You are here

A Complete Approach to Predict Biodistribution of Nanomaterials Within Animal Species from In-vitro Data

Download pdf | Full Screen View

Date Issued:
2019
Abstract/Description:
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.
Title: A Complete Approach to Predict Biodistribution of Nanomaterials Within Animal Species from In-vitro Data.
34 views
15 downloads
Name(s): Price, Edward, Author
Gesquiere, Andre, Committee Chair
Huo, Qun, Committee Member
Kolpashchikov, Dmitry, Committee Member
Rex, Matthew, Committee Member
Ebert, Steven, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2019
Publisher: University of Central Florida
Language(s): English
Abstract/Description: 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.
Identifier: CFE0007900 (IID), ucf:52747 (fedora)
Note(s): 2019-05-01
Ph.D.
Sciences,
Doctoral
This record was generated from author submitted information.
Subject(s): nanomaterial -- biodistribution -- PBPK -- pharmacokinetics -- in-vitro -- in-vivo
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007900
Restrictions on Access: public 2019-11-15
Host Institution: UCF

In Collections