Laboratory sample management | IDBS

Managing the precious biological samples needed for BioPharma product discovery is an essential skillset that continues through clinical trials and product commercialization. Sample management in laboratory contexts typically involves manual assays while sample management in manufacturing QC labs often entails automated sample management systems. From discovery to finished product, proper lab sample management is crucial to advance science and accelerate time to patient. Sample management software products can play an important role at every stage of the BioPharma lifecycle, improving sample tracking and sample integrity no matter the modality, including biopharmaceuticals and cell and gene therapies.

Effective management of laboratory samples is essential for any laboratory to run smoothly. This article explores what laboratory sample management is and describes what the key concepts are. It also identifies the key benefits of improving laboratory sample management and lastly, it addresses the limitations of common solutions used for lab sample management.

What is laboratory sample management?

Laboratory sample management is the process of correctly selecting, collecting, recording, processing, tracking, analyzing, storing and recovering laboratory samples when needed. Samples are used in all stages of BioPharmaceutical development and can be taken from humans, animals, plants, chemicals and manufactured substances. Proper sample management is essential to ensure the accuracy and reliability of analytical testing and results, which, in some cases, could impact product quality and/or patient health. While tracking and storage requirements are more stringent for manufacturing samples than research samples, there are general best practices that can improve sample management at any stage. For modern laboratories, this includes using software, such as a Laboratory Information Management System (LIMS), to manage samples effectively. To learn more about LIMS, read our article on What is a LIMS?

What are the key concepts of laboratory sample management?

The key concepts of laboratory sample management are:

  1. Sample collection – samples are collected from a variety of sources and are registered, carefully labeled and often stored. If barcode label printing is available, these labels can simplify sample registration and tracking and have the additional benefit of eliminating transcription errors and saving time.
  2. Sample storage – samples may require controlled environmental factors such as temperature, humidity and light during storage. This helps maintain the quality of the sample and prevents the samples from being spoilt, which could affect analytical test results.
  3. Sample tracking – samples are often collected by one group and tested by another and may be held in inventory between steps. All the sample details – including the sample’s location and status level, both past and present – are carefully recorded and documented so samples can be tracked and reviewed. Maintaining a record of available inventory is particularly important for limited quantities of samples, such as research cell banks and reference standards, which may be used by different groups.
  4. Sample quality control – when laboratory samples follow a strict quality control regime, the accuracy and traceability of the sample management process are continuously measured. This process can help with audits and enables the laboratory to adhere to relevant regulatory guidelines.

What are the key benefits of a LIMS for sample management?

Implementing software, such as a LIMS, to improve sample management can also boost the efficiency of how the laboratory is run from day to day and helps ensure quality. Here are some of the key benefits:

  1. Increased data and sample quality
    Ensuring data accuracy and completeness during sample registration and tracking can help reduce the risk of samples being lost or mixed up with other samples. This can include associating metadata such as site, group, project or study with each sample for easier identification. Implementing procedures to ensure correct sample handling, labeling, storage and retrieval can reduce or eliminate errors and prevent the accidental use of expired materials.
  2. Improved sample traceability
    Traceability across the sample lifecycle, including preparation status and location, is essential for a laboratory to run smoothly and efficiently. This includes documenting each stage of the sample’s lifecycle with dates and locations for greater clarity when scientists need to refer back to the sample later. Using barcode scanners to automatically record sample handling simplifies the process, saves time and eliminates transcription errors.
  3. Better sample data availability and shareability
    Having effective sample management, including associated metadata, means that samples can be easily found in the laboratory and made available when needed – either for analysis or for distribution to other laboratories. Also, having a single place to store sample data provides one single source of truth, so information regarding the samples can easily be shared with other departments and laboratories.
  4. Enhanced workflow and increased productivity
    Implementing a LIMS can automate repetitive and time-consuming manual sample management tasks, including recording sample information and associated data points. This saves time, reduces error and increases up-time for the laboratory, enabling laboratory staff to focus on their core tasks. Lab workflows can even be supported by a scientific voice assistant, which enables hands-free data entry and recording.

What are the limitations of using a LIMS for sample management?

LIMS are designed to manage samples and typically excel at summarizing sample test results and enabling automated workflows. This sample-centric view is ideal for routine quality control (QC)-related activities but doesn’t provide the flexibility and process/experiment context required for most research and development work. Additional limitations include:

  1. Gaps in critical information associated with processes and experiments limit the visibility of end-to-end workflows;
  2. Structured workflow design does not support the variable and ad-hoc nature of research and development experiments;
  3. Limited ability to record observations and scientific narrative to provide the context needed to interpret results.

Even LIMS designed for specific functional areas, such as bioanalytical laboratories, can result in siloed data if supporting activities such as reagent preparation and equipment checks aren’t captured in the same system. BioPharmaceutical companies looking to leverage advanced data scientific tools such as artificial intelligence and machine learning to gain value from their data need a holistic view that extends not just across the sample lifecycle but also includes process and experiment-related activities that happen before and after the samples are collected and tested.


Effective laboratory sample management is critical to the success of any laboratory and is essential for sample collection, storage, tracking and quality control. In addition, investing in software that supports sample management, such as a LIMS, can help manage increased data volumes, improve traceability and availability and boost data sharing. There are, however, some limitations to implementing a LIMS in research and development, such as lack of flexibility and experimental context.

Legacy systems often struggle to keep up with the demands of modern BioPharma development. The growing reliance on cross-team collaborations, outsourcing and advanced laboratory instruments that generate exponentially increasing amounts of digital information requires a data-centric rather than application-centric approach.

IDBS’ digital workflows are designed to create a persistent, dynamic data backbone throughout the BioPharma lifecycle. By providing a solid foundation for advanced analytics and enabling insights to be shared across internal and external teams, this, in turn, accelerates process and product understanding.

One of the most fundamental examples of why sample management matters in drug development is bioanalysis, the quantitative measurement of drugs and their metabolites in biological systems. Polar BioAnalysis is a bioanalytical software system that enables CROs and BioPharma to improve sample quality and reproducibility, reduce study cycle times and accelerate time-to-market.


Learn more about Polar BioAnalysis from IDBS today.


If you have questions about other types of sample management, such as biologics research or bioprocess development, contact us or access our website ChatBot.