Abstract

The high failure rate of drugs in the clinical pipeline is likely, in part, the result of inadequate preclinical models, particularly those for neurological disorders and neurodegenerative disease. Such preclinical animal models often suffer from fundamental species differences and rarely recapitulate all facets of neurological conditions, while conventional two-dimensional (2D) in vitro models fail to capture the three-dimensional (3D) spatial organization and cell-to-cell interactions of brain tissue that are presumed critical to the function of the central nervous system. Recent studies have suggested that stem cell-derived neuronal organoids are more physiologically-relevant than 2D neuronal cultures, due to their cytoarchitecture, electrophysiological properties, human origin, and gene expression. Hence, there is interest in incorporating such physiologically-relevant models into compound screening and lead optimization efforts within drug discovery. However, despite their perceived relevance, compared to previously utilized preclinical models, little is known regarding their predictive value. In fact, some have been wary to broadly adopt organoid technology for drug discovery due to the low throughput and tedious generation protocols, inherent variability, and lack of compatible moderate-to-high throughput screening assays. Consequently, microfluidic platforms, specialized bioreactors, and automated assays have been, and are being, developed to address these deficits. This mini review provides an overview of the gaps to broader implementation of neuronal organoids in a drug discovery setting, as well as emerging technologies that may better enable their utilization.

Significance Statement Neuronal organoid models offer the potential for a more physiological system in which to study neurological diseases, and efforts are being made to employ them, not only in mechanistic studies, but also in profiling/screening purposes within drug discovery. In addition to exploring the utility of neuronal organoid models within this context, ongoing efforts in the field aim to standardize such models for consistency and adaptation to standard screening platforms for more throughput evaluation.