What question did this study set out to answer?

The review aims to explore how machine learning can improve predictions in pharmacokinetics and pharmacodynamics for personalized medicine.

Machine learning models for predicting pharmacokinetics and pharmacodynamics: A step toward personalized dosing

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Review finds machine learning predicts pharmacokinetics and pharmacodynamics effectively, indicating progress in personalized therapy.

The review aims to explore how machine learning can improve predictions in pharmacokinetics and pharmacodynamics for personalized medicine.
Comprehensive literature review of machine learning algorithms applied to pharmacokinetics and pharmacodynamics.
Discussion of various ML algorithms including random forests, support vector machines, and neural networks.
Evaluation of challenges such as data quality and clinical integration of machine learning models.
Machine learning models showed improved capability in predicting drug profiles compared to traditional methods.
The use of ML enhances understanding of individual variability in drug disposition and effectiveness.
Challenges in data quality and representativeness were identified as key barriers to clinical application.

Gupta et al. (2026) studied this question.

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