Abstract
Within scientific discovery efforts, efficient and compliant tracking and movement of samples, whether chemical, biological, or clinical, are the primary goals of a sample management organization. Sample management practices have advanced significantly during the past 25 years, and the importance of growing and maintaining sample collections has been underscored by high-throughput screening in early drug discovery through to collection and storage of human clinical samples for future biological research in clinical development. Today’s sample management operations can range from centralized and highly automated robotic storage systems with complex data integration, to local manual storage with simple spreadsheets for inventory tracking, to (in more cases than may be admitted) the long-trusted combination of freezer, Sharpie-written label, and the scientist’s memory. Regardless of the selected sample management methodology, this discipline remains necessary and imperative for enabling science from drug discovery to the clinic.
Because sample management generally operates in the background, it may be undervalued until critical mass is exceeded and expectations around sample delivery timelines, sample quality, and/or sample identification are unable to be met. This is often seen in small startup organizations or in academic labs where there are no considerations for formalizing sample management until the number of transactions exceeds more than a person with an Excel spreadsheet can manage. In a larger organization, right-sizing sample management infrastructure can be equally challenging in the era of mergers, acquisitions, and/or site closures. Similarly, obsolescence of existing sample management equipment can create challenges for maintaining business continuity while implementing new technologies. In all cases, the importance of developing, maintaining, and future-proofing a robust and flexible sample management infrastructure must not be underestimated.
In this special issue of SLAS Technology, Janzen et al. provide a guide to best practices that can be used by both novice and experienced sample managers alike to either initiate or improve operations in their own organizations. With combined input from experienced sample managers from small startups to large pharma companies to biorepositories, this article covers the basics, including sample identification and inventory systems, sample stability and storage conditions, sample ordering and tracking, and logistics associated with sample management. In addition, recommendations for disaster planning and mitigation are discussed. These components are all necessary when considering either a de novo sample management build, or an upgrade or overhaul of existing infrastructure.
Standardization of sample management operations is key for maintaining an efficient process, but sample management functions must also evolve to ensure that the operations remain relevant to the research being performed. As biological assays are becoming more and more miniaturized, the sample management lab is challenged with how to best deliver samples to these assays in an automated fashion. Microarrays, nanowells, and microfluidic devices are newer technologies that can be implemented but would require a paradigm shift in sample delivery. Bhatt et al. describe the landscape of miniaturized technologies as well as infrastructure considerations for building optimal sample-handling support for miniaturized biological assays.
As previously mentioned, automation can be a vital component within sample management operations. From storage and retrieval to distribution of samples for biological assays, the creation of automated workflows drives throughput and creates impact within the research organization. While automation of small-molecule sample management workflows is well established, porting these technologies to manage biological samples has underscored the complexities inherent in ultra-low-temperature storage. Perkinson et al. illustrate their strategy and approach toward building automated workflows for biological substances, and they describe the challenges and opportunities that exist in this area.
As sample management has evolved from local supply of materials to adjacent, co-located testing laboratories to a global enterprise supplying both internal laboratories and external collaborators, the process now includes added complexities related to shipping and compliance with regulations, laws, and standards. While not as visible as next-generation sample-handling technologies, robust compliance procedures are equally complex and arguably more critical for maintaining the privilege to move materials within and across jurisdictions. Crimmin et al. highlight several areas in which compliance is key to both protect internal intellectual property (IP) and ensure compliance with external regulations, including those pertaining to controlled drugs, bio-licensing, and Nagoya. In addition, this article details efforts within GlaxoSmithKline for automated review of internal sample inventories against both internal restrictions and external legislative information sources. International material transfers also present compliance requirements, including correct classification of materials as well as obtaining the appropriate licenses for import and export of certain sample types. The true challenge for sample management groups is to build efficient tools that enable strict adherence to regulations while maintaining agreed-on sample transfer cycle times.
Several years ago, the science of sample management was explored and documented in a comprehensive edited volume by Wigglesworth and Wood. 1 Building on that foundation, this SLAS Technology special issue endeavors to highlight additional best practices, challenges, and new trends in the science of sample management. From the basics of sample management and best practices in compliance to complex, new frontiers in ultra-low-temperature automation and next-generation microfluidic sample handling, sample management continues to increase in complexity but remains a basic and integral driver for scientific discovery.