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European Journal of Drug Metabolism and Pharmacokinetics
Published in: European Journal of Drug Metabolism and Pharmacokinetics 3/2024

08-03-2024 | Artificial Intelligence | Systematic Review

Predicting Pharmacokinetics of Drugs Using Artificial Intelligence Tools: A Systematic Review

Authors: Mahnaz Ahmadi, Bahareh Alizadeh, Seyed Mohammad Ayyoubzadeh, Mahdiye Abiyarghamsari

Published in: European Journal of Drug Metabolism and Pharmacokinetics | Issue 3/2024

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Abstract

Background and objective

Pharmacokinetic studies encompass the examination of the absorption, distribution, metabolism, and excretion of bioactive compounds. The pharmacokinetics of drugs exert a substantial influence on their efficacy and safety. Consequently, the investigation of pharmacokinetics holds great importance. However, laboratory-based assessment necessitates the use of numerous animals, various materials, and significant time. To mitigate these challenges, alternative methods such as artificial intelligence have emerged as a promising approach. This systematic review aims to review existing studies, focusing on the application of artificial intelligence tools in predicting the pharmacokinetics of drugs.

Methods

A pre-prepared search strategy based on related keywords was used to search different databases (PubMed, Scopus, Web of Science). The process involved combining articles, eliminating duplicates, and screening articles based on their titles, abstracts, and full text. Articles were selected based on inclusion and exclusion criteria. Then, the quality of the included articles was assessed using an appraisal tool.

Results

Ultimately, 23 relevant articles were included in this study. The clearance parameter received the highest level of investigation, followed by the  area under the concentration-time curve (AUC) parameter, in pharmacokinetic studies. Among the various models employed in the articles, Random Forest and eXtreme Gradient Boosting (XGBoost) emerged as the most commonly utilized ones. Generalized Linear Models and Elastic Nets (GLMnet) and Random Forest models showed the most performance in predicting clearance.

Conclusion

Overall, artificial intelligence tools offer a robust, rapid, and precise means of predicting various pharmacokinetic parameters based on a dataset containing information of patients or drugs.
Appendix
Available only for authorised users
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Metadata
Title
Predicting Pharmacokinetics of Drugs Using Artificial Intelligence Tools: A Systematic Review
Authors
Mahnaz Ahmadi
Bahareh Alizadeh
Seyed Mohammad Ayyoubzadeh
Mahdiye Abiyarghamsari
Publication date
08-03-2024
Publisher
Springer International Publishing
Published in
European Journal of Drug Metabolism and Pharmacokinetics / Issue 3/2024
Print ISSN: 0378-7966
Electronic ISSN: 2107-0180
DOI
https://doi.org/10.1007/s13318-024-00883-7

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