Design Optimization of Tumor Vasculature-Bound Nanoparticles

Abstract

Nanotherapy may constitute a promising approach to target tumors with anticancer drugs while minimizing systemic toxicity. Computational modeling can enable rapid evaluation of nanoparticle (NP) designs and numerical optimization. Here, an optimization study was performed using an existing tumor model to find NP size and ligand density that maximize tumoral NP accumulation while minimizing tumor size. Optimal NP avidity lies at lower bound of feasible values, suggesting reduced ligand density to prolong NP circulation. For the given set of tumor parameters, optimal NP diameters were 288 nm to maximize NP accumulation and 334 nm to minimize tumor diameter, leading to uniform NP distribution and adequate drug load. Results further show higher dependence of NP biodistribution on the NP design than on tumor morphological parameters. A parametric study with respect to drug potency was performed. The lower the potency of the drug, the bigger the difference is between the maximizer of NP accumulation and the minimizer of tumor size, indicating the existence of a specific drug potency that minimizes the differential between the two optimal solutions. This study shows the feasibility of applying optimization to NP designs to achieve efficacious cancer nanotherapy, and offers a first step towards a quantitative tool to support clinical decision making.

Evaluation of Drug-Loaded Gold Nanoparticle Cytotoxicity as a Function of Tumor Vasculature-Induced Tissue Heterogeneity

“…In the case of the 24 h IC50 value, the concentrations are similar regardless of level of tissue heterogeneity, with an initial sharp peak at 2.5 h post-treatment initiation followed by a sharp drop to 35% of initial concentration within 4 h. The concentration then declines slowly afterwards, to 10% of initial by 30 h. For the other three drug strengths, the LOW case exhibits the highest concentration of nanoparticles overall, with 30% still in tissue after 30 h. For the 48h IC50 case, the VERY LOW case retains the second highest concentration, while for both 72 and 96 h, it is similar to the MEDIUM and HIGH conditions, decreasing to 20% of initial value by 30 h. Noticeably, the nanoparticle concentrations are more heterogeneous in time for the 48 and 72 h cases, while the 24 and 96 h evince more consistent profiles. This suggests that the drug strength is also a key parameter that influences the nanoparticle concentration as the tissue responds temporally and spatially to the drug, and is consistent with recent findings from an optimization model applied to this tumor model system 3 .…”

“…Since drug strength is a key clinical parameter underlying both response and systemic toxicity, the overall results support the notion that drug strength remains a critical modeling parameter for predictive evaluation. This is consistent with recent modeling work that combined an optimization approach to determine optimal nanoparticle sizes for maximum tumor regression 3 .…”

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

“…Biodistribution/physiological pharmacokinetics Li et al, 2010Li et al, , 2012Li and Reineke, 2011;Dogra et al, 2020a Transport in avascular tumors Gao et al, 2013;Curtis et al, 2016a Transport in irregularly vascularized tumors van de Ven et al, 2013;Wu et al, 2014;Curtis et al, 2016a;Miller and Frieboes, 2019a,b Transport based on nanoparticle physical characteristics Decuzzi and Ferrari, 2006;Decuzzi et al, 2009;Godin et al, 2010b Binding to tumor vasculature Frieboes et al, 2013;Curtis et al, 2015;Chamseddine et al, 2018Chamseddine et al, , 2020 Interactions with macrophages Leonard et al, , 2017Leonard et al, , 2020Mahlbacher et al, 2018 Intracellular pharmacokinetics Li et al, 2013;Miller and Frieboes, 2019b For tumor detection Reichel et al, 2015 For hyperthermia applications Kaddi et al, 2013 For drug delivery van de Ven et al, 2012;Li et al, 2013;Curtis et al, 2015Curtis et al, , 2016aLeonard et al, , 2017Leonard et al, , 2020Chamseddine et al, 2018Chamseddine et al, , 2020Miller and Frieboes, 2019a,b; FIGURE 4 | Effect of repeated therapy on simulated breast cancer liver metastsis lesions over 9 day, showing (A) drug (as% of maximum blood levels) and (B) tumor effect (as% of initial lesion diameter) after nAb-PTX and MSV-nAb-PTX injection. In all cases, therapy is initiated at 0, 3, and 6 day.…”

In silico modelling of cancer nanomedicine, across scales and transport barriers

“…This coupled model of tumour growth, vasculature system and nanoparticle adhesion has since been used as a tool for optimising nanoparticle design 77 . Here, the model was used to find the optimal nanoparticle diameter for accumulation and penetration.…”