Leveraging machine learning in limited sampling strategies for efficient estimation of the area under the curve in pharmacokinetic analysis: a review

Objective: Limited sampling strategies are widely employed in clinical practice to minimize the number of blood samples required for the accurate area under the curve calculations, as obtaining these samples can be costly and challenging. Traditionally, the maximum a posteriori Bayesian estimation has been the standard method for the area under the curve estimation based on limited samples. However, machine learning is emerging as a promising alternative for this purpose. Here, we review studies that utilize machine learning approaches to develop limited sampling strategies and compare the strengths and weaknesses of these machine learning methods. Methods: We searched the literature for studies that used machine learning to estimate the area under the curve using a limited sampling strategy approach. Results: We identified ten studies that developed machine learning models to estimate the area under the curve for six different drugs. Several of these models demonstrated good accuracy and precision in area under the curve estimation in reference to the traditional Bayesian approach, highlighting the potential of machine learning models in precision dosing. Conclusions: Despite these promising early results, the development of machine learning for limited sampling strategies is still in its early stages. Further research might be needed to validate machine learning models with larger, high-quality clinical datasets to ensure their reliability and applicability in clinical settings.