Machine Learning for Prediction of Drug Concentrations: Application and Challenges
7
Issued Date
2025-01-01
Resource Type
ISSN
00099236
eISSN
15326535
DOI
Scopus ID
2-s2.0-85216991485
Journal Title
Clinical Pharmacology and Therapeutics
Rights Holder(s)
SCOPUS
Bibliographic Citation
Clinical Pharmacology and Therapeutics (2025)
Suggested Citation
Huang S., Xu Q., Yang G., Ding J., Pei Q. Machine Learning for Prediction of Drug Concentrations: Application and Challenges. Clinical Pharmacology and Therapeutics (2025). doi:10.1002/cpt.3577 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/104272
Title
Machine Learning for Prediction of Drug Concentrations: Application and Challenges
Corresponding Author(s)
Other Contributor(s)
Abstract
With the advancements in algorithms and increased accessibility of multi-source data, machine learning in pharmacokinetics is gaining interest. This review summarizes studies on machine learning-based pharmacokinetics analysis up to September 2024, identified from the PubMed and IEEE Xplore databases. The main focus of this review is on the use of machine learning in predicting drug concentration. This review provides a comprehensive summary of the advances in the machine learning algorithms for pharmacokinetics analysis. Specifically, we describe the common practices in data preprocessing, the application scenarios of various algorithms, and the critical challenges that require attention. Most machine learning models show comparable performance to those of population pharmacokinetics models. Tree-based algorithms and neural networks have the most applications. Furthermore, the use of ensemble modeling techniques can improve the accuracy of these models' predictions of drug concentrations, especially the ensembles of machine learning and pharmacometrics.