How will the application of AI in the life science sector impact on the demand for labs?
The COVID-19 crisis has highlighted the important and growing role of AI in the life science sector.
It is clear that Artificial Intelligence (AI), both deep learning and machine learning, will become increasingly important within the sector in the coming years. Already a merger of tech and life sciences is changing how the industry operates on a day-to-day basis. This is because of the breadth of application that AI has on the ability to cover the drug development process, from discovery to manufacturing that is significant.
The use of AI in the sector will be increasingly adopted for the following reasons:
- Discovery: Scientists in laboratories across the world are executing numerous complex experiments every day generating huge amounts of data. The challenge is in finding useful information in the flow of data and to make meaningful insights which could ultimately lead to getting a product to market faster. The use of AI allows the researchers to predict the potential benefits of a new compound.
- Clinical Trials: A significant cost in validating a drug is in clinical trials and this is also where many drugs fail, leading to pharma often having to write off their investment. AI offers the potential to support trial design, better choice of subject and improved data analysis. It is interesting to note that Insilico Medicine a Hong Kong based company offering a solution in this space recently raised $255m for their work.
- Manufacturing: The manufacturing of a drug especially biologicals is an extremely complex process. The use of AI has the potential to improve quality control, reduce materials wastage and potentially lead to faster production (depending on the product). AI can achieve this by analysing the production data to offer suggestions and improve processes.
Taking one step further, the impact of AI is now supporting scientific methodology. Science since the 17th century has been based on developing a hypothesis and proving or disproving, by gathering the evidence and looking for relationships in the data. As data sets are getting bigger and more disparate, identifying the relationships with standard statistical model is getting progressively more challenging.
The introduction of AI into traditional scientific research provides scientists with analytical tools that are able analyse data in a completely different way and offers the potential to provide more powerful insights from protein folding to chemical interaction. This is truly revolutionary and the possibility, in terms of drug discovery and the speed of bringing solutions to disease and ill health, could be truly impactful.
So, what does this mean in relation to real estate? We are seeing the coming together of tech and life sciences and in the future, it is feasible, with the laboratory automation, we will see pharma companies and clinical research organisations establishing contractual wet labs that operate autonomously. This could mean that laboratory services become more efficient with a higher throughput leading to a scaling down of global lab space and the scaling up of computational lab space, with real estate focusing more on power, cooling requirements and data provision.
The practice of outsourcing chemistry work is already prevalent and taking this automated approach further could lead to an acceleration of innovation and ideas coming through with spinouts and academics no longer facing costly barriers to entry which including both real estate and expensive laboratory equipment.
Clearly, for property owners and developers looking at trying to future-proof their buildings for the life science sector, flexibility in design remains key in order to be able to accommodate the changes that are going to be seen in the way that science is undertaken in the future.
We look forward to sharing more insights on the implications of the evolution, and advancements in the life sciences sector and their real estate implications over the coming months.
Rob Beatson, Partner, with thanks to Sue Foxley, Research Director and Anil Vaidiya, Life Science Commercialisation Specialist
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