Artificial Intelligence and Machine Learning Applications to Pharmacokinetic Modelling and Dose Prediction of Antibiotics: A Scoping Review
Iria Varela-Rey, Enrique Bandín‐Vilar, Francisco José Toja-Camba, Antonio Cañizo-Outeiriño, Francisco Cajade-Pascual, Marcos Ortega, Victor Mangas Sanjuan, Miguel González‐Barcia, Irene Zarra‐Ferro, Cristina Mondelo‐García, Anxo Fernández‐Ferreiro · Preprints.org · 2024
Background and Objectives: The use of Artificial intelligence (AI) and, in particular, Machine Learning (ML) techniques is growing rapidly in the healthcare field. Their application in pharma-cokinetics is of potential interest due to the need to relate enormous amounts of data and to the more efficient development of new predictive dose models. The development of pharmacokinetic models based on these techniques simplifies the process, reduces time and allows more factors to be con-sidered than with classical methods, and is therefore of special interest in the pharmacokinetic monitoring of anti-biotics. This review aims to describe the studies that use AI, mainly oriented to ML techniques, to dose prediction and analyze their results in comparison with the results obtained by classical methods. Furthermore, in the review, the techniques employed and the metrics to evaluate the precision are described to improve the compression of the results. Methods: A sys-tematic search was carried out in the EMBASE, OVID and PubMed databases and the results ob-tained were analyzed in detail. Results: Of the 13 articles selected, 10 were published in the last three years. Vancomycin was monitored in 7 and none of the studies were performed on new an-tibiotics. The most used techniques were XGBoost and neural networks. Comparison was conducted in most cases against population pharmacokinetic models. Conclusion: AI techniques offer prom-ising results. However, the diversity in terms of the statistical metrics used and the low power of some of the articles make the overall assessment difficult. For now, AI-based ML techniques should be used in addition to classical population pharmacokinetic models in clinical practice.