Machine Learning for Valproic Acid Therapy: A Scoping Review of Pharmacokinetic-Prediction Models and Pharmacokinetic-Informed Clinical Outcome Models

Background/Objectives: Population pharmacokinetics (PopPK) has been used to aid valproic acid (VPA) dose individualization. However, this approach faces limitations owing to the complexity and high dimensionality of datasets. Machine learning (ML) can handle these challenges. However, the comparative evaluation of these ML algorithms and their practical application in optimizing VPA therapy have not yet been established. This review aims to summarize the current evidence, identify research gaps, and outline ML applications for VPA in clinical practice. Methods: PubMed, ScienceDirect, Scopus, the Association for Computing Machinery (ACM) Digital Library, and IEEE Xplore were searched from inception to October 2025. Eligible studies included original research articles using ML models to predict VPA pharmacokinetics or clinical outcomes (e.g., seizure control). Data on study design, population characteristics, features, predicted targets, ML algorithms, validation method, and model performance metrics were extracted. Results: Eleven studies were included. Most studies were retrospective, single-center designs. Ensemble tree-based models such as Random Forest, CatBoost, Gradient Boosted Regression Trees, and other ensemble methods, were consistently among the top-performing algorithms. Final models retained 3 to 19 input features, with daily dose, albumin, and body weight being the most common predictors. Only five studies performed external validation, limiting the generalizability of the models. Conclusions: Current VPA ML models demonstrated promising predictive performance. Nonetheless, most models are retrospective and single-center, with only limited external validation. Future VPA ML studies should use multicenter datasets and apply external evaluation to enhance model generalizability for clinical use.