Keywords
Adverse Drug Reactions
How to Cite
Abstract
The development of machine learning models for predicting drug pharmacokinetics is crucial for optimizing dosing regimens and enhancing therapeutic outcomes. This paper presents a comprehensive review of machine learning approaches for predictive modeling of drug pharmacokinetics. We discuss the importance of pharmacokinetics in drug development and clinical practice, highlighting the challenges and opportunities in this field. We then review various machine learning techniques, including traditional regression models, neural networks, and ensemble methods, applied to predict drug pharmacokinetics. We also discuss the integration of pharmacokinetic data with other types of data, such as pharmacogenomics and clinical data, to improve prediction accuracy. Finally, we provide insights into future research directions and the potential impact of machine learning on drug development and personalized medicine.
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