ArticleJournal of pharmacokinetics and pharmacodynamics2024
Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM.
Article in Journal of pharmacokinetics and pharmacodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Context Engineering for AI-Assisted Pharmacometrics: A Practical Tutorial.CPT: pharmacometrics & systems pharmacology · 2026Article
- A Generative AI Framework for Pharmacokinetic Clinical Study Report Authoring.Clinical and translational science · 2026Article
- Using ChatGPT to Solve Clinical Radiobiology Problems.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- PKGPT: Expert-Orchestrated Recursive LLM Agent for Automated NONMEM PopPK Modeling with Human Benchmarking.Pharmaceutics · 2026Article
- Innovative applications of artificial intelligence technology in pharmacometrics.European journal of clinical pharmacology · 2026Review
- Retrieval Augmented Generation (RAG) for Evaluating Regulatory Compliance of Drug Information and Clinical Trial Protocols.CPT: pharmacometrics & systems pharmacology · 2026Article
- Retrieval Augmented Generation (RAG) for Natural Language Querying of Immunogenicity Data for Protein Drugs.The AAPS journal · 2026Article
- AI for NONMEM Coding in Pharmacometrics Research and Education: Shortcut or Pitfall?CPT: pharmacometrics & systems pharmacology · 2025Article
- Scoping review of the role of pharmacometrics in model-informed drug development.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Pharmacometrics in the Age of Large Language Models: A Vision of the Future.Pharmaceutics · 2025Article
- Assessing the role of large language models in adolescent idiopathic scoliosis care: a comparison between ChatGPT and Google Gemini.Acta orthopaedica et traumatologica turcica · 2025Article
- The dawn of a new era: can machine learning and large language models reshape QSP modeling?Journal of pharmacokinetics and pharmacodynamics · 2025Review
- Leveraging large language models in pharmacometrics: evaluation of NONMEM output interpretation and simulation capabilities.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Leveraging In Silico and Artificial Intelligence Models to Advance Drug Disposition and Response Predictions Across the Lifespan.Clinical and translational science · 2025Review
- Large Language Models and Their Applications in Drug Discovery and Development: A Primer.Clinical and translational science · 2025Review
- AI In Action: Redefining Drug Discovery and Development.Clinical and translational science · 2025Article
- State of Art of Dose Individualization to Support tacrolimus drug monitoring: What's Next?Transplant international : official journal of the European Society for Organ Transplantation · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.
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38656706What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.