385194 A Predictive Pharmacodynamic Model for the Transport and Biodistribution of Functionalized Nanocarriers Using Generalized Langevin Equations and the Dynamical Density-Functional Theory

Thursday, November 20, 2014: 2:45 PM
Crystal Ballroom A/F (Hilton Atlanta)
Hsiu-Yu Yu1, Natesan Ramakrishnan2, David M. Eckmann3, Portonovo S. Ayyaswamy4 and Ravi Radhakrishnan2, (1)Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, (2)Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, (3)School of Medicine, University of Pennsylvania, Philadelphia, PA, (4)Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA

Selectivity and specificity of intravascular delivery of functionalized nanocarriers to organs or tissues critically impact the efficacy of targeted therapeutic strategies. Complementary to exhaustive in vivo monitoring and imaging experiments to obtain the biodistribution of nanocarriers in different organs, a predictive pharmacodynamic model that has the capability to interface with genomic and radiological data would be valuable to guide the optimal design of functionalized nanocarriers for specific targeted biophysical environments. In this work, we focus on nanocarriers surface-functionalized with antibodies specific to the intracellular adhesion molecule-1 (ICAM-1) expressed on endothelial cells lining the blood vessel. ICAM-1 presents a viable and optimal target as it is expressed nominally in normal endothelial cells, but the expression levels increase at the site of tissue inflammation and/or injury, due to the activation of endothelial cells by several growth factors such as TNF-alpha. Animal experiments carried out by our collaborators [1] have established that the specificity of targeting such nanocarriers to inflamed endothelium in the lung can be achieved by suitably optimizing design parameters such as antibody grafting density. However, these experiments have also shown that the optimal design parameters are organ specific and have to be varied depending on physiological and genomic markers including vascular geometries, hemodynamics, and protein expression levels. Given the difficulty in performing animal experiments to exhaustively cover the many scenarios that would be presented, and the uncertainty in extrapolating these results to physiological conditions in the human, an in silico framework for the prediction of biodistribution is highly desirable. Recent genomic experiments have quantified cell type specific expression of ICAM-1 across different organs in three organisms, namely mice, rat, and human. We show here that through combining this information with radiological data on vascular and hemodynamic characterization in these organisms, we can construct a highly quantitative and predictive computational framework for in silico pharmacology.

Specifically, we show how a coarse-grained approach that combines continuum fluid mechanics and statistical mechanics may be used to predict organ-specific drug delivery and distribution. With the vessel diameter and segmental length distributions for a given organ vasculature obtained from micro computed tomography (micro CT) images, we determine the blood flow rate and the mean hematocrit within each vessel tube. Within a segment of vessel tube, the migration of red blood cells (RBCs) and the corresponding margination of the nanocarriers are predicted through a dynamical density-functional theory (DDFT) that minimizes the RBC free energy in the presence of flow. Given the organ-specific ICAM-1 expression from genomic data, the potential of mean force (PMF) characterizing the free energy landscape of the marginated nanocarriers with the endothelial cells in the given organ is obtained by exhaustively sampling over the conformational degrees of freedom and the multivalent binding interactions using Metropolis Monte Carlo (MC) simulations [2]. To integrate (i.e. bridging the multiple spatial and temporal scales) the results of the DDFT and the MC simulations, we perform a generalized Langevin dynamics simulation for nanocarrier motion and ligand-receptor pair relaxation subject to the aforementioned RBC-driven marginating effect, ligand-receptor binding interaction (PMF), glycocalyx resistance, hydrodynamic interactions, and thermal fluctuations. Through analyzing the velocity and position autocorrelation functions of the nanocarrier as well as the position autocorrelation function of the receptor protein binding site, we determine the nanocarrier-cell binding affinity in the presence of physiological conditions along the entire vascular network defined by the micro CT data. Comparing the margination and adhesive landscape of nanocarriers in different organs, we predict the biodistribution of ICAM-1 targeting nanocarriers across different organs in three organisms, namely, mice, rat, and human.

We acknowledge support from NIH through grant NIH 1R01EB006818-05 for the development of the DDFT methods and NIH U01-EB016027 for the development of multiscale bridging techniques.

References

[1] Reduction of nanoparticle avidity enhances the selectivity of vascular targeting and PET detection of pulmonary inflammation, B. Zern, A.-M. Chacko, J. Liu, C. F. Greineder, E. R. Blankemeyer, R. Radhakrishnan, V. Muzykantov, ACS Nano, 7(3), 2461-2469, 2013.
[2] A computational model for nanocarrier binding to endothelium validated using in vivo, in vitro, and atomic force microscopy experiments, J. Liu, G. E. R. Weller, B. Zern, P. S. Ayyaswamy, D. M. Eckmann, V. Muzykantov, R. Radhakrishnan, Proc. Natl. Acad. Sci. U.S.A., 107(38), 16530-16535, 2010.


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See more of this Session: Thermodynamics at the Nanoscale II
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