Large Language Models in Pharmacometrics: Opportunities, Challenges, and Future Directions

Pharmacometrics has long provided a scientific foundation for quantitative decision-making in drug development and therapeutics. Yet, much of its workflow—such as data preparation, documentation, and interpretation—remains labor-intensive. In parallel, large language models (LLMs) have emerged as powerful tools capable of natural language understanding, reasoning, and code generation across scientific domains. Their ability to interpret, summarize, and generate structured information offers new ways to accelerate and augment pharmacometric workflows. However, their integration into this rigorously quantitative field requires a careful evaluation of reliability, transparency, and domain adaptation. Understanding where and how LLMs can add value, while preserving scientific and regulatory standards, is essential for responsible innovation in pharmacometrics.The goal of this Research Topic is to examine the practical/scientifically defensible roles of LLMs in pharmacometrics and related areas of pharmacological modeling (e.g. PK/PD, NLME, PBPK). We aim to identify how LLMs can enhance efficiency, reproducibility, and accessibility in modeling and simulation, while maintaining methodological rigor and regulatory compliance. Key questions include:• How can LLMs support model-informed drug development (MIDD) in drug discovery and clinical development (e.g. protocol interpretation, structured literature synthesis, documentation/code assistance)?• What are the best practices for validating LLM-assisted pharmacometric tools?• How can MIDD improve communication, education, and transparency in model-based research?Through original research, perspectives, and reviews, this topic will create a forum for critical assessment of AI’s evolving role in pharmacometrics and offer guidance for its responsible application. Submissions using LLMs must clearly describe evaluation/validation procedures, provide reproducibility details (data/code/prompts/models/versions where possible), explicitly discuss limitations, and address numerical fidelity (including hallucination risk or quantitative error propagation when relevant), with verification steps for any generated values, equations, or code outputs.This Research Topic welcomes original research, reviews, methodological papers, and opinion articles on the use of LLMs and related AI tools in pharmacometrics and pharmacological modeling. Relevant themes include automated knowledge extraction from literature, LLM-assisted data and model curation, AI-driven simulation workflows, regulatory and ethical considerations, and integration with quantitative systems pharmacology. We encourage both theoretical and applied contributions, including benchmark studies, open-source tools, and reproducible case studies. Submissions addressing transparency, validation, and interdisciplinary collaboration between pharmacometricians, data scientists, and regulatory experts are especially encouraged. Topic editors: Ahmed Elmokadem, Tursun Alkam MD PhD MSc AI(c), Alioune Ngom Source: https://www.frontiersin.org/research-topics/78466/large-language-models-in-pharmacometrics-opportunities-challenges-and-future-directions