Informatics
(CDS) and laboratory informatics management systems (LIMS), and provide structured data cap- ture and reporting of results. l Pharmacokinetics (PK) need to manage studies, co-ordinate dosing and sample collection, integrate with instruments, import and manipulate data, provide summary analysis and integrate with domain tools such as WinNonlin®.
Figure 1
The preclinical environment presents multiple challenges
for an Enterprise ELN. It must bring together data and interpretation, and enable collaboration between many varied groups
Supporting interdepartmental workflows in preclinical development Each department in preclinical development needs to be supported by efficient data capture and knowledge management, to enable information and results to be used in project teams and regula- tory documentation. A successful Investigational New Drug (IND) application requires a large range of department level solutions, each deliver- ing specific elements of data and interpretation (see Figure 1). Each discipline needs to be sup- ported, and each has different requirements of what an ELN must do and how it should work, for example:
l Formulations need to capture the design of batches, processes and dosage forms, calculations, test results and batch records, and deliver cross- domain reports to project teams. l Analytical groups need to manage requests for testing, capture method development and valida- tion details, integrate with clinical data systems
Figure 2
Each preclinical department should be able to view, from
its own perspective, data from many different groups across the organisation; in this case, formulations and analytical have a different but
overlapping data landscape to pharmacokinetics
At the R&D macro level, using the same ELN platform for each area means that departmental solutions can combine to form a comprehensive view of compound/biologic progression. It also enables each discipline to see not only their own data, but also the relevant information from other departments. With Enterprise ELNs, both the cap- ture and viewing of data can be tailored towards each user, based on their specific department/ domain needs (see Figure 2). For example, a for- mulator looks at results from a batch perspective, but a bioanalyst who conducts the work sees the data from a worksheet and test perspective. This means that the concept of good data management is of paramount importance. Systems must consid- er the consumption of data as well as the capture, as data use is often aligned with the working prac- tices of other groups. This fact alone is responsible for limiting the impact of new systems that only provide an ELN to a single domain and focus on replicating a paper system.
Careful review is required in assessing what data is to be promoted to management oversight level and what remains a departmental view, with consideration also given to differences in termi- nology3. The resulting executive-level view improves research oversight and portfolio man- agement by giving an integrated up-to-the-minute view of all research activities.
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Drug Discovery World Summer 2010
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