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HighRes Biosolutions to Provide Cutting-Edge Automated Robotic Platforms for UK National Phenotypic Screening Centre

August 4, 2014



By a News Reporter-Staff News Editor at Robotics & Machine Learning -- HighRes Biosolutions (HighRes), a global provider of automated robotic systems to the Life Sciences industry, announced that they have been selected by the Scottish Universities Life Sciences Alliance (SULSA) to provide multiple automated robotic platforms to support the UK National Phenotypic Screening Centre's (UK-NPSC) research activities. The robotic systems will be deployed at two sites, the University of Dundee and the new Target Discovery Institute at the University of Oxford. Together the partnership will focus on operating a world-class phenotypic drug discovery facility that will collaborate with a wider network of centres from across the UK, Europe and beyond, to bridge between academia and pharmaceutical companies and drive innovation in the sector.

UK-NPSC will focus on screening chemical libraries in a smart cost-effective way, mostly using human cells and tissues, in order to identify new drug candidates that address unmet therapeutic needs - particularly in complex multifaceted diseases. The deployment of HighRes' flexible modular robotic systems and software will enable the UK-NPSC to implement a wide range of cell-based phenotypic screens using monolayer and 3D cultures - in live cells and fixed samples, and importantly, to reconfigure the systems to suit each screen.

The more holistic approach to drug discovery used in phenotypic screening is a particularly powerful method for drug discovery because it offers the opportunity to go beyond the focus on single targets - traditionally the most widely adopted approach for drug discovery. It relies heavily on advanced high-throughput microscopy combined with sophisticated image analysis techniques employing mathematical and statistical modelling to identify drug candidates based on complex changes in multiple cellular or tissue characteristics.

The current cost of drug development is extremely high and is plagued by low clinical efficacy and late stage failures. Evidence indicates more "first-in-class" FDA-approved drugs come from phenotypic screening rather than target-based screening. Phenotypic screening represents a relatively non-biased, disease and patient-centric approach to drug discovery that embraces the complexity of living cells and tissues in order to identify effective treatments. The Centre's mission is to reduce the time and cost to translate basic research into a clinical setting: it aims to improve drug efficacy by using more realistic models of disease and ensure safety and reduce side-effects by employing a greater proportion of complex human models along the drug discovery pipeline.

"SULSA selected HighRes Bio as the preferred automation platform for UK-NPSC because of its flexible, high throughput and end-to-end robotics solution that can support a broad range of assays. This allows us to adapt the functionality of the system depending on our varying needs in phenotypic screening. Our goal ultimately is to help improve the success rate of new drugs coming into the market by providing academics who have innovative ideas, access to the state of the art screening systems. This investment will put Scottish universities at the forefront of academic drug discovery worldwide," said Professor Andrew Hopkins, Director of SULSA.

"We, at HighRes, are truly excited about the opportunity to work with the UK's National Phenotypic Screening Centre Sites at the University of Dundee and University of Oxford," said Louis Guarracina, President of HighRes Biosolutions. "This partnership will allow each University the opportunity to expand their screening capabilities beyond traditional automation solutions and allow HighRes the opportunity to develop stronger relationships within the academic and government communities of the UK."

Keywords for this news article include: Robotics, Machine Learning, Emerging Technologies, HighRes Biosolutions Inc..

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Source: Robotics & Machine Learning


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