AI for NONMEM Coding in Pharmacometrics Research and Education: Shortcut or Pitfall?

ABSTRACT: Artificial intelligence (AI) is increasingly being explored as a tool to support pharmacometric modeling, particularly in addressing the coding challenges associated with NONMEM. In this study, we evaluated the ability of seven Large Language Models (LLMs) to generate NONMEM codes across 13 pharmacometrics tasks, including a range of population pharmacokinetic (PK) and pharmacodynamic (PD) models. We further developed a standardized scoring rubric to assess code accuracy and created an optimized prompt to improve LLM performance. Our results showed that the OpenAI o1 and gpt-4.1 models achieved the best performance, both generating codes with great accuracy for all tasks when using our optimized prompt. Overall, LLMs performed well in writing basic NONMEM model structures, providing a usef

SUBMITTER: Zheng W 

PROVIDER: S-EPMC12706393 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature