Machine Learning: A New Approach for Dose Individualization

• Published in: Clinical pharmacology and therapeutics Vol. 115; no. 4; pp. 727 - 744
• Main Authors: Li, Qiu‐Yue, Tang, Bo‐Hao, Wu, Yue‐E, Yao, Bu‐Fan, Zhang, Wei, Zheng, Yi, Zhou, Yue, Anker, John, Hao, Guo‐Xiang, Zhao, Wei
• Format: Journal Article
• Language: English
• Published: United States 01-04-2024
• Subjects: Child; Humans; Machine Learning; Prognosis; Risk Assessment
• ISSN: 0009-9236; 1532-6535; 1532-6535
• DOI: 10.1002/cpt.3049

The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N = 23, 35.9%) and anti‐infectives (N = 21, 32.8%), and there is currently only very limited attention for special populations, such as children (N = 22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.