AI and policy reform set to reshape UK drug development

Every year, pharmaceutical companies invest billions in research and development, yet only a small fraction of newly discovered medicines reach patients. Lengthy clinical timelines, complex regulations and fragmented data infrastructures continue to delay progress. The UK Government’s recent plan to reduce clinical trial set-up times from nine months to ten weeks is an attempt to remove one of the most persistent bottlenecks in the drug development process.

For Chris Meier, Managing Director and Partner at Boston Consulting Group (BCG) based in London, this initiative arrives at a critical time. Leading BCG’s global work on AI, data and analytics in biopharma, Meier advises pharmaceutical companies on how digital innovation can shorten timelines, improve efficiency and enable new modes of drug discovery.

“The UK Government's plan to cut clinical trial set-up times is a welcome move. The UK has many world-class hospitals where pharmaceutical companies want to run trials, and shortening start-up timelines will make the UK even more competitive and attractive globally.”

Streamlining trials for faster access

Clinical trials remain one of the most resource-intensive stages of drug development. Administrative processes alone can consume months, slowing access to innovative treatments. Meier believes that faster start-up times will make a measurable difference both commercially and scientifically.

By reducing administrative delays, innovative medicines can be studied and hopefully reach the broader population more quickly.

“By reducing administrative delays, innovative medicines can be studied and hopefully reach the broader population more quickly. We expect that, as a result, more pharmaceutical companies will choose to conduct trials in the UK benefiting both the industry and most critically, patients.”

If realised, the policy could also create a feedback loop that strengthens the UK’s research ecosystem. More trials mean more data, better infrastructure and closer partnerships between hospitals, industry and academia.

Beyond policy changes, AI is beginning to make a difference

Meier’s background as a pharma R&D scientist gives him a practical view of where AI can have the most immediate impact. He identifies two areas where the technology is already accelerating progress.

“First, in drug discovery, AI can screen and generate molecules with the right properties, reducing the number of discovery cycles required. Second, in target discovery and validation, AI can mine vast genomic and other datasets to identify promising biological targets. Already today, these applications are delivering a growing AI-originated pipeline.”

AI can also help researchers identify which patients are particularly likely to respond to certain treatments. This can help with the design and planning of clinical trials.

By improving the quality and speed of early discovery, AI can shorten the journey from idea to clinical candidate. But its role does not stop there.

“AI can also help researchers identify which patients are particularly likely to respond to certain treatments. This can help with the design and planning of clinical trials.”

Patient stratification – the ability to predict who will benefit most from a therapy – is becoming one of AI’s most valuable contributions to translational research.

The impact of large language models (LLMs) in clinical trials

As AI evolves, pharmaceutical companies are increasingly using it to automate routine tasks, such as documentation.

“In clinical trials, many large and complex documents need to be produced – for example, clinical trial protocols, clinical study reports etc. Generating these documents often takes weeks and months. In the last few years, many pharmaceutical companies have started using large language models (LLMs) to help generate the first-draft versions of some of these documents – the output from the LLM is subsequently reviewed and checked by human experts.”

However, Meier cautions that traditional LLMs often fall short when applied to regulated scientific contexts.

Traditional LLMs can draft text very quickly, but due to a lack of data and training, their outputs sometimes miss the clinical or scientific fact base and the correct scientific terminology and references.

“Traditional LLMs can draft text very quickly, but due to a lack of data and training, their outputs sometimes miss the clinical or scientific fact base and the correct scientific terminology and references.”

For high-stakes documents such as clinical trial protocols, accuracy is paramount. Any error can delay approval or compromise safety. This is where specialized AI techniques, such as retrieval-augmented generation (RAG) offer a solution.

“The RAG approach grounds the LLM in trusted, up-to-date sources, enhances the quality of the output significantly. This approach also strengthens traceability and accelerates the drafting of complex documents, such as protocols.”

The integration of RAG ensures that generative models reference approved, verifiable data sources. The result is content that not only reads well but aligns with scientific and regulatory standards.

Smarter trial design and patient recruitment

Beyond documentation, AI’s influence extends into trial optimisation, site selection and recruitment.

“AI algorithms can analyse proposed clinical designs and benchmark them to large numbers of previous trials. Based on these benchmarks, AI can derive suggestions of how to optimise trial design. In a recent study, we have shown that certain changes, for example to eligibility criteria and endpoints, can substantially accelerate clinical trials.”

Such adjustments may sound minor but can significantly reduce patient and investigator burden. Leaner trials are easier to run, recruit for and complete on schedule.

AI can also be used to simulate trial outcomes before a single patient is enrolled.

“AI can be used to predict how a trial is likely to run. It can run simulations, sometimes called a ‘digital twin’ of a trial, forecasting potential outcomes.”

By modelling performance in silico, researchers can refine their approach, anticipate bottlenecks and improve success rates.

Closing the loop with discovery

The insights generated during clinical development can also feed back into early-stage research, creating a virtuous cycle of improvement.

“AI can analyse trial data, for example, to identify specific markers or genetic mutations observed in certain diseases. These insights can then be fed back to the laboratory to identify and discover the next generation of drug molecules.”

In this way, AI helps connect traditionally separate parts of the R&D continuum – discovery, clinical trials and post-marketing analysis – through data.

The UK’s competitive position

While faster trial set-up times may not directly influence how drug molecules are discovered, they can strongly affect where trials are conducted.

“Faster clinical trial set-up times will likely influence where the companies choose to run their studies. This could make the UK more competitive as a destination for clinical trials.”

That competitiveness, Meier argues, is vital for maintaining the UK’s reputation as a life sciences leader. The country already hosts world-class research institutions and a growing biotech sector, but access to integrated, high-quality data remains a key constraint.

The ideal AI-enabled ecosystem

“The UK is already an important hub for life sciences globally, but there is more that can be done.”

An ideal AI-enabled clinical trial ecosystem, he explains, would connect datasets and enable responsible data sharing.

The UK is already an important hub for life sciences globally, but there is more that can be done.

“AI algorithms require large amounts of high-quality data. In an ideal AI-enabled clinical trial ecosystem, R&D organisations would have access to life science data, improving AI outputs. Disparate data sources would be interconnected, enabling AI to gain more insights into diseases and potential new treatments.”

When combined with policy initiatives such as reduced start-up times, data integration could transform the speed and quality of clinical research in the UK.

“Together with the proposed plans to cut clinical trial set-up times, this could be transformative to further enhance the UK’s role as a major life science hub.”

Data-driven clinical research

The convergence of AI, analytics and policy reform represents a pivotal opportunity for UK biopharma. If companies, regulators and healthcare institutions can collaborate effectively, the country could establish itself as a model for efficient, data-driven clinical research.

For Chris Meier, the goal is not automation for its own sake but acceleration with accountability – ensuring that new tools improve accuracy, transparency and outcomes for patients.