Cell therapies are the future of healthcare, why are we struggling to make them at scale?

The future of healthcare is undeniably heading toward cell therapies, as they offer hope for diseases where, to date, we have not been able to provide treatments or cures. These therapies could revolutionise how we treat conditions like cancer, diabetes, and neurodegenerative diseases. In the UK alone, for example, we expect to see increases from two advanced therapies being approved per year to 10-15 by 2030, and the industry is predicted to grow 23% from 2024 to 2030.

Yet, even the largest pharma and therapeutics companies are still facing an uphill battle in making them viable at scale. There are many promising cell therapies on the horizon, but we must ensure they are accessible and affordable for those who need them most. Take the example of macular degeneration. Researchers are already working on cell therapies that can replace damaged retinal cells, potentially restoring vision for millions. Scaling this breakthrough requires overcoming challenges around the cost of development, and maintaining consistency in cell behaviour and performance. Similarly, diabetes treatments involving insulin-producing cells could transform care for millions – if we find ways to make these therapies affordable and accessible.

Overcoming these currently unsolvable bottlenecks in cell therapy development is what will bring this future closer to reality.

The process of finding and developing new cell therapies has many bottleneck

The first hurdle is in the research and development phase. Identifying and producing robust and meaningful cell types that can form the foundation for therapies is not straightforward. Even when promising candidates are found, developing them into therapeutic realities can be difficult. One reason is the inconsistency in processes. Cells are living systems, and their behaviour can vary significantly depending on even minute changes in conditions or handling practices.

For example, researchers working on CAR-T cell therapies must navigate a delicate process of modifying cells outside the body and reinfusing them into patients. Small differences in culture conditions can lead to variability in the therapy’s efficacy or safety profile. The complexity of ensuring every batch meets the same high standards adds layers of cost and time to development.

The transition from lab-scale development to large-scale manufacturing introduces another set of challenges. Scaling up cell production while maintaining quality, consistency, and efficiency is incredibly complex. Cells are highly sensitive to their environment, and even slight deviations in temperature or other parameters can affect their viability.

The level of complexity means that batch failure is frequent, leading to higher costs per dose, and delays in delivering therapies to patients. Cell culture media is also expensive, and the efficiency use of materials contributes to high waste. All of these factors together can then contribute to the overall high costs of a cell therapy for patients. For example, the cost of a single dose of Novartis’ CAR-T cell therapy was set at $475,000, which puts them out of reach for many who need them most.

Finally, the regulatory landscape for cell therapies is both rigorous and uncertain. Proving that therapies are safe and effective requires extensive testing, often spanning years and involving enormous financial investments. Being able to ensure traceability and compliance throughout the manufacturing process adds another layer of complexity and cost, especially when scaling up production.

The potential of AI to overcome challenges

These bottlenecks are having a serious impact - in the next three years, failed clinical assets will cost cell therapy developers billions of dollars.

But AI has the potential to help overcome these bottlenecks, and reduce sunk costs. AI can make it possible to accelerate discovery and asset development, improve manufacturing processes, and navigate regulatory requirements more effectively.

The technology could analyse huge datasets to find novel cell types. Predictive modelling could also allow scientists to simulate cell behaviours and interactions in silico, significantly narrowing down the pool of candidates before moving to the lab. This approach could therefore not only reduce the time required for discovery, but also cut costs with less trial-and-error experimentation.

When it comes to manufacturing, AI could predict the conditions required for cell growth and differentiation and, by ensuring consistent production, minimise waste and lower costs.

In addition, AI could simplify compliance by making sure that manufacturing processes meet regulatory guidelines. Cell therapy development generates massive amounts of data, and AI could organise and analyse it much faster than can be done manually, to simplify regulatory submissions.

Finally, AI tools could monitor cell therapy development in real time and detect deviations early, or even predict them before they happen, to make sure batches meet stringent quality standards, and could also help to assess risks related to safety and efficacy, so scientists can refine therapies before they reach the clinical trial stage.

The road ahead for cell therapy

Cell therapies have the power to change how we approach healthcare. They can help to cure diseases, rather than simply manage symptoms, offering hope to millions of patients worldwide. However, realising this potential at scale requires addressing the bottlenecks in R&D, manufacturing, and regulation that exist today.

AI offers a powerful and unparalleled solution to these challenges, accelerating discovery, streamlining production, and ensuring compliance. By embracing AI and fostering innovation, we can pave the way for a future where cell therapies are not only effective, but also scalable and accessible to all who need them.