Many labs turn to automation to increase throughput, lower costs, and reduce operator-related errors. Transferring a manual, bench-top assay to an automated platform, however, is often more complicated than expected. As part of this challenge, laboratories often rely on pipetting precision alone to assess and mitigate assay variability. However, both the accuracy and precision of the liquid handler need to be considered. In addition to liquid handler calibrations, other facets of the assay workflow should be evaluated in order to reduce overall assay variability. For example, assay mixing, plate washing efficiencies, disposable tip lot-to-lot measurement, and pipetting viscous solutions are all integral parts of an assay workflow and can severely impact the data quality. Another consideration is liquid class optimization for an automated liquid handler, whereby default values are typically selected during transition from manual assay to an automated platform. We will discuss the effect of variability of various assay workflow steps and how to assess them through an approach called process optimization. By assessing a complex assay workflow in this manner, the assay variability can be improved which ultimately saves time during a method transfer. This can be particularly important when planning for a transfer from manual operation to automated systems or lab to lab. Case studies involving complicated assay workflows (e.g., ELISA and qPCR) will be presented. By evaluating the effect of volume variability systematically, this methodology can help the user pinpoint which assay steps are critical for success during a transfer. It also allows the user to make the necessary corrections to the liquid handler by taking a proactive stance.
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