ELISA Intelligence System
A cognitive ELISA analytical platform integrating 4PL/5PL curve fitting, curve reliability scoring, sample validation, assay performance characterisation, sigma metrics, and clinical interpretation narrative.
Core Lab Tools provide rapid calculations and can be extended into full Clinical Intelligence Systems.
Input & Computation
Standard Curve
| Conc. | OD Rep 1 | OD Rep 2 | OD Rep 3 | |
|---|---|---|---|---|
Samples
| Label | OD Rep 1 | OD Rep 2 | OD Rep 3 | Dil. | |
|---|---|---|---|---|---|
Plate Layout (optional)
Evidence & References
Engvall E & Perlmann P (1971)
Original ELISA paper. Enzyme-linked immunosorbent assay (ELISA). Quantitative assay of immunoglobulin G. Immunochemistry 8(9):871–874.
Findlay JWA & Dillard RF (2007)
Appropriate calibration curve fitting in ligand binding assays. AAPS J 9(2):E260–E267. Establishes 4PL as the standard model for immunoassays.
CLSI EP17-A2 (2012)
Evaluation of Detection Capability for Clinical Laboratory Measurement Procedures. Defines LLOQ, ULOQ, LoD, and LoB for quantitative assays.
Boulanger B et al. (2003)
An analysis of the ELISA and immunoassay validation parameters. J Pharm Biomed Anal 32(4–5):753–765. Defines acceptance criteria for back-calculated standards (±15%).
Broto M et al. (2019)
New perspectives in immunoassay quality control. Anal Bioanal Chem 411:7503–7514. Sigma metrics application to ELISA assay monitoring.
Sittampalam GS et al. (2004)
Recommendations for the design, optimization, and qualification of cell-based assays used for the detection of neutralizing antibody responses elicited by biological therapeutics. J Immunol Methods 289:1–16.
ECIS v1.0 architecture: 4PL/5PL Levenberg-Marquardt fitting → back-calculation recovery (±15% = PASS, ±20% = FAIL) → sample quantification with dilution correction → LLOQ/ULOQ range checking → replicate CV assessment (Grubbs outlier detection) → sigma metrics integration → interpretive narrative generation.
PM Lab Suite
Clinical Laboratory Intelligence Platform
When to Use
- Evaluating ELISA assay reliability beyond R²
- Validating samples within or outside calibration range
- Characterising assay performance: LLOQ, ULOQ, dynamic range, signal-to-noise
- Applying sigma metrics to ELISA performance assessment
- Generating clinical interpretation narratives for biomarker data
- Teaching ELISA analytical concepts with viva preparation
Common Pitfalls
- Reporting concentrations from extrapolated OD values (outside LLOQ–ULOQ)
- Accepting R² ≥ 0.98 without checking standard back-calculation errors
- Ignoring hook effect at high concentrations
- Using CV% without clinical context of the biomarker
- Failing to correct for dilution factor in sample back-calculation