ArticleThe AAPS journal2025
Opportunities for AI-based Model-informed Drug Development: A Comparative Analysis of NONMEM and AI-based Models for Population Pharmacokinetic Prediction.
Article in The AAPS journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- From Small Data to Big Decisions: How Clinical Pharmacology Shapes Rare Disease Development.Journal of clinical pharmacology · 2026Review
- Deep-Learning: An Emerging Tool to Support Model-Informed Drug Development.Clinical and translational science · 2026Article
- Article
- Innovative applications of artificial intelligence technology in pharmacometrics.European journal of clinical pharmacology · 2026Review
- Deep-Learning- versus Hypothesis-Driven Modeling in Model-Informed Drug Development: A PK/PD Case Study.Journal of clinical pharmacology · 2026Article
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Authors and funding
6 authors.
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Abstract
Model-informed drug development (MIDD) plays an important role in pharmacometrics by leveraging mathematical models to optimize drug dosing strategies. Traditional methods such as nonlinear mixed effects modeling (NONMEM) have long been the gold standard in population pharmacokinetic (PPK) modeling. However, the development of artificial intelligence (AI) presents a potential improvement in predictive performance and computational efficiency. This study evaluates the effectiveness of AI-based MIDD methods for PPK analysis by comparing them against traditional nonlinear mixed-effects (NLME)-based methods (e.g., NONMEM). We tested five machine learning (ML) models, three deep learning (DL) models, and a neural ordinary differential equations (ODE) model on both simulated and real clinical datasets under different scenarios, assessing predictive performance with metrics such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R
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