AI's Potential Must Reconcile With R&D And Regulatory Bottlenecks
By Daniel Chancellor, VP of thought leadership, Evaluate
The pharmaceutical industry is nearing a period of sizable advancement driven by the integration of AI. The implementation of AI promises to significantly reduce drug discovery time, enhance efficiency, and maximize cost-effectiveness throughout the drug development pipeline. Over half of all companies are now routinely harnessing AI in drug development programs, according to a recent survey by Norstella. And this will only increase as proof points are generated.
As AI continues to advance and becomes more embedded in the pharmaceutical industry, its impact is not only streamlining drug discovery but also affecting clinical trials, various aspects of the regulatory landscape, and even how diseases are classified and diagnosed. The FDA has already seen a rapid boost of drug and biological product submissions that have been developed using AI. With so many new drugs entering clinical trials, the Agency must expand the scope of its regulatory oversight to accommodate a surge in new drugs.
Accelerated R&D Timelines
Thanks to AI drug development, timelines can be significantly reduced, enabling an unprecedented number of drugs to enter Phase 1 trials. For example, Insilico Medicine reduced the six-year timeline for its idiopathic pulmonary fibrosis drug to two and a half years using AI. Likewise, Genetic Leap is partnering with Lilly to leverage its RNA-targeted AI platform to accelerate genetic medicine therapeutics. Major pharma companies like these are tapping into AI’s data analysis and predictive modeling capabilities to introduce new therapies at a record pace.
AI not only accelerates the identification of promising drug discoveries, but it also mitigates risks associated with drug toxicity and efficacy before entering clinical trials. This helps to address falling success rates, which have previously dropped below 40% in Phase 1 trials. This all suggests that biopharma companies will be able to advance more drugs, at a faster rate, into the early pipeline, while efficiently rooting out failures before incurring expensive clinical trial costs.
Clinical Trial Bottlenecks
AI holds the potential to introduce a wealth of new drugs that cater to a broader patient population and ultimately increase profitability. However, this promising future is only possible if the pipeline can sustain the influx of Phase 2 and 3 trials. Since R&D budgets can only support a limited number of late-stage studies, funding will be crucial to ensuring that this surge can be accommodated effectively. Only then will AI’s full potential in transforming drug development and improving patient outcomes be realized.
We must also recognize that advancing novel drugs to pivotal studies is constrained by finite resources. According to Trialtrove data, the industry’s current capacity for Phase 3 trials is around 1,000 each year. Any influx of new drugs, enabled by AI technologies, will inevitably reach this bottleneck. Despite AI’s potential for cost savings, pharmaceutical companies will face challenging decisions regarding budget allocations as these new drugs progress through the pipeline. Given these constraints, the momentum around novel drugs driven by AI will realistically be slowed later in the pipeline.
Regulatory Roadblocks For AI-Driven Drug Discovery
Regulatory bodies must also anticipate the influx of new drugs entering the market. Notably, the FDA has already taken proactive steps to address the complexities of AI in therapeutic development by forming the CDER AI Steering Committee in February 2022. This committee represents an initial effort to coordinate the use of AI throughout various stages of drug development.
Although the regulatory process may slow down the advancement of new drugs, it is also crucial to ensure that these drugs are thoroughly vetted. Given the potential risks associated with AI, regulatory bodies must take steps to prepare and adapt by forming specialized committees and actively staying informed about the latest AI advancements. In doing so, they could learn from drug manufacturers that have partnered with AI agencies to expand their AI expertise and to integrate AI responsibly – like Sanofi’s partnership with Atomwise.
With many touchpoints between drug developers and regulators in the drug approval process, the FDA must be prepared for its workload to increase. In this evolving landscape, a collaborative effort between regulatory bodies and pharmaceutical companies will be essential not only for advancing drug development but also for ensuring that the integration of AI is both effective and safe.
Modernizing Diagnostic And Treatment Strategies
The goal is to harness AI to support more and higher-quality drug approvals, but new technologies also will shape the broader disease landscape. We will see clinical trials move toward early-stage and preventive treatments, as new therapies aim to address diseases before they progress. This could be a game-changer for diseases that become devastating in their late stages, like dementia and lung cancer.
Earlier this year, the FDA approved BrainSee, an AI-powered tool that assists in the earlier detection of Alzheimer’s disease and other dementias through quantitative brain volume analysis. Additionally, for lung cancers — which have the highest fatality rates due to late diagnoses — researchers in Bangkok, Thailand, used an AI-powered chest X-ray interpretation tool that identified lung cancer nearly six months earlier than the definitive diagnosis. This renewed approach to diagnostics offers hope for improved patient outcomes, prompting a shift toward preventive care strategies and trials. This will have dramatic consequences on healthcare funding and delivery. Such modernization is likely to be measured on a timeline of decades rather than months and years.
Navigating Challenges And Opportunities Of AI In Healthcare
AI is revolutionizing the pharmaceutical industry, offering unprecedented opportunities to accelerate drug discovery and enhance efficiency in clinical trials. AI's ability to streamline drug discovery, reduce clinical trial timelines, and facilitate earlier disease detection unlocks a new era of rapid innovation and improved patient outcomes. However, this transformation also brings challenges, including the potential for bottlenecks in clinical trials and increased regulatory demands. By working together to expand AI resources and adapt regulatory frameworks, regulatory bodies and pharmaceutical companies can ensure that the benefits of AI are fully realized while maintaining high standards of safety and efficacy.
About The Author:
Daniel Chancellor, vice president of thought leadership for Evaluate, produces materials that support clients across a range of hot topics in the biopharma industry. Daniel has over a decade of experience as an analyst in the biopharma industry, spanning roles in drug discovery, market analysis, competitive intelligence, and strategic consulting. Daniel regularly participates in webinars, conferences, and other speaking arrangements, and he has provided expert insights across a wide range of leading industry and business publications.