Pharmacogenomics Meets Generative AI: Transforming Clinical Trial Design with Large Language Models

ABSTRACT

Background: Pharmacogenomics aims to optimise drug therapy based on genetic makeup, but traditional clinical trial design faces challenges with complexity, cost and data integration. Objectives: This study explores integrating generative artificial intelligence (AI), specifically large language models (LLMs) like Llama3 8B, Mistral 7B v0.3 and Phi-3 Mini 3.8B, into pharmacogenomics clinical trial design through Retrieval-Augmented Generation frameworks and local knowledge bases to address the challenges. Materials and Methods: We conducted a comparative analysis of LLMs, evaluating the accuracy, relevancy, response time and operational efficiency with a case study that assessed LLMs’ capacity to address key trial design elements. The LLMs were locally run using an RTX 4080 mobile graphics card and Intel Core i9-13980HX central processing unit, with Open-WebUI employed. Results: Our results show that Llama3 8B and Phi-3 Mini 3.8B both achieved an accuracy and relevancy score of 0.92 and 0.89, showcasing their underscore of advanced capabilities in delivering both accurate and contextually relevant outputs. More thorough results showed that Phi-3 Mini 3.8B excelled in efficiency and scalability, while Llama3 8B provided greater contextual depth. Conclusion: This study indicates that generative AI offers transformative potential in pharmacogenomics clinical trials, enhancing efficiency and outcomes. However, challenges such as potential bias and the need for further validation remain. Addressing these limitations and advancing multimodal AI capabilities will further support inclusive and effective trial designs.

KEYWORDS

• Clinical trial design
• generative artificial intelligence
• large language models
• pharmacogenomics
• Retrieval-Augmented Generation

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• DOI10.1177/0976500X251321885Copy

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