ArticleCPT: pharmacometrics & systems pharmacology2025
AI for NONMEM Coding in Pharmacometrics Research and Education: Shortcut or Pitfall?
Article in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Context Engineering for AI-Assisted Pharmacometrics: A Practical Tutorial.CPT: pharmacometrics & systems pharmacology · 2026Article
- PKGPT: Expert-Orchestrated Recursive LLM Agent for Automated NONMEM PopPK Modeling with Human Benchmarking.Pharmaceutics · 2026Article
- Retrieval Augmented Generation (RAG) for Evaluating Regulatory Compliance of Drug Information and Clinical Trial Protocols.CPT: pharmacometrics & systems pharmacology · 2026Article
- Pharmacometrics in the Age of Large Language Models: A Vision of the Future.Pharmaceutics · 2025Article
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Authors and funding
6 authors.
Funding
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 useful foundation for pharmacometrics model coding. However, user review and refinement remain essential, especially for complex models with special dataset alignment or advanced coding techniques. We also discussed the applications of AI in pharmacometrics education, particularly strategies to prevent overreliance on AI for coding. This work provides a benchmark for current LLMs' performance in NONMEM coding and introduces a practical prompt that can facilitate more accurate and efficient use of AI in pharmacometrics research and education.
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Registered trials
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