A multiscale modeling study of particle size effects on the tissue penetration efficacy of drug-delivery nanoparticles

Abstract

Background: Particle size is a key parameter for drug-delivery nanoparticle design. It is believed that the size of a nanoparticle may have important effects on its ability to overcome the transport barriers in biological tissues. Nonetheless, such effects remain poorly understood. Using a multiscale model, this work investigates particle size effects on the tissue distribution and penetration efficacy of drug-delivery nanoparticles. Results: We have developed a multiscale spatiotemporal model of nanoparticle transport in biological tissues. The model implements a time-adaptive Brownian Dynamics algorithm that links microscale particle-cell interactions and adhesion dynamics to tissue-scale particle dispersion and penetration. The model accounts for the advection, diffusion, and cellular uptakes of particles. Using the model, we have analyzed how particle size affects the intra-tissue dispersion and penetration of drug delivery nanoparticles. We focused on two published experimental works that investigated particle size effects in in vitro and in vivo tissue conditions. By analyzing experimental data reported in these two studies, we show that particle size effects may appear pronounced in an in vitro cell-free tissue system, such as collagen matrix. In an in vivo tissue system, the effects of particle size could be relatively modest. We provide a detailed analysis on how particle-cell interactions may determine distribution and penetration of nanoparticles in a biological tissue. Conclusion: Our work suggests that the size of a nanoparticle may play a less significant role in its ability to overcome the intra-tissue transport barriers. We show that experiments involving cell-free tissue systems may yield misleading observations of particle size effects due to the absence of advective transport and particle-cell interactions.

Physical Properties of Nanoparticles That Result in Improved Cancer Targeting

“…Furthermore, using a mathematical model, Islam et al [ 81 ] found that the NP-cell interactions may moderate the particle size effect. The addition of coatings and surfactants such as peptides (Tat, PEG, and BVB) becomes important in order to achieve an active targeting of NPs that does not rely solely on the EPR effect.…”

“…They concluded that particle-size effect may dominate in a cell-free system in the absence of cell-particle interactions, and this effect is more pronounced in vitro. On the other hand, in vivo studies have shown that particle-cell interactions may moderate the particle-size effect, which might be the primary determinant of tissue penetration [ 81 ].…”

“… Illustration of the time-adaptive BD algorithm: the green circle represents the NPs, the large gray area represents the cells, and P is the probability of NPs to be captured by the NPs (adapted from [ 81 ]; open access no permission required). …”

Biodegradable Polymeric Nanoparticles for Drug Delivery to Solid Tumors

“…The physico-chemical properties of nanoparticles, such as size, shape, stability, drug-release profiles and surface characteristics, can all affect their behavior in complex biological environments. At the same time pH and ionic strength of the dispersion medium can influence biodistribution, pharmacological efficacy, and safety of the entrapped drug(s) ( Figure 4 ) ( Islam et al, 2017 ). Indeed, these parameters can significantly change in the biological milieu, due to the adsorption of proteins onto the nanoparticle surface.…”

Furin-responsive triterpenine-based liposomal complex enhances anticervical cancer therapy through size modulation

“…Whether the delivery system could efficiently accumulate at tumor sites and deeply penetrate within tumor tissues is an obstacle that needs to be overcome. The particle size of a nano-sized drug delivery system is the most crucial factor for accumulation and penetration in tumor tissues (Ding et al., 2012 ; Wang et al., 2015 ; Scenario, 2016 ; Islam et al., 2017 ; Zhang et al., 2017 ; Ruan et al., 2019 ; Yu et al., 2019 ; Tang et al, 2013 ).…”