Nanoparticle (NP)-based drug delivery is a promising method to increase the therapeutic index of anticancer agents with low median toxic dose. The delivery efficiency, corresponding to the fraction of the injected NPs that adhere to the tumor site, depends on NP size a and aspect ratio AR. Values for these variables are currently chosen empirically, which may not result in optimal targeted drug delivery. This study applies rigorous optimization to the design of NPs. A preliminary investigation revealed that delivery efficiency increases monotonically with a and AR. However, maximizing a and AR results in nonuniform drug distribution, which impairs tumor regression. Therefore, a multiobjective optimization (MO) problem is formulated to quantify the trade-off between NPs accumulation and distribution. The MO is solved using the derivative-free mesh adaptive direct search algorithm. Theoretically, the Pareto-optimal set consists of an infinite number of mathematically equivalent solutions to the MO problem. However, interesting design solutions can be identified subjectively, e.g., the ellipsoid with a major axis of 720 nm and an aspect ratio of 7.45, as the solution closest to the utopia point. The MO problem formulation is then extended to optimize NP biochemical properties: ligand–receptor binding affinity and ligand density. Optimizing physical and chemical properties simultaneously results in optimal designs with reduced NP sizes and thus enhanced cellular uptake. The presented study provides an insight into NP structures that have potential for producing desirable drug delivery.

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