From bench to bedside: Overcoming translational hurdles in nanoparticle research with pharmacokinetic modeling.

Nanoparticles represent a major advancement in drug delivery, enhancing stability, solubility, and targeted delivery while reducing non-specific toxicity. Despite promising preclinical results, few nanoparticle platforms have successfully translated to clinical use due to limited pharmacokinetic (PK) data, off-target effects, manufacturing complexity, and a lack of long-term studies. Emphasizing the pivotal role of PK in this development process is essential for harnessing the full therapeutic potential of nanoparticle applications in clinical practice. In this review, we introduce the concept of model-informed nanoparticle development (MIND), an extension of model-informed drug development (MIDD) principles to nanomedicine that integrates knowledge of nanoparticle interactions with biological systems and state-of-the-art pharmacokinetic modeling to support evidence-based decision-making. Physiologically-based PK (PBPK) models simulate the absorption, distribution, metabolism, and elimination (ADME) of nanoparticles within the body, incorporating physiological parameters to predict their PK behavior. Population-based PK (PopPK) models utilize population variability to characterize nanoparticle PK across diverse patient groups, optimizing dosing strategies and personalized medicine approaches. Mechanistic models elucidate the intricate interactions between nanoparticles and biological systems, integrating cellular pathways and disease mechanisms to predict therapeutic outcomes and guide nanoparticle design. Through the MIND framework, these modeling approaches can enhance our understanding of nanoparticle PK, foster innovation in active agent delivery systems, and accelerate nanoparticle translation from research to clinical applications.