At its core, the bioanalytical discipline strives to deliver timely quantitative data to drug discovery and development project teams. Within the discipline there are many considerations that affect how bioanalytical data is generated and subsequently applied for informative decision making related to drug portfolio progression. Over the course of the past 20 years many scientific and technology advances in drug research and development (R&D) have resulted in new and interesting bioanalytical challenges. Fortunately, these advances have positively impacted the evolution of the bioanalytical discipline which, in turn, has resulted in improved assay quality, reliability and throughput.

One area that has helped advance the bioanalytical discipline through the evolving R&D landscape has been the continued use of automation in bioanalysis. The value of automation has been well recognized within the literature and continues to be an area of continued expansion and innovation. As a result, automation continues to be broadly and successfully applied within the bioanalytical discipline. Some routine examples include automated biospecimen sample organization and storage systems, fully integrated robotic liquid handling sample preparation platforms and automated data collection, processing and report generation.

Fully leveraging automation in the bioanalytical laboratory requires considerations that should include biospecimen sample logistics and lifecycle management (collection, receipt, storage and organization, analysis, disposal), as well as the strategic management of the corresponding bioinformatic data (subject/animal, position, barcodes, etc.) and traveling with the biospecimen sample. A few examples are provided below to detail how these considerations can be complementary to automation, as well as some new approaches that will enable more streamlined and integrated approaches to bioanalytical data generation.

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