A Review of Compartmental Models for Personalized Drug Delivery Kinetics: Intelligent Modeling, Electronics Integration, and Real-World Applications

Abstract

Compartmental pharmacokinetic (PK) models play a crucial role in understanding drug distribution, metabolism, and elimination processes within the human body. These models simplify complex biological systems into interconnected compartments, enabling quantitative analysis of drug kinetics and facilitating personalized drug delivery strategies. Recent advancements between 2018 and 2023 have significantly enhanced the applicability of compartmental models through integration with intelligent computational techniques, such as machine learning, stochastic modelling, and physiologically based pharmacokinetic (PBPK) frameworks. These developments allow for improved prediction accuracy, individualized dosing, and better understanding of inter-patient variability. Furthermore, the integration of electronics and smart medical devices has enabled real-time monitoring and adaptive drug delivery systems, enhancing therapeutic outcomes. Wearable biosensors, implantable drug delivery systems, and IoT-enabled healthcare platforms have been increasingly combined with PK models to enable closed-loop drug administration. In addition, fractional and stochastic compartmental models have emerged as powerful tools for capturing complex biological variability and non-linear drug dynamics. This review comprehensively examines recent literature on compartmental models, focusing on their evolution, intelligent enhancements, and real-world applications. It also highlights challenges such as parameter estimation, model identifiability, and clinical translation, while identifying future research directions in personalized medicine and digital health integration.