Brain-on-a-chip Devices for Drug Screening and Disease Modeling Applications

Abstract

:Neurodegenerative disorders are related to the progressive functional loss of the brain, often connected to emotional and physical disability and, ultimately, to death. These disorders, strongly connected to the aging process, are becoming increasingly more relevant due to the increase of life expectancy. Current pharmaceutical treatments poorly tackle these diseases, mainly acting only on their symptomology. One of the main reasons of this is the current drug development process, which is not only expensive and time-consuming but, also, still strongly relies on animal models at the preclinical stage.:Organ-on-a-chip platforms have the potential to strongly impact and improve the drug screening process by recreating in vitro the functionality of human organs. Patient-derived neurons from different regions of the brain can be directly grown and differentiated on a brain-on-a-chip device where the disease development, progression and pharmacological treatments can be studied and monitored in real time. The model reliability is strongly improved by using human-derived cells, more relevant than animal models for pharmacological screening and disease monitoring. The selected cells will be then capable of proliferating and organizing themselves in the in vivo environment thanks to the device architecture, materials selection and bio-chemical functionalization.:In this review, we start by presenting the fundamental strategies adopted for brain-on-a-chip devices fabrication including e.g., photolithography, micromachining and 3D printing technology. Then, we discuss the state-of-theart of brain-on-a-chip platforms including their role in the study of the functional architecture of the brain e.g., blood-brain barrier, or of the most diffuse neurodegenerative diseases like Alzheimer’s and Parkinson’s. At last, the current limitations and future perspectives of this approach for the development of new drugs and neurodegenerative diseases modeling will be discussed.