- 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
Associate Scientist III
About the Authors
Dr Ming Li is a Principal Scientist in the Global Biomarker Discovery and Development department at Biogen, Inc., in Cambridge, MA. Formerly, he was Principal Scientist, Technology Development and Implementation at Pharmacokinetics Dynamics and Metabolism, Pfizer Global Research and Development in Groton, CT, and before that Research Scientist II at DMPK, Roche Palo Alto in Palo Alto, CA. Ming is a member of a number of professional societies, including the American Chemical Society, American Society for Mass Spectrometry, Society for Laboratory Automation and Screening, and the American Association of Pharmaceutical Scientists. Dr Li received his B.S. in Polymer Science from Fudan University in Shanghai, China and Ph.D. in Analytical Chemistry from State University of New York at Buffalo (UB). His recent research interests and publications are in the area of bioanalytical laboratory automation.
Judy Chou is an Associate Scientist III in the Global Biomarker Discovery and Development department at Biogen, Inc., in Cambridge, MA. She received her B.S. in Computer Science and Mathematics & Statistics from the University of Massachusetts Amherst with a focus in Information Assurance. Judy is a member of the Association for Computing Machinery, Society for Laboratory Automation and Screening, and American Association of Pharmaceutical Scientists.
Current bioanalytical wet chemistry automation development is more about software development on top of existing robotic hardware than hardware development. Bioanalytical automation software tools need to be SMART – Simple and intuitive to use, Mindful of user errors, Assay class automation, Rugged, robust and reliable, and Tied in with the organization’s IT systems. SMART features of latest automation tools include LIMS connectivity; 1D and/or 2D barcoding; data extraction automation; messaging; reporting and electronic lab environment integration. Integration of these electronic features in automation tools is the trend.
Bioanalytical lab work can be roughly divided into wet chemistry work, which includes assay development and sample preparation, as well as quantitative instrumental analyses. The quantitative instrumental analyses (and the resulting quantitative data analyses) part has been automated to a great extent with the past few decades of advancement in spectroscopy and mass spectrometry based detectors, chromatography instrumentation as well as information technology . The wet chemistry part is still largely done manually in most of the bioanalytical laboratories and is the bottleneck in current bioanalytical workflows, and thus is the focus of current bioanalytical automation development.
SMART bioanalytical wet chemistry automation
Bioanalytical wet chemistry automation started with early generation liquid handling robots, and picked up speed with the proliferation of microtiter plates and compatible contemporary liquid handling robots [2-12].The current generation of liquid handling robots all have single channel, multichannel, 96 channel and plate transport capabilities, and the ability to integrate third party accessories such as incubators, washers, shakers, vacuum boxes, temperature controlled accessories and barcode readers, and all have their own proprietary scripting environment. Hardware wise, they are capable of handling most of the tasks in bioanalytical wet chemistry work. However, the current generation of liquid handling robots are all general-purpose robots by design. Robots from different vendors all have their own independent but self-sufficient scripting environment. To tailor them for bioanalytical workflows and applications, custom development work is needed. Therefore, the current bioanalytical wet chemistry automation development is mostly custom software development on top of existing robotic hardware [13, 14], rather than hardware development.