Development of a fully automated platform for agar-based measurement of viable bacterial growth

Dynamic in vitro antibacterial studies provide valuable insight on effective dosing strategies prior to translating to in vivo models. Frequent sampling is required to monitor the pharmacodynamics (PD) of these studies, leading to significant work when quantifying the bacterial load of the samples....

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Bibliographic Details
Published in:SLAS technology Vol. 27; no. 4; pp. 247 - 252
Main Authors: Squadroni, Brian, Newhard, William, Carr, Donna, Trinh, Huong, Racine, Fred, Zuck, Paul, Howell, Bonnie, Hazuda, Daria J., Cassaday, Jason
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01-08-2022
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Summary:Dynamic in vitro antibacterial studies provide valuable insight on effective dosing strategies prior to translating to in vivo models. Frequent sampling is required to monitor the pharmacodynamics (PD) of these studies, leading to significant work when quantifying the bacterial load of the samples. Spreading a bacterial suspension on agar to allow colony counting is a proven process for measuring very low levels of growth, but commercial automation equipment to handle agar plating and colony counting at scale is not readily available. We describe a process to greatly decrease the hands-on time required for PD assays by utilizing general-purpose liquid handling robots to plate bacteria and a custom-made plate imager to automate colony counting. The platform developed handles the biological assay from beginning to end as well as sample tracking at each step of the process. The process relies heavily on custom automation scheduling software to enable dynamic process decisions and coordinate data flow throughout. Using the described platform, we can efficiently quantify >100 PD samples per day while maintaining the necessary dynamic range of the assay. Alleviating the main bottleneck in the dynamic antibacterial studies has allowed us to accelerate the rate of experiments to provide antibacterial dosing data within shorter timelines.
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ISSN:2472-6303
2472-6311
DOI:10.1016/j.slast.2022.03.003