Informatics
Table 1: Summary of key requirements for Enterprise ELNs
Scientific adaptability – support for study design with flexible protocol templates across many domains
Good data management – flexible views and data interrogation
Protocol templates need flexibility to support multiple, domain-specific design configurations. For example, in vivo protocols need to dynamically support multiple treatment regimes, randomise subject allocation to treatment, and add additional observation points or sampling times during the course of a study. Formulation requires the ability to easily vary excipient composition, process parameters and number of samples taken for in-process analyses. Modification of the design during the lifetime of a study should dynamically update associated calculations and analysis methods, as well as report structure.
An Enterprise ELN must be a full data management solution, ensuring that data is not held in static documents, but in a relational database specifically designed to take scientific data and support subsequent cross-experimental searching and data collation. In this way data consolidation and analysis across different projects and studies is automated for rapid reporting. This eliminates a tedious and lengthy process for the scientist and improves quality by reducing transcription errors. Multiple data views are essential to allow the researcher to easily pivot data without having to make changes to individual cells in nested Excel® worksheets. Viewing and interrogation is also part of the study reporting process, so the scientist needs to be able to report his or her findings easily.
Electronic signatures and validation support
Integrated and industry accepted electronic sign-off and witnessing (eg SAFE BioPharma6) are essential, and the ELN should also be a compliant system that can be validated without putting unnecessary complications and compliance overheads on the scientists. A granular security model is needed to allow multiple authors to contribute to a study. This must be done in an auditable and controllable manner.
Managing method libraries
Task flow and flexible multiple step experimental process control is required to support study execution, especially when working on projects requiring contribution from multiple scientists, often in separate departments or even locales. For example, the development of analytical methods requires great flexibility in an ELN; it also requires structured data entry once methods are validated for the lab. Capturing deviations and controlling vocabulary at the point of entry reduces error rates and makes support of regulatory workflows feasible.
Open and extensible architecture – integration with LIMS, instruments, statistics and analysis
Communication with laboratory equipment and LIMS is essential to enable sample orientated data to be automatically brought into the ELN environment. Access to compound registration systems, animal management systems and other internal data is also important. Enterprise ELNs must be developed using an open, industry standard platform and provide documented APIs and a modern web services framework to allow customers to easily add extensions and custom components, and to facilitate integration with other enterprise systems. This provides scientists with greater autonomy in the design and execution of experiments, and greater confidence in the results. This should not require any cut and paste or manual data transcription.
Collaboration
To ensure internal groups can effectively share information, an Enterprise ELN needs to support multiple teams working together on matrix projects, with suitable hierarchies and security restrictions. In addition, the system should be able to support sharing of data with contract research organisations (CROs) to enable new data to be uploaded directly into the appropriate projects using consistent data capture procedures. The growth in globalisation and outsourcing has required Enterprise ELNs to support collaborative environments where an ecosystem of CROs and internal groups are working together.
A foundation for quality by design The growth in interest and application of Quality by Design (QbD) in the pharmaceutical industry requires a more systematic approach to achieving quality, and characterising acceptable variations in manufacturing processes5. This brings a different approach to regulatory submissions and manufac- turing flexibility, but fundamentally necessitates a good data management approach to all data from the earliest stages of development.
Much of the preclinical and early development environment uses paper and electronic reporting, with tabular data embedded in flat documents. This makes accessing relevant data from the typi- cal document store very difficult, and limits
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reusability because it cannot be searched and extracted in a structured manner. Relying on these flat documents, which lack any data management foundations, as systems to support QbD is there- fore challenging. However, the use of Enterprise ELNs in preclinical development enables each study/project, be it in stability, analytical or bio- analysis, to be easily retrieved at any time for:
l Assessment of current project design l Use in regulatory submissions l Analysis of post market issues
This enables Enterprise ELNs to form a founda- tion to support QbD, by providing the data and
Drug Discovery World Summer 2010
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