Building a State-of-the-Art Automation Platform to Support High-Throughput 96-Well Caco-2 Permeability Assay
<i>Automation plays a crucial role in enhancing efficiency and increasing capacity for <i>in vitro</i> absorption, distribution, metabolism, and excretion profiling in early drug discovery. Building an automation platform requires a careful balance of innovation and practical considerations to align assay needs with technological capabilities. In this study, we present a state-of-the-art automation system designed to support the miniaturization of the Caco-2 permeability assay from the 24-well to the 96-well format. This platform integrates advanced infrastructure for cell culture and assay execution, along with several key features, including innovative cleaning protocols, cutting-edge plate tracking, and dynamic scheduling capabilities. The fully automated 96-well platform delivers significant efficiency gains, increased capacity, and faster turnaround for permeability assay support while maintaining high predictive accuracy. It correctly classified 94% of 50 literature compounds, demonstrating strong concordance with the established 24-well format. Moreover, the platform enables large-scale permeability data generation, advancing our "predict-first" modeling paradigm, in which predictive models guide experimental design and compound prioritization</i>.
Authors
- David Connors
- Xianmei Cai
- Shivani Anil Patel (ORCID: https://orcid.org/0000-0003-0082-5857)
- Gregory Barker (ORCID: https://orcid.org/0000-0002-2317-2556)
- Mary Ellen Cvijic
- Anthony Paiva
- Wilson Z. Shou (ORCID: https://orcid.org/0000-0001-9522-0409)
- Peter Chase
- Yongnian Sun
- Nicole Diaz
- Chunli Huang
Institutions
- Bristol-Myers Squibb (Germany) (DE)
- Analytical Services (US)
- Bristol-Myers Squibb (Sweden) (SE)
Publication Details
- Journal
- Assay and Drug Development Technologies
- Published
- 2025-12-30
- DOI
- https://doi.org/10.1177/1540658x251408742
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00