Emma Glass

2019 REU Student | Radhakrishnan Lab

Emma Glass is a junior studying computational and applied mathematics and statistics (CAMS) with a biomathematics concentration at The College of William and Mary in Williamsburg, Virginia. This summer Emma is part of Dr. Ravi Radhakrishnan’s lab and is working on a multi-scale computational PK/PD model for nanoparticles used in targeted drug delivery.  In the future, she plans to attend graduate school and pursue a career in academic or industrial research.

Research Abstract:

Determining Nanoparticle Biodistribution Using a Time Dependent Physiologically Based Pharmacokinetic Multi-Scale Model

In translational settings, nanoparticles (NPs) are increasingly being explored as vehicles for targeted drug delivery to healthy and cancerous tissues. Because there are nearly endless NP constructs (e.g., rigid, spherical, polymeric, etc.), sizes (nm to microns), and experimental models for translational studies, researchers are beginning to turn toward physiologically based pharmacokinetic (PBPK) models to guide in vivo experimentation and understand NP targeting behavior and performance in the human body. The purpose of this study is to create a novel multiscale model that describes NP dynamics at the subcellular, cellular, and vascular/organ levels to determine temporal biodistribution of NP in five target organs. We first developed a multicompartment organ-scale model to describe the flow of an intravenous concentration of NPs through the body and organ tissue using a combination of algebraic and ordinary differential equations (ODEs). We then created a cellular-scale model consisting of three ODEs to describe the movement of the NPs from the capillaries through the ECs and ultimately into the organ tissue. The ODEs of both scale models are solved in a coupled fashion (using a stiff ODE solver in MATLAB) to determine the temporal biodistribution of the NPs. We have successfully developed and validated a biophysically inspired multi-scale model that can describe the temporal biodistribution of NPs using experimental data, and achieving high correlation values (R). In the future, this model could be modified to include arterial branching, which will include NP uptake constants. Using PBPK models to predict NP biodistribution will ultimately result in more effective drug therapy development for humans.