Ask 10 people what a Laboratory Information Management System (LIMS) is, and you’ll likely get 10 different answers. That’s because both the functionality that any given LIMS provides, as well as the way that any given company uses their LIMS, can vary dramatically. In this article, we’ll discuss specifically LIMS for large molecule R&D.
At its core, the purpose of a LIMS is to help scientists and lab heads track samples, aggregate data from across experiments and teams, automate workflows, and integrate instruments into their data infrastructure. These functions are particularly important for companies doing large molecule R&D, where teams have to manage, collaborate on, and query across an unprecedented amount of data. The end goal of all of this is to generate results more quickly and with greater detail, and to cut down on the wasted time, duplicated efforts, and unnecessary expenditures of messy R&D processes.
When it comes to recording, processing, and learning from the data that large molecule R&D produces, systems like spreadsheets, Google Docs, and pen and paper fall far short. Spending hours searching for the right assay result, or agonizing over whether a sample’s record is up to date, is a regular occurrence for scientists. Beyond adding hours of busywork to scientists’ days, these outdated approaches can even call into question the accuracy of their results.
Biotech and pharma companies have long-since realized that they need to modernize their approach to tracking, centralizing, and querying their R&D data. The purpose of a LIMS is to give their teams a single source of truth where, rather than flip through spreadsheet after spreadsheet, scientists can enter in simple queries, or follow a straightforward trail of links, to find their desired data. Oftentimes, LIMS also have built-in validation rules that ensure all data is up to date and compliant, so teams don’t have to question its accuracy.
Biotech and pharma companies have long-since realized that they need to modernize their approach to tracking, centralizing, and querying their R&D data.
A LIMS can also serve as an effective lookup and reporting tool. The ideal LIMS will record and interlink the experiments conducted, results generated, and new output samples produced by any given sample. A scientist should be able to scan a barcode on a particular sample and see that sample’s complete experimental history. This, in turn, equips scientists to make decisions with complete experimental context. And when it comes time to make a go/no-go decision on a particular candidate, decision-makers can base their decisions on the candidate’s complete experimental history, rather than on only a few readouts.
Workflow Management and Instrument Integration
Beyond being a record-keeping system, a LIMS can play a proactive role in structuring R&D teams’ workflows and handoffs into executable stages. These stages often include explicit protocols for scientists to follow, as well as built-in rules around the type of data that each stage should produce. This significantly streamlines the work of individual scientists, but it also serves as an effective tool for team handoffs. Downstream teams get to see what work immediately preceded theirs, as well as the work that the teams downstream of them will have to do. As a result, each team can work more effectively, with the needs of their downstream colleagues in mind.
The ideal LIMS will record and interlink the experiments conducted, results generated, and new output samples produced by any given sample.
Some LIMS can also integrate directly with instruments through an API. This automates the data capture process, so that upon completing a plate transfer or other instrument-based activity, the samples’ digital records are automatically updated with the instrument’s outputs. Like the other functions that a LIMS provides, this cuts down on scientists’ busywork, centralizes data, and ensures the accuracy of records.
Where does LIMS fall short?
If all these features and benefits sound too good to be true, it’s because they often are. In fact, many LIMS are clunky and outdated, which means scientists rarely actually use them, negating all of the LIMS’s promised benefits. Configuring and implementing one of these LIMS is a daunting task, given the diverse nature both of large molecule R&D workflows and of the data infrastructure of different companies. Essentially every pharmaceutical company has a LIMS horrorstory to share, from protracted rollouts, to a lack of promised functionality, to the dreaded “shelfware” status attained by many unusable LIMS.
Many times, a LIMS that supposedly works for large molecule R&D is actually just a small molecule LIMS in everything but name.
Watch out for these key signs of a LIMS that might not be all it’s cracked up to be:
- An unattractive or slow interface
- Unintuitive or overly complicated functions
- A slow or nonexistent API
- Inability to model complex large molecule data types, such as DNA sequences
- Inability to model complex large molecule R&D workflows
- Implementation staff or vendors unfamiliar with large molecule R&D
- Lack of clarity around how/where it could fit in with your existing infrastructure
- Legacy vendor works primarily in the small molecule R&D space
The Benchling platform is a modern alternative to legacy LIMS, built for large molecule R&D workflows of all kinds. Its attractive and intuitive interface produces over 90% user adoption, while its flexibility makes it ideal for modeling large molecules and large molecule R&D workflows. Unlike any LIMS, it combines a Lab Notebook and a Molecular Biology Suite with a cutting-edge version of traditional LIMS functions.