Major pharmaceutical companies are increasingly turning to artificial intelligence (AI) to revolutionize drug development processes, significantly expediting clinical trials and potentially saving millions of dollars.
Human studies, a critical aspect of drug development, are both time-consuming and expensive, often taking years to recruit patients and conduct trials, costing over a billion dollars from drug discovery to approval, Reuters news report said.
Research firm Deep Pharma Intelligence estimates that investments in AI-driven drug discovery companies have tripled over the past four years, reaching $24.6 billion in 2022.
GlobalData reveals that the total spend on AI by the pharma industry is forecast to grow to over $3 billion by 2025.
“Drug discovery and development is an incredibly expensive and time-consuming process. The time needed for a drug to reach the market ranges from 12 to 18 years, with an average cost of about $2.6 billion,” Kitty Whitney, Senior Director of Thematic Analysis at GlobalData, said.
An analysis of GlobalData’s Deals database shows that the number of AI-based drug discovery strategic alliances has increased significantly, from 10 in 2015 to 105 in 2021, of which almost 70 were with pharma companies.
Examples of leading AI vendors include BenevolentAI, Exscientia, Insilico Medicine, Recursion Pharmaceuticals, and Atomwise, while leading adopters of AI include Janssen, AstraZeneca, Pfizer, Bayer, Bristol Myers Squibb, GSK, Sanofi, and Takeda.
Pharmaceutical giants like Amgen, Bayer, and Novartis are leveraging AI to swiftly identify suitable patients for clinical trials and reduce the number of participants needed, accelerating drug development timelines. This approach offers immense potential in shaping the future of drug development, potentially cutting years off the conventional timelines and enhancing overall efficiency, Reuters news report said.
AI is being utilized to scan vast amounts of public health records, prescription data, medical insurance claims, and internal data to efficiently identify potential trial participants, reducing the time it takes to enroll them by up to half in some cases. These advancements are beyond the experimental stage, showcasing a substantial and growing role of AI in human drug trials.
The U.S. Food and Drug Administration (FDA) has received approximately 300 applications integrating AI or machine learning in drug development from 2016 to 2022, with over 90 percent of these applications submitted in the last two years. Most of these applications focus on using AI during the clinical development stage, highlighting the rapid integration of AI technology in the pharmaceutical sector.
Amgen, for instance, has developed ATOMIC, an AI tool that significantly expedites patient recruitment for trials. ATOMIC analyzes extensive internal and public data, identifying and ranking clinics and doctors based on their past performance in recruiting patients. By doing so, Amgen has successfully reduced the enrollment time for mid-stage trials, potentially shaving off two years from their drug development timeline by 2030.
Bayer has also capitalized on AI, utilizing it to predict long-term risks in trial populations based on mid-stage trial results linked with real-world data. This innovation significantly reduced the number of trial participants required and expedited recruitment.
Additionally, Bayer plans to leverage real-world patient data through AI to generate an external control arm for a pediatric trial, a move that could eliminate the need for a placebo group in situations where finding suitable patients is challenging.
While the use of AI in clinical trials presents remarkable opportunities, concerns regarding overestimation risks persist. Scientists and regulators emphasize the need for cautious consideration and adherence to evidentiary standards to ensure drug safety and effectiveness, ultimately safeguarding patients’ well-being.
In conclusion, the integration of AI in drug development marks a pivotal advancement in the pharmaceutical industry, offering the promise of accelerated timelines, significant cost savings, and ultimately, improved healthcare outcomes for patients worldwide.
