Informatics
knowledge management backbone across preclini- cal development, and bring significant benefits by:
l Improving data quality – by reducing and poten- tially removing the risk of transcription errors from paper binders, instruments and Excel® spreadsheets, the quality of data is improved and ensures there is a time-stamped, electronically signed record of all data and IP generated. l Supporting knowledge reuse – scientists can reduce the time spent repeating studies by having a single point of access to a knowledge base that is searchable and retrievable. l Supporting knowledge mining – allowing past data to be mined effectively enables predictive models to be built and multivariant analyses to be run, to characterise the variables with significant impact and requiring more analysis.
Key capabilities in enterprise ELNs Enterprise ELNs are differentiated from earlier generations of ELNs by their data management orientated approach. Having a properly architect- ed ELN, based on good data management prac- tices, brings many benefits in terms of searchabili- ty and scalability, and moves the ELN from a sim- ple patent IP capture tool to the portal environ- ment in which scientists spend most of their time working. Some of the key capabilities for Enterprise ELNs are described in the following sec- tion and Table 1.
ELNs and regulatory compliance – review by exception
When ELNs are used in a regulatory environment, the capabilities required and implications of deployment change significantly. Enterprise ELNs must support the deployment requirements of good laboratory and manufacturing practice (GLP and GMP), with system audit trails, version con- trol, process enforcement, real time data validation and e-signatures. When working in a GLP/GMP environment, an important function is to enforce and monitor compliance to Standard Operating Procedures (SOPs). The ability of an ELN to pro- vide easily configurable, structured forms/tem- plates that ensure user data capture conforms to specific business rules is fundamental. However, even with a GxP environment, there are exceptions to the rules, and when they occur it is important that the ELN not be prescriptive, but rather allow a user to complete a task with comments, and track such deviation for future reference and reporting. This approach improves data quality but remains flexible, by ensuring data was cap-
Drug Discovery World Summer 2010
tured correctly at its source, and that any devia- tions are tracked in a searchable manner. At any point, a Quality Assurance (QA) review- er or auditor should be able to generate a report on all process deviations or exceptions with a given scope of work – a notion called ‘Review by Exception’. Such reports document all process exceptions and user comments, and provide links or references to the relevant experiment or record. Thus QA personnel no longer have to review all notebook content and associated paper binders for compliance, and instead only need to focus on doc- umented exceptions. This significantly expedites the QA process and reduces the cycle time for study completion and issuing of reviewed reports.
Reuse of corporate data
The complexity of the preclinical and early devel- opment formulations process, for small and large molecules, means there is considerable difficulty in finding information on historical data. This means that the sample questions below are often difficult and time-consuming to answer:
l I need particle size and density data for each for- mulation containing API (Compound 1234, batch- es 01, 02, 03). l I want to compare content uniformity and impu- rity data for all formulations using excipient X, Y and Z. l Get me all instances of protein degradation and their associated excipient selection, responses and analytical method/instrument selection.
To answer these types of question, the ELN must have a combination of good data management and powerful search capabilities providing enhanced visibility of all past data around formulations, their analysis, excipients, PK and stability. By capturing and storing all data, corporate knowledge is fully searchable, promoting reuse of high value knowl- edge and enabling better risk-based decisions.
Report generation and regulatory validation
Once the cycle of formulations, PK and analytical
Figure 3
Exceptions can be used to provide QA teams with reports and alerts of deviations from SOPs, replacing paper log books and reducing the burden of regulatory compliance. The image shows a series of bioanalysis issues captured using IDBS E-WorkBook
References 1 Rubacha, Michael et al. A Review of Electronic Laboratory Notebooks Available in the Market Today. The Association of Laboratory Automation, available online March 2010. 2 Elliott, Michael H. What You Should Know Before Selecting an ELN: Electronic Laboratory Notebooks Have Evolved into Four Distinct Types. Scientific Computing, June 2009. 3 Elliott, Michael H. Thinking Beyond ELN. Scientific Computing, December 2009. 4Vanderlaan, Martin. Implementing an Electronic Lab Notebook for a Large Bioprocess Organization. IQPC’s ELN and Advanced Lab Solutions Conference, September 2009. 5 Somma, Russ. Development Knowledge Can Increase Manufacturing Capability and Facilitate Quality by Design. J Pharm Innov, 2007, 2:87-92. 6
www.SAFE-BioPharma.org.
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