Assay Analysis With Biphasic Standard Curves for Low-Level Quantification
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Solution Overview
Problem
Existing immunoassay technologies face limitations in sensitivity and dynamic range, particularly at low analyte concentrations, leading to inadequate quantification of analytes like tumor necrosis factor (TNF) due to insufficient assay sensitivity and poor fitting of standard curves using 4-PL and 5-PL models.
Innovation Solution
A method involving a biphasic standard curve model using a mathematical transformation function to amplify signal values at lower concentrations, incorporating a combination of higher and lower affinity interactions, and accounting for background and nonspecific signals, to improve quantification accuracy and dynamic range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 4-PL or 5-PL models are used for standard curve fitting, then the assay can be operated with standard procedures, but the quantification accuracy at low analyte concentrations is insufficient and the dynamic range is limited
Solution Approach 1:
The patent applies parameter changes by transforming the standard curve model from conventional 4-PL or 5-PL models to a biphasic model with parameters Bmax1, Kd1, Bmax2, and Kd2 representing two distinct binding phases. This transformation enables accurate quantification across a broader concentration range, particularly at low analyte concentrations, while maintaining a manageable computational framework through systematic parameter estimation procedures.
2Measurement precision
If the assay sensitivity is increased to detect low abundance analytes, then the lower limit of quantification is reduced, but the dynamic range may be compromised
Solution Approach 1:
The patent segments the binding interaction into two distinct phases characterized by different affinity constants (Kd1 and Kd2) and maximum binding capacities (Bmax1 and Bmax2). This segmentation allows the assay to simultaneously capture high-affinity binding at low analyte concentrations and lower-affinity binding at higher concentrations, thereby achieving both low detection limits and extended dynamic range without compromise.
Solution Approach 2:
The biphasic binding model acts as a composite analytical framework that integrates two distinct binding paradigms into a unified quantitative model. By combining the mathematical descriptions of two different binding phases, the system achieves enhanced sensitivity at low concentrations while preserving the ability to quantify across a broad concentration spectrum, effectively creating a composite measurement capability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy and sensitivity of analyte quantification, particularly at low concentrations, by improving the goodness of fit and reducing the lower limit of quantification (LLOQ) while extending the assay's dynamic range.
Implementation Method 1
The dependence on radioactive signalling for analyte detection was replaced by the use of enzyme-based colour generation as a route for signal amplification. This gave rise to the generation of enzyme immunoassays (EIA) and enzyme-linked immunosorbent assay (ELISA). These common forms of assay system incorporate molecules or antibodies that bind to (or 'capture') analyte molecules prior to their quantification in an assay.
Implementation Method 2
The dependence on radioactive signalling for analyte detection was replaced by the use of enzyme-based colour generation as a route for signal amplification.
Implementation Method 3
A method involving a biphasic standard curve model using a mathematical transformation function to amplify signal values at lower concentrations, incorporating a combination of higher and lower affinity interactions, and accounting for background and nonspecific signals, to improve quantification accuracy and dynamic range.
Data Source
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AI summary
A method for determining the amount of an analyte in a sample is provided, the method including the steps of detecting signals resulting from reaction of captured analyte with detection molecules, applying a mathematical transformation function to the signal results, and using a computer to mathematically model a biphasic standard curve of analyte concentration vs transformed detected signal using an equation. A method for modelling calibration data, a computer programmed to mathematically model a biphasic standard curve of analyte concentration vs transformed detected signal, and a kit of parts for determining the amount of an analyte in a sample is also provided.