In Vitro Validation of Targeting and Comparison to Mathematical Modeling

  • Jill M. Steinbach-Rankins
  • Michael R. CaplanEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1831)


Nanoparticle and other drug delivery platforms have demonstrated promising potential for the delivery of therapeutics or imaging agents in a specific and targeted manner. While a variety of drug delivery platforms have been applied to medicine, in vitro and in silico optimization and validation of these targeting constructs needs to be conducted to maximize in vivo delivery and efficacy. Here, we describe the mathematical and experimental models to predict and validate the transport of a peptide targeting construct through a mock tissue environment to specifically target tumor cells, relative to non-tumor cells. We provide methods to visualize and analyze fluorescence microscopy images, and also describe the methods for creating a finite element model (FEM) that validates important parameters of this experimental system. By comparing and contrasting mathematical modeling results with experimental results, important information can be imparted to the design and functionality of the targeting construct. This information will help to optimize construct design for future therapeutic delivery applications.

Key words

Convection-enhanced delivery Diffusion Multivalent targeting Mass transport Finite element modeling Cancer targeting Peptide delivery 



The authors thank our funding sources: NIH (R01 CA097360, R21 NS051310), Arizona Biomedical Research Commission contract #0606 and our collaborators Robert J. Gillies (Moffitt Cancer Institute, University of South Florida), Michael Berens (Translational Genomic Institute), Josef Vagner (University of Arizona), and Heather Maynard (UCLA). Recognition is given to Dr. Michael Berens, Dr. Dominique Hoelzinger, and Dr. Tim Demuth for assistance with obtaining and validating cell lines. We thank Dr. Christine Pauken for assisting with the imaging. We also thank Dr. Dan Brune and John Lopez for their generous assistance and advice with the construct synthesis.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jill M. Steinbach-Rankins
    • 1
  • Michael R. Caplan
    • 2
    Email author
  1. 1.Department of Bioengineering and Center for Predictive MedicineUniversity of LouisvilleLouisvilleUSA
  2. 2.School of Biological and Health Systems Engineering, Fulton Schools of EngineeringArizona State UniversityTempeUSA

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