Current Modeling Approaches for Drug Delivery to the Central Nervous System

Central nervous system (CNS) disorders constitute a significant global health challenge; however, the development of therapeutic agents is considerably impeded by the difficulty in delivering effective concentrations within the brain. This comprehensive review delineates the current landscape of computational modeling techniques employed to address the formidable challenges associated with CNS drug delivery, with a particular emphasis on the anatomical barriers and physiological transport mechanisms pertinent to major neurological diseases. We categorize modeling approaches ranging from the atomistic scale, including molecular dynamics simulations of drug-blood-brain barrier (BBB) interactions, to macroscopic continuum and Physiologically Based Pharmacokinetic (PBPK) models that elucidate systemic distribution and overall brain exposure. We critically assess these models concerning established delivery routes, such as intranasal and intrathecal administration, as well as emerging methods, including focused ultrasound-mediated BBB opening and targeted nanoparticle delivery. This review underscores the growing importance of integrating complex physiological phenomena, such as glymphatic flow and cerebrospinal fluid (CSF) dynamics, into predictive models. Finally, we explore the emerging opportunities involving multiscale digital twins of the CNS that integrate molecular interactions, vascular hemodynamics, CSF and perivascular flow, and parenchymal transport within patient-specific anatomical geometries. The role of machine learning and surrogate modeling in expediting the prediction of drug transport parameters and optimizing delivery strategies is also examined. By providing a structured overview of current computational tools, this review aims to guide researchers in the design of more robust computational platforms for CNS drug delivery.