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Legacy informatics just can’t keep up with biologics R&D. From failing to express the complexities of large molecules, to rigid configurations that prevent process iteration, legacy informatics hinders the day-to-day work of scientists and forces R&D leaders to base decisions on incomplete data.
1. You can’t model complex large molecule relationships
Sample management is useless if you can’t track the data that you need about your biological entities. Legacy LIMS claim to have registration capabilities, but usually it’s just repackaged chemical compound registration. That can’t extend to complex use cases, such as antibody discovery, where many different entities (ex. plasmid maps, chains, individual lots of antibodies) have to be registered and interconnected. At the end of the day, scientists and directors miss out on crucial data.
2. You can’t structure biologics R&D workflows
Legacy LIMS are built for simple workflows with clear inputs and outputs. They’re ill-suited for biologics workflows, which vary significantly across companies, and which are often in flux. Workflow branches, cycles, and team handoffs are poorly modeled, if at all, by legacy LIMS. As a result, you can’t get a big picture view of your R&D progress, and you can’t measure the efficiency of your processes. Directors have no choice but to base decisions on incomplete information.
3. You can’t adjust configurations on the fly
The legacy model of LIMS breaks down whenever any changes to configuration have to be made. Doing something simple like adding a single field to an entity, or adding a new step to a workflow, can take many months of back-and-forth with an unresponsive vendor. Your informatics feels like it’s always lagging one step behind your science, causing data loss and endless IT headaches.
4. You can’t integrate with databases or other software
We’re all familiar with the pains of data silos, and legacy LIMS does nothing to alleviate these. Some legacy LIMS do have APIs, but more often than not, the right endpoints aren’t exposed – or the API is just plain slow. R&D and IT end up playing the blame-game, as scientists struggle to reference samples in their ELNs and query functional data across different repositories.
5. You can’t link assay data back to samples and experiments
What good is data if you can’t say for sure where it came from? Screening data, for example, shouldn’t exist separately from the lots that generated it – or from the experimental conditions behind it. If you can’t integrate your LIMS with instruments or reliably contextualize your data, then your results could be in question. Confidently making go/no-go decisions and learning from past candidates is much more difficult when your data doesn’t tell the whole story.
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