A New Beginning: The Effect of Artificial Intelligence on Clinical Trials, Medicine Creation

As the use of artificial intelligence (AI) has greatly accelerated in recent years, AI has been moving to reshape the clinical trial landscape by addressing critical challenges and improving outcomes across the entire process. According to Linical, AI is streamlining the clinical trial process through recruiting and enrollment, trial design, protocol optimization, data management and analysis, enhanced patient retention and compliance, regulatory compliance, post-trial analysis, and real-world evidence.¹

The article states that in trial design, machine learning (ML) models simulate outcomes and enable adaptive protocols to enhance efficiency and decision-making. Additionally, AI automates data collection and performs advanced real-time analyses, reducing errors and uncovering actionable insights, while also improving patient retention through personalized engagement and behavioral monitoring.¹

At this year’s Financial Times US Pharma and Biotech Summit, Tala Fakhouri, associate director, policy analysis, FDA, discussed the FDA’s point of view on ML and AI in drug development.²

“We're actually very excited about AI use,” said Fakhouri. “I think we're seeing that it's increasing efficiencies in different parts of the drug development process. If you think about things such as discovery or protein folding, which again, is outside of what we normally look at, it could potentially cut the development time by years. This is all very exciting, because it could translate into faster, safe and effective drugs coming into the market. It can also fill in certain gaps for rare diseases where we can see a lot of potential use for AI to accelerate the development of drugs. In this type of situation, that's what I would say would be the long term.”

Fakhouri added that in the short-term, the primary challenge that stakeholders are working to address is the adoption curve.

“You need to train your staff, you need to bring in the right expertise, and you need to develop the right tools to solve the right problems,” Fakhouri said. “That's going to take some time and that's why I think the short-term uses of AI are going to be mostly low hanging type of fruits where you're increasing operational efficiency, but then that will translate into the development of safe and effective drugs faster.”

On October 31, 2024, the FDA released updated guidelines on the use of AI for drug development. According to the guidance, the regulatory body recognizes the growing role of AI in drug development, spanning the entire drug lifecycle from nonclinical and clinical phases to manufacturing and post marketing.

In order to support industry innovation and maintain safety and efficacy standards, the Center for Drug Evaluation and Research (CDER) released a draft guidance earlier this year addressing AI’s use in regulatory decision-making. According to the updated FDA guidelines, the guidance is influenced by extensive public feedback, CDER’s review of over 500 AI-related submissions since 2016, workshops with stakeholders, and aligns with broader FDA efforts to ensure responsible AI use.³

Fakhouri previously stated that the FDA had no intention of placing restrictions on the use of AI and ML as time goes on.

“We get asked if the FDA regulates large language models or if we’re going to ban generative AI use,” she explained. “My response is that we typically don't regulate linear regression. We look at the data and the information that any modeling technique is producing, and we want to make sure that the information is trustworthy. So, I wouldn't say that we would be banning or prohibiting a certain AI or machine learning type of algorithm, what we're actually interested in is how robust how accurate and how credible the information from these models is.”

According to IBM, there are a number of benefits associated with implementing AI in clinical trials and medicine development, including:⁴

• Informed Patient Care: IBM states that a trained ML algorithm can help cut down on research time by giving clinicians valuable search results with evidence-based insights about treatments and procedures while the patient is still in the room with them.
• Error Reduction: Peer-reviewed studies have suggested that AI-powered decision support tools can help improve error detection and drug management.
• Reducing the Costs of Care: This includes reducing medication errors, customized virtual health assistance, fraud prevention, and supporting more efficient administrative and clinical workflows.
• Increasing Physician-Patient Engagement: AI can help provide around-the-clock support through chatbots that can answer basic questions and give patients resources when their provider’s office isn’t open.⁴

There are many thought leaders in the industry who believe that AI has the potential improve healthcare in all areas in the future. However, the biopharma industry has moved at a slower rate to implement AI compared to other fields.

At this year’s Young & Partners Pharmaceutical Executive Summit, Najat Khan, chief R&D officer, chief commercial officer, Recursion, offered her thoughts on why utilization has advanced at a slower rate and how companies can properly integrate AI into their everyday workflows.

“A huge part of it is the mindset, but pharma companies, in general, have high margins,” said Khan. “Whenever an industry has high margins, what’s the reason for change? That's the truth of it. Second, there’s a lack of understanding when it comes to using it. I think everybody knows what the pain points are. But then how do you deploy it? Sometimes, it means that your job changes. That's threatening, right? I've seen that firsthand, and this has happened over many generations. There’s two ways to respond. Either you evolve or you get evolved. You don't have to be a coder to actually understand AI. It's just like another science or another technical topic. But it's more important to understand how to use it, what not to do, what to do—given the complexity of the pharma industry, the regulations that are there, the limited understanding of biology today—there's just a fear of both personally being threatened.”⁵

References

1. How AI is Revolutionizing Clinical Trials. Linical. September 24, 2024. Accessed November 1, 2024. https://www.linical.com/articles-research/how-ai-is-revolutionizing-clinical-trials

2. Artificial Intelligence for Drug Development. FDA. October 31, 2024. Accessed November 1, 2024. https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/artificial-intelligence-drug-development

3. US Pharma and Biotech Summit 2024: Artificial Intelligence and Machine Learning Through the Eyes of the FDA Part II. Pharm Exec. May 21, 2024. Accessed November 1, 2024. https://www.pharmexec.com/view/us-pharma-and-biotech-summit-2024-artificial-intelligence-and-machine-learning-through-the-eyes-of-the-fda-part-ii

4. What is artificial intelligence in medicine? IBM. Accessed November 1, 2024. https://www.ibm.com/topics/artificial-intelligence-medicine

5. The Impact of Artificial Intelligence on the Creation of Medicines. PharmExec. October 24, 2024. Accessed November 1, 2024. https://www.pharmexec.com/view/young-partners-pharmaceutical-executive-summit-2024-impact-artificial-intelligence-creation-medicines