LIMS Deployment in an Assay Service Environment: Improving Efficiency and Effectiveness through Information Management

LIMS Deployment in an Assay Service Environment: Improving Efficiency and Effectiveness through Information Management

Roger Clark (AstraZeneca Pharmaceuticals, UK) and Jonathan Wingfield (AstraZeneca Pharmaceuticals, UK)
Copyright: © 2012 |Pages: 21
DOI: 10.4018/jcit.2012070102


In 2006 AstraZeneca (AZ) executed a strategy to centralise all biochemical screening activities within one of its Research Areas, into a single team. This team had the remit to deliver data faster and more consistently, whilst reducing the FTE’s deployed against such activities. Keeping the team small, AZ hoped to facilitate more flexible use of resources, remaining agile enough to respond to changing business demands; however this centralised approach brought with it a fresh set of challenges, not least of which was information management. This review describes a successful LIMS implementation within AZ (who deployed a customised COTS solution in just four months). It outlines the steps taken over the initial system development life cycle and highlights the requirement for dedicated in-house resource (with intimate domain knowledge) coupled with experienced vendor personnel. It goes on to explore the requirement for continued evolution of the system and the challenges this posed.
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Organizational Background

AstraZeneca is a major international healthcare business engaged in the research, development, manufacturing and marketing of prescription pharmaceuticals and is a supplier for healthcare services. In 2006 it had more than 12,000 Research and Development (R&D) employees spanning eight countries; the R&D function was divided into research areas (RA), each focusing on a particular disease setting and each operationally independent (Figure 1).

Figure 1.

AZ Global R&D structure and Organisational chart for the ‘Oncology & Infection, UK Bioscience section’ (in 2006). The key players in formation and sanction of the business case for centralisation are highlighted (and can be seen in greater detail within Figure 2). Where the reporting line continues beyond the margin, a similar organisational structure to that shown should be assumed.


Typically pharmaceutical R&D begins with either a High Throughput Screen (HTS) or a more directed Sub-Set Screen, whereby the company’s collection of chemical starting points is systematically tested against a biological target of interest. This process generally happens in a single high-cost activity and generates vast amounts of data which is then sifted in an attempt to derive an understanding of chemical structure versus biological activity, termed Structure-Activity Relationship (SAR). To add weight to SAR assumptions and to further enhance the pharmacological and physical properties of any ‘hit’ compounds identified, large amounts of chemistry resource is applied with the hope of turning these ‘hit’ molecules into appropriate ‘leads.’ With each round of chemistry, the new compounds are again tested against the biological target. This phase of SAR screening is iterative and will usually progress (with gradually decreasing throughput requirements) over a number of years until a given molecule’s properties have been refined enough for it to become a ‘Candidate Drug’ and progress into the clinical phases of R&D.

SAR screening (sometimes referred to as ‘Secondary Screening’ or ‘Efficacy Screening’) within the pharmaceutical industry had traditionally been carried out within defined project teams, in many cases the same resource being utilised to deliver biochemical and cellular assay builds, along with the routine delivery of data through those assays. The constant drive for efficiency gains within Pharmaceutical R&D required AZ to re-think the way resources were deployed against the SAR screening activities, asking the question:

‘How can we achieve rapid and efficient generation of high quality decision-making data, without compromising quality?’

The issues facing R&D within the pharmaceutical industry in 2006 were broadly the same as those today and have been summarised in many previous publications (Bleicher, Bohm, Muller, & Alanine, 2003; Paul, Mytelka, Dunwiddie, Persinger, Munos, Lindborg, & Schacht, 2010; Empfield & Leeson, 2010). Generating SAR data more quickly and efficiently, AZ hoped that it would be able to drive down the compound ‘make-test’ cycle time and ultimately accelerate research on the more promising chemical leads.


Setting The Stage

In late 2005 members of the Oncology & Infection Research Area within AZ put together a business case for centralisation of the distributed resource conducting the iterative biochemical SAR screening described. The primary driver for this was Full Time Equivalent (FTE) reduction against biochemical screening activities, though other ancillary benefits were predicted and are discussed in a previous paper (Wingfield, Jones, Clark, & Simpson, 2008). Coupling this centralised capability with flexible automation solutions, able to deliver across various aspects of the ‘screening cascade,’ AZ hoped to bring further improvements in efficiency. As the resource (both human and physical lab equipment) was already present within the organisation, the proposed financial cost was relatively low. There was no requirement to equip a biochemical screening laboratory with either expensive automation or skilled staff to operate it (in order to fully leverage the benefit of centralisation) as the key to success here was redeployment and reduction (Wingfield et al., 2008).

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