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ANALYSIS
AI’S IMPACT ON THE FUTU AND DRUG DISCOVERY The rise of artificial intelligence (AI) has been popular within the life sciences industry and in particular pharma. Find out why and what it means for the future of drug development.
T Charlotte WalkerOsborn, partner and international head of technology - Eversheds Sutherland Nabil Asaad - senior associate – Eversheds Sutherland
he recent surge in activity in deploying AI capabilities in pharmaceutical R&D shows no sign of abating, with ‘big pharma’ investing significant capital in streamlining or accelerating the drug discovery process and applying AI methods to identifying new targets and medicines. If AI delivers on its promise, then there will be a material increase in the rate of new medicines coming to market. This could lead to a corresponding up-tick in the demands on manufacturing. Running in parallel is the question of how the same technologies can be applied to increase efficiency and improve outcomes in manufacturing as well as how AI technology might help improve efficiencies within these businesses. AI MEETS ‘BIG PHARMA’ Major pharmaceutical companies are data-rich and experts at developing new medicines starting from an implicated gene, protein, process or pathogen. They have powerful computational tools at their disposal and are early adopters of new technology. The leading AI companies have developed tools capable of analysing vast data sets, including natural language documents. The convergence of the two means that the deployment of leadingedge AI to pharmaceutical R&D is accelerating. Pharma-AI collaborations are being structured in different ways.
For example, the AI partner may simply offer consultancy, and a technology platform for use with the pharma partner’s data, or the AI partner may wish to conduct its own pharmaceutical R&D and hence may wish to take learnings from and reserve usage rights in the collaboration outputs. INTELLECTUAL PROPERTY AI technologies evolve through use. Training an existing model on new data gives rise to a new model whose properties and behaviour are modified by that training data. Where the AI platform and data belong to different parties, the question arises as to which party should own the new intellectual property in the trained model and any derived data. There is no set rule. As with the fee and any royalty structure, this is ultimately a commercial discussion between the parties. Where AI technologies are used, for example, to prioritise follow-up research efforts, the downstream creative and technical input may generate IP which is distinct from the output of the AI system. The ownership and exploitation rights attaching to each should be dealt with clearly and separately. DATA USAGE, PRIVACY AND ETHICS Usage rights for the confidential/ proprietary information need to be considered. Are there any restrictions on the data being used? Is the data personal
data or personally identifiable information (PII)? AI is particularly suited to the analysis of genetic data. Analysis and application of privacy laws, including around whether data can be anonymised, is essential. The UK Information Commissioner’s Office has set out an auditing framework for artificial intelligence. The European Commission’s High-Level Expert Group on AI, Ethics Guidelines for Trustworthy AI (April 2019) – seven essentials has a strong focus on privacy and data governance as well as transparency and ethical application of AI. There will be more law and guidance emanating from regulators across the globe in these areas. TECHNOLOGY AND CYBERSECURITY AI technologies reside on servers, whether on-premise or in the cloud. It is critical that a detailed analysis takes place, early on, as to the technology set-up, data flows, and security arrangements. The analysis is broadly the same as for other technology projects, but is a critical step given the large amounts of data and the potential sensitivity of the data and the results, especially if any personal data being used is voluminous and not anonymised. FUTURE PROSPECTS AI technologies are now used at every stage in pharmaceutical R&D, as is evidenced by the