Brain physiome: A concept bridging in vitro 3D brain models and in silico models for predicting drug toxicity in the brain

Authors
Seo, YoojinBang, SeokyoungSon, JeongtaeKim, DongsupJeong, YongKim, PilnamYang, JihunEom, Joon-HoChoi, NakwonKim, Hong Nam
Issue Date
2022-07
Publisher
Elsevier
Citation
Bioactive Materials, v.13, pp.135 - 148
Abstract
In the last few decades, adverse reactions to pharmaceuticals have been evaluated using 2D in vitro models and animal models. However, with increasing computational power, and as the key drivers of cellular behavior have been identified, in silico models have emerged. These models are time-efficient and cost-effective, but the prediction of adverse reactions to unknown drugs using these models requires relevant experimental input. Accordingly, the physiome concept has emerged to bridge experimental datasets with in silico models. The brain physiome describes the systemic interactions of its components, which are organized into a multilevel hierarchy. Because of the limitations in obtaining experimental data corresponding to each physiome component from 2D in vitro models and animal models, 3D in vitro brain models, including brain organoids and brain-on-a-chip, have been developed. In this review, we present the concept of the brain physiome and its hierarchical organization, including cell-and tissue-level organizations. We also summarize recently developed 3D in vitro brain models and link them with the elements of the brain physiome as a guideline for dataset collection. The connection between in vitro 3D brain models and in silico modeling will lead to the establishment of cost-effective and time-efficient in silico models for the prediction of the safety of unknown drugs.
Keywords
CENTRAL-NERVOUS-SYSTEM; EXTRACELLULAR-SPACE; CEREBRAL ORGANOIDS; SYNAPSE FORMATION; CELL DIVERSITY; BARRIER; GLUTAMATE; NEURONS; MYELINATION; AUTISM; Brain physiome; In vitro 3D platform; Brain organoid; Brain-on-a-chip; In silico model
ISSN
2452-199X
URI
https://pubs.kist.re.kr/handle/201004/114941
DOI
10.1016/j.bioactmat.2021.11.009
Appears in Collections:
KIST Article > 2022
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