1. Define the analytical target
State the biological question, material and matrix, endpoint, reportable result and unit, expected range, required precision, decision context, throughput, and operating constraints. This target guides the plate and statistical design.
Decide whether the primary objective is relative potency, absolute dose response, inhibition, interpolation, screening, or monitoring. Similar-looking curves can require different models and evidence.
2. Stabilize biology and plate execution
Select controls that represent the response window and failure modes. Explore seeding, incubation, reagents, preparation, mixing, timing, plate type, instrument settings, and location effects.
Use designed experiments where practical rather than changing one factor until a curve looks acceptable. Record lots, instruments, operators, and timing so sources of variation can be identified.
3. Specify data handling before optimizing a fit
Define source formats, plate roles, normalization, blank handling, dilution calculations, replicate summaries, exclusions, concentration units, and rounding. Keep raw and derived values linked.
Well-designed analysis settings can be applied to development data, compared with alternatives, exported, reviewed, and eventually approved as a routine method version.
4. Evaluate models and suitability together
Compare linear, 4PL, 5PL, restricted, or other appropriate models using representative runs. Inspect residuals, coverage, parameter stability, uncertainty, and performance of reported results—not only R².
Develop system-suitability criteria around known failure modes and intended decisions. Challenge them with edge cases to understand false passes and false failures. Thresholds should be supported across relevant runs and factors.
5. Challenge, validate, and transfer the procedure
Robustness experiments deliberately vary relevant conditions around target settings. Validation studies address selected performance characteristics for intended use. Transfer or implementation verifies that the receiving environment can execute the procedure as planned.
Record versions for the method, reader profiles, plate layouts, calculation software, analysis settings, criteria, report template, and evidence. Define how changes will be assessed before routine use so improvements do not create uncontrolled drift.
6. Design the report early
A draft report reveals missing units, source records, parameters, criteria, and decision fields long before validation. Include fingerprints for the source data file and analysis configuration, model parameters and diagnostics, exclusions, calculated results, suitability outcomes, and version information.
Keep scientific values separate from presentation rules. This allows teams to change captions, reported digits, styles, and customer-specific sections without changing the calculation that produced the result.
Frequently asked questions
When should assay software enter development?
Early enough to preserve data and compare analysis settings reproducibly, while keeping development work separate from approved routine execution.
Should system-suitability thresholds be copied from literature?
Literature can inform hypotheses, but thresholds should be justified for the assay, design, intended use, and observed performance.
Why record the analysis-software version?
It lets a reviewer identify the software that produced a result, reproduce it with the same version, and assess the impact of later changes.