Cancer Modeling‐on‐a‐Chip with Future Artificial Intelligence Integration. Issue 50 (13th November 2019)

• Record Type: Journal Article
• Authors: Fetah, Kirsten Lee; DiPardo, Benjamin J.; Kongadzem, Eve‐Mary; Tomlinson, James S.; Elzagheid, Adam; Elmusrati, Mohammed; Khademhosseini, Ali; Ashammakhi, Nureddin
• Is Part Of: Small. Volume 15:Issue 50(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-13
• Subjects: artificial intelligence -- cancer models -- chemotherapy -- microfluidics -- organ‐on‐a‐chip; Nanotechnology -- Periodicals; Nanoparticles -- Periodicals; Microtechnology -- Periodicals; 620.5
• DOI: 10.1002/smll.201901985
• Languages: English
• ISSNs: 1613-6810

Abstract

Abstract: Cancer is one of the leading causes of death worldwide, despite the large efforts to improve the understanding of cancer biology and development of treatments. The attempts to improve cancer treatment are limited by the complexity of the local milieu in which cancer cells exist. The tumor microenvironment (TME) consists of a diverse population of tumor cells and stromal cells with immune constituents, microvasculature, extracellular matrix components, and gradients of oxygen, nutrients, and growth factors. The TME is not recapitulated in traditional models used in cancer investigation, limiting the translation of preliminary findings to clinical practice. Advances in 3D cell culture, tissue engineering, and microfluidics have led to the development of "cancer‐on‐a‐chip" platforms that expand the ability to model the TME in vitro and allow for high‐throughput analysis. The advances in the development of cancer‐on‐a‐chip platforms, implications for drug development, challenges to leveraging this technology for improved cancer treatment, and future integration with artificial intelligence for improved predictive drug screening models are discussed. Abstract : The tumor microenvironment is not recapitulated in traditional models, limiting translation of preliminary findings to clinical practice. "Cancer‐on‐a‐chip" platforms model the tumor microenvironment in vivo and allow for high‐throughput analysis. The latest advances in cancer‐on‐a‐chip platforms, implications for studying cancer pathophysiology, diagnostics, personalized cancer treatment, and future integration with artificial intelligence for improved predictive drug screening models are presented.