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Research Papers

Nanoparticle Optimization for Enhanced Targeted Anticancer Drug Delivery

[+] Author and Article Information
Ibrahim M. Chamseddine

Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0C3, Canada
e-mail: ibrahim.chamseddine@mail.mcgill.ca

Michael Kokkolaras

Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0C3, Canada
e-mail: michael.kokkolaras@mcgill.ca

1Corresponding author.

Manuscript received June 6, 2017; final manuscript received September 20, 2017; published online January 19, 2018. Assoc. Editor: Ram Devireddy.

J Biomech Eng 140(4), 041002 (Jan 19, 2018) (10 pages) Paper No: BIO-17-1243; doi: 10.1115/1.4038202 History: Received June 06, 2017; Revised September 20, 2017

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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Figures

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Fig. 2

Tumor structure model (dimensions are in μm)

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Fig. 3

Comparison of our results with those reported in Ref. [17]. (a) α = 1 × 1010 m−2, β = 1 × 10−5 m−2 s, K = 5, yields R2 = 0.9759. (b) α = 1 × 1010 m−2, β = 1 × 10−4 m−2 s, K = 5, yields R2 = 0.9713. (c) α = 1 × 1010 m−2, β = 1 × 10−3 m−2 s, K = 5, yields R2 = 0.9142. (d) α = 1 × 1012 m−2, β = 1 × 10−4 m−2 s, K = 1/25, yields R2 = 0.8845.

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Fig. 4

Comparison of our results with those reported in Ref.[5] (R2 = 0.9413)

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Fig. 5

Comparison of our results with those reported in Ref.[7] (R2 = 0.83)

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Fig. 8

Pareto front referring to the trade-off between η and λp

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Fig. 9

Areas that received drug exceeding EC = 20 μM after 50 s from transporting in the tissue: (a) single objective optimization: a = 1000 nm, AR = 10, η = 99.98%, λp = 16.2%. (b) MO: a = 720 nm, AR = 7.45, η = 96.80%, λp⋆=50.31%.

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Fig. 10

The isocontours of η are shown in black curves labeled by their values. The design space is shaded by the values of λp.

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Fig. 12

Pareto fronts referring to the cases of optimizing only physical properties, and physical and chemical properties

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Fig. 11

Pareto points corresponding to different average wall shear stresses in the tumor vessels

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Fig. 7

Number of NPs attached to each level of vessels: (a) a = 10 nm, AR = 1 and (b) a = 1000 nm, AR = 10

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Fig. 6

Delivery efficiency as a function of NP structure

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