How to rescue an analytical method without starting again
In veterinary pharma, analytical methods rarely fail dramatically. They fade. They drift. They become slower, fussier, and harder to trust. By the time the symptoms are obvious — OOS events, transfer failures, unexplained variability — the team is already firefighting.
The good news is that most struggling methods can be rescued with a structured, scientific approach. Here’s how we tackle it.
1. Strip the method back to first principles
Before changing anything, go back to the science.
- What is the method actually trying to measure?
- What are the critical quality attributes?
- Does the chemistry still make sense for the formulation?
Many methods fail because they’ve drifted away from their original purpose.
2. Challenge every inherited assumption
Legacy methods often carry years of “we’ve always done it this way.” We look for:
- unnecessary steps
- outdated column choices
- sample prep that no longer matches the product
- parameters that were never scientifically justified
A fresh pair of eyes can spot inefficiencies instantly.
3. Identify the true failure mode
Most method issues fall into a few categories:
- poor selectivity
- unstable sample prep
- column sensitivity
- inconsistent extraction
- system suitability that’s compensating for deeper problems
Once you know the root cause, the fix becomes obvious.
4. Make targeted, minimal interventions
A rescue doesn’t mean a redesign. Often, small changes unlock big improvements:
- adjusting pH
- simplifying sample prep
- choosing a more robust column
- tightening injection parameters
- redefining SST to reflect real performance
The goal is stability, not reinvention.
5. Re-establish method capability with real data
After optimisation, we always run:
- robustness checks
- forced degradation (if relevant)
- repeatability across analysts
- side-by-side comparisons with the original method
This builds confidence — and gives regulators a clear, defensible story.
6. Strengthen the documentation
A rescued method needs a rescued rationale. Clear justification, updated risk assessments, and a clean validation plan turn a fragile method into a lifecycle-ready one.
The outcome
A method that was causing delays becomes one that supports progress. A process that felt chaotic becomes predictable. And teams regain confidence in their data — which is often the biggest win of all.
If you’re wrestling with a method that’s drifted off-spec, get in touch — this is exactly the kind of problem we solve.
Questions about any of this?
If something here is relevant to a project you are working on, we are happy to talk it through.