Abstract

In the last decade, different research groups in the academic setting have developed induced pluripotent stem cell-based protocols to generate three-dimensional, multicellular, neural organoids. Their use to model brain biology, early neural development, and human diseases has provided new insights into the pathophysiology of neuropsychiatric and neurological disorders, including microcephaly, autism, Parkinson’s disease, and Alzheimer’s disease. However, the adoption of organoid technology for large-scale drug screening in the industry has been hampered by challenges with reproducibility, scalability, and translatability to human disease. Potential technical solutions to expand their use in drug discovery pipelines include Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) to create isogenic models, single-cell RNA sequencing to characterize the model at a cellular level, and machine learning to analyze complex data sets. In addition, high-content imaging, automated liquid handling, and standardized assays represent other valuable tools toward this goal. Though several open issues still hamper the full implementation of the organoid technology outside academia, rapid progress in this field will help to prompt its translation toward large-scale drug screening for neurological disorders.

Highlights

  • Organoids are stem cell-derived, three-dimensional (3D) cultures that are artificially generated

  • Many therapeutics successful in preclinical model trials fail in lateIPSC-derived organoids permit in vitro and in vivo investigations, representing a relevant stage clinical studies due to reproducibility issues between animal models and humans

  • We focus on the early use of neural organoids in the industry, the current challenges, and the possible technical solutions that might support their implementation into drug discovery pipelines

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Summary

Introduction

Organoids are stem cell-derived, three-dimensional (3D) cultures that are artificially generated. Examples of 3D cultures recapitulating human organs in vitro include the peripheral nerve, [8] the spinal cord [9], and the brain [1] These neural organoids proved to be useful platforms to model neurodevelopmental, neuropsychiatric [10], and neurodegenerative disorders [11], such as microcephaly [12], Miller-Dieker Syndrome [13], and Alzheimer’s disease [14]. Many therapeutics successful in preclinical model trials fail in lateIPSC-derived organoids permit in vitro and in vivo investigations, representing a relevant stage clinical studies due to reproducibility issues between animal models and humans. Drugs (such as PTC-124 and Ataluren) that were successful in non-neurological animal models were not efficacious in human intestinal organoids modeling cystic fibrosis These results turned out to be accurate in two-phase clinical studies [16], suggesting that organoids might have the potential to bridge preclinical and clinical trials [17]. We focus on the early use of neural organoids in the industry, the current challenges, and the possible technical solutions that might support their implementation into drug discovery pipelines

Neural Organoids for Drug Discovery in the Industry
Challenges for the Adoption of Neural Organoids for Drug Discovery
Heterogeneity
Scalability
Reproducibility
Maturity
CRISPR
Methods
Automation and High-Throughput Screening
Imaging
Single-Cell RNA Sequencing
Machine Learning
Machine Learning in Drug Discovery Using Neural Organoids
Future Perspectives on Machine Learning Applied to Organoid Technology
Conclusions

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