@article {20443, title = {Breast tumor-on-chip models: from disease modelling to personalized drug screening}, journal = {Journal of Control Release }, year = {2021}, month = {2021-01-06 00:00:00}, publisher = {Elsevier B. V.}, abstract = {

Subia B, UR Dahiya, S Mishra, J Ayache, GV Casquillas, D Caballero, RL Reisand SC Kundu(2021) Breast tumor-on-chip models: from disease modelling to personalized drug screening. Journal of Control Release (in press) https://doi.org/10.1016/j.jconrel.2020.12.057\ 

Abstract:

Breast cancer is one of the leading causes of mortality worldwide being the most common cancer among women. Despite the significant progress obtained during the past years in the understanding of breast cancer pathophysiology, women continue to die from it. Novel tools and technologies are needed to develop better diagnostic and therapeutic approaches, and to better understand the molecular and cellular players involved in the progression of this disease. Typical methods employed by the pharmaceutical industry and laboratories to investigate breast cancer etiology and evaluate the efficiency of new therapeutic compounds are still based on traditional tissue culture flasks and animal models, which have certain limitations. Recently, tumor-on-chip technology emerged as a new generation of in vitro disease model to investigate the physiopathology of tumors and predict the efficiency of drugs in a native-like microenvironment. These microfluidic systems reproduce the functional units and composition of human organs and tissues, and importantly, the rheological properties of the native scenario, enabling precise control over fluid flow or local gradients. Herein, we review the most recent works related to breast tumor-on-chip for disease modeling and drug screening applications. Finally, we critically discuss the future applications of this emerging technology in breast cancer therapeutics and drug development.

}, keywords = {breast cancer, drug screening, Industrial applications, Microfluidics, Tumor-on-chip}, issn = {0168-3659}, doi = {https://doi.org/10.1016/j.jconrel.2020.12.057}, url = {https://www.sciencedirect.com/science/article/pii/S0168365921000031 }, author = {Subia, B. and Dahiya, U. R. and Mishra, S. and Ayache, J. and Casquillas, J. G. V. and Caballero, D. and Reis, R. L. and Kundu, S. C.}, editor = {Torchilin, V. P.} }