The predictive capacity of in vitro preclinical models to evaluate drugs for the treatment of retinoblastoma.

Details

Ressource 1Download: 36940901.pdf (2864.81 [Ko])
State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_28ED0A397FB1
Type
Article: article from journal or magazin.
Publication sub-type
Review (review): journal as complete as possible of one specific subject, written based on exhaustive analyses from published work.
Collection
Publications
Institution
Title
The predictive capacity of in vitro preclinical models to evaluate drugs for the treatment of retinoblastoma.
Journal
Experimental eye research
Author(s)
Sinenko I.L., Turnell-Ritson R.C., Munier F.L., Dyson P.J.
ISSN
1096-0007 (Electronic)
ISSN-L
0014-4835
Publication state
Published
Issued date
05/2023
Peer-reviewed
Oui
Volume
230
Pages
109447
Language
english
Notes
Publication types: Journal Article ; Review
Publication Status: ppublish
Abstract
Retinoblastoma is a rare childhood cancer of the eye. Of the small number of drugs are used to treat retinoblastoma, all have been repurposed from drugs developed for other conditions. In order to find drugs or drug combinations better suited to the improved treatment of retinoblastoma, reliable predictive models are required, which facilitate the challenging transition from in vitro studies to clinical trials. In this review, the research performed to date on the development of 2D and 3D in vitro models for retinoblastoma is presented. Most of this research was undertaken with a view to better biological understanding of retinoblastoma, and we discuss the potential for these models to be applied to drug screening. Future research directions for streamlined drug discovery are considered and evaluated, and many promising avenues identified.
Keywords
Humans, Child, Retinoblastoma/drug therapy, Drug Evaluation, Preclinical, Retinal Neoplasms/drug therapy, Drug discovery, Retinoblastoma, Three-dimensional models, Tumour organoids
Pubmed
Web of science
Open Access
Yes
Create date
24/03/2023 13:27
Last modification date
09/02/2024 9:44
Usage data