Life sciences commercial services company Eversana is one of the latest to throw its hat into the generative AI ring. Tapping a partnership with Amazon Web Services (AWS), Everasana is focusing on developing generative AI technologies in the pharmaceutical industry.
Also this month, the startup Synthetica Bio announced it would use generative AI to boost drug discovery innovation while Nvidia announced it would invest $50 million in the biotech Recursion to support its AI drug discovery efforts. Earlier this year, Nvidia debuted a cloud service for generative AI in drug discovery known as BioNemo.
Eversana’s approach to generative AI in pharma
Eversana aims to ‘pharmatise’ AI. The company’s chief digital officer Scott Snyder explains:
“When we talk about pharmatising, it’s overlaying all of the unique needs, requirements, and goals of pharma, but layering it on to the innovation capability of generative AI.”
The Eversana–Amazon partnership will focus on high-impact AI applications across pharmaceutical commercialization. As Snyder stated, Eversana wants to “solve customer challenges” by combining its own digital and AI capabilities with AWS services like Amazon Bedrock for deploying generative AI.
Key applications and potential impact of the Eversana-AWS partnership
Initial target areas include automating regulatory review, improving field rep and patient assistance through chatbots, and generating personalized disease and drug education content.
The collaboration will aim to drive efficiencies, business value and patient outcomes. Tapping AWS’s Amazon Bedrock platform for large language models, the partnership will focus on identifying, developing and deploying technologies across the pharmaceutical value chain.
Industry observers have varied opinions regarding the potential of generative AI in the pharma sector.
UBS’s Q-Series report titled ‘Will Generative AI deliver a generational transformation?’ came to somewhat guarded conclusions, viewing generative AI in biopharma as more of an evolution than a revolution. The bank notes that the technology holds promise in areas like protein mapping and compound screening but doesn’t expect it to spark an R&D renaissance in the near term.
In contrast, McKinsey paints a promising picture of generative AI’s potential to fuel transformation in the pharmaceutical industry and beyond. The consultancy projects that generative AI could help to pinpoint the most promising compounds and targets at each stage of the drug development value chain. It expects the AI subtype to streamline lab processes, translating to fewer, yet more successful experiments, to achieve the same number of leads. In terms of financial impact, McKinsey projects that generative AI could generate an equivalent of $2.6 trillion to $4.4 trillion annually across 63 use cases it studied. The consultancy put industries like banking, high tech and life sciences at the top of its list of sectors poised to see the most significant revenue impact from generative AI technology.
Envisioning AI-powered field rep assistants
Snyder envisions generative AI creating “awesome sidekick assistants” for pharma field reps. These AI assistants could aggregate relevant information on doctors, health systems, industry trends, and products to support field reps during customer interactions. The human rep would still be the decision-maker, with AI providing support. “You can really turn generative AI loose on your own data,” Snyder said. “It can be really powerful to bring all that history of what a good call or interaction looks like and how you inform one of those people at the edge.”
While the Eversana-AWS partnership aims to accelerate generative AI adoption in pharma, the industry has been relatively slow to embrace new technologies such as the cloud compared to sectors like retail and consumer goods. “I think in pharma, there’s such a heightened awareness of control and also, what could go wrong,” Snyder said. In a forthcoming article, we will explore the unique challenges and considerations for pharma in leveraging generative AI. In a related article, we will explore the unique challenges for pharma in leveraging generative AI, including not just concerns over risks but also fear of missing out on potential benefits.
Filed Under: Data science, Drug Discovery, Industry 4.0, machine learning and AI
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