AI in Pharma Shows Promise With Faster Development, Lower Costs

Artificial intelligence (AI) is transforming pharmaceutical research, compressing timelines and potentially slashing drug prices in an industry defined by decade-long development cycles and billion-dollar price tags.

This technological upheaval is exemplified by projects like the AI-driven RNA Foundry (AIRFoundry), part of a $75 million investment by the U.S. National Science Foundation (NSF) in five biofoundries. The initiative aims to leverage AI to streamline RNA research and drug development, representing a broader trend of integrating advanced technologies into pharmaceutical processes.

“There are great commercial benefits to be made by pharma companies that can incorporate the use of AI in their research activities,” Thomas Kluz, managing director at the venture capital firm Venture Lab, told PYMNTS. “First of all, there is the reduction of costs. If the drug discovery process is made faster and the rate of success of clinical trials is enhanced, then the companies can reduce their R&D costs.”

Accelerating Discovery

The AI acceleration is particularly evident in the early stages of drug discovery, where machine learning algorithms can rapidly analyze vast amounts of biomedical data, identifying potential drug candidates at an unprecedented pace.

The influence of AI extends beyond just the initial discovery phase. “AI is making it possible to do many clinical trial tasks faster, including, but not limited to, transferring data from the medical center’s electronic health record (EHR) to the sponsor’s electronic data capture (EDC),” Iddo Peleg, CEO and Co-founder at clinical trial company Yonalink, told PYMNTS.

Automation and error reduction can significantly speed up clinical trials, historically one of drug development’s most time-consuming and expensive parts.

“AI solutions for patient recruitment can help identify the patients most relevant for a specific trial and those who are most likely to finish until the end of the trial without adverse events or dropping out,” Peleg said.

Financial Implications

The financial implications of AI advancement could be significant.

“Recent studies put loss of prescription sales at $840,000 to $1.4 million per day (depending on therapeutic area),” Peleg said. “This means that every day trials are delayed poses a significant fiscal loss for sponsors.”

Cost reduction stems not just from faster development times, but also from improved success rates in clinical trials and more efficient use of resources throughout the R&D process.

“There are great commercial benefits to be made by pharma companies that can incorporate the use of AI in their research activities,” Kluz said.

The NSF’s investment in biofoundries represents a significant push to democratize access to these capabilities. By serving as user-facing facilities with complementary internal research programs, these foundries will provide broad access to advanced technologies, potentially leveling the playing field between large pharmaceutical companies and smaller biotech startups.

This democratization of technology could reshape the pharmaceutical industry’s competitive landscape.

“AI will reshape competition in the clinical trials industry by forcing sponsors to embrace AI-based technologies to be first to market,” Peleg said.

The impact of AI on drug development may extend beyond just accelerating timelines and reducing costs. It could also lead to more personalized and effective treatments. AI’s ability to analyze vast amounts of data could help identify subtle patterns and relationships that human researchers might miss, leading to more targeted therapies and potentially uncovering treatments for rare diseases.

The integration of AI into pharmaceutical research is not without challenges.

“There exist numerous biases within AI. Everything that was integrated into the AI building process from data, code and pictures is subjective and cannot guarantee an objective truth,” Kluz said, underscoring a need for rigorous validation of AI-generated results and careful oversight of AI systems in drug development.

Regulatory bodies will need to adapt to this rapidly changing landscape. The FDA and other watchdog agencies across the globe are already grappling with how to evaluate AI-assisted drug development processes and ensure they meet the same rigorous safety and efficacy standards as traditional methods.

The potential benefits of AI in drug development are significant.

“AI will reshape pharma pricing by reducing the cost of drug development, which will ultimately enable sponsors to reduce drug costs for consumers,” Peleg said. “Seven percent of Americans suffer from pharmaceutical poverty, a condition which may be reduced or alleviated if drug prices are lower.”