AI for NONMEM Coding in Pharmacometrics Research and Education: Shortcut or Pitfall?
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arXiv
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| Format: | Preprint |
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2025
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| author | Zheng, Wenhao Wang, Wanbing Kirkpatrick, Carl M. J. Landersdorfer, Cornelia B. Yao, Huaxiu Zhou, Jiawei |
| author_facet | Zheng, Wenhao Wang, Wanbing Kirkpatrick, Carl M. J. Landersdorfer, Cornelia B. Yao, Huaxiu Zhou, Jiawei |
| contents | 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 AI agents 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 AI agent 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, AI agents 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 over-reliance on AI for coding. This work provides a benchmark for current AI agents performance in NONMEM coding and introduces a practical prompt that can facilitate more accurate and efficient use of AI in pharmacometrics research and education. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08144 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI for NONMEM Coding in Pharmacometrics Research and Education: Shortcut or Pitfall? Zheng, Wenhao Wang, Wanbing Kirkpatrick, Carl M. J. Landersdorfer, Cornelia B. Yao, Huaxiu Zhou, Jiawei Other Quantitative Biology 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 AI agents 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 AI agent 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, AI agents 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 over-reliance on AI for coding. This work provides a benchmark for current AI agents performance in NONMEM coding and introduces a practical prompt that can facilitate more accurate and efficient use of AI in pharmacometrics research and education. |
| title | AI for NONMEM Coding in Pharmacometrics Research and Education: Shortcut or Pitfall? |
| topic | Other Quantitative Biology |
| url | https://arxiv.org/abs/2507.08144 |