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In a recent interview, Deloitte’s report co-author Kevin Dondarski highlighted a long-term decline in pharma R&D returns. “The first time we wrote it, the return on investment was around 10%,” Dondarski noted. “There has generally been a pretty steady decline in that figure throughout the years.” He attributed this trend to the confluence of factors, including the industry’s focus on more complex disease areas, specialty therapeutics, and nuanced patient populations, as well as the increasingly stringent reimbursement landscape. The pandemic disrupted the trend with a temporary spike due to successful vaccines and treatments, but R&D returns have remained stubbornly low, falling to 4.1% in 2023.
[Figure found in Deloitte’s Unleash AI’s potential report]
Those trends have “coincided with the broader trend in the reimbursement landscape, both public and private, becoming more stringent,” he added.
Why future pharma R&D returns remain uncertain
Despite a short-term R&D return bump, biopharma faces ongoing challenges: regulatory changes (e.g., Inflation Reduction Act with drug pricing negotiations), expiring exclusivity on high-value drugs and inflation. The IRA fuels industry concerns about recouping R&D costs as a result of uncertain future pricing. These changes have stoked more worries in the industry regarding recouping R&D costs amidst uncertainty regarding future price negotiations. “There’s a tremendous amount of uncertainty on how the IRA will continue to evolve in terms of the drugs that are selected in subsequent years, but also what the price negotiations will look like and where they will land,” Dondarski noted.
“The clear driver or trend that [pharma executives] were most concerned about was the uncertainty from a regulation and legislation standpoint,” Dondarski said. Almost unanimously, pharma execs the firm corresponded with cited regulatory worries as one of the most concerning worries. “Leaders have a higher degree of control on cost and cycle time than they do uncertainty,” Dondarski said.
Price wars and pipeline pressures
In addition, competitive intensity in the sector has skewed R&D spending toward certain therapeutic areas, particularly oncology and rare diseases while payers focus more on equitable allocation of healthcare spending. From a practical point of view, the saturation in areas like oncology makes it “difficult to find, recruit and enroll patients in your studies” Dondarski said. “There’s just a lot more choice from an investigator and patient standpoint.”
Competition is also heating up in the red-hot metabolic segment, which is among the fastest-growing pharma niches in recent memory. The report notes that the success of GLP-1 receptor antagonists in obesity has highlighted the condition as a “public health emergency and a lucrative franchise.” Dondarski pointed out the strong correlation between shareholder returns and companies with GLP-1s on the market over the past few years. Yet he cautioned that the value of being a late entrant into this class of drugs remains uncertain, particularly from a reimbursement perspective.
How generative AI could help revive pharma innovation
One bright spot amidst the hype is the transformative potential of AI, which offers a glimmer of hope for driving sustained innovation and productivity gains. Generative AI (Gen AI), in particular, stands out due to its relatability. As Dondarski pointed out, while a significant portion of the population may not have a firm grasp on machine learning techniques like neural networks, support vector machines, gradient boosting, or random forests, “everybody can understand how to use Gen AI tools.”
Dondarski emphasized the enormous potential that Gen AI holds as an innovation and capability advance. “The potential that it holds as an innovation and capability advance, and what it can mean, is enormous,” he noted.
The most significant opportunity and excitement surrounding Gen AI lie in its ability to transform how the industry learns and makes choices related to research, both in discovery and translational models. Dondarski explained, “I think the part that remains the largest opportunity, and what has people most excited, is how the proliferation of just existing and novel datasets coupled with Gen AI can really transform how the industry learns and makes choices as it relates to effectively research, either in discovery or in translational models.”
AI: A double-edged sword?
The transformative potential of AI, particularly Gen AI, offers a glimmer of hope for driving sustained innovation and productivity gains. As the industry continues to face an increasingly competitive landscape, the potential of Gen AI to accelerate research and development timelines could provide a significant advantage. “If AI can help Company A come to that conclusion 6, 12, 18 months before Company B, then all other things being equal, they would have success and get to the market 6, 12, 18 months earlier,” Dondarski said.
Yet the adoption of Gen AI is not without its challenges. Companies will need to invest in the necessary infrastructure, talent and processes to effectively integrate this technology into their operations. From a talent perspective, finding pharma-focused AI leaders remains an ongoing challenge. Flexible partnership models with academia, biotech startups, and technology companies can help the industry continue to access cutting-edge research techniques and novel modalities as the industry works to sharpen skillsets and change legacy ways of working.
For thoughtful early adopters, the advantages are considerable “Being able to make more informed choices far earlier in the value chain, that’s really where I think the most potential impact as far as innovation comes in,” Dondarski concluded.
Filed Under: Data science, Drug Discovery and Development, machine learning and AI, Regulatory affairs
