Method of calibrating an analysis of a plurality of immobilised capture agents on a substrate

Abstract

A method of calibrating an analysis of a plurality of immobilised capture agents on a substrate is disclosed, wherein the method comprises performing, in a process of analysing a plurality of immobilised capture agents on a substrate, a lot adjustment per immobilised capture agent by adjusting a result of an analysis of each immobilised capture agent using calibration correction parameters. Said calibration correction parameters are calculated for each immobilised capture agent for each production lot based on adjustments required to the calibration dataset required to align the calibration dataset with a master dataset. Also disclosed is a substrate for use in the method, and s substrate analysis apparatus configured to receive a container comprising a plurality of the substrates.

Description

[0001] METHOD OF CALIBRATING AN ANALYSIS OF A PLURALITY OF IMMOBILISED CAPTURE AGENTS ON A SUBSTRATE

[0002] FIELD OF INVENTION

[0003] The present disclosure is in the field of calibration methods for immunoassays, and relates in particular to a method of calibrating an analysis of a plurality of immobilised capture agents on a substrate.

[0004] BACKGROUND TO INVENTION

[0005] Analysis systems are available for automating the analysis of chemical or biochemical assays. Such assays are generally performed in a regular array of print areas that are configured to hold reactants and a test sample. Such assays generally test for the presence or level of an analyte in the test sample. A response, often in the form of a change in degree of opacity, colour, size or other detectable change, is associated with the presence or level of the analyte.

[0006] In an example, such a regular array of print areas may comprise an array of a biological material, such as proteins, DNA, antigens or antibodies, deposited and immobilised on a solid and typically flat substrate, typically made of functionalized glass or plastic. The substrate (sometimes referred to as a solid phase) is generally selected to provide adequate binding with the biological material, but also to avoid any modification of the material, such as denaturation in the case of a protein.

[0007] The analysis system is provided with a sensor, typically a digital camera, for identifying the response in each print in the array. In this way, for example, the digital camera image can be reviewed and the response (e.g. degree of change in colour or opacity) can be identified in order to determine the presence or level of analyte in any given print area.

[0008] Immunoassays, and more particularly semi-quantitative and quantitative tests, must be calibrated to provide a precise and robust result whatever the test conditions (batches and age of reagents, instruments, etc.). Typically, an external product calibrator is provided to the operator to calibrate the test. Each analyte must be calibrated with a specific calibrator.

[0009] That is, the manufacturer may be required to supply and / or formulate and / or adjust liquid product calibrators for each analyte. Furthermore, such calibration may introduce a risk of error, as manual actions are required in such a calibration process e.g. experimental and / or calculation errors.

[0010] Furthermore, such calibration may incur a delay in patient result delivery, due to such a necessity to calibrate before testing.

[0011] As such, it is therefore desirable to provide a simple, reliable, quick and low-cost method of calibrating immunoassays.

[0012] It is therefore an aim of at least one embodiment of at least one aspect of the present disclosure to obviate or at least mitigate at least one of the above identified shortcomings of the prior art.

[0013] SUMMARY OF INVENTION

[0014] The present disclosure is in the field of calibration methods for immunoassays, and relates in particular to a method of calibrating an analysis of a plurality of immobilised capture agents on a substrate (or solid phase).

[0015] As used herein, the terms “comprising”, “comprise” and / or “comprises” are used to denote aspects and embodiments of this disclosure that “comprise” a particular feature or features. It should be understood that these terms may also encompass aspects and / or embodiments which “consists of” or “consists essentially of” the relevant feature(s).

[0016] Moreover, in the following text any reference to an aspect of the present disclosure may refer to any embodiment or teaching of that aspect. Furthermore and for the avoidance of doubt, all embodiments, disclosures and / or teachings associated with the first aspect of this disclosure, apply mutatis mutandis to the second aspect (and its embodiments, disclosures and / or teachings) and any further relevant aspects (embodiments, disclosures and / or teachings) described herein.

[0017] According to a first aspect of the disclosure, there is provided a method of calibrating an analysis of a plurality of immobilised capture agents on a substrate. The method comprises: determining, in a substrate production process and by analysing characteristics of at least one substrate from each of a plurality of substrate lots, a master dataset for each immobilised capture agent of the plurality of immobilised capture agents, each master dataset corresponding to characteristics of each respective immobilised capture agent; determining, in the substrate production process and for each production lot of substrates, a calibration dataset corresponding to characteristics of each immobilised capture agent of the plurality of immobilised capture agents, calculating calibration correction parameters for each immobilised capture agent for each production lot based on adjustments required to the calibration dataset required to align the calibration dataset with the master dataset; and performing, in a process of analysing a plurality of immobilised capture agents on a substrate, a lot adjustment per immobilised capture agent by adjusting a result of an analysis of each immobilised capture agent using the calibration correction parameters.

[0018] Implementation of such a method of lot-to-lot calibration means that, for the manufacturer performing said substrate preparation / production process, there may be no need to supply and / or formulate and / or adjust liquid product calibrators for each immobilised capture agent.

[0019] Furthermore, the disclosed method of calibration may reduce a risk of experimental and / or calculation errors being introduced by an analysis system operator, as no manual actions are required for the calibration.

[0020] Furthermore, the disclosed method of calibration may reduce any delay in patient result delivery, as the disclosed method mitigates a necessity to calibrate before testing.

[0021] Furthermore, the calibration methods described herein may, at least in part, compensate for, reduce or otherwise mitigate errors in the process of analysing a plurality of immobilised capture agents on the substrate, and / or compensate for lot-to-lot variability of the capture agent and / or variabilities in the substrate preparation / production process. Such errors may otherwise be introduced by: different components of the analysis and / or substrate preparation system(s), e.g. instrument and environmental conditions, substrate lots, array to array, liquid reagent lots, spot to spot and patient sample; and / or a stability of arrays and liquid reagents.

[0022] The plurality of immobilised capture agents on a substrate may comprise, for example, a plurality of antigens (or antibodies) printed on the epoxy glass for antibody (or antigen) detection in a sample, for example a human sample. In examples, the substrate may be provided with a plurality of immobilised capture agents, wherein each immobilised capture agent may be deposited on the substrate by printing, or the like. Each immobilised capture agent may be deposited on the substrate as a spot, for example a discrete spot, and may hereafter be referred to as “diagnostic spots”.

[0023] The “substrate production process” may refer to a process of printing the plurality of immobilised capture agents on the substrate. That is, the “substrate production process” may refer to a substrate preparation process. As described in more detail below, the “substrate production process” may also refer to a process of printing other agents on the substrate, such as controls and / or gridding spots and / or calibration spots. The “master dataset” may refer to a plurality of data points. In some examples, said plurality of data points may correspond to a plotted curve, e.g. when plotted on a graph depicting the signal as a function of a concentration and / or sample dilution (expressed in international units or arbitrary units), as described in more detail below. As such, in at least some embodiments described herein, the master dataset may herein be referred to as the “master curve” and / or as comprising “curve data”. Similarly, in at least some embodiments described herein, the calibration dataset may herein be referred to as the “calibration curve” and / or as comprising “curve data”.

[0024] By way of example, the “master dataset” may be prepared using one or more test samples, each test sample comprising one or more targets for any of the immobilised capture agents described herein. The test samples may be subject to dilution and each test sample dilution may be contacted with at least one substrate from each of a plurality of substrate lots. Each test sample dilution may be contacted with a substrate in a ‘test sample contact step’ and under conditions which permit binding between an immobilised capture agent of the substrate and its respective target in the test sample. For each substrate lot, the test sample contact step may be repeated a number of times (for example 2, 3, 4 or 5 times) for each test sample dilution and using a number (for example at least 2) instruments. Once the test sample contact step is complete, binding between an immobilised capture agent and its target (forming a capture agent / target complex), may be determined via some form of detection step which may comprise contacting the capture agent / target complex with a test detection reagent. The test detection reagent may comprise a test detection agent. The test detection agent may comprise a conjugated antibody, wherein the antibody is conjugated to a detectable, for example optically detectable, moiety. One of skill will appreciate that as the test sample is diluted, the amount of target available to be bound by the respective immobilised capture agent is reduced (there being more target in an undiluted test sample versus the amount of target in a test sample which has been diluted). As such, the detection step may yield a binding signal which may be ‘strong’ for undiluted test samples and relatively weaker for diluted test samples. The data may be plotted on a graph of test sample concentration (or dilution) against target binding signal.

[0025] As such, the ‘master dataset’ may comprise, for each immobilised capture agent, a graph plotting binding signal against test sample dilution.

[0026] The “process of analysing a plurality of immobilised capture agents on a substrate” may correspond to a process distinct from the substrate production / preparation process. In an example, the substrate production process may be carried out by a manufacturer and / or supplier of prepared substrates, such as at a manufacturing / production / preparation facility. In the example, process of analysing the plurality of immobilised capture agents on a substrate may be carried out at a location remote from the location of the substrate production process. The substrate production process may be carried out by a party distinct from the manufacturer and / or supplier of prepared substrates, e.g. by a customer of the manufacturer.

[0027] As described below with reference to example embodiments, the “calibration correction parameters” may, for example, refer to parameters describing a shape and / or characteristics of a curve, such as a calibration curve, a master curve, or parameters for fitting one curve to another curve e.g. for adapting one curve to match, or approximate another curve. As non-limiting examples, said parameters may comprise any of: polynomial coefficients, a slope parameter, an intercept parameter, any / all parameters of a 4PL or 5PL regression model.

[0028] The term “immobilised capture agents” may, in some examples, refer to “analytes” or “immobilised analytes”. References to “analytes” in the ensuing description will be understood to more generally refer to immobilised capture agents.

[0029] References herein to an “analysis system” may be to MosaiQ™ by AliveDX, which is an example of microarray-based technology used for blood grouping and donor disease screening.

[0030] Determining the master data set may comprise, for each immobilised capture agent, generating curve data for signal as a function of a concentration.

[0031] Determining the master data set may comprise applying a mathematical model to the curve data to determine parameters corresponding to characteristics of the respective immobilised capture agent.

[0032] The mathematical model may be a least squares logistic regression model.

[0033] The least squares logistic regression model may be a four parameter logistic (4PL) or a five parameter logistic (5PL) regression model.

[0034] In other examples, the mathematical model may comprise a polynomial approximation.

[0035] Determining the calibration dataset may comprise determining a mean value of measurements for each immobilised capture agent replicated across a plurality of different measurement instruments.

[0036] Determining the calibration dataset may comprise generating curve data for each immobilised capture agent. The calibration correction parameters may comprise a slope parameter and an intercept parameter for adjusting the calibration dataset to the master dataset, based on a linear calibration model.

[0037] In examples, lot-to-lot (e.g. lot-specific) adjustments may be implement based on the linear calibration model for semi-quantitative assays.

[0038] The calibration correction parameters may be derived from a mathematical model of the calibration dataset adjusted to the master dataset.

[0039] Optionally, the mathematical model is a 4PL or 5PL regression model.

[0040] In examples, lot-to-lot adjustments may be implement based on the 4PL or 5PL regression model for quantitative assays.

[0041] The plurality of immobilised capture agents may comprise a plurality of different immobilised capture agents.

[0042] That is, the inventors have recognised that for purposes of calibration, a plurality of different immobilised capture agents may be effectively represented by as few as one immobilised immunoglobulin, as described below.

[0043] That is, a plurality of different immobilised capture agents may be effectively represented by a single spot comprising an immobilised calibration agent. The immobilised calibration agent may comprise an immunoglobulin. The immobilised calibration agent may comprise immobilised human immunoglobulin. The immobilised calibration agent may comprise immobilised IgG, for example, immobilised human IgG. The immobilised calibration agent may comprise a functional (e.g. antigen or epitope binding) fragment thereof. By way of example, the calibration agent may comprise an immunoglobulin (for example human immunoglobulin, human IgG) Fc fragment.

[0044] The method may comprise generating a calibrated analysis result by applying a 4PL / 5PL regression model using the calibration correction parameters to an analysis result signal for each immobilised capture agent.

[0045] The method may comprise generating a calibrated analysis result by subtracting a / the intercept parameter of the calibration correction parameters from an analysis result signal for each immobilised capture agent and dividing a result by a / the slope parameter of the calibration correction parameters.

[0046] Each immobilised capture agent of the plurality of immobilised capture agents may comprise antigens and / or antibodies deposited on a substrate for antibody and / or antigen detection respectively in a sample, for example a human sample.

[0047] Each substrate may further comprise at least one immobilised human immunoglobulin. The immobilised human immunoglobulin may be deposited on the substrate by printing, or the like. The immobilised human immunoglobulin may be deposited on the substrate by printing or the like during the substrate production process. Each immobilised human immunoglobulin may be deposited on the substrate as a spot, and may hereafter be referred to as a “calibration spot”. In example embodiments, a plurality of calibrating spots, such as two calibration spots, may be deposited on the substrate, as described in more detail below.

[0048] The human immunoglobulin may comprise human IgG and / or one or more derivatives. In an example, the one or more derivatives may comprise an active or functional fragment, such as, for example, an Fc fragment comprising heavy chains of type gamma.

[0049] The human immunoglobulin may comprise human IgA and / or one or more derivatives. In an example, the one or more derivatives may comprise an Fc fragment comprising heavy chains of type alpha.

[0050] The human immunoglobulin may comprise human IgE and / or one or more derivatives. In an example, the one or more derivatives may comprise an Fc fragment comprising heavy chains of type epsilon.

[0051] The human immunoglobulin may comprise human IgM and / or one or more derivatives. In an example, the one or more derivatives may comprise an Fc fragment comprising heavy chains of type mu.

[0052] The immobilised calibration agent may not bind anything present in the sample, but may instead ‘interact’ with or bind to the detection reagent. In one teaching the detection reagent may comprise a detection agent which (under suitable conditions) binds to the calibration agent. For example, where the calibration agent comprises an immunoglobulin (for example a human immunoglobulin as described herein), the detection reagent may comprise an anti-immunoglobulin antibody (for example an antihuman immunoglobulin antibody). The anti-immunoglobulin antibody may be conjugated or bound to some form of label, for example an optically detectable label. The label may comprise HRP. As such, the detection reagent may comprise an HRP labelled antiimmunoglobulin antibody. Without wishing to be bound by theory, a spot comprising an immobilised calibration agent should not be impacted by the sample but only by the instrument (optical detection, temperature) and the detection reagent (concentration adjustment and stability).

[0053] The method may comprise computing, for each lot or for a container of substrates from a lot, or for each substrate, an internal immobilised human immunoglobulin target value. Optionally, the internal immobilised human immunoglobulin target value may be based on an average of values obtained during the determination of the calibration dataset.

[0054] The method may comprise performing an internal calibration for each immobilised capture agent of the plurality of immobilised capture agents by performing a mathematical transformation of an analysis result signal for each immobilised capture agent by the internal immobilised human immunoglobulin target value, and using an analysis result signal of the at least one immobilised human immunoglobulin.

[0055] The method may comprise performing an internal calibration for each immobilised capture agent of the plurality of immobilised capture agents by adjusting an analysis result signal for each immobilised capture agent using the internal immobilised human immunoglobulin target value and an analysis result signal of the at least one immobilised human immunoglobulin.

[0056] The method may comprise performing an internal calibration for each immobilised capture agent of the plurality of immobilised capture agents by multiplying an analysis result signal for each immobilised capture agent by the internal immobilised human immunoglobulin target value divided by an average analysis result signal of the at least one immobilised human immunoglobulin. The term “average” may, in embodiments, refer to an arithmetic mean, geometric mean or median.

[0057] Such internal calibration may be implemented to mitigate effects of instrument variability, reagent stability, lot-to-lot variability. The term “internal calibration” may refer to a calibration that is performed for each sample analysed, e.g. patient blood sample, and for each diagnostic spot systematically and automatically by an analysis system, without any external action required by a user of the analysis instrument. The internal calibration may be implemented in combination with the above-described lot-to-lot calibration.

[0058] Advantageously, by basing the internal immobilised human immunoglobulin target value on an average of values obtained during the determination of the calibration dataset, the dataset prepared for lot-to-lot calibration serves the additional purpose of providing basis for internal calibration.

[0059] Furthermore, combining such an internal calibration with the above-described lot- to-lot calibration may improve repeatability and / or reproducibility and / or accuracy or results, and may advantageously provide an increased shelf-life of the product.

[0060] The method may comprise performing a validity check of the substrate by determining whether an analysis result signal of the at least one immobilised human immunoglobulin is within a predetermined range. That is, said validity check may be a test that an analysis result signal is within an expected range, wherein a result outside the expected range may be indicative of a defective substrate and / or analysis method.

[0061] The method may comprise storing data and / or a reference to data, on a container comprising a plurality of the substrates from a lot. The data may comprise at least one of: the master dataset; the calibration dataset; calibration correction parameters; the internal immobilised human immunoglobulin target value and / or the fitted curve (as described in more detail below).

[0062] In a non-limiting example, the method may comprise storing the data on an RFID tag associated with or coupled to the container.

[0063] In a non-limiting example, the method may comprise providing a reference to the data, such as a barcode, Quick-Response (QR) core, Uniform Resource Locator (URL) or the like, that is associated with or coupled to the container or the substrate.

[0064] In a non-limiting example, the method may comprise storing the data on an RFID tag associated with or coupled to each substrate. That is, in some embodiments each substrate, e.g. each microarray, may be effectively tagged, such as by an RFID , a QR- code, or the like.

[0065] According to a second aspect of the disclosure, there is provided a substrate comprising: a plurality of different immobilised capture agents deposited on the substrate, each immobilised capture agent for the detection of a target in a sample, for example a human sample. In one teaching, an immobilised capture agent may comprise an antigen and / or antibody for antibody and / or antigen (the target) detection respectively in a sample, for example a human sample; and at least one immobilised human immunoglobulin deposited on the substrate at a pre-determined concentration.

[0066] According to a third aspect of the disclosure, there is provided a container comprising a plurality of substrates according to the second aspect, and further comprising a reference to data and / or a data storage device comprising data, wherein said data comprises at least one of: calibration correction parameters for performing a lot-to-lot calibration of analysis results; an internal immobilised human immunoglobulin target value; a master dataset corresponding to characteristics of each immobilised capture agent across a plurality of production lots; and / or a calibration dataset corresponding to characteristics of each immobilised capture agent of the plurality of immobilised capture agents in a production lot.

[0067] According to a fourth aspect of the disclosure, there is provided a substrate analysis apparatus configured to receive a container according to the third aspect, the apparatus configured to perform a lot adjustment per immobilised capture agents by adjusting a result of an analysis of each immobilised capture agent using the calibration correction parameters for each respective immobilised capture agent in each production lot.

[0068] The above summary is intended to be merely exemplary and non-limiting. The disclosure includes one or more corresponding aspects, embodiments or features in isolation or in various combinations whether or not specifically stated (including claimed) in that combination or in isolation. It should be understood that features defined above in accordance with any aspect of the present disclosure or below relating to any specific embodiment of the disclosure may be utilized, either alone or in combination with any other defined feature, in any other aspect or embodiment or to form a further aspect or embodiment of the disclosure.

[0069] BRIEF DESCRIPTION OF DRAWINGS

[0070] These and other aspects of the present disclosure will now be described, by way of example only, with reference to the accompanying drawings, wherein:

[0071] Figure 1 depicts an example of a substrate, according to an embodiment of the disclosure;

[0072] Figure 2 depicts an example of an immunological reaction between a human sample and diagnostic and calibration spots in a microarray of the substrate of Figure 1 ;

[0073] Figure 3 depicts a high-level overview of a 2-step calibration method, wherein the method comprises both internal calibration and lot-to-lot adjustments, according to embodiments of the disclosure;

[0074] Figure 4 depicts the internal calibration method and associated calculations, according to an embodiment of the disclosure;

[0075] Figure 5 depicts examples of such master curves generated according to embodiments of the disclosure;

[0076] Figure 6 depicts an example of the 4PL regression model applied to a calibration curve, according to embodiments of the disclosure;

[0077] Figure 7 depicts an application of a linear model to the master curve for semi- quantitative assays, according to embodiments of the disclosure; Figure 8a-b depicts an application of the 5PL calibration model for quantitative assays, according to embodiments of the disclosure;

[0078] Figure 9 depicts a summary of the disclosed calibration method, wherein the method comprises both internal calibration and lot-to-lot adjustments, according to embodiments of the disclosure;

[0079] Figure 10 depicts an example of a workflow for master curve generation, according to embodiments of the disclosure;

[0080] Figure 11 depicts an example of a container lot calibration workflow, according to embodiments of the disclosure; and

[0081] Figures 12a-d depict examples of results of an implementation of the disclosed method of calibration;

[0082] Figure 13 depicts residual plots of the new lots to be adjusted against the reference master curve at various stages of calibration;

[0083] Figure 14 depicts residual plots of the new lots to be adjusted against the reference master curve at various stages of calibration;

[0084] Figure 15 depicts a coefficient of variation (CV) obtained for three different lots;

[0085] Figure 16a depict the evolution of the metrics throughout the calibration process;

[0086] Figure 16b depict the evolution of the metrics throughout the calibration process;

[0087] Figure 17 depicts plots of Calibrations Curves versus Master curves for each of three different lots; and

[0088] Figure 18 depicts the complete calibration process, including progression to a final Q-value.

[0089] DETAILED DESCRIPTION OF DRAWINGS

[0090] Embodiments implementing the disclosed method of calibrating an analysis of a plurality of immobilised capture agents on a substrate are now described with reference to the drawings.

[0091] Implementation of such calibration methods may be necessitated by identified variability factors that may influence a result of an analysis of immobilised capture agents on a substrate. For example, such variability factors may include the different components of the analysis system (instrument and environmental conditions, container lots, array to array, liquid reagent lots, spot-to-spot and patient sample). Such variability factors may include a stability of arrays, e.g. microarrays, and liquid reagents.

[0092] Figure 1 depicts an example of a substrate, according to an embodiment of the disclosure. The substrate may comprise, for example, glass, silicon, or a polymer such as nitrocellulose. The substrate may optionally be coated with a coating layer which may be selected so as to improve or alter properties of or interaction with the biological material, including adhesion, immobilisation, stabilisation, etc. The coating layer may comprise, may consist essentially of or may consist of a metal such as aluminium or gold, a polymer such as hydrophilic polymers or hydrophilic polymer, e.g. polyacrylamide, or the like.

[0093] The substrate may be prepared according to a method described in PCT / EP2021 / 069222, which is hereby incorporated by reference in its entirety.

[0094] The substrate may comprise a substantially planar surface suitable for deposition of, for example, the above described spots, e.g. calibration spots, diagnostic spots, etc.

[0095] In the non-limiting example of Figure 1 , a microarray is printed onto the substrate. While the example depicts a regular arrangement of spots defining a microarray, it will be appreciated that other geometries and / or arrangements of spots, including nonregular and / or non-uniform arrangements may be implement, still falling within the scope of the present disclosure.

[0096] The example microarray is composed of five different types of spots.

[0097] The microarray comprises a plurality of immobilised capture agents, denoted CA1 to CA10 in Figure 1 , hereafter referred to as diagnostic spots. In the example, said diagnostic spots comprise antigens (or antibodies) printed on the substrate for antibody (or antigen) detection in a human sample. In the non-limiting example, all the diagnostic spots are printed in two replicates, although it will be appreciated that in other examples the diagnostic spots may be printed in 1 , 3 or more replicates.

[0098] The microarray comprises a plurality of calibration spots, denoted “Cal” in Figure 1. Each calibration spot comprises immobilised human immunoglobulin. In the example, two calibration spots are provided printed at positions32 and 26. Said calibration spots may be used for an internal calibration of the assay, as described below. The Immunoglobulin in the calibration spots is printed at a pre-determined concentration.

[0099] The example microarray also comprises a plurality of gridding spots. In the example, four gridding spots are provided at positions 1 , 6, 61 and 66, e.g. substantially corner positions. Said gridding spots may be implemented for location purposes, such as for accurately identifying where a given spot may reside within the microarray.

[0100] The example microarray also comprises a positive control, denoted “POS Ctrl” in Figure 1 , at position 16. The example microarray also comprises a negative control, denoted “NEG Ctrl” in Figure 1 , at position 15. Application of the disclosed methods to generate semi-quantitative result in AU / ml and immobilised capture agent quantitative result using the output of a Five Parameter Logistic (5pl) calibration curve to report a result in lll / ml is now described with reference to the diagrams.

[0101] Figure 2 depicts an immunological reaction between a human sample and diagnostic and calibration spots in the microarray of Figure 1 .

[0102] In Figure 2.1 (left-side of Figure 2), the immunological reaction between patient sample and diagnostic spots in the microarray is depicted. It can be seen that Immunoglobulins contained in the sample bind to the antigen immobilized on the solid phase. Then, anti-lmmunoglobulin antibodies labelled with HRP bind to Immunoglobulin captured by the solid phase.

[0103] In Figure 2.2 (left-side of Figure 2), the immunological reaction between patient sample and calibration spots in the microarray is depicted. It can be seen that nonspecific human Immunoglobulins are coated on the solid phase. There is only limited interaction between coated human Immunoglobulins and sample. The main interaction is between coated human Immunoglobulins and anti-human IgG-HRP contained in the detection reagent.

[0104] Consequently, the result of the calibration spot should not be impacted by the sample but only by the analysis system, (e.g. optical detection, temperature) and the detection reagent (concentration adjustment and stability).

[0105] Figure 3 depicts a high-level overview of a 2-step calibration method, wherein the method comprises both internal calibration and lot-to-lot adjustments. Activities such as master curve / dataset creation and calibration curve / dataset creation for each immobilised capture agent and each lot as well as internal calibration target (an internal immobilised human immunoglobulin value) may be carried out at part of the substrate production process. In some examples, at least some of the aforementioned data, e.g. the master curves / dataset and / or calibration curves / dataset and / or internal calibration target, may then be stored on a container comprising a plurality of the substrates. In some examples, the data may be stored on an RFID tag that is coupled to the container. In some examples, a reference to at least some of the aforementioned data, e.g. the master curves / dataset and / or calibration curves / dataset and / or internal calibration target, may then be stored on or otherwise coupled to or associated with a container comprising a plurality of the substrates.

[0106] Advantageously, the analysis system may be configured to read and / or retrieve the data during a calibration of an analysis of a plurality of immobilised capture agents on a substrate without requiring any action or input from a user / operator of the analysis system. All calculations made as part of the method of calibration may be carried out automatically by the analysis system, which may directly provide a patient result in AU / mL or ILI / mL.

[0107] In the method depicted in Figure 3, in a first step a determination, in the substrate production process and for each production lot of substrates, is made of a calibration dataset (denoted calibration curve) corresponding to characteristics of each immobilised capture agent of the plurality of immobilised capture agents.

[0108] In the first step, a calculation is also made of an internal immobilised human immunoglobulin target value, e.g. a mean immunoglobulin calibration spot target.

[0109] During a subsequent a process of analysing a plurality of immobilised capture agents on a substrate, for each diagnostic spot the internal calibration is run. That is, for each immobilised capture agent of the plurality of immobilised capture agents on the substrate, an analysis result signal (denoted “signal”) for each immobilised capture agent is multiplied by the internal immobilised human immunoglobulin target value (denoted “Target Cal Spot”) divided by an average analysis result signal of the three calibration spots in the sample (denoted “Internal Cal Spot”).

[0110] The process of internal calibration is described in more detail with reference to Figure 4, which more clearly depicts the internal calibration calculations. The described internal calibration is used to mitigate reagent stability, lot-to-lot variability as well as to improve reproducibility by reporting for assay reactivity. In this process, one internal calibration spot target is computed per new container batch and assigned to all microarrays, e.g. each substrate, as part of the calibration parameters using a linear model.

[0111] In the internal calibration process, a target for the lot will be calculated by averaging all the values obtained during an experiment performed to generate the calibration dataset (described in more detail below with reference to lot-to-lot calibration). In example, this internal immobilised human immunoglobulin target value is encoded in the RFID tag of the lot and will later be used to normalize each sample by the analysis system.

[0112] Then, for each new sample tested by an operator of the analysis system, the result (denoted “Alnsignai” in Figure 4) of the patient sample will be obtained by multiplying the signal (denoted “Signaldiagspot” in Figure 4) of each immobilised capture agent by the internal immobilised human immunoglobulin target value (denoted “Internal CAL target” in Figure 4) and dividing by the average of the three spots of the sample (denoted “Internal Cal parray” in Figure 4”), e.g. by an average analysis result signal of the three calibration spots in the sample.

[0113] Several metrics may be used in the calibration methods described herein. Said metrics (SLM, ALM and GQM) are summarised in the table below. As a brief summary: SLM corresponds to no calibration; ALM corresponds to internal calibration only, and GQM corresponds to both internal and lot-to-lot calibration.

[0114] Table 1. Metrics used in Calibration Calculations

[0115] Calculations required for internal calibration are summarised in Table 2 below. Note that, for example “SLMCaH” refers to the Selected Metric for a first calibrator spot. It can also be seen in Figure 4 that a validity check (referred to as a “Sanity check” in Table 2) of the substrate may be performed, by determining whether an analysis result signal of the at least one immobilised human immunoglobulin is within a predetermined range.

[0116] Table 2: Summary of Internal Calibration Calculations

[0117] With reference to the average (X1 , X2, X3) described in Table 2 above, it will be appreciated that in other embodiments the average may alternatively be, for example a weighted mean (AX1 +BX2+CX3 / 3), wherein coefficients A, B and C may be lot specific.

[0118] Furthermore, in some examples the method may comprise storing said coefficients A, B and C on the above-described RFID tag associated with or coupled to the container or substrate.

[0119] Furthermore, in some examples the method may comprise storing said coefficients A, B and C on the above-described RFID tag associated with or coupled to the container or substrate. In a non-limiting example, the method may comprise providing a reference to said coefficients A, B and C, such as a barcode, Quick-Response (QR) core, Uniform Resource Locator (URL) or the like, that is associated with or coupled to the container or substrate.

[0120] Referring again to Figure 3, there is also depicted a lot-to-lot calibration method, for mitigating lot-to-lot variability. In this example, for each immobilised capture agent, a “master curve” to “calibration curve” correction is applied. In some examples, a linear model may be applied to achieve semi-quantitative results. In some examples, a 4 / 5PL model may be applied to achieve semi-quantitative results. The mathematical model may be applied to each signal (data point) individually. The averaging may be done afterwards, i.e. after the mathematical model is applied to each signal (data point) individually. The semi-quantitative results are provided as a signal relative to a threshold in units of All / ml). Alternatively, a further mathematical model, which in this example is a 4 / 5PL may be applied to the measure signal to provide a quantitative result. The quantitative results are provided as a signal relative to a concentration in units of lll / ml.

[0121] The method of lot-to-lot calibration comprises determining, in a substrate production process and by analysing characteristics of at least one substrate from each of a plurality of substrate lots, a master dataset, e.g. a master curve, for each immobilised capture agent of the plurality of immobilised capture agents, each master dataset corresponding to characteristics of each respective immobilised capture agent.

[0122] The process of creating master curves may be performed just a single time for a plurality of production lots. Dedicated samples used for calibration may herein be referred to as “calibrators”. In an example process, three prototype lots may be used to assign values to the different calibrators. For each immobilised capture agent, serial dilutions of calibrators will be assayed on the analysis system to obtain a value for each sample dilution.

[0123] The process of creating master curves may be carried out over several days, on several different analysis systems, using a plurality of detection reagents lots, substrate lots and replicates. In a non-limiting example, the experiment may be conducted on four days, three instruments, two detection reagent lots, three substrate lots, and two replicates corresponding to forty-eight values per sample dilution. For each immobilised capture agent, a master curve can be drawn by plotting the signal obtained for each sample dilution as a function of a concentration expressed in international units or arbitrary units depending on the availability of an international standard. Examples of such master curves are depicted in Figure 5.

[0124] Next, a mathematical model (which in this example embodiment is a 5PL mathematical model) may be applied for each immobilised capture agent, and the determined parameters used to calibrate future lots using the master curve to calibration curve correction for each immobilised capture agent.

[0125] That is, for each new lot of substrates manufactured in the subtract production / preparation process, a calibration curve per immobilised capture agent is created, and that calibration curve will be adjusted to the master curve.

[0126] As a non-limiting example, lot-specific calibration curves may be generated for each lot, by testing all sample dilutions in five replicates, on two instruments, one detection reagent, and one day. In some examples, the same samples as for master curves (named working calibrators) will be tested. An average value of each sample dilution will be used to generate the calibration curves.

[0127] Figure 6 depicts an example of the 4PL regression model applied to a calibration curve. The parameters are listed in Table 3. Note that in embodiments employing a 5PL regression model, an additional parameter “E” is included, as listed in Table 3.

[0128] Table 3: Summary of Parameters of 5PL Regression Model

[0129] As described above, a linear model may be applied for semi-quantitative assays. The goal of calibration is to generate the GQM, which is a corrected metric value that is independent of the production lot. It may be assumed that the GQM is a metric proportional (i.e. linearly related) to the signal from the analysis system, e.g. a signal extracted from an image captured by the analysis system (with or without the abovedescribed internal calibration).

[0130] In some examples, a 5PL regression model may be used for semi-quantitative assays. In some other non-limiting examples, a linear approximation of the calibration curve may be applied. That is, for semi-quantitative assays, it may be assumed the calibration curve for each immobilised capture agent can be approximated as linear, thus defined by a slope parameter and an intercept parameter. The slope and intercept parameters for the calibration curve for each immobilised capture agent are made available to the analysis system, such as by encoding in an RFID tag as described above.

[0131] The calibration curve may be applied to the single measure by the analysis system so that the corrected response is aligned with the master curve. This may be achieved by defining the slope and intercept such that the calibration points meet the master curve at the calibration points’ concentration. Note that in the example depicted in Figure 7, the calibration points C1 and C2 have been chosen at the minimum and maximum level of the response curve, but in other embodiments may be placed at other locations on the curve.

[0132] A summary of an example embodiment of the calibration steps for semi- quantitative assays is as follows:

[0133] 1. A master dataset, i.e. a master curve (Signal vs Dilution or Concentration, Arbitrary unit, III) must have been established for the immobilised capture agent of interest. The master curve must be a fitted curve (4 / 5PL) based on multiple lots and instrument results.

[0134] 2. Multiple dilutions of a specific calibrator sample at known levels of concentration of the considered immobilised capture agent (IU , Arbitrary units, dilutions, as a function of master curve definition) are run on Quality Control (QC) instruments in multiple replicates with the substrate lot to be calibrated.

[0135] 3. A 5PL curve fitting using the multiple calibrators is performed to a corresponding measured signal value across the calibrator and associated dilutions. Two adjuster points (SC1 and SC2) are calculated from the lot-specific fitted curve based on the QC Testing. Corresponding Master Signal Values (SM1 and SM2) are extracted from the Master curve based on the calibrator concentrations.

[0136] 4. The calibration curve is generated based on the values of SC1 , SC2, SM1 , SM2 and the Master curve

[0137] To perform the lot-to-lot calibration based on the above-described linear model the calculations summarized in Table 4 are performed.

[0138] Table 4: Summary of calculations for lot-to-lot calibrations for a linear model Referring again to Figure 3, there is also depicted a method using a 4 / 5PL applied to the measured signal) to provide a quantitative result. The quantitative results are provided as a signal relative to a concentration in units of lU / ml. Generation of quantitative results are described with reference to Figures 8a and 8b, which depict concentration unit calibration, for generating a result value (GQM) expressed in a unit representative of the sample concentration (III, Arbitrary unit,..). In this case, the calibration curve is of the 4 / 5 PL type and applied to the signal measure by the analysis system to calculate the concentration.

[0139] Figure 9 depicts a further summary of the complete calibration method, wherein lot-to-lot calibration is combined with internal calibration. Figure 9 depicts how diagnostic spots can be used for generation of semi-quantitative results by applying the linear model (using slope and intercept) and / or quantitative results by applying the 4 / 5PL model. Figure 9 also depicts use of the calibration spots to apply internal calibration, as described above.

[0140] For both the internal calibration and lot-to-lot calibration, validity checks may be performed. As depicted in Figure 9, for the internal calibration method, the validity checks may comprise determining whether a measured signal falls within a predetermined and expected range. As also depicted in Figure 9, for the lot-to-lot calibration method, the validity checks may comprise a qualitative grading of diagnostic spots and / or a check that 5PL parameters of the curves represented by each dataset fall within expected ranges.

[0141] Figure 10 depicts an example of a workflow for master curve generation, according to embodiments of the disclosure, wherein it can be seen that a generated master curve (which corresponds to a signal versus concentration curve for each immobilised capture agent) is defined by five numerical coefficients according to the 5PL regression model, as described above.

[0142] Figure 1 1 depicts an example container lot calibration workflow, according to embodiments of the disclosure, wherein it can be seen that for each immobilised capture agent a master curve adjustment is made using multiple calibration points to generate a calibration curve for each immobilised capture agent that is valid for the specific lot.

[0143] Figures 12a-d depict examples of results of an implementation of the disclosed method of calibration. A key to the information provided in Figures 12a-d is provided in Table 5 below.

[0144] Table 5: Summary of Calibration Test Results

[0145] As an example, it can be seen in Figure 12a that for the calibrator sample represented by “CAL_A03”, for the “Selected Metric (SLM)” with no calibration a repeatability is approximately 14%CV and a reproducibility is approximately 20%CV.

[0146] Continuing with the example, it can be seen in Figure 12b that for calibrator sample represented by “CAL_AO3”, for the “GQM Lot Specific” with no internal calibration and with lot-to-lot calibration, a repeatability is approximately 15%CV and a reproducibility is approximately 21%CV.

[0147] Continuing with the example, it can be seen in Figure 12c that for the calibrator sample represented by “CAL_A03”, for the “GQM Calibration +ICAL” with both internal calibration and with lot-to-lot calibration, a repeatability is approximately 13%CV and a reproducibility is approximately 17%CV.

[0148] Figure 12d depicts a summary, wherein for reproducibility the cumulative effects of lot-specific calibration and internal calibration can be readily compared.

[0149] Example results of the above-described method of calibration are now described in more detail with reference to Figures 13 to 18.

[0150] Figures 13 and 14 depict residual plots of the new lots to be adjusted against the reference master curve at various stages of calibration. (Figure 13: DFS70 sample D04, and Figure 14: Chromatin sample D08).

[0151] As described above, the calibration method disclosed herein is for achieving, for a given sample, a response as close as possible to that that would be obtained on the master curve.

[0152] A first metric obtained is SLM (see Table 1 above), which corresponds to the raw signal measured by the analysis system. The calibration process comprises two steps:

[0153] 1 . Microarray calibration using the ratio of the reference SLM signal from the internal calibrator to the microarray signal. This yields the ALM metric (see Table 1 above), calculated as:

[0154] SMN x Reference SMN signal from internal calibrator ALM = - - -

[0155] Sample SMN signal form internal calibrator

[0156] 2. Lot adjustment to the master curve. In examples, such lot adjustment is implemented using a 5PL correction on the lot's calibration curve. The final response obtained for a sample is the GQM, which may correspond to the ALM value read on the corrected curve.

[0157] In the graphs depicted in Figures 13 and 14: o The hatched bars represent the difference between the raw signal (SLM) obtained for a given sample in the lot being adjusted and the raw SLM signal obtained when creating the master curve (SLM residuals). o The dotted bar represents this difference after correction by the internal calibrator, relative to the master curve (ALM residuals). o The black bar represents the difference between the GQM of the sample in the adjusted lot and the master curve (GQM residuals). o For the master curve (MC), which serves as the reference: SMN=ALM=GQM. o It can be observed that for all samples SLM residuals >= ALM residuals >= GQM residuals (in absolute values). By applying the disclosed 2 steps of the calibration process the SLM metric is transformed into the GQM metric which is closer to the master curve value, advantageously making different assay lots over time comparable. Figure 15 depicts a coefficient of variation (CV) obtained for three different lots (Lot #1 , Lot #2 and Lot #3) for the DFS70 analyte, using the DFS70 T 1 _10 sample tested in five replicates. Figure 15 depicts the CV calculated across the five replicates at each stage of the calibration process. That is, Figure 15 depicts CV (n=5 replicates) improvement for the DFS70 assay through the calibration process:

[0158] 1 . On the SLM metric, directly obtained from the analysis system;

[0159] 2. On the ALM metric, after correction by the internal calibrator; and

[0160] 3. On the GQM metric, after the lot adjustment step to the master curve.

[0161] The graph in Figure 15 shows an important decrease in the CV at each calibration step for the three lots.

[0162] Figures 16a and 16b depict the evolution of the metrics throughout the calibration process, starting from the raw SLM signal and progressing to the final metric, Q-Value, which is calculated as GQM divided by the GQM threshold signal (signal to cut-off) and multiplied by 10 (alignment factor to adjust the scale of the signal-to-cut-off, “S / co”).

[0163] Three different lots are represented (Lot #1 , Lot #2 and Lot #3).

[0164] The left y-axis represents the average signal of the five replicates for SLM, ALM, and GQM. The right y-axis represents the average signal of the five replicates for Q- Values.

[0165] Figures 16a and 16b show that, despite initially very different SLM values — also reflected in the calibration curves relative to the master curves — the final Q-Value values obtained are similar.

[0166] For completeness, Figure 17 depicts plots of Calibrations Curves versus Master curves for each of the three different lots (Lot #1 , Lot #2 and Lot #3). In these examples, the calibration curve is of the 4 / 5 PL type and applied to the signal measure by the analysis system to calculate the concentration.

[0167] Figure 18 depicts the complete calibration process, including progression to a final Q-value. It can be seen that following generation of the GQM, a quantitative value (denoted “Semi-quantitate result”, also known as the above-described “Q-value”) may be determined. In some examples, said Q-value may be calculated as GQM divided by the GQM threshold signal (signal to cut-off) and, in some examples, may be scaled by an alignment factor to adjust the scale of S / co. Such an alignment factor may be available for configuration per analyte to allow scaling of GQM or of GQM / Cutoff Quantification Modes.

[0168] Although the disclosure has been described in terms of particular embodiments as set forth above, it should be understood that these embodiments are illustrative only and that the claims are not limited to those embodiments. Those skilled in the art will be able to make modifications and alternatives in view of the disclosure, which are contemplated as falling within the scope of the appended claims. Each feature disclosed or illustrated in the present specification may be incorporated in any embodiments, whether alone or in any appropriate combination with any other feature disclosed or illustrated herein.

Claims

CLAIMS:

1. A method of calibrating an analysis of a plurality of immobilised capture agents on a substrate, wherein the method comprises: determining, in a substrate production process and by analysing characteristics of at least one substrate from each of a plurality of substrate lots, a master dataset for each immobilised capture agent of the plurality of immobilised capture agents, each master dataset corresponding to characteristics of each respective immobilised capture agent; determining, in the substrate production process and for each production lot of substrates, a calibration dataset corresponding to characteristics of each immobilised capture agent of the plurality of immobilised capture agents, calculating calibration correction parameters for each immobilised capture agent for each production lot based on adjustments required to the calibration dataset required to align the calibration dataset with the master dataset; and performing, in a process of analysing a plurality of immobilised capture agents on a substrate, a lot adjustment per immobilised capture agent by adjusting a result of an analysis of each immobilised capture agent using the calibration correction parameters.

2. The method of claim 1 , wherein determining the master data set comprises: for each immobilised capture agent, generating curve data for a sample dilution as a function of a concentration; and applying a mathematical model, such as a least squares logistic regression model, to the curve data to determine parameters corresponding to characteristics of the respective immobilised capture agent.

3. The method of claim 2, wherein the least squares logistic regression model is a four parameter logistic (4PL) or a five parameter logistic (5PL) regression model.

4. The method of any preceding claim, wherein determining the calibration dataset comprises at least one of: determining a mean value of measurements for each immobilised capture agent replicated across a plurality of different measurement instruments; and for each immobilised capture agent, generating curve data for the immobilised capture agent .

5. The method of any preceding claim, wherein the calibration correction parameters comprise a slope parameter and an intercept parameter for adjusting the calibration dataset to the master dataset, based on a linear calibration model.

6. The method of any preceding claim, wherein the calibration correction parameters are derived from a mathematical model of the calibration dataset adjusted to the master dataset, and optionally wherein the mathematical model comprises a polynomial approximation or a 4PL or 5PL regression model .

7. The method of any preceding claim, wherein the plurality of immobilised capture agents comprises a plurality of different immobilised capture agents.

8. The method of any preceding claim, comprising generating a calibrated analysis result by: applying a 4PL / 5PL regression model using the calibration correction parameters to an analysis result signal for each immobilised capture agent; or subtracting a / the intercept parameter of the calibration correction parameters from an analysis result signal for each immobilised capture agent band dividing a result by a / the slope parameter of the calibration correction parameters.

9. The method of any preceding claim, wherein each immobilised capture agent of the plurality of immobilised capture agents comprises antigens and / or antibodies deposited on a substrate for antibody and / or antigen detection respectively in a human sample.

10. The method of claim 9, wherein each substrate further comprises at least one immobilised human immunoglobulin.11 . The method of claim 10, wherein the human immunoglobulin comprises human IgG and / or derivatives (Fc fragment).

12. The method of claim 10 or 1 1 , comprising computing, for each lot or for a container of substrates from a lot, or for each substrate , an internal immobilised human immunoglobulin target value, and optionally wherein the internal immobilised human immunoglobulin target value is based on an average of values obtained during the determination of the calibration dataset.

13. The method of claim 12, comprising performing an internal calibration for each immobilised capture agent of the plurality of immobilised capture agents by adjusting an analysis result signal for each immobilised capture agent using the internal immobilised human immunoglobulin target value and an analysis result signal of the at least one immobilised human immunoglobulin.

14. The method of claim 12, comprising performing an internal calibration for each immobilised capture agent of the plurality of immobilised capture agents by multiplying an analysis result signal for each immobilised capture agent by the internal immobilised human immunoglobulin target value divided by an average analysis result signal of the at least one immobilised human immunoglobulin.

15. The method of any of claims 10 to 14 comprising performing a validity check of the substrate by determining whether an analysis result signal of the at least one immobilised human immunoglobulin is within a predetermined range.

16. The method of any preceding claim comprising storing data and / or a reference to data, on a container comprising a plurality of the substrates from a lot, wherein the data comprises at least one of: the master dataset; the calibration dataset; calibration correction parameters; and / or the internal immobilised human immunoglobulin target value.

17. A substrate comprising:a plurality of different immobilised capture agents deposited on the substrate, each immobilised capture agent comprising antigens and / or antibodies deposited for antibody and / or antigen detection respectively in a human sample; and at least one immobilised human immunoglobulin deposited on the substrate at a pre-determined concentration.

18. A container comprising a plurality of substrates according to claim 17, and further comprising a reference to data and / or a data storage device comprising data, wherein said data comprises at least one of: calibration correction parameters for performing a lot-to-lot calibration of analysis results; an internal immobilised human immunoglobulin target value; a master dataset corresponding to characteristics of each immobilised capture agent across a plurality of production lots; and / or a calibration dataset corresponding to characteristics of each immobilised capture agent of the plurality of immobilised capture agents in a production lot.

19. A substrate analysis apparatus configured to receive a container according to claim 18, the apparatus configured to perform a lot adjustment per immobilised capture agents by adjusting a result of an analysis of each immobilised capture agent using the calibration correction parameters for each respective immobilised capture agent in each production lot.

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