3D Collagen Vascular Tumor-on-a-Chip Mimetics for Dynamic Combinatorial Drug Screening

Abstract

Disease models, including in vitro cell culture and animal models, have contributed significantly to developing diagnostics and treatments over the past several decades. The successes of traditional drug screening methods were generally hampered by not adequately mimicking critical in vivo features, such as a 3D microenvironment and dynamic drug diffusion through the extracellular matrix (ECM). To address these issues, we developed a 3D dynamic drug delivery system for cancer drug screening that mimicks drug dissemination through the tumor vasculature and the ECM by creating collagen-embedded microfluidic channels. Using this novel 3D ECM microsystem, we compared viability of tumor pieces with traditionally used 2D methods in response to three different drug combinations. Drug diffusion profiles were evaluated by simulation methods and tested in the 3D ECM microsystem and a 2D 96-well setup. Compared with the 2D control, the 3D ECM microsystem produced reliable data on viability, drug ratios, and combination indeces. This novel approach enables higher throughput and sets the stage for future applications utilizing drug sensitivity predicting algorithms based on dynamic diffusion profiles requiring only minimal patient tissue. Our findings moved drug sensitivity screening closer to clinical implications with a focus on testing combinatorial drug effects, an option often limited by the amount of available patient tissues.

Document Details

Document Type
Pub Defense Publication
Publication Date
Mar 30, 2021
Source ID
10.1158/1535-7163.mct-20-0880

Entities

People

  • Carola A. Neumann
  • John J. Skoko
  • Jun Yin
  • Li Wan
  • Mei Zhang
  • Philip R. LeDuc
  • Russell Schwartz

Organizations

  • Air Force Office of Scientific Research
  • Carnegie Mellon University
  • National Institutes of Health
  • Office of Naval Research
  • Pennsylvania Department of Health
  • University of Pittsburgh

Tags

Fields of Study

  • Biology

Readers

  • Computational Modeling and Simulation
  • Integrated Circuit Design and Technology.
  • Oncology (Cancer Research).