BUSINESS
structure information may be available from chemists’ individual electronic laboratory notebooks (ELN) but the affiliated unit operation details and the complete supporting molecular characterisation data are not usually directly
available. Then some of that data and interpreted information may have been
transcribed into spreadsheets. In those spreadsheets, synthetic
Developers are challenged to process, assemble, and review appropriate material and process information in order to perform this important risk assessment effectively
process and supporting analytical and chromatographic data are abstracted to numbers, text and images, and the raw data is stored in archives. Separate reports are often needed to assemble subsets of analytical characterisation information and interpretations. The analytical information is
transposed for decision-making purposes, but review of the decision-supporting data is, at best, impractical because it has been sequestered into different systems. Batch-to-batch comparison data
is also transcribed into spreadsheets in an attempt to bring all the relevant information together into one system—unfortunately not one well suited to support rich chemical
and scientific information. Project teams spend weeks on the assembly of this information for internal reporting and external submissions. This abstracted and repeatedly transcribed information is then reviewed to establish and implement control strategies in compliance with a QbD approach. Users need the ability to aggregate all of the information, data and knowledge in a single, integrated, and interoperable platform. Project teams must be able to search, review and update the information on a continual basis as projects progress and evolve. Data access should support sharing of data for collaborative research while protecting data integrity. Furthermore, from the perspective
of preserving the rich scientific information therein, an ideal informatics system will also limit the need for data abstraction. While data abstraction serves a purpose – reduction of voluminous data to pieces of knowledge – it also brings limitations since important details, knowledge and contextual information can be lost. In order to address all these
challenges, a next-generation informatics infrastructure is required.
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