@article {20203, title = {Monitoring pH variation of a 3D breast cancer model in a flow-through microreactor}, booktitle = {Forecast 2nd Workshop}, year = {2019}, month = {2019-07-12 00:00:00}, address = {porto}, abstract = {

Cancer is responsible for an estimated 9.6 million deaths in 2018, being the second leading cause of death worldwide.1 However, due the complexity and diversity of the disease, an effective treatment, with insignificant health risk for the patient, is still not available. Therefore, cost-effective drug testing platforms capable of reproducing the complexity of cancer biology and monitoring its response are urgent and necessary.\  We present a novel 3D drug testing platform capable of sustaining a breast cancer model and simultaneously monitor the tumor extracellular pH, which can be used as a diagnostic biomarker for tumor aggressiveness and metastases.2

The platform consists of 300 {\textmu}l central chamber, where 3D gellan gum matrix measuring 6.6 {\textpm} 0.3 x 4.9 {\textpm} 0.5 mm is inserted, and tumor cells are cultured. The micro-reactor allows controlled flow rates mimicking in vivo tumor microenvironment. In addition to continuous monitoring of pH, since it contains three integrated electrodes for voltammetry measurements. {\textquotedblleft}Spongy-like{\textquotedblright} hydrogels fabricated using gellan gum are highly porous, capable of 100\% recovery after deformation, presenting 12 months of-the-shelf stability. The chemistry of gelan gum and microstructure of the prepared hydrogels allow the entrapment, growth and proliferation of cells.3 MCF7 breast cancer cells and fibroblasts are dynamically cultured in the 3D microreactor, showing 70\% of cell viability at day 15. The extracellular pH measure after day 15 was 5.8, similar value was reported in literature.4

The combination of the microreactor, with a 3D tissue engineered tumor model and the electrodes makes this platform a potential tumor-on-chip device for monitoring the response of personalized tumor models to drugs.

1\ \ \ \ \ \ \ \ \ \ \  Cancer Research UK, Worldw. Cancer Mortal. Stat., https://www.cancerresearchuk.org/health-professional/cancer-statistics/worldwide-cancer/mortality, (accessed 28 February 2019).

2\ \ \ \ \ \ \ \ \ \ \  M. Chen, C. Chen, Z. Shen, X. Zhang, Y. Chen, F. Lin, X. Ma, C. Zhuang, Y. Mao, H. Gan, P. Chen, X. Zong and R. Wu, Oncotarget, 2017, 8, 45759{\textendash}45767.

3\ \ \ \ \ \ \ \ \ \ \  L. P. da Silva, M. T. Cerqueira, R. A. Sousa, R. L. Reis, V. M. Correlo and A. P. Marques, Acta Biomater., 2014, 10, 4787{\textendash}4797.

4\ \ \ \ \ \ \ \ \ \ \  X. Zhang, Y. Lin and R. J. Gillies, J. Nucl. Med., 2010, 51, 1167{\textendash}1170.

}, keywords = {3D cancer model, pH monitoring}, author = {Barbosa, A. I. and Kant, K. and Rebelo, R. and Brancato, V. and Abalde, S. and Wu, L. and Kundu, S. C. and Correlo, V. M. and Di{\'e}guez, L. and Reis, R. L.} }