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.
Highlights
Targeted cancer nanotherapy relies on nanocarriers to deliver anticancer agents safely to tumors while minimizing systemic toxicity
Other in vivo studies are reviewed in Zhang et al.[10], for which nanoparticle design recommendations were based on increasing circulation time, taking advantage of the enhanced penetration and retention (EPR) effect, and maintaining high drug entrapment efficiency in the nanoparticle synthesis stage
Large nanoparticles have strong binding affinity, but they are exposed to high hemodynamic loadings that may dissociate them from the endothelium
Summary
Targeted cancer nanotherapy relies on nanocarriers to deliver anticancer agents safely to tumors while minimizing systemic toxicity. Quantifying nanotherapy efficacy using in vitro assays may require lengthy preparatory steps, which include setting up proper cell lines and reagents, synthesizing nanoparticles, and tailoring experimental protocols. The complexity of these studies is further escalated in vivo. Is acquiring and maintaining animal models expensive, but there may exist a long process from the initiation of oncogenic mutation or transplantation of xenografts, tumor proliferation, to monitoring tumor regression after nanoparticle injection This requires advanced imaging techniques and multidisciplinary expertise. For these reasons, computational modeling offers an attractive option for exploratory evaluation of nanoparticle design that complements experimental work, including investigation of a wide range of variables
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