139© 2013 David G. Wild. Published by Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/B978-0-08-097037-0.00010-5

Qualitative immunoassays bypass concentration determi-nation, which is the output of the quantitative immuno-assay, advancing to the “diagnosis” or classification of a sample into two or more populations, such as antibody/antigen positive or negative. Qualitative immunoassays provide answers to questions, often in the simplest possible way with the answers yes or no. The first impression of a qualitative assay is that it is somehow simpler than its quan-titative counterpart, and it is certainly simpler to use. But because this type of test is taking on the burden of inter-preting the signal generated rather than merely interpolating the concentration, it is a highly attuned version of immu-noassay in which great care needs to be taken in assay design, and users need to understand the limitations of each application. For example, many such tests have gray zones where the diagnosis is inconclusive and requires con-firmation. A positive result, for example, on a blood dona-tion used for transfusion, is invariably confirmed using an alternative qualitative test or a quantitative assay. How-ever, a negative result in a qualitative immunoassay can also have significant consequences without further verifica-tion, such as a woman assuming she is not pregnant. Such a result is not usually repeated without clinical interpreta-tion. Hence, the understanding of customer requirements and user interpretation is fundamental to assay design.

Soon after the invention of immunoassay, qualitative immunoassays started to appear. However, the need for standardization of every assay run (because of the finely balanced dependence on the exact reagent concentrations and the decay of radioactive isotopes) limited their value and reliability. This variation was managed by using con-trols to obtain a fix on the signal level relevant to the cutoff point. Once nonradioactive, robust, immunometric immu-noassays were introduced, such as ELISA and lateral flow immunoassay, qualitative assays flourished. Many early qualitative assays were for the measurement of antibody titers, such as those targeted at hepatitis B virus, so the equivalent “quantitative” assay produces results in arbitrary units, which vary from method to method. This neutralized a potential benefit of quantitative assays for the purist.

Because they are used widely for screening, particularly in blood banks, the volume of qualitative assay tests used in laboratories is very significant. Qualitative assays are the method of choice for mass screening of donated blood, using large automated equipment. But at the opposite end of the spectrum, they are also the method of choice for the home user, with the most well-known application being the home pregnancy test. In between these two areas lie many situations where qualitative assays are used by professionals who do not have specialist analytical skills, ranging from GPs and veterinary surgeons through police officers to environmental and security specialists.

Qualitative immunoassays fall into two groups: those that detect the presence or absence of an analyte, such as a viral antigen or antibody, or an illicit drug, and those that dis-tinguish between background and elevated concentrations of analyte, such as hCG in a pregnancy test. In the first instance, the sensitivity and specificity of the immunoassay are the key factors; in the second, normal biological varia-tion is an additional consideration for the assay designer.

In terms of growth potential, due to the emerging mar-ket from developing countries and the growing application of blood screening assays, the volume of qualitative immu-noassays is steadily increasing.

Qualitative assay development has its own characteristics and unique features in assay design, optimization, verifica-tion, and validation. There are also additional regulatory requirements for qualitative assays. Although the concep-tual design and development of qualitative assays discussed in this chapter are associated with development of auto-mated immunoassay tests, the principles are applicable to other qualitative immunoassays.

Features of Qualitative ImmunoassayDEFINITIONImmunoassays can be differentiated on the basis of out-come. In this way, immunoassays can be classified as quali-tative, semiquantitative, and quantitative. The word qualitative refers to a quality or characteristic rather than the quantity or measured value. A qualitative immunoas-say is designed to distinguish between two or more mutu-ally exclusive characteristics and answer a question of importance, based on the presence or absence of the ana-lyte or the concentration relative to an established refer-ence point. In some applications, a qualitative immunoassay may require the determination of two or more analytes. The result of a qualitative assay is usually binary and expressed as either reactive or nonreactive, which can be interpreted as positive or negative depending on the assay design. A quantitative or semiquantitative assay provides an estimated concentration of the analyte. A qualitative immunoassay does more than simply interpreting the qualitative result from the measured concentration and a reference interval. It takes on the responsibility of deter-mining the sample characteristics, without first measuring the concentration, although there are areas of overlap between some quantitative and some qualitative assays.

In a qualitative assay, a certain threshold level of analyte (cutoff value, to be discussed below) is defined to differen-tiate presence and absence. Presence or absence of the analyte

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Simon Parker

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should not be considered to be an absolute measure related to a concentration level of zero, but rather to the previ-ously defined cutoff value based on the balance of assay sensitivity and specificity, and compared to a predicate device or clinical results. Because of that, there is usually a gray zone around a cutoff value for a qualitative assay.

Semiquantitative assays are usually considered to be most like qualitative assays, giving categorical results based on a quantitative determination in which a clinically mean-ingful gradation of results often exists. In general, semi-quantitative assays provide categorical information such as negative, low positive, medium positive, and high positive. This categorization is relevant to the application of the assay, e.g., measuring antiviral antibody levels to monitor patient response to antiviral therapy. To put it another way, the term semiquantitative refers to the ability to determine an increase or decrease in the concentration of antigen or titer of antibody to provide additional qualita-tive information, i.e., a change in a qualitative result. As with any testing method, there is some uncertainty associ-ated with the results from semiquantitative assays. In prac-tice, there are overlaps between semiquantitative assays and both qualitative and quantitative assays. From a regu-latory perspective, a semiquantitative assay must be vali-dated as a qualitative assay to claim its gradation with clinical significance.

As in some quantitative assays, a calibration curve derived from multiple calibrator points is used to trans-form the instrumental signal response into numerical units to determine the levels of analyte present in the specimens. Depending on the clinical utility of the individual assay, semiquantitative immunoassays sometimes provide a sub-stantial amount of quantitative information.

One of the great advantages of a qualitative immunoas-say for the presence or absence of an analyte is that it can be designed to achieve the highest sensitivity possible to report a positive or reactive result when there is a very low concentration of antigen or titer of antibodies in the speci-men. Enabling a quantifiable or numerical response to the presence of the targeted analyte is not usually a require-ment when designing qualitative assays.

ANTIGEN AND ANTIBODY DETECTIONQualitative immunoassays can be used in the detection of either antigen or antibody, or both (Mire-Slusa et al., 2004). However, the objectives and requirements of assay design for the detection of antigen and antibody are quite different. When intended for antigen detec-tion, for example, in the detection of viral antigen, the assay has to be designed for maximum sensitivity. This is largely to enable the detection of trace amounts of exog-enous viral antigens in the presence of low constituent background where specificity is not unduly affected. For antibody detection, however, the assay design, while still focused on optimal sensitivity, requires a balance to be struck to ensure better clinical specificity. Moreover, unlike antigen assays, antibody assays measure the titra-tion of the specific antibody by means of functionally measuring the avidity of the specific antibody binding with the specific antigen. Therefore, the results are often reported in arbitrary units.

When designing assays to achieve high sensitivity or specificity, it is important to take into account the relative clinical impact of false-positive and -negative results. Since achieving high sensitivity is usually at the expense of speci-ficity (and vice versa), it is essential to balance assay sensitiv-ity and specificity for optimal analytical and clinical performance, taking confirmatory tests into account. An assay with too high sensitivity is more likely to have rela-tively low specificity, causing false-positive results. For instance, for diagnostic use, an HBsAg assay with too high sensitivity will report high initial reactive results, which need to be confirmed or rejected through a complicated algorithm. Likewise, a false-positive result will cause unnec-essary repeat testing, or in the case of a blood screening algorithm, unnecessary delay to results and the waste of valuable blood products. Alternatively in the case of a mis-diagnosis, for example, an anti-HTLV false positive, the patient maybe be subjected to undue stress and alarm, espe-cially bearing in mind the relatively low rate of associated morbidity associated with this virus. Similarly in diagnosis of cancer, the assay needs to have high specificity to mini-mize possible false-positive results to prevent iatrogenic disease as a result of treatment.

Another example is viral IgM testing. Since viral IgM testing is intentionally used to replace isolation methods and for rapid diagnosis of acute or primary infections, it must be demonstrated that the assay will detect a true disease state with viral infection, and not miss possible positive samples. As such, a well-optimized design with both high specificity and sensitivity is required for viral IgM testing. As can be seen from these examples, the costs and risks associated with the relevant confirmatory tests cannot be overlooked when optimizing a screening immunoassay.

Finally, it must be acknowledged that there is a differ-ence between designing a screening assay for the normal population (e.g., blood donors) and a diagnostic test for patients with symptoms or a clinical history.

The latest fourth generation qualitative immunoassays are able to detect both antigen and antibody simultane-ously in a combination assay. Assay design is primarily focused on achieving maximum sensitivity, for example, in the inclusion of the detection of HIV p24 (gag) viral anti-gen and also low-level antibody developing during early seroconversion. Optimal specificity is still important in these assays as the confirmation of the diagnosis of HIV infection must always be carried out using an alternative supplementary test. The presence of HIV antibodies is a reliable indicator for HIV infection, but only after the ini-tial window period of about 3–4 weeks (although the win-dow period is longer in some individuals). The advent of HIV Combo assays has resulted in a significant decrease in this window period to as little as 2.35–4.04 mean days compared with NAT screening (Ly et al., 2007). Because of the high degree of antigenic variability within immuno-dominant regions of the HIV genome, multiple antigens derived from different viral strains that represent con-served regions are favored in assay design. Fourth genera-tion assays such as the HIV Combination are increasingly desirable for regulatory submission in both the US and EU but because of their complexity can be more challenging to develop and manufacture.

141CHAPTER 2.7 Qualitative Immunoassay—Features and Design

DEVELOPMENT AND REGULATORY REQUIREMENTSAs with quantitative assays, assay development for qualita-tive assays needs to go through major assay development phases, typically feasibility, optimization, verification and validation, and launch. Among these, the phase of verifica-tion and validation is very critical to developing qualitative assays. The requirements and approaches for validation for qualitative assays are more assay specific than for quantita-tive assays, such as the verification and validation of the cutoff values, and determination of assay sensitivity for dif-ferent panels and stages of diseases. More details about qualitative assay development are described later.

It is also essential to understand regulatory require-ments for the particular analyte and assay. Regulatory authorities, such as the Department of Biologics at the FDA for blood screening tests and the USDA for veteri-nary screening assays, have critical concerns regarding the safety and effectiveness of qualitative assays, espe-cially for antiviral IgM antibody testing. Laboratories may depend on IgM testing for the diagnosis of viral infections. Therefore, the IgM assay has to be performed with high specificity. If an IgM test is not capable of dis-tinguishing between a true positive and a false positive in the specific population tested, it could have adverse con-sequences on the patients concerned, such as a healthy individual receiving unnecessary antiviral therapy. Like-wise, false-negative results may cause therapy to be with-held or delayed. Moreover, misdiagnosis or false diagnosis of some viral infections could have significant adverse implications for pregnant women and the fetus. There-fore, there are specific requirements for developing anti-viral IgM antibody assays. These are the major elements for qualitative antibody–antigen assays but can also apply in principle to IgM-specific assays.

� Antigen. What is the antigen? Why is it selected? Is the antigen native or recombinant? Is it a protein or pep-tides? Is it a purified protein or cell lysate? What are the sources of viral strains, antisera, and synthetic materials used in the assay design? All these are critical factors defining the specificity and sensitivity of the assay.

� Cutoff value. How is the cutoff value determined and validated? This must be supported with a Receiver Operating Characteristic (ROC) Curve.

� Quality control (QC) materials. The very critical require-ment for QC is that the QC material should be repre-sentative of and correlate with the intended use and clinical utility of the assay. At a minimum, make avail-able or recommend the use of two controls (positive and negative) in the same matrix as test specimens indi-cated for use with the qualitative assay.

According to FDA requirements, if the assay is semiquan-titative, perform appropriate studies to show the relation-ship of results to the stage of infection (e.g., early, acute, waning infection) for which value ranges have been estab-lished. For each stage of infection, present results from a minimum of 10 patients. If semiquantitation is claimed, the linearity of the device must be proven over the claimed range.

CLINICAL APPLICATIONIn general, qualitative assays can be used for the purposes of screening, diagnosis, and confirmation. Different quali-tative assays have different characteristics, depending on their applications. In a screening assay, the qualitative assay needs to be designed to achieve the highest sensitiv-ity possible, to minimize the risk of false-negative results. However, as alluded to earlier, achieving extreme high sensitivity can be at the expense of specificity. As a conse-quence, a screening assay design may give false-positive results. For a qualitative assay used for screening purposes, the harm of false-positive results should be much less than the harm of false-negative results. For example, in screen-ing of blood donors using HBsAg assay, high sensitivity is both very important and also a regulatory requirement as false-negative results impose a risk to the blood supply. In contrast, false-positive results in blood screening cause little risk to the blood supply. Moreover, false-positive results can be corrected by following confirmatory testing if needed although the confirmation of the presence of some analytes may be more problematic, time consuming and expensive than others. Confirmation of excessive false-positive samples and the consequent shortening of the blood product shelf life and loss of blood donors from the donor pool have important consequences to the blood banks. Also of note, these same qualitative assays are often used for diagnostic purpose. Therefore, it is necessary to minimize the occurrence of false-positive results as much as possible.

In a diagnostic assay, a qualitative assay needs to achieve both high sensitivity and specificity. A critical aspect in the design of a diagnostic assay is to achieve an optimum bal-ance between sensitivity and specificity. Some assay designs do not just define reactive and nonreactive but have a built-in gray or equivocal zone based on the cutoff of the assay. This is usually around the critical minimum sensitiv-ity of the assay. Equivocal results often need to be repeated.

In some applications, samples defined as reactive sam-ples after testing in a qualitative assay screening are subse-quently analyzed by a suitable confirmatory method. This is important and, in some cases, mandatory to ensure that the qualitative assay provides reliable and accurate results. As a confirmatory assay, the qualitative assay needs to achieve high specificity and high positive value prediction. Confirmatory assays can usually be designed around the primary screening qualitative assay by adding a specific neutralizing antibody to confirm the presence or absence of specific analyte, for example, anti-HBs in the case of HBsAg detection. Alternatively, some screening results can be confirmed by other methods, such as western blot-ting or immunoblotting, for example, in the detection of antibody to HCV and HIV. Potential outcomes after con-firmatory testing are confirmed (positive), not confirmed (neg-ative), or indeterminate (equivocal).

In the algorithm in Fig. 1, the cutoff of the Access HBsAg assay is 1.0 S/CO (where the ratio of sample signal to cutoff value equals one) with a gray zone of 0.9–1.0 of S/CO. The specimen is tested with the screening assay. If the result is ≥1.0 S/CO, it is called initially reactive (IR). If the result is within 0.9–1.0 S/CO, it is called gray zone (indefinite or equivocal). Samples with reactive or indefinite results need

142 The Immunoassay Handbook

to be repeated with the screening assay in duplicate. If two of the three total screening tests are ≥1.0 S/CO, it is called repeatedly reactive (RR). Samples with RR results are then retested with the confirmative assay. If the control of con-firmative results ≥1.0 S/CO and neutralization rate is ≥40%, the result of the sample is called confirmed positive. If the control of confirmative results ≥1.0 S/CO, however, the neutralization rate is <40%, and the sample needs to be diluted and repeated. Finally, if the control of confirmative results is <1.0 S/CO, it is called not confirmed reactive.

The algorithm to confirm a reactive screen result is time-consuming. For diagnostics, in this case, every IR should go through the algorithm to be confirmed or not confirmed as HBsAg positive. Therefore, it makes sense to fully optimize the screening assay to achieve the best sen-sitivity and specificity thereby minimizing the false IR rate.

In each application, appropriate clinical studies must be carried out to show the relationship of results to disease state compared with the predicate device or “golden method” if there is one.

Qualitative Assay Design and DevelopmentASSAY FORMAT SELECTIONQualitative assays use the same assay formats as quantitative assays. The selection of assay format for a particular assay depends upon the intended use and requirements of the assay, e.g., detection of antigen or antibody, requirements of patients, users and regulatory agencies, and so on. An appropriate format has to be carefully chosen for a qualita-tive assay to meet the design inputs. Below are the common considerations based on the features of qualitative assays:

� For antigen detection, the most commonly used format is the immunometric (sandwich format) with two anti-bodies. This format has a much wider dynamic range and a more robust result. The competitive format may also be used for the detection of either antigen or anti-body. It is the simplest option for small molecules, e.g., steroid hormones and drugs.

� For antibody detection, the choice can be indirect, immunocapture, or sandwich with two antigens.

� Indirect formatThe first step is to capture specific antibodies (IgG, IgM, or total) using a solid phase (e.g., paramag-netic particles) coated with synthetic proteins or peptides, purified viral antigens, or even virus-infected cell lysates. The second step is to detect the captured antibodies using either enzyme or fluores-cent conjugated antihuman IgG or antihuman IgM, or even an immunocomplex of antigen with conju-gated antihuman IgG or IgM.

� Immunocapture formatThe first step is to capture specific antibodies (IgG, IgM, or total) using a solid phase coated with anti-human antibodies (e.g., mouse antihuman IgG, goat antihuman IgM, or even protein G/A) to capture corresponding human antibodies. The captured antibodies can be detected in the second step by either enzyme- or fluorescent-conjugated specific antigen or an immunocomplex of antigen with con-jugated antihuman IgG or IgM.

� Sandwich formatThe specific antibodies can also be captured using a solid phase coated with specific antigen and detected by conjugated specific antigens. However, this requires availability of defined and purified specific antigen and a suitable molecular weight of the anti-gens. The two-antigen sandwich format does not distinguish between IgG and IgM, detecting total antibodies.

There are pros and cons for each format in the detection of antibodies. In general, the selection of assay format depends on the detection of IgG, IgM, IgA, or total, the availability of specific antigen, the competition between subclasses of immunoglobulins for binding to specific antigen, susceptibility to interference, etc. Taking an IgM assay as an example, in the immunocapture format, the captured IgM is separated from other serum compo-nents in the first step, preventing subsequent competi-tion between IgM and IgG and other subclasses of

Initial Result > 0.9 to < 1.0 S/C0Gray zone

Initial Result < 0.9 S/C0Negative

Initial Result > 1.0 S/C0Initial Reactive

Recentrifuge and retest in duplicate

2 of 3 results < 1.0 S/C0Negative

2 of 3 results >1.0 S/C0Repeat Reactive

Recentrifuge and retest in duplicate

Control < 1.0 S/C0Neutralization Any valueNot Confirmed Reactive

Control > 1.0 S/C0Neutralization < 40%

Dilute and repeat

Control > 1.0 S/C0Neutralization > 40%Confirmed Positive

Sample VolumeLimiting

FIGURE 1 An example of an HBsAg algorithm from Beckman Coulter, Inc. (The color version of this figure may be viewed at www.immunoassayhandbook.com).

143CHAPTER 2.7 Qualitative Immunoassay—Features and Design

immunoglobulin. This is in contrast to the indirect for-mat, where the sensitivity of the assay can be influenced by non-IgM immunoglobulins. However, specific IgM will still compete with other nonspecific IgM molecules for binding with captured antihuman IgM antibodies. As a consequence, the sensitivity of this format is influenced by the ratio of specific IgM antibodies to total IgM. The disadvantage of the immunocapture format compared to the indirect format is the need for conjugated antiserum or antigen for each specific IgM to be tested, which often requires purified specific antigen or antigen–antibody immunocomplex. Moreover, labeling antigens is more difficult in practice than labeling immunoglobulins.

For the detection of specific antiviral IgM, the indirect format is prone to interference from rheumatoid factors (RFs). RFs are autoimmune antibodies, usually of the IgM class, that recognize human IgG. Therefore, RFs can bind with the antiviral-specific IgG that is captured by the viral specific antigens in the first step using the indirect format. The bound RFs can then be detected in the next step using conjugated antihuman IgM. This is particularly an issue when the specimen has a high titer of viral-specific IgG. In this case, the presence of viral-specific IgG may compete with specific viral IgM for binding of antigen coupled on the solid phase, which may impact on the assay sensitivity. On the other hand, the immunocapture format is less prone to RF interference than the indirect format. If using the indirect format, a sample treatment to deplete IgG is desirable to improve the assay robustness to a high titer of specific viral IgG.

CHOICE OF REAGENTS AND OPTIMIZATIONReagent optimization is critical for qualitative assay design, just as it is for quantitative assays. Selection of appropriate biological reagents, such as antibody pairs and antigens, optimization of reagent formulations, and process optimi-zation, are important elements. Reagent optimization begins with an evaluation of the major factors, including the range of concentrations possible, likely cross-reactants and other interfering substances. Taking reagent optimi-zation to the next level, the critical individual factors are

� Antibody pairs: avoidance of HAMA interference with monoclonals on both sides of the assay design.

� Antigens: native or recombinant, complementary on both sides of assay, heterogeneity, use of different expression and cell culture systems.

� Subtypes or genotypes represented. � Solid-phase chemistry; myriad of choices in literature. � Conjugation chemistry; myriad of choices in literature. � Incubation time; for sensitivity and specificity and to

suit user needs. � Assay format; as above. � Reagent composition and concentration; blockers, sta-

bilizers, preservatives, impact of chemicals on environment.

� Reagent stability during storage at 2–8 °C, frequent removal by user to ambient temperature for testing to be carried out and transport from source to end user.

� Antigen/antibody for making calibrators and QC con-trols; recombinant or native.

� Matrices for calibrators and controls; correct for sam-ple handling

� Sample type: EDTA, heparinized, citrate plasma, and serum equally effective.

Assay developers should assess each of these factors as well as their interactions for their impact on assay accuracy, precision, repeatability, sensitivity, and specificity. In a qualitative assay, setting up the right cutoff value is fundamental.

ROC CURVE, CUTOFF VALUE DETERMINATION, AND GRAY ZONEOne of the most important parameters in qualitative assay design is the definition of the cutoff value. The cutoff value is the level of signal response, which distinguishes reactive from nonreactive and is a prerequisite of an assay to allow for the correct clinical interpretation. Various methods have been developed to determine cutoff values. It is a common practice to define the cutoff as the mean plus 2–3 SD of the negative control value. However, cutoff values are best determined using an ROC Curve. An ROC curve is a plot of the sensitivity of a diagnostic test over all possible false-positive rates (1—clinical specificity). ROC curves have been widely accepted as the standard method for characterizing the clinical sensitivity (detection of dis-ease when disease is truly present) and clinical specificity (recognition of disease absence when the disease of inter-est is truly absent) (Obuchowski et al., 2004). The ROC curve shows the trade-offs between the clinical sensitivity and specificity of an assay. With considerations of the bal-ance of sensitivity and specificity according to the assay design inputs, an optimum cutoff value can be determined for a qualitative assay (Fig. 2).

In principle, the ROC curve is generated to set the cut-off value to achieve the minimum false-positive and -nega-tive results, achieving the best balance between sensitivity and specificity. The harm and severity of consequences from either a false-positive result or a false-negative result is dependent upon the assay’s intended use. For example, assays for blood donor screening should have high sensi-tivity since false-negative results will impose a high risk on

Assay 1

Assay 3

Assay 5

Assay 4

Assay 2

100

Se

ns

itiv

ity

(%

)

80

60

40

20

0

0 20 40

100-Specificity (%)

Increasing usefulness

60 80 100

FIGURE 2 Theoretical Receiver Operating Characteristics (ROC) Curve using theoretical data from five possible modifications to the assay cutoff. Assay 1 represents the optimal (ideal) 100% sensitivity and 100% specificity, Assay 5 would be least useful as it is unable to differentiate true and false positive. (The color version of this figure may be viewed at www.immunoassayhandbook.com).

144 The Immunoassay Handbook

blood transfusion safety. As another example, assays intended for aiding diagnosis of malignant tumors should have a high specificity since false-positive results could impose a higher risk of morbidity to the patients due to unnecessary treatment. Determination of optimum cutoff value is unique to each application and, to a lesser extent, unique to each assay design. “Accuracy” for a qualitative assay is therefore validated by demonstrating that the cut-off value gives the required diagnostic sensitivity and spec-ificity to yield the best predictive value in the populations encountered in screening situations.

In practice, as a result of biological and systemic factors, there is no such assay with perfect sensitivity and specific-ity. In some situations, the objective of setting up a cutoff value is to maximize the probability of identifying a true positive, while minimizing false-negative results in an often overlapped population. In the other situations, a cut-off value should be set up to achieve maximal probability of true negative but minimal probability of false-positive results.

To summarize, the single objective of clinical usefulness is met by a combination of reagent selection and optimiza-tion, and cutoff setting, within the constraint of the inher-ent biological limitations of the biomarker under measurement.

In assay design, the cutoff value needs to be formulated and programmed. There are a few approaches to define the formula using assay calibrators (Xu et al., 1997). For example, a two-level calibration may be used, with one calibrator lacking the specific analyte to indicate system noise and the other prepared with a concentration based on functional sensitivity. The cutoff value can be calcu-lated from the general equation:

where x, y, and z are parameters defined by the ROC curve to achieve the best results for sensitivity and specificity tai-lored to the assay. The results for qualitative assays are usu-ally reported as the ratio of sample signal to cutoff value (S/CO). The reactivity outcome of S/CO is defined and sub-sequently validated by internal and external studies. In most cases, the S/CO ≥ 1.0 would mean reactive or positive.

In assay development, the prevalence of the analyte in a normal population (healthy individuals without symptoms) is defined using the specified cutoff by assessing a statisti-cally significant number of specimens that are representa-tive of the population for the intended clinical application, including the appropriate matrix of the specimens.

The most important element in the validation of a qual-itative assay is the analytical and or diagnostic reliability of the cutoff value in which, in reality, there is ambiguity (Coste et al., 2006). Gray zone is a range of values around the cutoff in a qualitative assay that is considered neither reactive nor nonreactive. It is a zone of “uncertain, inde-terminate, and inconclusive” results. The upper limit of the gray zone is defined by the value of the test associated with the minimal positive likelihood ratio that allows rul-ing in the diagnostic hypothesis. The lower limit is con-sistent with the maximal negative likelihood ratio that allows ruling out the diagnostic hypothesis. The gray

zone avoids the misleading binary constraint of a “black or white” outcome, which can be inappropriate for the imprecision of detection systems and clinical practice. The gray zone and cutoff value should be defined based on a statistically large enough sample size, and the dis-eased and nondiseased individuals must be representative of the population to be tested routinely. A major objective in the optimization and validation of the assay is to ensure that the gray zone is as narrow as possible. Thus, the ideal goal for the design of a qualitative assay is to achieve no gray zone.

In practice, different cutoff values are sometimes set up for the same assay used in different populations or for dif-ferent applications.

CALIBRATION AND QUALITY CONTROLUnlike a curve plotted using multiple calibrator concen-trations for quantitative assays, qualitative assays often use bi-level calibration to define a cutoff value. In cer-tain situations, for example, microplate EIA, the cutoff is calculated using the mean of replicate calibrators and a predetermined value based on assay validation using a large sample population. For calibrators in qualitative assays used for detection of antigen, antigens are usually prepared in a synthetic matrix to have low nonspecific binding and better stability. For calibrators in qualita-tive assays for antibody detection, antibodies are often prepared in serum or plasma-derived native matrices. A negative calibrator (calibrator 0, often the matrix itself), an indicator of system variability, is usually included in the formula to calculate cutoff value. The concentration of antigen or titer of antibody in the second calibrator (calibrator 1) is usually at a very low level. As mentioned above, the cutoff value is often a function of calibrator 0 and calibrator 1. This should be defined through the ROC curve by running a number of negative samples and low-positive samples. And, it needs to be fine-tuned to allow for the imprecision of the assay from instru-ment to instrument, day to day, and reagent lot to lot. Because the cutoff value is largely defined by the signal response of calibrator 1, the stability of calibrator 1 is critical to the manufacturability and reproducibility of the assay.

The purpose of quality controls is to monitor the per-formance and qualify the assay during manufacturing. There is no coherent theory of QC for qualitative assays (Simonet, 2005). In theory, controls should resemble sam-ples in composition and behavior to capture aberrant assay performance, e.g., loss of assay sensitivity (Garrett, 1994). Therefore, it is always desirable to use native matrix, anti-gens, and antibodies as materials. The Clinical Laboratory Improvement Act of 1988 requires that laboratories use positive controls separate from those used to calculate the cutoff value, and some require use of a positive control in addition to controls in the reagent kit provided by the manufacturer. QC controls should at least have two levels, i.e., negative and positive control, in the same matrix as tested samples indicated for use with the assay.

Depending upon the assay and availability of the posi-tive sample, pooling and spiking of positive human samples are usually acceptable.

145CHAPTER 2.7 Qualitative Immunoassay—Features and Design

VIRAL ANTIBODY AND AVIDITY TESTINGIt is well known that the affinity of antibodies increases with time after immunization, and this maturation of the affinity is associated with a switch of class from IgM to IgG (Hedman et al., 1989). The goal of avidity testing is to measure the binding strength of targeted IgGs with the antigen bound on a solid phase. Antibody avidity testing is used to confirm results of screening qualitative assays for recent infection. Antibody avidity can also be determined by a qualitative assay. However, the assay design is some-what different from a typical qualitative assay. Quite often, a denature step is required to demonstrate the binding changing for the antibody.

Avidity is measured by the differential binding affinity between assays in the presence and absence of denature agent. High-affinity titer can be determined by addition of a chaotropic detergent, e.g., urea, SDS, diethylamine, or ethanolamine, to remove low-avidity IgGs. Total IgG titer is measured using the same assay reagents but without adding chaotropic detergent. The detergent can be added to the patient sample to inhibit the binding of low-avidity antibodies to the solid phase or during the incubation on the solid phase or to wash the solid phase. Bound, high-avidity IgGs are then detected with the same antihuman IgG conjugate.

The index is calculated by readings in the presence and absence of a denaturing agent using the following equation:

Typically, avidity indices of 50% or less are considered low-avidity indices.

The validation of avidity assays should be done on large, well-characterized panels of samples in order to determine a threshold above which the risk of complication is low.

Results of antibody avidity assays should be interpreted with caution since the maturation of the immune response varies between individuals. In particular, immunocompro-mized patients or individuals under medication, including some antibiotics, may exhibit a slowed-down increase of avidity following infection by Toxoplasma gondii or CMV (Lefevre-Pettazzoni et al., 2006, 2007).

VERIFICATION AND VALIDATIONWithout doubt, qualitative methods should be verified and validated (Taverniers et al., 2004). Although there are numerous qualitative assays, it is interesting to note that there are few systematic approaches to characterizing binary “yes/no” responses and the corresponding qualita-tive assays (Trullols et al., 2005). In practice, the strategy of verification and validation depends on the specific charac-teristics and features of the qualitative assays being used and should be designed accordingly. The most important quality parameters for the different qualitative methods should be defined before and during verification and vali-dation. In general, the major analytical features of qualita-tive assays are similar to quantitative assays, so the

requirements and specifications of verification and valida-tion are alike for both qualitative and quantitative assays. However, there are also some unique features for qualita-tive assays.

First of all, the cutoff value needs to be validated to ensure its appropriateness for assay analytical and clinical performance. The cutoff value is crucial because it directly defines the regions where negative and positive responses are obtained. Moreover, the limits that define the region where inconclusive sample results are obtained depend on the error associated with the cutoff value. For validation, the samples should be determined by the relationship of the sample response to the derived cutoff value. It is preferable to have controls of negative, borderline, or positive. Sam-ples can be artificially created by spiking antibodies to target titers. Sample choice, clinical decision points, and the area around the cutoff are important. In clinical applications, the cutoff may determine a diagnosis, and therefore, during validation, diagnostic specificity and sensitivity are major foci of attention.

Qualitative results can be achieved only at concentra-tions above the limit of detection. Determination of the cutoff value should be validated by providing data to show how the cutoff value differentiates positivity from negativ-ity. The number of specimens included in each disease syndrome should be statistically significant in relation to the intended use and proposed clinical utility of the device. A good validation should consider different geographical population characteristics.

To validate a gray zone, analyte concentration in the samples should be prepared close to the cutoff value. Pre-pare sufficient samples to run 20 replicates with concentra-tions at the cutoff value, 20% higher and 20% lower than the cutoff value. Run 20 replicates of each sample, calcu-late the results as percentage negative and positive, then evaluate if the cutoff is accurate by assessing whether the range of −20% to +20% is within the 95% confidence interval (see Fig. 3). The gray zone is ideally between the lower 95th percentile and the upper 95th percentile.

Second, it is critical to define the assay clinical sensitivity and specificity during verification and validation, which is different from quantitative assays. Clinical sensitivity and specificity are assessed using patient samples in conjunc-tion with validation of the assay cutoff value determination. As stated earlier, sensitivity is often a driver in the design of qualitative assays, therefore, it is understandable if a few false-positive results are encountered in achieving high sensitivity. It is advisable to determine both false-positive and false-negative rates, ideally under routine working conditions, e.g., with the instructed sample handling and processing.

Third, assay performance needs to be evaluated through a valid method comparison. Ideally, all performance evalu-ations shall be carried out in direct comparison with an established state-of-the-art device. Caution should be taken over sample selection, ensuring a significant portion of samples is around the cutoff, and in the gray zone, besides clearly negative and positive samples. Results should be evaluated using concordance of qualitative yes or no outcomes, against predefined criteria. In some cases, it is also informative to assess the correlation between meth-ods using quantifiable S/CO ratio changes. If discrepant

146 The Immunoassay Handbook

results are identified as part of an evaluation, these results should be resolved as far as possible, for example:

� by evaluation of the discrepant sample in further test systems,

� by use of an alternative method or marker, including avidity testing,

� by a review of the clinical status and diagnosis of the patient, and

� by testing follow-up samples.

Studies to assess the performance of qualitative methods generally require substantially more replication than vali-dation of quantitative methods. There are considerable advantages to including a range of analyte concentrations or (if concentration cannot be determined) samples of dif-fering response probabilities as determined by a reference or gold standard method.

ConclusionQualitative assays are increasingly used for a variety of purposes in clinical laboratories throughout the world. In the absence of a systematic publication dedicated to quali-tative assays, this chapter provides guidance based on experience and best practice. In principle, a qualitative assay aims to provide a rapid, simple, and reliable “yes or no” result for sample qualification and classification. In contrast to the quantitative assay, the qualitative assay design has its own unique features, especially those associ-ated with the determination of the cutoff value. Because the well-known analytical features of quantitative analysis cannot be directly extrapolated to qualitative assays, a qualitative assay has to be validated for its own unique characteristics.

Progress in immunoassay technology has been impres-sive, and the demand has risen. The breadth of qualitative tests available has increased dramatically in the past 2 decades. There has also been a shift in demand to the emerging markets of Asia and Africa, where infectious dis-eases are still epidemic.

Serological assays are typically designed for the detec-tion of infectious agents and corresponding antibodies and are usually semiquantitative or qualitative, often

demanding high sensitivity. The capability of qualitative assays to provide quick and definitive answers meets these user needs. The trend toward simplification of the information, straightforward results, and the increasing technological advances in qualitative assay design, auto-mation, and miniaturization are considerations for the future.

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FIGURE 3 Ideal position of gray zone. (The color version of this figure may be viewed at www.immunoassayhandbook.com).

147CHAPTER 2.7 Qualitative Immunoassay—Features and Design

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