Companies need the flexibility to change the way they do things, thus generic ELNs are often chosen. They can adapt to the language of specific disciplines and offer flexible hierarchies, metadata setup and capture, highly granular security, and ease of use.
“Generic ELNs do have certain strengths like process control, centralizing data, and IP capture but they also have limitations,” cautioned Paul Denny-Gouldson, Ph.D., product manager, IDBS (www.idbs.com). Generic systems can’t easily retrieve structured biological data like absorption, metabolism, distribution, elimination, and toxicology, particularly when multidimensional data is sought.
Text searches through flat files and Excel documents used by generic ELNs can’t retrieve secondary data successfully because there is no way to understand the structure of the data or how to link it in a relational way to other data.
Consequently, Dr. Denny-Gouldson continued, “it is almost impossible to find, for example, all compounds with IC50 less than 10 mM of a given receptor, or all pharmacokinetics or pharmacodynamics for a given compound, or related compounds on a set of subjects over the past two years stored in a set of unstructured Excel and Word docs.”
It can more easily be found, however, when one uses a biologic data-management solution combined with a generic ELN in a way that supports structured and unstructured searches, allows structured capture, is easy to maintain, extendable, and can combine structured and unstructured data into one format that can be searched on both fact and contextual data, pointed out Dr. Denny-Gouldson.
“One of our customers, a large Spanish pharma company, used the E-WorkBook Suite and integrated 200 biology, chemistry, pharmacology, and preclinical development users into one enterprise-wide ELN by providing each group with specific add-in features for their scientific discipline.”
The time savings with such a system is significant, he said. IDBS compressed a one-week endeavor into six minutes when it put together a study report using BioBook. That’s possible because “we can template the entire workflow and analysis so that as you input raw data, the report is set up,” Dr. Denny-Gouldson explained.
BioBook begins at the experiment level, letting users capture experimental details (e.g., text and images), then moves into the capture of the structured data in spreadsheets and includes instruction sheets that guide users through their experiment. Additionally, text and hyperlinks to discreet tables and charts are added, creating a workflow for the spreadsheet template.
Flexible experiment setup can handle multiple types of experiments, designs, and disciplines as well as on-the-fly changes to treatment groups, sampling time points, and configuration of the standard curve. The sample worklist setup creates multidimensional relationships between tables without the need to write code and supports pivoting and filtering so users can see only the data they need, Dr. Denny-Gouldson said.
Data can be viewed many different ways and can be exported in many file formats including native Excel. Data can also be imported directly from instruments, and a variety of calculations can be made automatically or with user intervention as a result of BioBook’s math engine. Any changes to the data are captured by the audit log. Interactive graphing lets users examine data, with dynamic calculating and reformatting in real time.
One of the benefits, Dr. Denny-Gouldson noted, is that users can chart all the data in the templates without preparing discreet charts in advance for every variable or measure. The fit engine automatically reflects design changes in the charts, calculations, and reports as data is added.