S&P Global — 10 October 2024
Daily Update: October 10, 2024
AI Comes to Big Pharma
By Nathan Hunt
Start every business day with our analyses of the most pressing developments affecting markets today, alongside a curated selection of our latest and most important insights on the global economy
The pharmaceutical industry is dominated by a few large companies that have the scale to manufacture drugs in massive quantities and the experience working with regulators to get new drugs approved. The core group is AbbVie, Amgen, AstraZeneca, Bristol Myers Squibb, Eli Lilly, Gilead Sciences, GSK, Johnson & Johnson, Merck, Novartis, Novo Nordisk, Pfizer, Roche, Sanofi and Takeda Pharmaceuticals. Unsurprisingly, it is also this group that has moved aggressively to develop AI capabilities. Generative AI will transform pharmaceutical research and development by strengthening innovation, offering cost efficiencies and accelerating the traditionally ponderous research-to-market cycle. A team of analysts at S&P Global Ratings analyzed its impact in “AI In Pharmaceuticals Promises Innovation, Speed, And Savings.”
According to industry research, bringing a drug to market takes 10-15 years, with an average cost of $1.3 billion. Any efficiencies that can be wrung from that process could be hugely beneficial. While applying AI to pharmaceutical research and development is still in its early stages, money is already being spent to accelerate the process. The pharmaceutical AI market was valued at $1 billion in 2022, according to Boston Consulting Group, and is expected to expand to almost $22 billion by 2027.
According to the team at S&P Global Ratings, AI can shorten pharmaceuticals’ long road to market through improved and innovative compound screening and selection of molecular combinations that could yield new drugs. These applications fall under synthetic biology, a topic covered in previous research by S&P Global. As a new drug moves into clinical development, potential AI applications include clinical trial design and selection of subjects to match trial requirements. Finally, when a drug is ready for market approval, AI can help with pharmacological and faster US Food and Drug Administration dossier submissions.
Big Pharma often outsources research and analysis to biotechnology companies to accelerate drug discovery and design. This relationship helps the latter with funding; manufacturing at scale; management of stakeholders, including regulators; and marketing new drugs. Because of biotechnology companies’ role in the market, they have become some of AI’s earliest adopters.
While AI finds its footing in healthcare broadly and pharmaceutical specifically, there are challenges that must be addressed. First, the quantity and irregularity of drug discovery data is massive. Drug discovery will require deep learning models sensitive to unexpected outcomes and variables. In addition, there are always ethical and legal considerations when accessing and training deep learning algorithms on patient data. Security lapses or lack of transparency on how data is being used could have reputational and legal repercussions. Finally, the use of AI in pharmaceutical development will require a new and unfamiliar set of skills for pharmaceutical and biotechnology companies.
Today is Thursday, October 10, 2024, and here is today’s essential intelligence.
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—Listen and subscribe to the podcast from S&P Global Commodity Insights
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—Read the article from S&P Global Ratings
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—Read the article from S&P Global Market Intelligence
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—Read the article from S&P Global Commodity Insights
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—Read the article from S&P Global Commodity Insights
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—Register for the in-person event from S&P Global Ratings