Multiscale modeling and uncertainty quantification in nanoparticle-mediated drug/gene delivery

Liu, Ying; Stroberg, Wylie; Lee, Tae-Rin; Kim, Hansung; Man, Han; Ho, Dean; Decuzzi, Paolo; Liu, Wing Kam

doi:10.1007/s00466-013-0953-5

Cited by 56 publications

(45 citation statements)

References 195 publications

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“…They predicted a critical NP size for endocytosis consistent with experimental results 87 . The studies of Yang and Ma 88 and Ying Li 89 both show that shape and initial orientations affect the endocytosis process. Endocytosis is governed by the bending energy of the cell membrane and the ligand-receptor binding energy.…”

Section: Modeling Applications In Drug Delivery and Nanoparticle Dmentioning

confidence: 99%

Design of Nanoparticle-Based Carriers for Targeted Drug Delivery

Trase

Ren

et al. 2016

Journal of Nanomaterials

211

130

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Nanoparticles have shown promise as both drug delivery vehicles and direct antitumor systems, but they must be properly designed in order to maximize efficacy. Computational modeling is often used both to design new nanoparticles and to better understand existing ones. Modeled processes include the release of drugs at the tumor site and the physical interaction between the nanoparticle and cancer cells. In this article, we provide an overview of three different targeted drug delivery methods (passive targeting, active targeting and physical targeting), compare methods of action, advantages, limitations, and the current stage of research. For the most commonly used nanoparticle carriers, fabrication methods are also reviewed. This is followed by a review of computational simulations and models on nanoparticle-based drug delivery.

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Section: Modeling Applications In Drug Delivery and Nanoparticle Dmentioning

confidence: 99%

Design of Nanoparticle-Based Carriers for Targeted Drug Delivery

Trase

Ren

et al. 2016

Journal of Nanomaterials

211

130

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“…Relatively recently, it has been recognized that multiscale modelings and simulations with uncertain computational model require adapted algorithms and very powerful computers [93]. In this framework, advanced multiscale modelings under uncertainties have been developed (see for instance [13,52,55,83,84,101]). When these works were published, design optimization with stochastic computational model was not still really possible for large scale multiscale computational models.…”

Section: Stochastic Modeling Of Biological Tissuesmentioning

confidence: 99%

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

Soize

2017

Comput Mech

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This paper deals with the optimal design of a titanium mesoscale implant in a cortical bone for which the apparent elasticity tensor is modeled by a non-Gaussian random field at mesoscale, which has been experimentally identified. The external applied forces are also random. The design parameters are geometrical dimensions related to the geometry of the implant. The stochastic elastostatic boundary value problem is discretized by the finite element method. The objective function and the constraints are related to normal, shear, and von Mises stresses inside the cortical bone. The constrained nonconvex optimization problem in presence of uncertainties is solved by using a probabilistic learning algorithm that allows for considerably reducing the numerical cost with respect to the classical approaches.

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“…However, there is not a truly multi-scale computational method connecting these different models together to bridge different spatial and temporal scales [10]. In future, we expect rsif.royalsocietypublishing.org J. R. Soc.…”

Section: Uncertainty Quantification Of Drug Delivery Processmentioning

confidence: 99%

USNCTAM perspectives on mechanics in medicine

Bao

Bazilevs

Chung

et al. 2014

J. R. Soc. Interface.

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Over decades, the theoretical and applied mechanics community has developed sophisticated approaches for analysing the behaviour of complex engineering systems. Most of these approaches have targeted systems in the transportation, materials, defence and energy industries. Applying and further developing engineering approaches for understanding, predicting and modulating the response of complicated biomedical processes not only holds great promise in meeting societal needs, but also poses serious challenges. This report, prepared for the US National Committee on Theoretical and Applied Mechanics, aims to identify the most pressing challenges in biological sciences and medicine that can be tackled within the broad field of mechanics. This echoes and complements a number of national and international initiatives aiming at fostering interdisciplinary biomedical research. This report also comments on cultural/educational challenges. Specifically, this report focuses on three major thrusts in which we believe mechanics has and will continue to have a substantial impact. (i) Rationally engineering injectable nano/microdevices for imaging and therapy of disease. Within this context, we discuss nanoparticle carrier design, vascular transport and adhesion, endocytosis and tumour growth in response to therapy, as well as uncertainty quantification techniques to better connect models and experiments. (ii) Design of biomedical devices, including point-of-care diagnostic systems, model organ and multi-organ microdevices, and pulsatile ventricular assistant devices. (iii) Mechanics of cellular processes, including mechanosensing and mechanotransduction, improved characterization of cellular constitutive behaviour, and microfluidic systems for single-cell studies.

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Multiscale modeling and uncertainty quantification in nanoparticle-mediated drug/gene delivery

Cited by 56 publications

References 195 publications

Design of Nanoparticle-Based Carriers for Targeted Drug Delivery

Design of Nanoparticle-Based Carriers for Targeted Drug Delivery

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

USNCTAM perspectives on mechanics in medicine

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