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58
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()
xx
=×
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F. Ceriotti and M. Panteghini
conditions” refers to the different ways in which precision
can be estimated. These conditions are indicated as repeat-
ability when we speak of the same measurement procedure,
performed by the same operator with the same operating
conditions, on the same or similar samples within a short
interval of time. On the other hand, intermediate precision
refers to repeated measurements extended over a wide interval of time (days or weeks) and may involve different operators, various calibrations, and the use of different lots of
reagents. In this case, we also speak of reproducibility.
Precision is a qualitative characteristic that can be dened as
“good,” “poor,” etc. What can be quantied in numerical
terms is the level of imprecision, which is expressed in terms
of standard deviation (SD):
=ini
SD =
1
n
2
−
1
or in terms of coefcient of variation (CV):
SD
CV
100
x
Systematic Error
A systematic error is an error that remains constant or varies
in a predictable way in repeated measurements. For example,
it may result from a pipette that always dispenses a larger
volume of sample than it should, a lower reaction temperature, an error in the value assigned to the calibrator, or the
use of a different lot of reagents. The dening characteristic
of systematic error is trueness, dened as “closeness of
agreement between the average of an innite number of replicated measured quantity values and a reference quantity
value”. Trueness is a qualitative characteristic, like precision,
and for the quantication of systematic error, bias is used,
which can be expressed in absolute value or in percentage as:
bias=(measured value− reference value)/reference value×
100, where “measured value” is the average of several measurements of the same sample.
Unlike random error, systematic error can theoretically be
eliminated. According to the above denitions, repeated
measurements are required to identify the presence of random or systematic errors. When a single measurement is performed, the level of agreement between the measured value
and the reference value is dened as accuracy. Good accuracy can only be obtained when both random and systematic
errors are well controlled and result in as small as possible
measurement uncertainity. Measurement uncertainty is
dened as “a nonnegative parameter characterizing the dispersion of the quantity values being attributed to a measurand, based on the information used.” It is based on the
Table 7.1 Classication of the measurement error
Type of error
Random error Precision Standard deviation
Systematic error Trueness Bias
Measurement error Accuracy Uncertainty
Feature of the clinical
presentation Expression
concept that the true value is not knowable, but it is possible
to dene, based on the measurement obtained, a range within
which, with a dened probability, the “true” (unknown)
value is contained. In other words, the uncertainty range that
characterizes the measurement denes the range in which,
with reasonable certainty (usually with a probability of
95%), the value falls. Accordingly, the result of any measurement should not be considered complete if its uncertainty is
missing. The concepts expressed above are schematized in
Table7.1.
The ISO 15189:2022 “Medical Laboratories.
Requirements for Quality and Competence” at paragraph
7.3.4 states that the laboratory shall evaluate and regularly
review the measurement uncertaintyof measured quantity
values, where relevant.
The measurement uncertainty concept assumes to eliminate (by an appropriate correction) the bias and to combine
the variability due to the random error (u
to the elimination of the bias (u
bias
) and that related
imp
) according to the error
propagation formula:
uu u
=
cimp bias
2
+
()
2
where uc is dened as combined standard uncertainty. To
pass to the so-called extended combined measurement uncer-
tainty (Uc), which allows the denition of a 95% probability
range, it is necessary to multiply this value by a “coverage
factor” k. To obtain 95% coverage, the factor k is 1.96, but it
is usually rounded to 2.
Information about the inaccuracy and measurement
uncertainty of the laboratory methods can be obtained
through internal quality control (IQC) programs and external
quality assessment (EQA) schemes.
Biological Variation
The biological variation represents the physiological variability of the concentrations of a measurand in the biological uid of interest, for example, serum, plasma,
whole blood, urine, or others. Two types of biological
variation are recognized: intraindividual (concentration
variations within the same individual) and interindividual
(dispersion of average concentration values among different individuals belonging to a population with dened
characteristics).
120
Creatinine (µmol/L)
Subjects
7 The Quality ofLaboratory Results: Sources ofVariability, Methods ofEvaluation, andEstimation ofTheir Clinical Impact
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59
Intraindividual Biological Variation (CVI)
CVI is the random uctuation of a constituent of the organism, measured at different times in the same individual,
around its homeostatic point. The extent of this variation
depends on the conditions that underlie the control of the
concentrations of a given analyte in the biological uid of
interest. If the analyte levels are critical for the proper functioning of the organism as a whole (e.g., sodium, whose
variations in plasma lead to a shift of water from the intracellular to the extracellular compartment, with potential problems in the central nervous system), the homeostatic control
will be very tight, and the concentrations of the analyte will
uctuate within a narrow range. Conversely, if the measurand level does not have specic functions in the evaluated
biological uid (e.g., the enzymes indicating cytolysis) or its
variations in the uid are related to tissue needs or dietary
intake (e.g., the plasma concentrations of iron or triglycerides), the intraindividual variation will be higher. The CVI is
therefore characteristic of a specic measurand (analyte in a
specic biological uid), although there may be signicant
differences between individuals.
Interindividual Biological Variation (CVG)
CVG is dened as the difference in the average results of the
same constituent obtained in different individuals, all under
the same physiological conditions, due to the diversity of
their homeostatic points. It represents the variability due to
the different characteristics of the individuals belonging to a
specic population. The extent of this variability is represented by the width of the range of values that a given measurand can assume in a population of appropriately selected
subjects with criteria similar to those employed for dening
a “reference population”.
An example of the two types of biological variation is
presented in Fig. 7.1, showing serum creatinine values
obtained in 90 subjects without renal dysfunction, whose
blood samples were drawn once a week for 10 consecutive
weeks. It is noteworthy that, while the CVI in different individuals is narrow, medians of the various subject results are
dispersed over a wide range, with different values between
males and females. The case of creatinine, presented in
Fig.7.1, is typical for a measurand that has sex-specic differences and a very low ratio between CVI and CVG, that is,
an individuality index [II] equal to ∼0.3. In this situation,
when the CVI is lower than the CVG, the population-based
reference intervals lose their sensitivity in identifying individuals who may already present variations indicating possible renal dysfunction. In Fig.7.2, the individual with the
lower mean creatinine (∼65 μmol/L) will almost have to
double his/her creatinine value to overcome the upper limit
of the reference interval (104μmol/L), which for this individual may already imply a relevant reduction of renal
function.
Knowing the biological variation of the various measurands is important because, in addition to being one of
the models for the denition of APS (see below), it allows
one to estimate the reference change value (RCV), that is,
the reference to statistically evaluate the signicance of
the variation between two consecutive measurements on
the same individual beyond the total variation of the measurement. The RCV estimate allows one to establish if
two consecutive measurements of the same measurand in
the same subject can be considered different from the statistical point of view (with a given level of probability)
from the variation only due to analytical and biological
sources.
Fig. 7.1 Biological variability of
creatinine in serum of 90 subjects
with physiological renal function,
subjected to sampling once a week
for 10 consecutive weeks. The data
are divided by gender and, within
the same group, sorted by
increasing age (from 20 to 69years
for women and from 20 to 59years
for men). The horizontal line
indicates the median, and the
vertical line indicates the range of
dispersion of the values
(maximum-minimum) of each
subject
Females < 50 years Females
110
100
90
80
70
60
50
40
30
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
78 13 49 50 69 86 62 53 70 45 88 35 79 58 19 66 87 84 22 15 51 83 08 44 81 27 65 80 17 06 52 36 90 57 63 55 64 03 25 02 11 34 31 09 04 41
> 50 years
Males
60
Serum creatinine (µmol/L)
AI
22
TA
=+
22
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104
60
Biological variability
in stable subjects
Population reference
interval
F. Ceriotti and M. Panteghini
the correct denition of the APS for each measurand is
extremely important for several reasons: it can be the reference for the development of new analytical methods and
technologies, for their evaluation, and for the assessment of
the results obtained in IQC programs and in EQA schemes.
The Conference held in Milan in 2014 dened three models to be used in dening APS:
• A model based on the impact of analytical performance
on clinical outcome
• A model based on biological variation of the measurand
• A model based on the state-of-the-art of the
measurement
Fig. 7.2 Example of the impact of biological variability on the interpretation of the creatinine level. The intraindividual biological variability is much lower than the population reference range (in other words,
the interindividual biological variability). Consequently, variations in
the serum creatinine concentration in a subject, evaluated by the estimated critical difference (~12%), can signal a change in glomerular
ltration even when the results are still within the reference range of the
population
The RCV is calculated as follows:
RCV CV CV
=× +277
.
In the formula, the total variation (which derives from the
square root of the sum of the analytical variance and the
intra-individual biological variance) is multiplied by the
factor 2.77, which is obtained from 1.96 (the statistical factor to estimate the 95% probability) multiplied 2 (because
two measures are compared). From the formula, it appears
that the smaller the analytical variation, the smaller the RCV
is. It is also clear that if the intraindividual biological variation is high, the RCV value will be larger and, therefore, it
will be more difcult to assess the actual clinical signicance of a result variation. A limitation of the use of RCV is
that the available biological variation data are usually
obtained as a mean of a group of individuals, but the biological variation of the individual on which the measurements were performed and the RCV employed could be
signicantly different than the average of the data used to
derive RCV.
Model Based ontheImpact onClinical
Outcome
This model should be based on dening the maximum permissible error of measurements that cannot affect the interpretation and, therefore, the clinical use of the laboratory
data. It should be theoretically applied in all cases where
there are dened decision thresholds, such as, for instance, in
the case of serum cholesterol or blood glycated hemoglobin.
The problem with this type of model is that studies to obtain
the estimation of APS should allow the effect of measurement error to be quantied objectively. These types of studies are difcult to perform and, even when they employ
simulations, are rarely available.
Model Based onBiological Variation
oftheMeasurand
This model is based on the concept that the acceptable
“noise” caused by analytical variation should depend on the
biological variation of the analyte in question. As mentioned
above, each measurement carried out in the medical laboratory is inuenced by these two types of variability. To calculate their combined effect, it is necessary to obtain the square
root of the sum of the two sources of variance according to
the following formula:
CV CV CV
I
Denition ofAPS
APS represent the level of analytical quality required for a
given measurement to be suitable for clinical use. ISO
15189:2022, in paragraph 7.3.4, asks the medical laboratory
to compare and document the measurement uncertainty of
their measures against performance specications.Therefore,
where CVT is the total variation affecting the laboratory
result. Considering that biological variation is an incompressible factor, the contribution of analytical variation
should be minimized. Starting from the above formula, it is
possible to calculate the effect of analytical variation on the
total variation of the result: it can be easily veried that if
CVA=0.5 CVI, CVT increases by 11.8%; if CVA=0.25 CVI,
CVT increases by only 3%; and if CVA = 0.75 CVI, CVT
IG
≤+
22
7 The Quality ofLaboratory Results: Sources ofVariability, Methods ofEvaluation, andEstimation ofTheir Clinical Impact
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61
Table 7.2
Quality level
Optimal 0.25 +3%
Desirable 0.50 +12%
Minimum 0.75 +25%
CV coefcient of variation
Analytical quality levels and their effect on total variability
K
[CVA≤K CVI] Increase of CV
T
increases by 25%. These ratios between CVA and CVI dene
three quality levels of APS, classied as “minimum,” “desirable,” and “optimal,” respectively (Table7.2).
The concept behind this approach is that if the CVI is
high, even the CVT will be high, so it is not necessary to have
a very low CVA. On the contrary, if the CVI of the measurand
is low, the CVA has to be very low to minimize its inuence
on the CVT; otherwise, its contribution to increasing the total
variation of the result will be such that it could potentially
invalidate its correct clinical interpretation. Data about the
biological variation of many analytes are available in the literature, and it is, therefore, possible to calculate APS based
on this principle for almost all the most frequently performed
tests. However, it must be kept in mind that for the denition
of APS based on biological variation, it is necessary that the
measurand in question have a certain level of stability in the
biological uid in which it is measured. For example, this
does not happen for urinary components for which APS
derived from biology are not relevant.
Using biological variation, it is also possible to dene
APS as the maximum acceptable bias. As mentioned above,
when a nonnegligible bias is present, it is appropriate to
eliminate it. However, in any case, the denition of negligible bias is useful to be able to dene a maximum acceptable
limit for it. The principle for its denition is based on the
comparability between the obtained values and the reference
interval adopted. Considering the value distribution of the
reference population and the theory of the reference values
that foresees considering the central 95% of the distribution
in dening the reference limits, taking off the two tails (2.5%
below and above the limits), a systematic deviation that leads
to wrongly reclassifying a limited percentage of subjects is
considered acceptable. Based on this principle, the following
formula has been proposed:
bias CV CV
%.
025
Model Based onState-of-the-Art
oftheMeasurement
This model is not related to clinical needs or biological characteristics but depends only on the quality of available technology. However, it is very important to note that the
denition of “state-of-the-art” corresponds to the highest
level of quality obtainable at that given time and not, for
instance, to the average of the performance provided by a
group of laboratories. This model could lead to clinically
inadequate APS if the quality of available technology is poor,
or to stringent APS that is unnecessary for the clinical application of the test. However, when other models are not applicable or adequate data are not available, the quality achievable
by, for example, the top 20% of laboratories participating in
an EQA program may represent a good goal for all other
participants.
Recommended Readings
Braga F, Panteghini M (2016) Generation of data on within-subject bio-
logical variation in laboratory medicine: an update. Crit Rev Clin
Lab Sci 53:313–325
Braga F, Panteghini M (2020) The utility of measurement uncertainty in
medical laboratories. Clin Chem Lab Med 58:1407–1413
Braga F, Panteghini M (2021) Performance specications for measure-
ment uncertainty of common biochemical measurands according to
Milan models. Clin Chem Lab Med 59:1362–1368
Ceriotti F (2014) The role of external quality assessment schemes in
monitoring and improving the standardization process. Clin Chim
Acta 432:77–81
Ceriotti F, Fernandez-Calle P, Klee GG etal (2017) EFLM Task and
Finish Group on Allocation of laboratory tests to different models
for performance specications (TFG-DM). Criteria for assigning
laboratory measurands to models for analytical performance specications dened in the 1st EFLM Strategic Conference. Clin Chem
Lab Med 55:189–194
EFLM biological variation database. https://biologicalvariation.eu/
Panteghini M, Ceriotti F, Jones G, Oosterhuis W, Plebani M,
Sandberg S, Task Force on Performance Specifications in
Laboratory Medicine of the European Federation of Clinical
Chemistry and Laboratory Medicine (EFLM) (2017) Strategies
to define performance specifications in laboratory medicine: 3
years on from the Milan Strategic Conference. Clin Chem Lab
Med 55:1849–1856
Sandberg S, Fraser CG, Horvath AR etal (2015) Dening analytical
performance specications: consensus statement from the 1st stra-
tegic conference of the European Federation of Clinical Chemistry
and Laboratory Medicine. Clin Chem Lab Med 53:833–835
Principles ofImmunochemistry
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AntonioFortunato
8
Introduction
The term immunochemistry identies analytical methods
characterized by high sensitivity and specicity. Identifying
of the substance to be determined exploits the specic binding between molecules with complementary three-dimensional structures. These techniques are called ligand assay,
and in most cases, they are based on the use of an antibody
(Ab) to identify the analyte, therefore considered as antigen
(Ag). The quantication of the analyte occurs by a tracer
that, under certain conditions, allows detecting a signal proportional to the number of complexes between Ag and Ab
formed in the reaction.
In addition to these characteristics, an immunochemical
determination is based on several analytical steps:
• Incubation for the formation of Ag-Ab complexes
• Separation of Ag-Ab complexes from unbound Ag and
Ab molecules
• Detection of the tracer analytical signal
• Data processing and extrapolation of analyte
concentrations
The main classication of immunological methods, based
on the architecture of the determination, distinguishes them
into:
• Competitive (or in default of Ab/excess Ag)
• Non-competitive (or in excess of Ab/Ag)
In competitive methods, only one Ab identies the Ag,
and the analytical signal is inversely proportional to its concentration. In contrast, in non-competitive methods, the reaction involves two Abs, and the signal is directly proportional
to the Ag concentration.
A. Fortunato (*)
Clinical Pathology Laboratory, Ascoli Piceno, Italy
e-mail: antonio.fortunato@sanita.marche.it
The use of immunochemical methods began in the 1950s
with the research of Rosalyn Yalow, who, in collaboration
with Solomon Berson, developed the detection of insulin.
Roger Ekins made a signicant contribution to the development of methods for measuring molecules of various structures, to the introduction of non-competitive methods and
systems on microchips.
The Immunochemical Assay
The fundamental components of the immunochemical assay,
in addition to the analyte (Ag) to be determined, are the binding molecule (Ab), which allows the recognition of the Ag,
and the tracer, which emits the analytical signal as a function
of the bond between Ag and Ab.
The Analyte
In the immunochemical assay, the analytes are Ags toward
which specic Abs are produced. Ags are generally macromolecules (proteins, glycoproteins, or polysaccharides) that
can express many different epitopes. An antigenic determinant, or epitope, is dened the Ag region that is specically
recognized by the Ab. Ags capable of eliciting an immune
response are called immunogens. Low-molecular- weight
molecules (less than 2kDa) can be antigenic, i.e., able to be
recognized by an Ab, but not immunogenic, and are called
haptens. To produce Ab directed toward a hapten, the latter
must be conjugated to a carrier, consisting of a protein or a
glucidic group, which makes it immunogenic.
Immunochemical methods can be used to measure any analyte that can be recognized and bound by another molecule,
so the Ab itself can be the Ag that is bound by an anti-Ab Ab
(this is the case of the use of immunochemical methods to
search for Ab produced during some viral infections or autoimmune diseases).
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
M. Ciaccio (ed.), Clinical and Laboratory Medicine Textbook, https://doi.org/10.1007/978-3-031-24958-7_8
63
64
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A. Fortunato
The Binding Molecule
The binding molecules used in almost all immunochemical
methods are Ab; only in some cases other molecules are used
(e.g., receptors). The fundamental characteristics of an Ab
are afnity (measured in chemical–physical terms by the
equilibrium constant K of the reaction in which it binds the
Ag) and specicity (ability to recognize the analyte, distinguishing it from molecules with a similar structure). The Abs
most used for developing immunochemical methods are
immunoglobulin G (IgG).
Ab used in immunochemical determinations can be either
polyclonal or monoclonal. The production of polyclonal Ab can
occur by immunization of an animal with an Ag from a different
animal species. In this case, the Abs, produced by different
clones of plasma cells, are heterogeneous and are directed
against different Ag epitopes; their afnity for the Ag presents a
high variability, but it can recognize different molecular forms
of the same Ag. The characteristics of polyclonal produced Ab
are specic for the response obtained in the individual animal,
so the production of Ab with specicity is related, over time, to
the survival of the animal itself.
Monoclonal Abs are produced by a single line of plasma
cells. This phenomenon is obtained in vivo in certain
myelomas in which tumor degeneration of a plasma cell
clone leads to the hyperproduction of monoclonal immunoglobulin. The creation of hybrids between secretory B lymphocytes capable of producing specic Ab and
plasmacytoma cells, with immortal characteristics, has
made it possible to produce monoclonal Ab in adequate
quantities to produce diagnostic reagents, reproducible
over time (Table8.1).
Therefore, the specicity of the immunochemical measurement is strictly dependent on the Ab used as a binding
reagent and is dened as the ability of the Ab to recognize
the Ag to be determined. The methods that use polyclonal
Table 8.1 Characteristics of monoclonal antibodies
Advantages Disadvantages
The characteristics of afnity and
specicity are known and constant
A small amount of Ag is sufcient
for production
You can select the Ab with the
desired specicity
Can be produced in unlimited
quantities, and the process of
purication is simple
They are characterized by high
specicity
Specicity may be excessive
It requires more sophisticated
technologies for production
They do not always react with
protein A
They do not form a precipitate
following binding with the Ag
They are often characterized
by a low afnity
Ab, show a lower specicity than those using monoclonal
Ab, which constitutes a homogeneous population of Ab
directed toward a single specic epitope. The degree of specicity can be expressed as a percentage of cross reaction
relative to the ability of an Ab, typical for a given Ag, to
recognize another Ag, called interferer.
The Tracer
The detection of the analytical signal in an immunochemical
determination represents a critical step for the sensitivity of
these methods used to measure concentrations of substances
of the order of nanomoles and picomoles.
The term identies the indicator of the analytical reaction.
The substance to be measured is made detectable by introducing an appropriate marker into the molecule but without
modifying its immunological behavior. The portion of Ag or
Ab conjugated with the tracer in the immunochemical
method is dened labeled.
The evolution of the tracers used has allowed the applicability of the immunochemical technique, even in laboratories
not equipped for using radioactive tracers, which were initially the only ones used, and the development of automated
instrumentation.
All tracers must possess certain general characteristics:
• Their chemical bond with the Ag or Ab molecule must
have the least interference with the Ag-Ab complex.
• The analytical signal emitted shall have a high intensity
per unit mass, which depends on the detection limit of the
tracer type.
• The specicity of the signal shall be as high as possible in
order to ensure a low background noise and, thus, contrib-
ute to improved detectability.
• The signal shall not be modied by the characteristics of
the surrounding matrix.
• Whenever possible, the measurement should be repeated.
• The tracer must be detected over a wide range of
concentrations.
The characteristics of the analytical signal depend on the
nature of the signal itself and the instrumentation to measure
it.
The tracers can be grouped into one of the following
categories:
• Radioactive
• Enzymatic
p-nitrophenylphosphate pnitrophenol Pi- +.
eH
02++
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65
Table 8.2
Tracers Emitted signal
Radiosotope
Fluorophore Light radiation
Enzyme Absorption of
Enzyme Light radiation
Enzyme Cyclic
Luminogenic
molecule
Tracers in immunochemical techniques
Instrumentation for
the measurement
γ o β radiation
emission
emission
light radiation
emission
amplication
Light radiation
emission
γ o β radiation
counter
Fluorimeter
Photometer
Luminometer
Luminometer
Luminometer
Mass (moles
/ tube)
–10
–15
–15
–16
–20
–16
×10
– 10
– 10
– 10
– 10
– 10
−15
−18
−16
−18
−21
−18
0,1
10
10
10
10
10
• Fluorescent
• Chemiluminescent
Each category of tracer is characterized by a different
type of emitted signal and, consequently, by the type of
detector used for its measurement (Table8.2).
Some tracers provide an explicit signal (e.g., radioactivity, luminescence) that can be measured directly with the
appropriate detection instruments; others carry an implicit
signal (e.g., enzymatic activity, uorescence) that requires an
additional step before the measurement.
Radioactive Tracers
Radioactive tracers have long been the only tracers used in
immunochemical determinations (isotopic methods).
Most natural elements are mixtures of various isotopes.
Some isotopes, called radioactive isotopes, can be unstable
and become stable after the emission of particles or electromagnetic radiation through disintegration. The two main
radioelements used as markers in radioimmunochemistry
are 125iodium (which emits γ radiation) and 3H (tritium)
(which emits β radiation). The radioactive signal has the
following advantages: it is a direct signal (emitted directly
from the marker); it is positive (emission of electromagnetic or particle radiation); it is spontaneous (no need for an
external energy source); and it is very specic (spurious
radiation or contamination is exceptional). The emission of
the signal is independent of the environment, and the activity (intensity of the physical signal) is proportional only to
the number of labeled molecules. Signal detection is a simple numerical count and, thus, easily corrected for background noise.
Compared to other tracers, radioactive tracers have a
reduced steric hindrance (in particular tritium), which
slightly modies the immunological behavior of the labeled
concerning the analyte to be determined. In addition, the
radioactive tracer emits a signal that is not destroyed during
the measurement, and therefore, the measurement itself can
be repeated several times. The limitations to the use of radioactive tracers are related to the regulations that govern the
use of radioelements and dene the methods of use (authorizations, radioprotective regulations) and, above all, to the
difculty in realizing fully automated instruments.
Enzyme Tracers
Immunochemical assays using an enzyme as a tracer (enzyme
immunoassays) have been used since 1971 as an alternative
to determinations using radioisotopes. In enzyme immunoassays, the label consists of an Ag, or an Ab, associated with
an enzyme. The measurement of the enzyme activity allows
for evaluating the amount of free or labeled bound in the
immune complex. Quantication is made possible by the
addition of the appropriate substrate that triggers the enzymatic reaction. Enzymatic markers do not emit a direct signal: the quantitatively measurable signal is proportional to
the catalytic activity of the enzyme and, therefore, to concentration. The analytical signal emitted depends on the type of
transformation induced on the substrate by the enzyme: it
can be either the absorption of light radiation (absorbance) or
the emission of light radiation (uorescence or
luminescence).
Absorption Signal
The light radiation absorption signal of an enzymatic reaction can be obtained by different methodologies described
below.
Use ofaChromogenic Substrate
A chromogen is a molecule whose chemical transformation
catalyzed by an enzyme (tracer) changes its absorption
spectrum.
Example
In this enzymatic reaction, alkaline phosphatase converts
the chromogenic sub-layer (p-nitrophenylphosphate) to
p-nitrophenol (whose peak light absorption is at a wavelength of 405nm) and inorganic phosphate (Pi).
Use ofTwo Substrates, One ofWhich Is Chromogenic
Example
HO o phenyldiaminenitroanilin
22 2
O---
In this enzymatic reaction, horseradish peroxidase catalyzes the oxidation of o-phenyldiamine (reduced chromogen) to o-nitro-aniline and transfers electrons to hydrogen
peroxide, which is reduced to water. The wavelength of the
peak light absorption of o-nitro-aniline is at 492nm.
66
Glucose phosphate NADP
--
6
++
++
44-- --
methyl umbelliferyl galacto pyranoside
β
22
++
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A. Fortunato
Use ofNAD+ andNADP+ (Cosubstrates That Absorb
at340nm Only intheReduced Form)
Example
phospho Dgl
---
6
uucono lactone NADPHH--
δ
+
In this enzymatic reaction, the NADP+ cosubstrate, which
does not absorb at 340 nm, is reduced to NADPH + H+,
which exhibits an absorption peak at 340nm.
Emission Signal
The light emission signal of an enzymatic reaction can be
obtained by several methodologies described below.
Use ofaFluorogenic Substrate
A uorogen is a molecule whose transformation gives rise to
a uorescent molecule.
Example
--
methyl umbelli
fferone
In this enzyme-mediated reaction, the substrate is hydrolyzed to 4-methyl-umbelliferone, which is uorescent,
whose maximum excitation wavelength is 364nm, that of
the emission peak at 448nm.
Use ofTwo Substrates, One ofWhich Is Luminogenic
A luminogen is a molecule whose chemical transformation
produces an emission of light (chemiluminescence
reaction).
Example
HO oPhenyldiamineAmino phthalate ionHO-
22
In this reaction mediated by horseradish peroxidase, the
luminogen is oxidized to aminophthalate ion, which is in an
excited energy state. The spontaneous return of this ion to the
fundamental state is associated with light emission (peak
emission wavelength at 430nm). Hydrogen peroxide functions as electron and proton acceptor.
Direct Fluorescent Tracers
Fluorescent tracers (uorophores) re-emit, in most cases at a
longer wavelength and, therefore, lower energy, the electromagnetic waves received after excitation by ultraviolet or
visible wavelength radiation. In addition to the qualities
common to all tracers (ease of marking, high marking efciency, stability, low cost, etc.), uorescent tracers should, if
possible, emit an intense and specic signal. The intensity of
a signal depends mainly on the power of the excitation source
and the quantum yield of the uorophore. Conventional uorophores, such as uorescein, emit an intense but unspecic
signal because various interfering phenomena combine to
increase the optical background noise in the detection passband. These interfering phenomena include Rayleigh and
Raman scattering phenomena and other interfering uorescence phenomena, such as the uorescence of measuring
cuvettes and the uorescence of some natural plasma compounds (proteins, bilirubin, NADH, porphyrins, etc.) or
reagents. Interfering diffusions increase the optical background noise when the Stokes shift (wavelength interval
separating the maximum peaks of the excitation and emission spectra) is limited. Regarding spurious uorescence,
most frequently in blue and green, they contribute to the
optical background noise when the uorophore emits in the
same wavelength region. In immunochemical determinations, in order to achieve an adequate limit of detection, a
high signal-to-noise ratio is required, i.e., uorophores that
emit an intense, measurable signal with the highest specicity should be used. This measurement specicity can be
improved by wavelength and time. When a uorophore has a
high emission wavelength (red), the contribution of interfering uorescence phenomena is low at the level of the measurement passband. On the other hand, when the Stokes shift
is important, scattering photons are not detected.
Direct Chemiluminescent Tracers
Chemiluminescent tracers are molecules whose excitation
and subsequent emission of light radiation are achieved by
a chemical energy input, usually resulting from the rapid
oxidation of the molecule. Chemiluminescent compounds
and marking methods that can be used in immunochemical
assays must have the following characteristics: the ability
to form covalent bonds with the molecule to be labeled;
the chemical reaction that leads to the formation of this
bond must ensure that the immunoreactivity of the complex formed remains unaltered; the quantum yield of luminescence must not decrease excessively after bonding.
Some families of chemiluminescent compounds can be
identied: phthalhydrazides, acridinium esters, and
dioxyethanes.
Phthalhydrazide
Luminol, isoluminol, and isoluminol substitution derivatives
are the most widely used. These molecules are transformed
into excited species, which then return to the fundamental
state with the emission of photons of energy hυ, in oxidation
reactions in the presence of hydrogen peroxide and an enzymatic catalyst (peroxidase) according to the following
scheme:
−
,
Luminol HO Aminophthalate ion
+→ +
22
OHperoxidase
430
nm
=
λ
()
max
h
υυ
()
()
∗∗
8 Principles ofImmunochemistry
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67
These molecules can be used in direct and indirect chemiluminescence. In direct chemiluminescence, the tracer is the
luminogenic substrate; at the end of the assay, the peroxidase
is added together with the hydrogen peroxide. In indirect
chemiluminescence, the tracer is the peroxidase, and the
luminogenic substrate is added later together with hydrogen
peroxide.
Acridinium Esters
The addition of hydrogen peroxide in an alkaline environment is sufcient to trigger the chemiluminescent reaction:
−
OHperoxidase
Acridinium esterHONmethyl ac--
+→
22
,
nm
=
λ
430
()
max
rridone + h
υ
The forming compound (N-methyl-acridone) in the
excited singlet state returns to the fundamental state with the
emission of light radiation.
Dioxetans
Dioxyethanes are spontaneous chemiluminescent compounds. However, it is possible to stabilize them by conjugating them to a phosphate or galactose group. In this case,
the light emission is obtained by the action of the enzyme
corresponding to the stabilizing group (ALP or
β-galactosidase). The most common substrate in commercially available systems is adamantyl-1,2-dioxyethane-phenyl-phosphate (AMPPD) ester.
Architecture oftheImmunochemical Assay
The architecture of the immunochemical assay refers to the
mode by which the concentration of analyte bound by the Ab
is detected. It relies on the denition of the molecule (Ag or
Ab) bound to the tracer, how many types of Ab are used to
identify different epitopes of the Ag, and the relationship
between the analytical signal measured and the Ag concentration. The sensitivity and specicity of immunochemical
assays have greatly improved with the evolution of their
architecture, replacing, where possible, the initial competitive method in the homogeneous phase with the non-competitive method in the heterogeneous phase.
Competitive Methods
Competitive methods are dened, in addition to Ab deciency or Ag excess, also as displacement or saturation methods. The architecture of these methods is based on the
competition of the analyte to be measured (Ag), in variable
quantity in the sample, and of the labeled (Ag*) added as a
reagent, in constant concentration, for the binding sites of the
Ab in the reaction environment in constant concentration and
in any case lower than the concentration of the same Ag*.
Even in the absence of Ag, only a fraction of the total amount
of Ag* added to the reaction environment can be bound by
the Ab. The analytical signal represents the maximum binding capacity of the system. On the contrary, at increasing
concentrations of Ag, at the end of the reaction, the amount
of Ag* that will be bound in the Ab-Ag* complex will be
lower because of competition.
Thus, the competitive reaction scheme involves the presence of the following components in the reaction
environment:
1. The analyte to be analyzed contained in the sample (Ag)
2. The same Ag previously bound with the reaction reporter
(labeled: Ag*)
3. An Ab for which both Ag and Ag* are immunologically
indifferent, leading to the simultaneous formation of
Ag-Ab and Ag*-Ab complexes according to the following reaction pattern:
Ag Ag Ab Ag-AbAg-Ab++↔
+
If the concentrations of Ab and Ag* remain constant, an
increase in the concentration of Ag in the biological sample
to be analyzed leads to an increase in the concentration of the
Ag-Ab complex at the expense of the formation of the
Ag*-Ab complex (Fig. 8.1). If the nature of the tracer
requires, before measuring the signal, the separation of the
free and bound phases, it is essential to use a method that
does not alter the balance of the concentrations of the Ag-Ab
and Ag*-Ab complexes that have been formed. The measurement of the analytical signal emitted by the tracer used
in the reaction makes it possible to determine the concentration of free Ag* (F*) and of the Ag*-Ab complex (B*) as the
concentrations of Ag change. When equilibrium is reached in
the formation of the complexes with Ab, the concentrations
of F* and B* are present in the same ratio as the concentrations of the free (F) and bound (B) forms of the Ag being
determined. For any level of Ag concentration, the following
relationship is valid: B/F=B*/F*.
The main advantage of immunometric methods based on
the competition principle is that they can be applied to all
antigens, regardless of size. Moreover, they are the only
method that can be used to determine haptens exposing only
one epitope. However, some conditions are necessary for
their application. In order to detect low concentrations of Ag,
it is essential to use an Ab with a high- afnity constant and a
tracer with high specic activity; in order to obtain adequate
repeatability of the determinations, the presence of a constant number of Ab-binding sites must be guaranteed. This is
68
Addition of
12 3
and bound
Analyte concentration
phase
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reagents
Reaction
Free
Tracer
Analyte
Solid
A. Fortunato
++
separation
Bonded tracer
4
4
4
0416
Fig. 8.1 Competitive methods. (Copyright EDISES 2021. Reproduced
with permission)
particularly critical and is related to the fact that the concentration of Ab is much lower than the concentration of
Ag+Ag*.
Since in the determination with competitive architecture,
the Ag is recognized by the Ab through the binding of only
one epitope, Ag fragments or metabolites containing this epitope are also measured as the intact Ag molecule. In this context, the Ag concentration is consequently overestimated,
and the specicity of the method is reduced.
Non-competitive Methods
The non-competitive methods, or in excess of Ab or defect of
Ag or sandwich, were introduced in the second half of the
1980s and have evolved signicantly with the introduction of
monoclonal Ab to replace most competitive methods in the
determinations of high molecular weight molecules. These
methods are characterized by two Abs, present in excess of
the concentration of Ag to be measured, which recognize two
different non-interfering epitopes of Ag. The second Ab is
labeled with the tracer that allows the detection of the analytical signal (Fig.8.2).
In the structure of these immunometric determinations in
the heterogeneous phase, the rst Ab (binding Ab or capture
Ab) is xed on solid support (tubes, marbles, different particles). The amount of immobilized Ab must guarantee several sites for Ag binding higher than the number of Ag
molecules that may be present in the sample (calibrator or
unknown sample). The Ag binds to the specic sites of the
Separation
Ab capture
Ag analyte
Ab marked
Fig. 8.2 Schematic representation of the non-competitive reaction
(two-site immunometric methods or sandwich techniques in excess of
Ab) in the heterogeneous phase. (Copyright EDISES 2021. Reproduced
with permission)
rst Ab and then the Ab* binds to the same Ag (detection
Ab). The second Ab can be added simultaneously with the
other reaction components (one- step methods), or after an
initial incubation and washing (two-step methods). The
name sandwich given to this type of determination derives
from the complex formed in the reaction in which the Ag is
blocked between the two Abs. In these heterogeneous phase
methods, a simple washing procedure separates the
Ab-Ag-Ab* complexes from the free Ab* in excess of the
Ag. In general, for the Ab* to bind to the Ag, already involved
in the reaction with the rst Ab, the two Abs must recognize
different epitopes (if the Ag molecule exposes the same epitope in a repetitive manner, the same Ab can be used as capture Ab and as tracer Ab*). Capture Ab and tracer Ab* can be
polyvalent with different specicity. This is the choice solution. Combinations of monoclonal Ab binding and polyclonal Ab tracing and the association of different monoclonal
Ab can be used indifferently. The curves describing the relationship between the analytical signal deriving from the
labeled complex (Ab-Ag-Ab*), shown in the ordinate, and
the concentration of Ag in the abscissa have an increasing
trend, contrary to what is obtained for the determinations by
competitive methods in which this trend is decreasing.
The denition of the concentration of excess reagents
must be carefully determined according to the analyte concentration range to be measured. Indeed, if the concentration
of Ag approaches the concentration of Ab, both capture and
detection, the relationship between concentration and signal
tends to reach a level of saturation (plateau), tending to a
situation of defect of Ab. On the contrary, an exaggeratedly
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