- Chapter 1: Laboratory informatics for the bioanalytical laboratory
- Chapter 2: Laboratory information management systems (LIMS) for bioanalysis
- Chapter 3: Development of an integrated informatics solution for advanced bioanalytical business analytics
- Chapter 4: Electronic notebooks in the bioanalytical lab: a perspective on determining return on investment (ROI)
- Chapter 5: Electronic notebooks: the paperless laboratory
- Chapter 6: Computerized system validation
- Chapter 7: Importance and application of electronic standards in bioanalysis
- Chapter 8: Automation tools
- Chapter 9: The future of big data in regulated bioanalysis: clouds, trends and transparency
- Appendix: Pertinent Regulations and Guidances in Electronic Data Use
About the Author
Dana Vanderwall is currently Associate Director of Cheminformatics in Research & Development Information Technology at Bristol-Myers Squibb (NY, USA). He earned his B.S. in Biochemistry from the University of Wisconsin (WI, USA), and Ph.D. in Biochemistry in from the University of Maryland (MD, USA).
With 20 years of experience supporting Drug Discovery, he has held leadership roles in Computational Chemistry, Cheminformatics, and IT, and has worked in structure-based, and pharmacophore driven approaches, as well as data integration, data mining, and visualization. His research has covered areas such as hit progression, expression profiling, pathway-focused screening analysis, cell-based profiling, and chemical biology.
His current responsibilities include working with scientists in R&D to identify and create solutions for data analysis, visualization and decision support capabilities across Drug Discovery, as well as supporting the laboratory informatics and automation for purification, characterization, in-vitro bioanalysis in Discovery Analytical Chemistry.
Importance and application of electronic standards in bioanalysis
Conversations about data standards don’t typically generate a lot of excitement and the topic does not often grace the covers of scientific or trade journals. Whereas topics like ‘dealing with the data deluge’, ‘big data’ and the ‘electronic/paperless laboratory’ are getting splashy coverage and are the subject of entire conferences, real, sustainable progress in these areas will, in fact, hinge on data standards. Because of the lack of standards, the pharmaceutical/biopharmaceutical industry, including the bioanalysis domain (analysis of drugs and metabolites in biological samples), suffers from a language barrier, in effect, which is characterized by a deep-rooted inability to efficiently exchange laboratory data. This barrier arises from the juxtaposition of constantly changing science, continually evolving technology, and a broad landscape of instrument and software providers. This environment has led to an equally diverse array of data formats and systems to consume them. To compound these problems, our effective local ‘languages’ often differ in describing the contexts in which data are generated. This means without parsing and translating all these languages and their associated data formats, all of our instruments and systems speak different languages. This chapter will describe the problems associated with the language barrier, or lack of electronic standards or their effective application and the benefits of building standards into the applications and systems that support bioanalysis.
To help understand why it is important to standardize data and its description as well as the problems that will result if we do not, it is helpful to consider the lifecycle of an experimental data point as context, as illustrated in Figure 1.
The data lifecycle in the context of a bioanalysis laboratory: