Strategic Intellectual Property Considerations For Protecting AI Innovations In Life Sciences

• Jurisdiction: United States,Federal
• Law Firm: Finnegan, Henderson, Farabow, Garrett & Dunner, LLP
• Subject Matter: Corporate/Commercial Law, Intellectual Property, Corporate and Company Law, Patent, Trade Secrets
• Author: Mr Charles Collins-Chase, Kassandra M. Officer and Xirui Zhang, Ph.D.
• Published date: 27 January 2023

Artificial intelligence ("AI") is all around us. It allows us to unlock our smartphones with just a glance. It can customise the temperature of our home or recommend television shows based on things we enjoyed watching before. It may soon drive our cars for us. Through the combination of increasing computing power and massive amounts of data, AI has made unprecedented advances in recent years in its ability to make predictions and solve problems. As a result, AI has become a vital part of our everyday lives.

And soon, the medications we take each day may also be identified and developed at least in part by AI. This article examines strategic intellectual property considerations for innovative pharmaceutical and biotechnology companies that are developing AI systems or using third-party systems to enhance drug discovery, clinical trials, manufacturing, or other processes.

AI is Transforming the Life Sciences Industry

AI and machine learning ("ML") are revolutionising the pharmaceutical and biotechnology industries. While drug discovery may be the most well-known use of AI and ML in these fields, the technologies have a wide range of other applications in these industries, as shown in Figure 1 below. AI and ML are also accelerating innovation in developing pharmaceutical formulations, predicting protein structures, designing clinical trials and analysing the data, and speeding up manufacturing and ensuring better quality control.^1,2,3

Figure 1: Application of AI in various aspects of the pharmaceutical industry

For many life sciences companies, data such as compound libraries may be among their most valuable assets. AI allows companies to leverage those data to more rapidly identify drug targets and advance them through clinical trials. As shown in Figure 2, AI can use those data sets to predict which compounds might have desired chemical or biological properties, drastically reducing the time needed to identify candidates for further laboratory or clinical testing.

Figure 2. A workflow applying AI to accelerate lead compound identification

As just one recent example, the biotechnology company Evotec recently announced a phase 1 clinical trial on an anticancer molecule that was co-invented and developed in partnership with Exscientia, whose AI platform technology computationally analyses the properties of millions of small-molecule candidates to identify a handful suitable for further testing.^4,5 Using AI allowed the companies to identify the candidate molecule in just 8 months.

AI thus has the power to reduce the time and costs of drug discovery and increase the number of new therapies available. A recent analysis by Morgan Stanley Research concluded that even modest improvements in early-stage drug development success rates made possible by AI and ML could lead to an additional 50 novel therapies over a 10- year period, reflecting a $50 billion market opportunity.⁶

It is therefore no surprise that pharmaceutical and biotechnology companies are racing to implement AI and ML to improve their pipeline and reduce costs. Indeed, a recent survey of pharmaceutical and biotechnology professionals indicated that 80% were already using AI technologies in their work or were planning to do so.4 AI is critical to remaining competitive in the pharmaceutical field by reducing the high costs of bringing new drugs to market.

Given the rapid pace at which pharmaceutical and biotechnology companies are developing or adopting AI, companies must have a plan to safeguard their innovations and avoid common intellectual property ("IP") pitfalls.

Claim Strategies to Satisfy Subject Matter Eligibility Requirements for AI- Related Inventions

One of the most critical issues facing AI and ML inventions is the increasing difficulty of meeting the patent eligibility requirement under 35 U.S.C. ' 101, particularly for software- and computer-implemented inventions. The Supreme Court's decision in Alice Corp. Pty. Ltd. v. CLS Bank International created a two-part test for determining patent-eligibility, which has led to more patents being invalidated.⁸ The first step under Alice asks whether the claims are "directed to" a patent-ineligible abstract idea, natural phenomenon, or law of nature. Under the...