Ph.D. Candidate University of Georgia, United States
Disclosure(s):
Ashley Ray, B.S., Biochemistry & Molecular Biology: No financial relationships to disclose
Objectives: P-glycoprotein (Pgp) contributes to drug distribution (oral bioavailability[1], blood-brain barrier[2], stem cells and cancer chemoresistance[3]). Predicting drug access to tissues and cells is important to understand the drug response in-vivo. Unfortunately, Pgp specificity for FDA drugs is unclear due to technical variability in assays that quantify barrier ratios and enzyme kinetics (kcat/Km) using non-cellular experimental models[4]. Our research improves Pgp specificity scores by leveraging new Pgp expression and function datasets including drug screening data, Pgp expression data (cell lines, tissues) and barrier ratio datasets.
Methods: Emerging big datasets (gene expression, drug response etc.) provide an opportunity to simultaneously measure Pgp quantity and function across tissues and cell lines. At the same time, physics-informed machine learning infers biophysical kinetic parameters from these datasets. Here, we experimentally and computationally integrate functional dataset information to better understand Pgp specificity. Pgp function is measured through barrier ratios (tissue and cells—Calcein AM[5]) and drug response data in cells. Pgp expression is quantified through RNAseq[6], proteomics and flow cytometry. We take these pre-existing datasets and create an experimental procedure that leverages both data types. We utilize multilinear regression as our machine learning approach. Our rationale is based on underlying Michaelis-Menten physics and enables us to make functional assays for patient-specific diagnostics.
Results: We obtained consensus scores for Pgp specificity across 1,500 FDA approved drugs (PRISM[7]) and validated them experimentally in a subset of 76 substrates selected to represent top and bottom drugs for Pgp specificity. These scores can be used to calibrate clinical diagnostics (Pgp expression), and our experimental platform can be used to quantify Pgp function in clinical samples.
Conclusions: Overall, we have developed a parallel computational and experimental procedure to estimate Pgp selectivity in live cells. The long term implications for this research include multidrug resistance diagnostics, tissue distribution predictions, drug-drug interaction predictions and transporter kinetics measurement (cellular vs protein) improvements.
