A complete concentration-response workflow
Start by defining the endpoint and response direction. An agonist response, inhibition readout, binding signal, and relative-potency comparison can use similar sigmoid models while answering different questions.
Preserve individual wells and replicate identities before deriving normalized responses. A plotted mean without its source wells can hide plate position, exclusions, and variability.
- Retain raw response, plate position, sample, concentration, replicate, and control labels.
- Apply blank correction or normalization according to the method, recording the exact transformation.
- Inspect concentration coverage and confirm that the fitted midpoint is supported by observations on both sides.
- Fit the candidate model with a documented equation and constraints.
- Review residuals, parameter uncertainty, curve shape, and assay-specific suitability checks.
- Report the estimate with units, model version, exclusions, diagnostics, and fingerprints for the source data file and analysis configuration.
Choose the model with scientific and diagnostic evidence
A 4PL is a common starting point for symmetric sigmoidal behavior. A 5PL adds asymmetry, but the extra parameter also needs enough informative data. Constraining an asymptote may stabilize a fit when scientifically justified; it also changes the estimand and must be visible.
R² alone is not a model-selection rule. Residual patterns, parameter precision, convergence, concentration coverage, weighting assumptions, and performance on known samples provide more useful evidence.
| Question | Evidence to inspect |
|---|---|
| Is the midpoint interpolated? | Observed concentrations and fitted midpoint |
| Is asymmetry supported? | Residual pattern, parameter uncertainty, repeated-run behavior |
| Are constraints justified? | Method rationale and performance against unconstrained fits |
| Is weighting needed? | Variance across the response or concentration range |
Make the result reproducible
Record whether concentrations were fitted on the raw or logarithmic scale, how zero-dose controls were handled, whether responses were normalized, and which definition of EC50 or IC50 was reported.
The report should connect the plotted curve to model parameters, well-level observations, exclusions with reasons, suitability results, the software version, and the analysis settings used to generate it.
Frequently asked questions
Should zero concentration be included on a logarithmic x-axis?
Logarithms of zero are undefined. Preserve zero-dose controls as observations, but display or model them using an explicitly documented method rather than silently replacing zero with an arbitrary small concentration.
Is the model with the highest R² always best?
No. R² may improve when parameters are added even when the added flexibility is unstable or scientifically unhelpful. Inspect residuals, uncertainty, coverage, convergence, and repeated-run performance.