Experimental and Computational Modeling of Nanocarrier Transport in Biological Flows

Nanocarriers, including lipid-based, polymeric, biomimetic, stimuli-responsive, and hybrid systems, can improve drug solubility, targeting, controlled release, and therapeutic selectivity. Yet their performance is shaped by processes underrepresented in static assays, including hydrodynamic forces, carrier deformation, aggregation, adhesion, cellular uptake, transport across biological barriers, and interactions with disease microenvironments. These processes are important in vascular, lymphatic, mucosal, interstitial, and tissue-specific environments, where dynamic conditions can alter biodistribution and treatment response. Advances in microfluidics, high-content imaging, computational fluid dynamics, particle-based simulation, multiphysics modeling, artificial intelligence, machine learning, and digital twins now support studies of nanocarrier behavior. Integrating these methods across molecular, cellular, tissue, and systemic scales can strengthen the connection between carrier design, biological performance, and clinical translation.This Research Topic aims to advance experimentally grounded and computationally rigorous approaches for understanding and predicting nanocarrier transport in biologically relevant flows. It will connect drug delivery, biomaterials, pharmacology, biomedical engineering, and computational modeling to identify the principles governing carrier movement, mechanics, interactions, release, and therapeutic performance. It also seeks to support targeted and precision nanomedicine through flow-resolved computational models that guide nanocarrier design for transport barriers and disease microenvironments.We welcome studies using experiments, simulations, data-driven methods, or integrated workflows to address delivery challenges. The collection seeks to connect nanocarrier formulation and physicochemical properties with flow-dependent transport, biological barriers, cellular interactions, biodistribution, efficacy, safety, and treatment outcomes. Of special interest are studies that move beyond descriptive formulation characterization toward mechanistic and predictive drug delivery science. Artificial intelligence, machine-learning, and digital-twin methods should predict or explain transport-related outcomes and be linked to experimental or clinically relevant data.This Research Topic focuses on biologically relevant dynamic-transport models and experimentally validated computational analysis of nanocarriers. Standalone carrier synthesis, routine formulation optimization, and broad nanomedicine studies without a clear dynamic-transport, mechanistic, or predictive component are outside the scope. Artificial intelligence or machine-learning studies limited to formulation screening, molecular design, manufacturing, or static physicochemical-property prediction without a biological-transport component are also excluded.Topics welcome include, but are not limited to:- Computational fluid dynamics, particle-based, multiphysics, multiscale, artificial intelligence, machine-learning, and digital-twin models of nanocarrier motion, deformation, margination, adhesion, aggregation, release, and transport-dependent effects on biodistribution, pharmacokinetics, efficacy, or safety across molecular, cellular, tissue, and systemic scales- Microfluidic, organ-on-chip, microphysiological, and patient-derived platforms that reproduce vascular, lymphatic, endothelial, mucosal, interstitial, tissue, inflammatory, or oxidative transport environments under defined dynamic conditions- Experimental and computational validation workflows combining imaging, particle tracking, molecular readouts, cellular assays, patient-derived models, ex vivo systems, or in vivo data to test transport predictions- Flow-mediated interactions between nanocarriers and endothelial or immune cells, extracellular matrices, biological barriers, and disease microenvironments, including mechanisms governing adhesion, penetration, uptake, and clearance- Relationships between carrier size, shape, stiffness, surface chemistry, and cargo release and transport performance, including computationally guided design of biomimetic, stimuli-responsive, targeted, and precision nanomedicines for specific barriers and dynamic disease microenvironments- Benchmark datasets, uncertainty quantification, model reproducibility, interoperable workflows, and standardized methods for comparing experimental and computational nanocarrier transport studies Topic editors: Berwin Singh Swami Vetha, Amin Rahmat Source: https://www.frontiersin.org/research-topics/85506/experimental-and-computational-modeling-of-nanocarrier-transport-in-biological-flows