For many organisations looking at the horizon right now there is a lot of uncertainty.”
He adds, “Also a lot of companies are facing patent cliffs. Their portfolios have drugs that have lost or are losing patent protection, and they are not generating, or expected to generate the kind of revenue the company needs. Companies anticipate a period of financial struggle, and so they want to improve their operating profitability.”
AI Transforming Regulatory and Safety Functions
“The themes which have dominated the life sciences regulatory and quality landscape in 2024 are the integration of advanced data analytics and AI in regulatory compliance. This shift is driven by the need for more efficient and accurate compliance processes from the molecule to the product,” says Jens Marburg, Principal Consultant at MAIN5. He points out that evidence of this can be seen in the increased adoption of AI-driven tools for data validation, risk assessment, and regulatory reporting, adding: “2025 is likely to be the year of digital transformation and adoption in regulatory compliance.”
ArisGlobal’s CEO Aman Wasan cautions: “As one of the most safety- and risk-conscious industries there is, it’s important that pharma/biopharma gets AI right. Where patient safety is concerned, there can be no tolerance for ‘black box’ mystery; nor data security/ privacy breaches. Results must be reliable, robust, explainable, consistent, and trust-inspiring. It’s why the sector is pushing hard not only to harness trailblazing applications but also to test the boundaries of AI explainability, optimised human sampling, and transparency (e.g. through the combination of LLMs and retrieval augmented generation or RAG), so that regulators can see and assess outcomes for their reliability and consistency.”
Marburg at MAIN5 has seen clients increasingly embracing risk based approaches to computerised system validation (CSV), moving away from exhaustive validation processes to more targeted, risk focused strategies that allows for more efficient use of resources and faster project timelines. However, he says, “Companies are still not fully appreciating the importance of data governance and ownership. Many are at risk of non-compliance due to fragmented data management practices. The urgency around this issue is high, as regulatory bodies are increasingly scrutinising data integrity. Companies should establish clear data governance frameworks and assign ownership to ensure compliance and data quality.”
