Abstract
Smart drug-design for antibody and nanomaterial-based therapies allows for optimization of drug efficacy and more efficient early-stage pre-clinical trials. The ideal drug must display maximum efficacy at target tissue sites, but to track and predict distribution to these sites, one must have a mechanistic understanding of the kinetics involved with the individual cells of the tissue itself. This process can be tracked through biological simulations coupled with in-vitro approaches, which result in a rapid and efficient in-depth understanding of drug transport within tissue vasculature and cellular environment. As a result, it becomes possible to predict drug biodistribution within live animal tissue cells without the need for animal studies. Herein, we use in-vitro assays to translate transport kinetics to whole-body animal simulations to predict drug distribution from vasculature into individual tissue cells for the first time. Our approach is based on rate constants obtained from an in-vitro assay that accounts for cell-induced degradation, which are translated to a complete animal simulation to predict nanomedicine biodistribution at the single cell level. This approach delivers predictions for therapies of varying size and type for multiple species of animals solely from in-vitro data. Thus, we expect this work to assist in refining, reducing, and replacing animal testing, while at the same time, giving scientists a new perspective during early stages of drug development.
Notes
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Graduation Date
2019
Semester
Spring
Advisor
Gesquiere, Andre
Degree
Doctor of Philosophy (Ph.D.)
College
College of Sciences
Department
Chemistry
Degree Program
Chemistry
Format
application/pdf
Identifier
CFE0007900
URL
http://purl.fcla.edu/fcla/etd/CFE0007900
Language
English
Release Date
November 2019
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
STARS Citation
Price, Edward, "A Complete Approach to Predict Biodistribution of Nanomaterials Within Animal Species from In-vitro Data" (2019). Electronic Theses and Dissertations. 6689.
https://stars.library.ucf.edu/etd/6689