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LabAutomation


of data generated by automated systems can lead to bottlenecks if not collated in an effective way. This, in turn, compromises the whole workflow and the samples it contains – hold-ups expose pre- cious cell cultures to additional risk. The term data handling encompasses many


tasks, from data generation and reformatting to suit individual requirements, through to communi- cation with third party software programmes and databases. Full sample tracking and ease of opera- tion is highly advantageous for efficient routine use, and each factor must remain flexible to fit around the great variety of demands of individual laboratories and their existing data infrastructure. Often, due to regulatory requirements such as


FDA approvals, stored data needs to be traced back to a corresponding sample, perhaps even a number of years after the initial experiment. Therefore, it is vital that effective data handling systems are in place to protect critical data. Without such automated data handling, resource- intensive data analysis can also create a bottleneck that undermines the efficiency benefits afforded by the automated set-up. Database tracking functionalities ensure that a


full history of each sample is securely stored and readily available to the operator. This can serve many purposes, such as tracing the origin of out- liers when the results are reviewed. This capability also facilitates assays requiring the association of multiple plates, for example when cell culture supernatant is assayed on one plate, while the cells remain in culture on the second plate. The results from the first plate containing the supernatant must then be tracked to the corresponding cells, providing the position and barcode for easy loca- tion of the relevant sample. It is equally important to be aware of the chal-


lenges involved in retrospectively organising such complex data acquired over months or even years of development. To place this into context, consid- er having to present data covering nine years of work, where five different technicians were involved in data handling. It can easily require months to organise the data, retrieving it from multiple network locations and unformatted spreadsheets. Some work might even need repeat- ing if results have become lost over this length of time, and each file must be reformatted to provide directly comparable data across the entire project. With data management systems in place, data is easily accessible and consistently formatted to streamline the reporting process. It is important then to considering data handling as an integral part of the automated system build.


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This often-overlooked function is key to maintain- ing efficiency and also for reaching the system’s full capacity from the outset by protecting against bot- tlenecks. Build-only automation solutions may ini- tially appear a cost-effective means to automation, however the hidden costs of this are later revealed when it comes to software and data handling – this can be extremely expensive in terms of time and cost when trying to integrate retrospectively. Integrating data handling into automated plat- forms from the outset delivers significant advan- tages, such as securing data and protecting samples by avoiding bottlenecks – which are crucial to con- sider, and detrimental when overlooked.


Summary Automating cell line development makes a tremen- dous difference to drug discovery pipelines, speed- ing up processes, increasing reliability and repro- ducibility, reducing risk from human error while also saving time and money. Due to the extremely fragile nature of cultured cells, safety should be front of mind when looking to automate to protect investment and product. However, this should also extend to operator safety, particularly when work- ing with human cell lines and potentially infectious agents. As a part of this, an automated workcell should comply with, and ideally go beyond, stan- dards, and also provide flexibility, ease of use and, importantly, adequate data handling to circumvent issues down the line. Good solution providers will understand these requirements and have the neces- sary expertise and experience to guide the creation and installation of the best automation solution possible for the required workflow.


DDW


Jon Newman-Smith is Engineering Operations Director at PAA. Jon’s background is in engineer- ing, specialising in robotics and automated sys- tems. He is also a certified Machine Safety Expert with 17 years’ direct experience at PAA, where he leads the design, specification and delivery of bespoke automated systems for PAA’s Life Science and Personal Healthcare customers. His team of highly skilled engineers, scientists and project man- agers guide PAA’s customers through the project lifecycle to achieve a successful outcome for each customer. Jon has a passion for developing new ideas for PAA’s laboratory workcells and applying new concepts such as collaborative robotics to break down traditional barriers of automation.


Drug DiscoveryWorld Summer 2019


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