Lab Systems & Automation

The problem in your words

Laboratory estates grow one instrument and one system at a time, and the validation follows the same pattern: a LIMS validated years ago and patched since, a chromatography data system with audit-trail settings nobody has reviewed, electronic notebooks half-adopted, and instrument software qualified to whatever the supplier's binder suggested. Data-integrity findings concentrate in the lab for exactly this reason: the evidence is fragmented across systems that were each validated alone, and nobody owns the whole picture.

Automation raises the stakes. Robotic sample preparation, automated stability pulls and connected instruments multiply throughput, and they also multiply the number of systems that create, transform and move GxP data without a human watching.

How I approach it

Treat the laboratory as one validated data estate rather than a shelf of individually qualified boxes. The work starts with a lab systems inventory and data-flow map: every system that creates or changes a result, from balance to LIMS to CoA. Each system then gets a risk classification under the same DRIVE risk decision used estate-wide, with GAMP categories doing the sorting: vendor evidence carries standard instrument software, configuration-level assurance covers the LIMS and CDS, and full lifecycle validation is reserved for the genuinely custom. Audit-trail review, access control and ALCOA++ controls are designed once, as lab-wide standards, instead of being rediscovered system by system.

For automation, each robotic cell or interfaced instrument is validated as part of the data flow it serves, with the interface evidence and error handling treated as first-class requirements. The result is a lab where every result is attributable end to end, and where the next instrument lands into a defined pattern instead of a blank page.

Inspectors go to the lab first because that is where the data is born. Win the lab and the rest of the inspection changes tone.