DLA Piper’s inaugural Life Sciences Index is a survey and study series about perceptions of innovation and growth in the life sciences industry (get your copy here). The survey explores the current drivers of - and barriers to - innovation and growth for the world’s largest biopharma and medtech companies. The Index tracks four specific themes: dealmaking; sustainability and ESG; intelligent technology; and the future of care delivery, with insights and data resulting from a survey of 200 respondents from the top 100 innovative biopharma and the top 100 innovative medtech companies (according to 2021 global revenues). 

The Index will no doubt spark discussion and debate amongst the global life sciences community. I was lucky enough to sit down with some industry stakeholders recently to hear their perspective on some of the key findings of the Index. This is our first Index in the Life Sciences sector, with a plan to release it on a biannual basis. And whilst we therefore didn’t have any past data for comparison, it didn’t stop a certain amount of crystal ball gazing and sharing of opinions as to where things are going. We joked that we should have a sweepstake on which statistics will change most dramatically over the next two years. My money is on intelligent technology, with the rise of AI.

The results of the Index survey showed that 36% of those questioned considered the application of intelligent tech (AI / ML) to be a significant strategic priority, ranking it ‘5’ on a scale of 1 to 5, with a further 28% ranking it ‘4’. Something as ground-breaking as AI should undoubtedly be high on the agenda for those in the life sciences and, given the potential of AI to make an extraordinary impact in this field, I expect many of those currently in the ‘3’, ‘2’ and ‘1’ camp will be scoring it more highly by the time the 2026 Life Sciences Index rolls around. 

One area of potentially exponential growth is the use of AI in drug research and development. The current process for drug discovery and development is widely regarded as expensive, slow and inefficient. On average, it costs approximately $1.3bn and 10 years to bring a new therapeutic drug to market[1]. AI can play a role to reduce drug discovery costs and timelines, and increase the probability of success, with potential application at numerous stages of the drug discovery and development process. 

Given the ability of AI to analyse vast datasets, to identify patterns and to make predictions, the opportunities for putting AI to good use in this field are potentially boundless. With natural language processing algorithms able to scan and analyse millions of documents (such as research papers and patents), the equivalent of innumerable years of desk-based research can be carried out relatively quickly and easily and connections can be made which humans would have no hope of finding on their own, and may even not have thought to look for. AI can also analyse biological and clinical data from patients, identifying biomarkers, searching for correlations that may be too complex for humans to see, and freeing research from reliance on the use animal models as a proxy for experimentation in humans. In this way, AI can discover relationships that have implications for biology and the treatment of disease. 

Efforts to use AI both to identify a disease-specific target and then to come up with a drug candidate to interact with that target in a therapeutic way are on the increase.  AI can be used to screen databases of existing compounds or natural products, and to enable the design and synthesis of both small molecules and proteins with desired characteristics, which may not even currently exist in nature. AI is also valuable in finding new indications for existing drugs and/or optimising their therapeutic properties. Further, as AI is well suited to understanding the role of complex genetic interactions, medical history and lifestyle in disease, it is likely to be a very powerful tool in personalised medicine so that drugs can be tailored based on genetic data, biomarkers and other information to offer more effective treatments with fewer side effects.  The potential of AI does not end there as it may also be useful in numerous other applications, such as predicting side effects and toxicity, evaluating potential drug candidates in silico, accelerating high content screening assays through automated data analysis, and in supporting clinical development of drug candidates by assisting in identifying suitable patient cohorts for clinical trials, predicting patient responses, improving efficiency, and enhancing trial monitoring.

In academia, a number of universities are leading the way in integrating AI with pharmaceutical research. For example, Queen Mary, University of London's Digital Environment Research Institute (DERI) has assembled researchers with diverse backgrounds such as bioinformatics, natural language processing, computer vision, computational chemistry, cell biology and molecular pharmacology to explore new AI drug discovery strategies at the drug, target, and system levels[2]. The Chemoinformatics Research Group at the University of Sheffield applies AI techniques to various aspects of drug discovery, including chemical synthesis and toxicity prediction[3].

In industry, notable success stories are emerging. A paper published recently in Nature Biotechnology[4] showcases the use of AI to discover, develop and test a lead drug candidate, reporting the work of AI-driven biotech Insilico Medicine in its search for a treatment for Idiopathic Pulmonary Fibrosis (IPF), an aggressive lung disease with a high mortality rate. Insilico Medicine identified a novel anti-fibrotic target by using a natural language processing engine to analyse various data sources, including patents, research publications, and clinical trial databases, in order to assess a target's novelty and disease association. They then used an AI-based generative chemistry platform to produce a library of drug-like molecular structures with suitable physicochemical properties. From this, they created a candidate small molecule inhibitor which exhibits drug-like properties and anti-fibrotic activity across different organs in vivo. The safety, tolerability and pharmacokinetics of this molecule were validated in phase 1 clinical trials. This work was completed in around 18 months from target identification to preclinical candidate nomination and, according to Insilico[5], at 10% of the cost of a conventional R&D programme. The inhibitor is currently in phase 2 trials and Insilico is now making its AI platforms available to others. 

Another key player grabbing attention in this space is Isomorphic Labs. Building on Google DeepMind's pioneering research and revolutionary systems – like AlphaFold, which achieved a holy grail of structural biology: predicting 3D protein structures – Isomorphic Labs is developing cutting-edge computational techniques in fields like deep learning to solve some of the toughest challenges in drug discovery and some of the most stubborn scientific problems in biology, chemistry and medical research[6]. This holistic approach, using AI to understand biology in order to avoid the long, expensive and risky traditional process of drug discovery, has enormous potential in the quest for the medicines of tomorrow.   

Aside from the discovery of new drugs, finding new uses for existing drugs is a highly attractive goal. Approved medicines have already undergone extensive testing so aspects of their pharmacological activity and their toxicity profiles are well understood, reducing the time and cost of bringing them to market for new uses and reducing the risk of unexpected adverse effects. AI has a role to play here too, with proven success. In 2020, during the COVID-19 pandemic, BenevolentAI set out to find an existing drug that could be repurposed as a COVID-19 treatment. They used their AI technology, which had been designed to develop new drugs for disease, to look at mechanisms related to viral infection and inflammatory response by extracting novel information from published literature. In just 48 hours, BenevolentAI had identified baricitinib, a drug owned by Eli Lilly and approved for rheumatoid arthritis, as a potential treatment for COVID-19. Baricitinib is now approved and recommended for the treatment of hospitalised COVID-19 patients[7].

Patentability

As a patent specialist, these exciting developments lead me to ponder what impact the use of AI in drug R&D might have on patentability. Patent law in the UK as we know it today dates back to the 1970s, when the idea of AI was the stuff of science fiction. As the legal framework for protecting innovation was established without AI in mind, it is inevitable that the law is having to play catch-up with the commercial reality of using AI to assist in innovation and with the development of AI technology itself (such as the AI platforms being used to identify drug targets and candidate molecules). The increasing prevalence of AI is demanding a reassessment of fundamental patent law concepts and their application to new technology. 

Conversations and consultations are going on worldwide. The UK Intellectual Property Office (UKIPO) sought views on AI and IP in 2020, following up with a further consultation specifically on whether the patent system is equipped to deal with AI in 2022. It concluded that there was no evidence that UK patent law is currently inappropriate to protect inventions made using AI, but it remains open to the possibility that change may be needed in the future when there is a stronger technological case. The UKIPO has published, and subsequently updated, guidelines for examination of patent applications relating to AI. 

So, the position remains that patents may be granted in the UK for AI-related inventions, provided that the application satisfies the legal requirements set out in the Patents Act 1977. Specifically, the application must claim something that is novel, involves an inventive step, is capable of industrial application and does not fall within one of the statutory exclusions from patentability, which include computer programs, mathematical methods, business methods and presentation of information. The invention must also be disclosed clearly and completely enough for it to be performed by the skilled person. 

Many questions and challenges are likely to arise as AI is increasingly used in the drug development process. Inventiveness may become an increasingly difficult hurdle as AI platforms become ever more sophisticated and widely used, making many elements of research and development routine and leading to an increase in the threshold for obviousness. There is no obligation to disclose in the specification that AI was involved in the inventive process, so will all applications have to be examined on the assumption that it was?  Inventiveness also has to be assessed by considering AI as it was at the priority date, ignoring capabilities developed later.  However, trying to pinpoint the state of the art some years down the line in an area as fast-moving as AI is likely to be a challenge. And are we going to need the concept of 'the skilled AI' alongside 'the person skilled in the art' in order to consider what is obvious?

And what of the AI systems themselves? One of the main issues faced when trying to patent AI inventions in the UK is the law relating to exclusions from patentability. UK legislation[8] declares that anything that consists of certain categories of excluded matter, such as "a program for a computer … as such" or a mathematical method, is not an invention and therefore cannot be protected by a patent. AI by its nature is based on computational models and mathematical algorithms, so these statutory exclusions raise tricky questions as to whether it is possible to patent AI-related technology. A recent decision of the UK Court of Appeal in the Emotional Perception case[9] has provided some much needed guidance on this. As things stand currently (subject to any appeal to the UK Supreme Court), AI- implemented inventions are in no better and no worse position than other computer-implemented inventions – they are patentable if there is sufficient 'technical contribution' – and the UKIPO has updated its guidelines for examining patent applications relating to AI in light of the case.  My more detailed analysis of the Emotional Perception judgment can be found here: Patentability of AI – A New Perspective from the UK on Emotional Perception | DLA Piper. 

It is an exciting time in the life sciences industry. AI will undoubtedly play a significant role in shaping its future, potentially transforming it in ways that we cannot currently predict. Technological advances will no doubt challenge the legal system that is the bedrock of protection for innovation and which facilitates a return on investment in research. With the rise of digital biology, patentability of inventions comprising or derived from AI is likely to be replete with questions and challenges for many years to come. We need to ensure that the answers that develop do not stifle innovation, so that the life sciences industry can reap the full benefit of modern technology. 

[1] AI for drug discovery - Queen Mary University of London (qmul.ac.uk)

[2] AI for drug discovery - Queen Mary University of London (qmul.ac.uk)

[3] AI/Machine learning for drug discovery | Information School | The University of Sheffield

[4] A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models, Nature Biotechnology, March 2024 

[5] Insilico: linking target discovery and generative chemistry AI platforms for a drug discovery breakthrough (nature.com)

[6] Our Approach - Isomorphic Labs 

[7] COVID-19 | Drug Repurposing | BenevolentAI Platform

[8] Patents Act 1977, section 1(2)

[9] Comptroller-General of Patents v Emotional Perception AI Ltd [2024] EWCA Civ 825