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HomeHealth & Medical NewsMajor Drugmakers Harness Artificial Intelligence to Accelerate Clinical Trials and Save Costs

Major Drugmakers Harness Artificial Intelligence to Accelerate Clinical Trials and Save Costs

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In a groundbreaking move towards revolutionizing the drug development process, major pharmaceutical companies are increasingly turning to artificial intelligence (AI) to expedite patient recruitment for clinical trials and reduce costs. This innovative approach has the potential to accelerate drug development timelines and potentially save millions of dollars.

Challenges in Drug Development

Human studies constitute the most resource-intensive and time-consuming phase of drug development, often taking years to recruit suitable patients and conduct trials. The costs involved can skyrocket to over a billion dollars from the initial discovery of a drug to its eventual market approval. Pharmaceutical companies have long sought ways to streamline this process and make it more efficient.

The Role of Artificial Intelligence in Drug Trials
Artificial intelligence impact on drug clinical trials
Artificial intelligence on drug trials

While pharmaceutical companies have been experimenting with Artificial intelligence for some time, it is now becoming a pivotal component of human drug trials. Companies such as Amgen, Bayer, and Novartis are harnessing the power of AI to sift through vast amounts of data, including public health records, prescription data, medical insurance claims, and their own internal data. AI is being used to identify suitable trial patients, reducing the time it takes to recruit them, and ultimately speeding up the clinical trial process.

Jeffrey Morgan, Managing Director at Deloitte, noted that AI’s role is growing in the pharmaceutical industry, stating, “I don’t think it’s pervasive yet, but I think we’re past the experimentation stage.”

Regulatory Perspective of Artificial Intelligence 

The U.S. Food and Drug Administration (FDA) reported that it received approximately 300 applications that incorporated AI or machine learning in drug development between 2016 and 2022. The majority of these applications, over 90%, were submitted in the past two years, with many focusing on the use of AI during the clinical development stage.

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Case Studies: AI in Action
  • Amgen: Prior to utilizing AI, Amgen spent months sending surveys to doctors worldwide to identify clinics and hospitals with suitable patients for trials. Now, Amgen employs an AI tool called ATOMIC to expedite this process, potentially reducing the time required to enroll patients by half.
  • Bayer: Bayer used AI to optimize the participant selection process for a late-stage trial of the experimental drug asundexian. By linking mid-stage trial results with real-world data, Bayer was able to predict long-term risks in a similar population, reducing costs and recruitment time.
  • Novartis: Novartis has also reported significant improvements in trial enrollment, efficiency, and cost savings through the use of AI.
Challenges and Ethical Considerations

While AI shows great promise in reshaping clinical trials, challenges and ethical considerations persist. Concerns include the potential for bias in AI algorithms, the need for regulatory oversight, and the debate around using AI to replace control groups in trials. Other concerns include the potential for overestimating a drug’s success due to differences between trial conditions and real-world patient experiences.

The Future of AI in Drug Development

Despite these challenges, pharmaceutical companies are committed to leveraging AI to transform the drug development landscape. They anticipate that AI will help shave years off the typical decade-plus timeline required to bring a drug to market. As AI continues to evolve and mature, it is poised to play an increasingly significant role in drug development, ultimately benefiting patients and the industry as a whole.

Artificial intelligence is emerging as a powerful tool in the pharmaceutical industry, enabling faster patient recruitment, cost savings, and potentially shorter drug development timelines. While the industry is still in the early stages of integrating AI into clinical trials, the technology’s potential for transforming drug development is clear. As AI applications continue to expand, the pharmaceutical landscape is set to undergo a significant transformation in the coming years.

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