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Automation of multiplex biochemical assays to enhance productivity and reduce cycle time using a modular robotic platform

Authors :
Buyun Tang
Becky Lam
Stephanie Holley
Martha Torres
Theresa Kuntzweiler
Tatiana Gladysheva
Paul Lang
Source :
SLAS Technology, Vol 29, Iss 6, Pp 100233- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Pharmaceutical and biotechnology companies are increasingly being challenged to shorten the cycle time between design, make, test, and analyze (DMTA) compounds. Automation of multiplex assays to obtain multiparameter data on the same robotic run is instrumental in reducing cycle time. Consequently, an increasing need in automated systems to streamline laboratory workflows with the goal to expedite assay cycle time and enhance productivity has grown in industrial and academic institutions in the past decades. Herein, we present a customized robotic platform with operational modularity and flexibility, designed to automate entire assay workflows involving multistep reagent dispensing, mixing, lidding/de-lidding, incubation, centrifugation, and final readout steps by linking spinnaker robot with various peripheral instruments. Compared to manual workflows, the system can seamlessly execute processes with high efficiency, evaluated by standard assay validation protocols for robustness and reproducibility. Furthermore, the system can perform multiple, independent protocols in parallel, and has high-throughput capacity. In this publication, we demonstrate that the modular robotic platform can fully automate multiplex assay workflows through ‘one-click-and-run’ solution with tremendous benefits in liberating manual intervention, boosting productivity while producing high-quality data combined with reduced cycle time (>20 %), as well as expanding the capacity for higher throughput.

Details

Language :
English
ISSN :
24726303
Volume :
29
Issue :
6
Database :
Directory of Open Access Journals
Journal :
SLAS Technology
Publication Type :
Academic Journal
Accession number :
edsdoj.667b3d6cd55948858c5f1cd5d51f9adb
Document Type :
article
Full Text :
https://doi.org/10.1016/j.slast.2024.100233