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LABORATORY INFORMATICS Design automation


The role of Antha in developing a more effective platform for design of experiments


optimisation of their transfection reagent mixes. Lentiviral vectors are able to modify a


Automation offers higher throughput and reproducibility than manual R&D. However, programming automation requires large amounts of time and specialist expertise, and can result in inflexible workflows that require substantial re-coding when needs change, as they often do. To combat this challenge Synthace have


developed a platform to help automate the design of experiments (DoE) so that organisations can increase the complexity of their investigations, leading to more robust and productive experiments. Markus Gershater, Synthace co-founder


and CSO, said: ‘Antha takes in much higher- level specifications of the automated protocol that a scientist wants to run, and automatically generates the script needed to carry out that protocol on the make and model of automation that the scientist has specified. This capability is based on the Antha language, which allows the logic of lab protocols to be expressed in code. ‘These Antha workflows are equipment agnostic, and equipment-specific instructions are generated by Antha automatically. What this means, in practice, is that automation can be programmed extremely rapidly, and automated protocols can then in turn be rapidly adapted on demand.’ While, in principle, all manual R&D could


ultimately benefit from being automated, for most organisations automation has traditionally focused on high volume workflows that require lots of repetition with little overall change. ‘A good example of this is high-


throughput screening, where the same assay is to be run thousands or even millions of times,’ said Gershater. ‘Antha’s dynamic generation of automation scripts enables automation to be rapidly adapted for more changeable contexts, which encompasses much of biologics R&D. Experiments can be automated where it would previously have been impractical to do so. ‘Any experimentation by hand is limited by the amount of complexity that can


26 Scientific Computing World Autumn 2020


broad range of dividing and nondividing cell types. This leads to stable and long-term expression of the gene or genes of interest. They are used in in-vivo gene therapy applications for central nervous system indications, such as Parkinson’s disease and Amyotrophic lateral sclerosis. To investigate this complex biological


“Experiments can be automated where it would previously have been impractical to do so”


be handled by the scientist doing the work. This means that the experimental designs people would ideally like to run are unfeasible in practice. Antha can effortlessly generate the instructions needed for even highly complex experiments, freeing scientists to do the experiments they need to address biological complexity,’ suggested Gershater. ‘Automation offers huge benefits over manual experimentation: reproducibility, throughput and decreased hands-on time, freeing scientists to spend more time designing experiments and analysing results. ‘Antha adds to this, introducing


transferability and sharing of automated protocols across sites, even if different locations are using different equipment makes, and the ability to do experiments that were previously intractable with either manual experimentation or automation programmed in a conventional way. We have seen our users gain large amounts of additional scientific insight, while saving large amounts of time that would otherwise be spent in laborious manual work or programming.’


Optimising therapy development One example is Oxford Biomedica, who started using Antha in 2018. Oxford Biomedica employed the Antha platform in their research and development to improve the efficiency and robustness of their in- house lentiviral vector production through


system a multifactorial experimental approach was taken that would have been very challenging via a more traditional approach using manual execution. This is largely due to the number of experimental runs and the complexity of the experimental design. Oxford Biomedica shortlisted 10 factors


for investigation in the first iteration of the DoE optimisation. This allowed for a more complex analysis of how different factors interact. The Oxford Biomedica team also investigated the benefits and value of deviating from a traditional iterative DoE methodology for a quicker optimisation strategy. Full details of the DoE process can


be found in the case study but Oxford Biomedica reported an 83 per cent time saving from combined design, planning and physical execution, and a 32 per cent resource saving. In a webcast with Synthace: ‘DoE Optimisation of Advanced Therapy Development with Antha’, André Raposo automation group leader, Oxford Biomedica, noted that using the platform for DOE had ‘fundamentally changed the way we work’. ‘In the old days, DOE could be a bit


insecure somehow. The way that is now we do not have to worry, the system does everything and the capacity is much larger, so you can do DoE’s with a capacity of 200 to 500 wells. That would be impossible before,’ added Raposo.


Webinar: Oxford Biomedica Discuss DoE Optimisation of Advanced Therapy Development with Antha https://vimeo.com/393991672/5ea57a90a3


Case study: Design of Experiments (DoE) with Oxford Biomedica www.synthace.com/customers/case-studies/detail/design- of-experiments-doe-with-oxford-biomedica/


@scwmagazine | www.scientific-computing.com


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