标题： 人工智能在药动学研究与教育中的非线性混合效应模型编程：捷径还是陷阱？ 显示英文标题

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

摘要： 人工智能（AI）正日益被探索作为支持药动学建模的工具，特别是在解决与NONMEM相关的编码挑战方面。 在本研究中，我们评估了七种AI代理在13个药动学任务中的生成NONMEM代码的能力，包括一系列群体药动学（PK）和药效学（PD）模型。 我们进一步开发了一个标准化评分标准来评估代码准确性，并创建了一个优化的提示来提高AI代理的性能。 我们的结果表明，OpenAI o1和gpt-4.1模型表现最佳，当使用我们优化的提示时，它们能够为所有任务生成具有高度准确性的代码。 总体而言，AI代理在编写基本的NONMEM模型结构方面表现良好，为药动学模型编码提供了有用的基础。 然而，用户审查和改进仍然是必不可少的，特别是对于具有特殊数据集对齐或高级编码技术的复杂模型。 我们还讨论了AI在药动学教育中的应用，特别是防止过度依赖AI进行编码的策略。 这项工作为当前AI代理在NONMEM编码中的表现提供了一个基准，并介绍了一个实用的提示，可以促进在药动学研究和教育中更准确和高效地使用AI。 显示英文摘要

摘要： 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.