4.2 Article

Multiscale Modeling of Functionalized Nanocarriers in Targeted Drug Delivery

期刊

CURRENT NANOSCIENCE
卷 7, 期 5, 页码 727-735

出版社

BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/157341311797483826

关键词

Monte Carlo; absolute binding free energy; multivalent interactions; shear enhanced binding; glycocalyx; cell membrane; molecular dynamics

资金

  1. NSF [CBET-0853389, CBET-0853539]
  2. NIH [NIBIB-1R01EB006818, NHLBI-1R01HL087036]
  3. National Partnership for Advanced Computational Infrastructure (NPACI) [MRAC MCB060006]
  4. Division Of Materials Research
  5. Direct For Mathematical & Physical Scien [1120901] Funding Source: National Science Foundation
  6. Div Of Chem, Bioeng, Env, & Transp Sys
  7. Directorate For Engineering [0853539, 0853389] Funding Source: National Science Foundation

向作者/读者索取更多资源

Targeted drug delivery using functionalized nanocarriers (NCs) is a strategy in therapeutic and diagnostic applications. In this paper we review the recent development of models at multiple length and time scales and their applications to targeting of antibody functionalized nanocarriers to antigens (receptors) on the endothelial cell (EC) surface. Our mesoscale (100 nm-1 mu m) model is based on phenomenological interaction potentials for receptor-ligand interactions, receptor-flexure and resistance offered by glycocalyx. All free parameters are either directly determined from independent biophysical and cell biology experiments or estimated using molecular dynamics simulations. We employ a Metropolis Monte Carlo (MC) strategy in conjunction with the weighted histogram analysis method (WHAM) to compute the free energy landscape (potential of mean force or PMF) associated with the multivalent antigen-antibody interactions mediating the NC binding to EC. The binding affinities (association constants) are then derived from the PMF by computing absolute binding free energy of binding of NC to EC, taking into account the relevant translational and rotational entropy losses of NC and the receptors. We validate our model predictions by comparing the computed binding affinities and PMF to a wide range of experimental measurements, including in vitro cell culture, in vivo endothelial targeting, atomic force microscopy (AFM), and flow chamber experiments. The model predictions agree quantitatively with all types experimental measurements. On this basis, we conclude that our computational protocol represents a quantitative and predictive approach for model driven design and optimization of functionalized NCs in targeted vascular drug delivery.

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