Introduction
Clinical trials play a critical role in the development of new drugs, but they are often associated with inflated costs, long durations, and low success rates. However, the advancement of Artificial Intelligence (AI) and Machine Learning (ML) may be able to mitigate these challenges. This blog post explores how the International Council for Harmonisation and Good Clinical Practice (ICH-GCP) guidelines safeguard the safety and well-being of trial subjects and data integrity in the era of AI/ML
Trial Design
Let’s start with the most crucial part —the trial design. It’s possible for AI/ML to actually generate trial protocols! However, it’s important to ensure that these protocols follow all the rules and regulations. That means the process of gathering and validating data needs to be thoroughly vetted and meets industry standards. Plus, we have to consider things like how complex the trials are and even the use of new technologies like remote site visits. All of this means any AI used in trial design needs expert review of the outputs to avoid errors.
Investigator Selection and Patient Recruitment
Those who have worked on trials know that finding the right investigators and sites for clinical trials can be a long, arduous process. That’s where AI/ML may be able to help! Using these tools, we can greatly speed up the process by searching through mountains of data in minutes. This obviously requires some human touch, but with machine learning, we can get trials off the ground much quicker by making this process more efficient. Similarly, patient recruitment often requires pouring through a ton of data to find eligible candidates. Just like with investigator selection, leveraging AI/ML tools can help us dig through the pile of candidates that much quicker and spend less time searching.
Monitoring and Analysis
During clinical trials, it’s mission-critical to monitor the data closely and identify (and mitigate) any potential risks. AI/ML’s data processing capabilities can quickly flag any deviations or safety concerns for further review—it’s like having a super-powered monitoring system. Results can also be analyzed by AI/ML algorithms, which speeds up the analysis process for biostatisticians. It’s important that the data is verified by a human hand, of course. However, having these algorithms monitor and analyze the data in real time can both limit risks and increase efficiency in analysis.
The Future of AI/ML
The upcoming ICH-GCP Revision 3, expected later in 2024, does not explicitly discuss the role of AI/ML. However, it addresses innovative technologies that support a risk-based approach to clinical trial design and management. These changes might indirectly influence the utilization of AI/ML in future clinical trials and potentially offer added guidance on incorporating these technologies securely into clinical research practices.
The advancement of AI/ML technology holds great promise for the pharmaceutical industry in terms of finding effective treatments for diseases, shortening trial durations, and reducing the burden of data analysis. As AI/ML becomes an increasingly valuable tool in clinical research, adherence to ICH-GCP guidelines ensures the safety and well-being of trial subjects and maintains data integrity. By combining the power of AI/ML and robust regulatory frameworks, we can accelerate the development of life-saving medications and improve patient outcomes.
