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OPINION


Global centralisation: accelerates decisions in early drug discovery


Roger Clark, Senior Scientist, CIRA Bioscience at AstraZeneca and Trish Meek, Life Sciences Strategist at Thermo Fisher Scientific, discuss how global centralisation can accelerate decision- making in early drug discovery.


Roger Clark, maître de recherches au sein du département CIRA Bioscience d’AstraZeneca, et Trish Meek, spécialiste en stratégie des sciences de la vie chez Thermo Fisher Scientific, discutent de la façon dont la centralisation mondiale peut accélérer la prise de décisions dans la découverte précoce de médicaments.


Roger Clark, leitender Wissenschaftler bei CIRA Bioscience der Firma AstraZeneca, und Trish Meek,


Biowissenschaftlerin bei Thermo Fisher Scientific, erläutern, wie eine gloable Zentralisierung Entscheidungsfindungen bei einer frühen Medikamenten-Erkennung beschleunigen kann.


T


he global pharmaceutical industry is projected to grow to approximately US$1.3 trillion by 2020. A recent report by PriceWaterhouseCoopers


indicates that the current pharmaceutical industry business model is both economically unsustainable and operationally incapable of acting quickly enough to produce the types of innovative treatments demanded by global markets. In order to take full advantage of the future


growth opportunities, the industry must fundamentally change the way it operates. Centralisation helps establish common


processes and evaluation standards across a company’s global operations, facilitating optimum performance and productivity. Integration of systems and procedures dramatically enhances collaboration and increases information sharing and learning. Ultimately, this results in the ability to drive efficiencies, connect better with customers and make more informed decisions faster, thus delivering high-quality products and services across the world. Clinical trials and discovery processes are


streamlined and collaboration is improved, allowing scientists to bridge the gap between research, discovery and drug delivery. Delivering high quality decision-making data at the earliest opportunity serves to accelerate research and maintain product pipelines that are critical to business success.


This article discusses how


centralisation of biochemical screening can accelerate decision-making in early drug discovery. Improved data turnaround can be addressed with the deployment of a laboratory information management system (LIMS) as part of a centralisation strategy.


Prescription drugs


AstraZeneca is a major international healthcare business engaged in the research, development, manufacturing and marketing of prescription pharmaceuticals as well as offering healthcare services through state-


of-the-art facilities spanning eight countries. A UK-based AstraZeneca team began


exploring the idea of centralising its biochemical screening operations to service (at that time) over 50 AstraZeneca bioscientists and chemists operating in R&D centres spread over four of those countries.


The Biochemical Screening Team (BST) at


AstraZeneca forms part of the Assay Sciences Group, which encompasses biochemical and cellular aspects of drug discovery projects. The team processes approximately 12 000 samples per month, originating on a daily basis from more than 80 internal customers globally. This requires assays to be run against 20–30 different biochemical targets every week.


The case for centralisation


Biochemical screening requests have been traditionally recorded, tracked and managed at the local laboratory level. One person would manually order and test samples, then follow these through to results, leading to an insufficient productivity of 2.5 assays per scientist. AstraZeneca identified that its decentralised, inefficient and manual process of biochemical screening could be improved. The company wanted to reduce its timeline to get results and speed up its ability to make decisions around whether to move a candidate to the next step or stop valuable research time and dollars on


Fig. 1. An AZ extension to Nautilus LIMS allows cherry- picking of samples which match various (dynamically configured) criteria.


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