The pharmaceutical industry is not only starting to take notice of artificial intelligence (AI), but many companies have started to future-proof their approaches to keep their competitive edge. Using GlobalData’s Thematic Research ecosystem, we look at which companies are leading the field in AI.

First, some background: one feature of the ecosystem is the Thematic Scorecard, which evaluates companies on how equipped they are in a certain theme, such as AI, over the next two-to-three years. This is based on their current AI activity and investment. Companies can garner a score between 1–5, with the score of 5 denoting a high AI commitment from the company. The score covers the use of AI for faster drug discovery and repurposing, enhanced clinical trial design, smarter supply chains, among others.

Among the companies monitored, the pharma companies with a score of 5 are AstraZenecaBayerBristol Myers SquibbGlaxoSmithKlineJohnson & JohnsonNovartis, and Takeda Pharmaceutical.

There are specific examples from these pharma companies investing in AI in clinical trials in 2021. One is AstraZeneca working with Singapore-based Oncoshot regarding the latter’s AI-driven patient-to-trial matching technology. Also, ConcertAI has several collaborations with big pharma, such as one announced last year with Janssen Pharmaceuticals. This specific collaboration aims to support trial designs, broaden new sites, and strengthen trial diversity. J&J is the parent company of Janssen.

AI in clinical trial patient matching

That said, while there has been increasing interest in AI in the pharma space, there are still untapped opportunities in using AI in clinical trials specifically. Most large AI companies with name recognition like DeepMind Technologies and Exscientia are relatively driven more by molecule optimisation, drug repurposing, or drug discovery compared with AI use in clinical development, explains Lucas Glass, vice president of IQVIA’s Analytics Center of Excellence.

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One of the clearer uses of AI in clinical trials is applying the technology to match patients to a clinical trial. Glass says there are two paradigms: The first is clinical trial sites sifting through their patient database to match patients with a given trial. The second is that patients themselves look for a clinical trial that are best suited for them. Of these two paradigms, the first has had a relatively robust speed of development because the funding mechanisms in that space are clear, Glass explains.

As for the second paradigm, can help patients find trials but it is not particularly patient-friendly, he adds. In that vein, a way to move the needle is to have a bedside technology that alert clinicians their patient may be eligible for ongoing trials, he says.

Yet the development of such an approach needs to be careful as to not significantly impact clinicians’ daily workflows, he notes. “I imagine it [the alerts] as a bit more of a of a passive thing, so it’s not like slamming it in the doctor’s face, but an alert on the side [of the screen] they can click on and see and explore.” Clinicians are likely to engage as they are used to doing their due diligence to explore all possible therapeutic options for their patients, he adds.