UC Santa Barbara

In this thesis, we outline the development of a multiscale physics-based platform for exploring and ultimately predicting the design-specific interaction of ~1-10 nm particles with model cellular membranes. Nanoparticles (NP) are ever-present in foods and beverages, cosmetics, packaging, cooking products, fertilizers, pesticides, and novel pharmaceuticals, and pose significant challenges related to their increased consumer, occupational, and environmental exposure and their unique bioactivity relative to small molecules and large colloids. Thanks to rapidly advancing fabrication and characterization techniques, NPs are highly tunable in physicochemical properties such as size, surface chemistry, shape, elasticity, roughness, and crystallinity. Currently, however, the influence of these NP design parameters is highly underdeveloped, and difficult to reproducibly demonstrate in in vivo, in vitro, and even model experiments. Specifically, NP interactions with and passive transport across cellular membranes play a significant role in pharmacological and consumer product performance (biodistribution) as well as adverse outcome pathways in toxicology. We thus focus on the fundamental problem of design-specific interactions between NPs and cellular membranes, modeled to a first approximation as lipid bilayers.

To provide accurate, efficient, and robust predictions for a range of NP designs, we construct a first-of-its kind, multiscale physics-based platform linking detailed molecular dynamics (MD) simulations, continuum mechanical theory, and multi-compartment modeling. Using this platform, we examine the two most influential design parameters--size and surface chemistry--and through two main case studies: (1) the membrane permeability of sub-nanometer particles and (2) the thermodynamic stability of larger-scale, ~1-10 nm particle-membrane interactions. Within (1), we first simulate the NP-membrane interactions and transport in full detail to test the validity of Overton's Rule, a longstanding structure-property relationship, and the inhomogeneous solubility-diffusion (ISD) model, a microscopic mechanistic continuum model for transport. We show that Overton's Rule is overly simplified for describing transport across a fluctuating lipid bilayer membrane, yet that ISD model holds for small enough particles. Within this range of particles where the solubility-diffusion mechanism holds, we directly link the impact of particle chemistry in the MD simulations to transient (time-dependent) transport outcomes in the macroscopic multi-compartmental models. This allows us to both compare with and evaluate models used in experimental permeability assays and close the orders of magnitude gap between simulation-predicted and experimentally-calculated permeabilities. We also leverage our platform to construct improved structure-property relationships for the steady-state membrane permeability and structure-kinetic relationships, accounting for a wider range of particle chemistries and highlighting the imperative of time in dictating the design rules for membrane permeation.

Within case study (2), we probe larger NP-membrane interactions that implicate macroscopic membrane deformations and restructuring. Using the molecular simulations, we map out in particle size and chemistry space the putative NP-membrane interaction configurations, some of which resemble and agree with small-scale solubility-diffusion theory or large-scale membrane elastic theory (e.g. for lipid bilayer or monolayer wrapping of the NP) in stability limits and free energies and some of which require closer examination in the simulation to explain the thermodynamics. We also discover an entirely novel mechanism of interaction for ~4 nm, rough crystalline hydrophobic particles that we call "asymmetric leaflet hopping," wherein the particle preferentially inserts in one bilayer leaflet, forming a pre-pore in the membrane and inducing large-scale membrane curvature, and flips to the other leaflet over extended time scales.

We conclude with preliminary phenomenologies to outline the phase behavior of ~1-10 nm particles of varying chemistry, as well as other areas where our platform shows great promise. By accounting for a vast range of NP designs, natural lipid diversity (lipidomics), and variable compartmental size, boundary layer, and transient conditions, this platform has the potential to more intuitively and effectively inform systems-level physiologically-based pharmacokinetic models for NP biodistribution predictions, as well as structure-activity relationships for direct predictions of product efficacy and toxicity. The end result of this multiscale platform is that we can directly link a NP's microscopic physicochemical properties to its macroscopic outcomes in a dynamic biodistribution setting.