Scientific AI trends in biopharma

December 21, 2023
Ryan Gutierrez

The biopharmaceutical industry stands at a crossroads. R&D costs are rising. Sales are falling. And the process from discovery to commercialization remains long and complicated. Many biopharma leaders believe artificial intelligence (AI) will reverse these trends, potentially revolutionizing the entire value chain. They’re placing big bets on AI through investments in technology, people, and partnerships. Let’s take a closer look.

The R&D productivity problem

Biopharma companies are spending more than ever on research and development, but the returns are diminishing. Last year it cost an average of $2.3 billion to bring a drug to market—76 percent higher than a decade ago. During the same period, sales have slumped 25 percent on a per-drug basis. Both trends have contributed to a five-fold reduction in R&D productivity.

Drug development is not only costly but also long and risky. It lasts over 10 years on average with a measly 8 percent of drug candidates earning regulatory approval.

The industry needs a paradigm shift.

graphs showing R&D productivity over last decade
Rising costs and slumping drug sales have contributed to declining R&D productivity over the last decade. Source: Deloitte, 2023.

Manufacturing is getting harder to scale

Producing safe and effective drugs is already complicated. But the next generation of therapies will make the process even more challenging. The last 20 years have seen the development of more than 17 new drug modalities, such as antibody-drug conjugates, bispecific proteins, and cell and gene therapies. Small molecules still make up the vast majority of products on the market, but drug development pipelines are shifting. In 2022, new modalities accounted for about half of drug approvals. By 2025, the FDA expects to greenlight 10 to 20 cell and gene therapy products annually.

Biologics and advanced therapies, derived from living cells, are more complicated to produce, handle, store, and analyze than small molecules. This makes their manufacturing costly and difficult to scale. The complexity is even more pronounced for personalized medicines like chimeric antigen T cell (CAR-T) therapy, where a patient’s cells are harvested, modified, and reintroduced.

Although far from perfect, manufacturing processes for traditional therapeutics are significantly more mature than those for newer modalities. Yields for small molecules are often measured in kilograms, whereas gram-scale batches are common for biologics like monoclonal antibodies. Cell and gene therapies are produced in much smaller quantities—micrograms to milligrams. They will need to lean on smart factory capabilities and AI to scale up production for large populations.

The promise of AI

Biopharma organizations are looking to AI to revolutionize the entire value chain for therapeutics. It has the potential to significantly shorten drug development timelines, boost R&D success rates, and radically improve manufacturing end to end.

The gains from Scientific AI could be immense. Morgan Stanley estimates that using AI in early-stage drug development over the next decade could bring an additional 50 therapies to market worth over $50 billion in sales. A McKinsey analysis predicts that generative AI could unlock the equivalent of 2.6 to 4.5 percent of annual revenue ($60 billion to $110 billion) for the industry. 

Executives in biopharma are becoming increasingly optimistic about the impact of AI on their business. Nearly half of the top 50 biopharma companies have mentioned AI on earnings calls over the past five years. Emma Walmsley, CEO of GSK, said AI can "improve the biggest challenge of the [pharmaceutical] sector, which is the productivity of R&D." Sanofi recently announced its ambition "to become the first pharma company powered by artificial intelligence at scale." And Christophe Weber, CEO of Takeda, said "AI will reduce the cost of R&D [per] molecule. It has to."

AI investment is surging

To bring AI ambitions to fruition, biopharma companies are poised to spend billions. According to one report, spending will climb from $1.64 billion in 2023 to $4.61 billion in 2027. Morgan Stanley predicts that AI investments will grow from 1.5 percent of R&D budgets in 2023 to 4 percent in 2030.

Top biopharma companies are building dedicated AI teams, with clear mandates from leadership. Hiring over the last year has been brisk. In late 2022, AI-related jobs accounted for 7 percent of new job postings by biopharma companies, more than double the average across all sectors. These new positions include both AI generalists and expertise-based roles. The latter typically require advanced degrees in data science and proficiency in the functional domain.

Biopharma companies are also looking to collaborate with partners to gain the necessary technology and know-how to carry out their AI initiatives. About 800 companies are currently applying AI to drug discovery and development. Many are startups offering an array of platforms and services, including software as a service (SaaS), custom data sciences services, drug discovery (drug candidate as a service), and clinical trial support. 

R&D partnerships between leading biopharma organizations and AI companies have surged over the last six years, totaling 249 by early 2023. Half of the 50 largest biopharma companies have entered into partnerships or licensing agreements with AI companies. They have invested over $1 billion in upfront payments over the last five years.

graphs showing increased AI spending and partnerships
Biopharma companies are ramping up their investments in AI. Source: Morgan Stanley, 2022; Deep Pharma Intelligence, 2023

The Scientific AI gap

Eager to get started with AI? Your scientific data may be holding you back. 

Read our white paper to understand why biopharma companies risk falling short of their AI goals due to their scientific data strategy. Learn how to close the gap between the vision of AI and present-day scientific data reality.