Multiscale modeling of protein membrane interactions for nanoparticle targeting in drug delivery

Abstract

Nanoparticle (NP)-based imaging and drug delivery systems for systemic (e.g. intravenous) therapeutic and diagnostic applications are inherently a complex integration of biology and engineering. A broad range of length and time scales are essential to hydrodynamic and microscopic molecular interactions mediating NP (drug nanocarriers, imaging agents) motion in blood flow, cell binding/uptake, and tissue accumulation. A computational model of time-dependent tissue delivery, providing in silico prediction of organ-specific accumulation of NPs, can be leveraged in NP design and clinical applications. In this article, we provide the current state-of-the-art and future outlook for the development of predictive models for NP transport, targeting, and distribution through the integration of new computational schemes rooted in statistical mechanics and transport. The resulting multiscale model will comprehensively incorporate: (i) hydrodynamic interactions in the vascular scales relevant to NP margination; (ii) physical and mechanical forces defining cellular and tissue architecture and epitope accessibility mediating NP adhesion; and (iii) subcellular and paracellular interactions including molecular-level targeting impacting NP uptake.

Figure 1:

(left) Schematic of the multiscale pharmacokinetic model showing the different length scales and resolutions of the transport problem in targeted drug delivery. (right) A flowchart for an algorithmic implementation of the multiscale model consisting of the “Hydrodynamics” component, the “Cellular Adhesion” component, and the “Pharmacokinetics” component. The arrows represent the flow of information between the suite of models and the flow of information for experimental validation.

Figure 2:

(a) Depiction of the live cell adhesion NP tissue targeting highlighting the parameter mapping (inputs and outputs). A Monte Carlo framework constitutes the live cell adhesion suite.