1. science
2. mathematics
3. statistics

Validation as a Quality Assurance Tool
in GLP Environment
Muraleedharan CV
Biomedical Technology Wing
Sree Chitra Tirunal Institute for Medical Sciences & Technology
Thiruvananthapuram – 695 012, INDIA
1. INTRODUCTION
The basic principles identified as important for laboratories carrying out testing or studies which
involve making measurements, are :

Measurements should be made to satisfy an agreed requirements;

Measurements should be made using methods and equipment which have been tested to
ensure they are fit for purpose;

Persons making measurements should be both qualified and competent to undertake the
task;

There should be regular independent assessment of the technical performance of a
laboratory;

Measurements made in one location should be consistent with those made elsewhere; and

Organisations making measurements should have well defined quality control and quality
assurance procedures.
Figure 1 shows a hierarchical approach to quality assurance within an organisation. The outer layer
represents the elements of quality assurance that apply to all levels of activity within the organisation so-called organisational quality elements. Examples at this level include a quality management
structure with a defined role within the organisation; a quality system; documented procedures for key
activities; a recruitment and training policy for all staff; etc.. The next layer, technical quality elements
forms a subset and comprises specific QA elements which apply to the technical activities of the
organisation, such as policy and procedures for instrument calibration and performance checks; use of
certified reference materials and reference materials, and use of statistical procedures. The inner layer,
study quality elements, represents the activities carried out for particular studies or individual tests It
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Organizational
Quality Elements
Figure 1 Hierarchical approach to quality
assurance in an organisation
Technical
Quality Elements
Study
includes the planning, control and reporting practices recommended at the start of, during, and at
completion of the study or testing.
1.1.
Organisational Quality Elements : Many systems and practices that the organisation adopts
for its routine functioning forms the organisational quality elements. These include, but is not
limited to :

Have staff with suitable managerial and technical abilities to plan, control, deliver and
report each study.
1.2.

A quality system established to implement the quality policies

Systems for coordination and management of study projects

Establish a good system of document control and record keeping

Establish a good monitoring / surveillance system including periodic audits
Technical Quality Elements : GLP study projects can be considered as a collection of
discrete tests or , each consisting of a number of unit processes composed of routine unit
operations. The unit processes are characterised as being separated by natural dividing lines
at which work can be interrupted and the test portion or extract can be stored without
detriment before the next step. This is illustrated in Figure 2. The benefit of this modular
approach to defining GLP studies is that new study is likely to contain at least some
components which are familiar to the laboratory and may even be performed routinely.
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EXAMPLES
Study Project
Figure 2 Illustration of break down
of GLP studies into unit
processes
Accelerated ageing
studies of heart valve
Tests
Microbial barrier of
Packaging
Unit test
Sterility test
Unit processes
Preparation of
culture media
This approach offers benefits in terms of establishing staff competence and also in
documentation of procedures. This means, if the laboratory has demonstrated its ability to
perform a particular method, it is also accepted as fit to perform similar closely related
methods. This logical, but knowledge and experience oriented approach, enables the
demonstration of valid measurements to external experts without the need for elaborate
validation of every single unit operation or module or process.
The major technical elements that need to be considered during establishing unit processes
include :

Calibration of measuring instruments to establish traceability to national / international
metrology systems.

Validation of the systems / methods to ensure that data produced is comparable with
data for similar measurements made at different times, or by different analysts or
laboratories, or using different methods.

Use of statistical methods in
o
o
o
o
experimental design;
characterisation of method performance;
quality control of the method and
interpretation of results.
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2. SYSTEM VALIDATION
Validation of systems, whether measurement systems as in GLP regulated testing environment or
production systems, as in GMP regulated production environment has been in practice for quite some
time. These validations attempt to ensure reliability and comparability of study data or reproducibility of
Hardware validation
Software validation
Method validation
Figure 3 Elements of system validation
SYSTEM
VALIDATION
System suitability
assessment
products manufactured, as the case may be. With respect to GLP studies, the validation process shall
address the total system which include the scope of measurement , measurement system, and the
measurement method. Hence the process of validation, in totality, can be depicted as in Figure 3. Any
validation program may have the following elements depending on the nature and scope of validation.

Documentation on the validation plan, procedures and acceptance criteria. These
should be generated validation begins;

Demonstrate the calibration and traceability of measurement systems used in the
validation process / system under validation;

Demonstrate that the process / method meets an established range of operation for
the set of parameters under consideration;

Demonstrate the ruggedness of the system by challenging at the limits of established
operating conditions;

Demonstrate the appropriateness, accuracy, precision and repeatability of the
methods used;

Demonstrate that the validation has been carried out as per the plan and that the
outcome meets the predefined acceptance criteria.
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2.1.
Prospective and Retrospective Validation approaches : The simplest approach in
validation is the prospective validation which means that after a system has been developed,
the system and the associated methods are validated before employing in GLP studies.
Prospective validation is not always feasible, especially for a laboratory who is employing
certain measurement systems in testing but are setting up a GLP environment for these
measurement processes. Retrospective validation means a validation process to qualify a
method or system that is already being used for a specific application. Normally, a
retrospective validation is a much more difficult and intensive task than prospective validation.
2.2.
Validation – a continuing process : The system validation can be considered as a process
which continues over the life span of the system, including the procurement phase, installation
phase and usage phase. Figure 4 provides the activities at these phases of validation. The
validation process starts with the manufacturer or the supplier carrying out the structural,
software and method validation of the measurement system. Once the system is brought to
the user’s premises, the functional validation of the system, establishing its suitability for the
user’s application is carried out. This will involve the
three stages, viz., Installation
Qualification (IQ), Operational Qualification (OQ) and Performance Qualification (PQ). Once
the system is put into use, it has to go through continuous quality assurance schemes, which
may involve routine calibration, maintenance and verification.
2.3.
Installation Qualification : The purpose of installation qualification is to document the
Figure 4 Activity time line for
a typical system validation
program
Supplier’s
Site
Structural,
software and
method
validation
User’s Site
User’s Site
FUNCTIONAL VALIDATION
Installation
Qualification
(IQ)
Before
procurement
Operational
Qualification
(OQ)
Before use
Performance
Qualification
(PQ)
Calibration,
maintenance
and
verification
to establish
continuing
suitability
During use
installation of an instrument, equipment or system in a manner that allows for the efficient
implementation of calibration and preventive maintenance programs and facilitates effective
control of change to the equipment, over time. The IQ documentation shall describe all the
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critical features of the equipment, i.e., any component or feature that, if changed, could
seriously affect the performance, operation or safety of the system. The following information
is considered appropriate for an IQ document :
2.4.

Technical drawings / description of the equipment / system

Primary specifications, including scope and range of operation

A statement declaring the adequacy for the intended use

System identity characteristics

Utility requirements

Safety features

Reference to operator and maintenance manuals

Reference to vendor support services and parts suppliers
Operational Qualification : The purpose of the operational qualification (OQ) is to confirm
that the equipment or the system operates as expected under controlled conditions. The IQ
should be completed before the OQ begins to ensure that any deviations in operational
expectations are not a result of improper installation. Activity usually associated with OQ
events include the following :

Calibration of sensors and measuring devices

Qualification of support processing such as cleaning, disinfection, passivation, or
sterilization, as applicable

Qualification of the monitoring and controlling software

Qualification of methods used to assess the performance

A systematic demonstration of its features and functions

A demonstration of cycle performance, when appropriate, including the performance
of controllers / software.

A demonstration of process uniformity / consistency (e.g. Temperature distribution in
an oven, fill volume consistency in a filling machine, moisture level uniformity in an
incubator etc)
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
A demonstration of safety features and reset procedures after likely events such as
power failure, shut down etc.
2.5.
Performance Qualification : The performance qualification (PQ) is the most important event
of a system validation. PQ must confirm, under routine and challenged conditions of operation,
that the system continues to operate as expected and the outcome of the process is
acceptable. This must be demonstrated repeatedly, which usually means a minimum of three
consecutive successful runs.
Challenging the system to confirm its operation within the established limits of operation is a
fundamental requirement of the PQ event.
This does not mean endless number of
demonstrations with each parameters pushed to a minimum or maximum limits; only means
that a reasonable approach to the performance evaluation.
3. METHOD VALIDATION
Method validation is the process of defining an analytical requirement, and confirming that the
method under consideration has performance capabilities consistent with what the application
require. A method should be validated when it is necessary to verify that its performance
parameters are adequate for use for a particular application and is necessary in the following
situations.

new method developed for particular problem;

established method revised to incorporate improvements or extended to a new problem;

when quality control indicates an established method is changing with time;

established method used in a different laboratory, or with different analysts or different
instrumentation;

to demonstrate the equivalence between two methods, e.g. a new method and a standard
one which is known to be validated.
The laboratory has to decide which method performance parameters need to be characterised in
order to validate the method. Characterisation of method performance is an expensive process and
inevitably it may be constrained by time and cost considerations. Starting with a carefully
considered analytical specification provides a good base on which to plan the validation process,
but it is recognised that in practice this is not always possible. The laboratory should do the best it
can within the constraints imposed, taking into account customer requirements, existing experience
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of the method, and the need for compatibility with other similar methods already in use within the
laboratory or used by other laboratories. Some of the parameters may have been determined
approximately during the method development stage. Often a particular set of experiments will yield
information on several parameters, so with careful planning the effort required to get the necessary
information can be minimised. The following sections describe a set of parameters, the estimation
of which could form the basis for any method validation.
3.1.
Applicability : This concept covers the identity of the analyte, its concentration change , the
protocol, the intended application and its uncertainty requirements in the measurement of the
analyte. It is necessary to establish that the signal being produced at the measurement stage,
which has been attributed to the analyte, is only due to the analyte and not from the presence
of something chemically or physically similar or arising as a coincidence
3.2.
Selectivity : Selectivity (or specificity) is the degree to which the method can quantify the
analyte under consideration, in the presence of interferences. It is the ability of a method to
determine accurately and specifically the analyte of interest in the presence of other
components in a sample matrix under stated conditions of test
Example : A peak in a chromatographic trace may be identified as being due to the analyte of
interest on the basis that a reference material (RM) containing the same analyte generates a
signal at the same point in the chromatogram. But, is the signal due to the analyte or to
something else which coincidently got eluted? It could be either or both. Identification of the
analyte by this means only is unreliable and some form of supporting evidence is necessary.
For example, the chromatography could be repeated using a column of different polarity, to see
whether the signal and the signal generated by the reference materials still appear at the same
time. Where a peak is due to more than one compound, a different polarity column may be a
good way of separating the compounds.
3.3.
Limit of Detection (LoD) : When measurements are made at low analyte or property levels, it
is important to know what is the lowest concentration of the analyte or property value that can
be confidently detected. LoD is relevant in qualitative tests also. LoD is also known in certain
documents as minimum detectable net concentration, detection limit, or minimum detectable
(true) value. Table 1 and 2 shows examples for estimation of an LoD for a quantitative and
qualitative tests involving concentration estimation.
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Table 1 : Estimation of LoD – Quantitative Estimation
What to do ?
How many times ?
What to do with data ?
Measure the result corresponding
to sample blank
Minimum 10
independent
measurements
Measure the result corresponding
to sample blank fortified at lowest
acceptable concentration
Minimum 10
independent
measurements
Express LoD as concentration
corresponding to mean + 3s (or
4.6 s), Where s is the sample
standard deviation (not of
population)
Table 2 : Estimation of LoD – Quantitative Estimation
Concentration (ppm)
No. of Tests
Positive / Negative Results
500
10
10/0
200
10
10/0
100
10
10/0
50
10
9/1
25
10
5/5
10
10
1/9
5
10
0/10
Note : 100 ppm is the LoD for concentration estimation in this case.
3.4.
Limit of Quantitation (LoQ) : The lowest concentration of an analyte, that can be determined
with acceptable precision (repeatability) and accuracy under the stated conditions of the test.
Neither LoQ nor LoD represent immeasurable values; but only indicates that below which the
measurement uncertainties approach the measured value itself. Many agencies specify the
LoQ as 10X values of the standard deviation of the blank mean. Table 3 provides an example
of LoQ estimation.
Table 3 : Estimation of LoQ – Quantitative Estimation
What to do ?
How many times ?
What to do with data ?
Measure the results for
Sample blanks
10 independent
measurements
Estimate LoQ as 10X standard
deviation of the mean.
Fortify aliquots of sample
blank at various
concentrations close to
LoD
At least 6 concentrations and
10 replicate measurements of
each concentration
Calculate the value of variance (s)
for each concentration and plot
against concentration. Assign LoQ
by inspection
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3.5.
Working and Linear Ranges : The working range is the range over which the method can be
employed. The lower limit of the working range may be the LoD (in qualitative estimations) and
LoQ (in quantitative estimations). At the upper end of the range, limitations will be imposed by
various effects, like instrument response. The linear range is the range of over which the
method gives test results proportional to the input.
Table 4 : Estimation of linear range
3.6.
What to do ?
How many times ?
What to do with data ?
Analyse blank + RM or
fortified sample blanks at
various concentrations
At least six
concentrations + blank
(independently prepared)
Plot the conc. Vs. result and identify
approximate working and linear ranges
Analyse RM or fortified
sample blanks within the
linear range
At least six
concentrations to be
tried
Calculate the regression coefficients in
the linear range. Calculate the residual
plots. Normal distribution of residuals
with zero mean establishes linearity.
Accuracy and precision : Accuracy expresses the closeness of result with the TRUE value
or CONVENTIONAL TRUE value. Accuracy is expressed as BIAS. i.e. the difference between
the mean value of the method and the mean value of the TRUE VALUE or CONVENTINAL
TRUE VALUE. Certified Reference Materials (CRM) can be used for establishing the bias of
the method Alternate method for estimation of bias is the use of any other standardised
method, that could be used for the estimation of the same result.
Table 5 : Estimation of accuracy
3.7.
What to do ?
How many times ?
What to do with data ?
Analyse blank and CRM
using the candidate
method
At least 10
independent
measurements
Estimate the difference between the means
certified value of the CRM to the value
obtained in the test
Reagent blank and
reference / test material
using alternate standard
method
At least 10
independent
measurements
Estimate the difference between the results
with the candidate method and the alternate
standard method.
Precision : Most common precision measures are REPEATABILITY and REPRODUCIBILITY
Repeatability will provide the dispersion of results when a method is performed by a single
person, on one measurement system, over a short period of time (time interval being
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comparable to the duration of the test). Reproducibility refers to the dispersion of results when
there occurs following conditions

Different operators

Different measurement systems (instruments)

Larger time / spatial gap between measurements
Normally standard deviation of the mean (SD) is estimated to represent the measure of
precision. But, the standard deviation of the mean may some times depend on the mean itself.
In such cases, relative standard deviation or coefficient of variation could be a better measure
of precision. A Precision estimation plan could have three distinct objectives :

Estimation of Repeatability precision

Estimation of Intra Laboratory reproducibility precision

Estimation of Inter Laboratory reproducibility precision
Table 6 : Estimation of precision measures
What to do ?
How many times ?
What to do with data ?
(Analyse RM or fortified sample blanks at various concentrations across working range)
3.8.
Same analyst, equipment,
laboratory, short time scale
10 independent trials
Estimate repeatability standard
deviation
Different analysts,
equipments, same laboratory,
extended time scale
10 independent trials
Estimate intra laboratory
reproducibility standard deviation
Different analysts,
laboratories, extended time
scale
10 independent trials
Estimate inter laboratory
reproducibility standard deviation
Sensitivity : It is the measure of the gradient of the calibration function and is a measure to
compare different techniques of measurement.
3.9.
Ruggedness and Robustness : The robustness of a method is a measure of its capability to
remain unaffected by small, but deliberate variations in method parameters and provides an
indication of its reliability during normal usage. Robustness is normally evaluated during the
method development (one laboratory) and subsequently, ruggedness estimation is attempted
using an inter laboratory study. Ruggedness requires an inter laboratory study to assess the
behavior of a method when small changes in the environmental and / or operating conditions
are made (which signifies the changes that could arise between different laboratories).
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Table 7 : Estimation of ruggedness / robustness
What do you do ?
Identify variables which
could have significant
effect on the method.
Conduct experiments to
monitor the effect of
each variable on
accuracy and precision
3.10.
How many times ?
What to do with data ?
Analyse each set of
experimental conditions
once.
Estimate the effect of each change in
condition on the mean.
Rank the variables in the order of the
greatest effect on method performance.
Concentrate on these variables during
method improvement
Measurement Uncertainty : It is a parameter that could be attributed to the dispersion of the
measurement. Uncertainty estimation of the measurement shall take into account,

Over all precision of the method

Bias and its uncertainty (method)

Calibration uncertainties

Any other significant factors and their uncertainty that might affect the results (Eg.
Environmental conditions, sample preparation, sampling …..)
3.11.
Recovery : Test for analytes do not always measure the analyte present in the sample.
Analytes may be present in different forms; either bound or unbound conditions, some of
which during the extraction process does not contribute towards the result. Since it is not
possible to know how much analyte is present in a sample, it is difficult to be certain how
successful the method is in extracting the analyte. Estimation of efficiency of recovery is an
acceptable way to ascertain the suitability of extraction method.
4. CONCLUDING REMARKS
Validation starts well before a system is put to use and continues long after system / method
development. After a system has been found suitable to be taken into use, it must be continuously
assessed as remaining stable over its entire life. A plan for its systematic control should be
established. This plan may contain parts of the elements of the system or the entire system,
depending on what is necessary and most practical. The laboratory should concentrate its efforts
on aspects where the sources of errors are greatest, i.e., on the control of adjustment and
calibration of the components and the verification and maintenance of procedures to ensure the
reliability of data. A well defined and documented validation process provides regulatory agencies
with evidence that the system and method are suitable for their intended use.
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5. REFERENCES
5.1.
USFDA, “Analytical processes and method validation - Guidance for Industry” , Centre for
Drug Evaluation and Research, Maryland 20857, USA ,2000
5.2.
EURACHEM, “Quality Assurance for Research and Development and Non-routine analysis”,
EURACHEM / CITAC Working Group, Middlesex, United Kingdom, 2003
5.3.
NATA, “ Format and Content of Test Methods and Procedures for Validation and Verification
of Chemical Test Methods”, National Association of Testing Authorities, Australia (NATA),
1998
5.4.
EURACHEM, “The Fitness for Purpose of Analytical Methods - A Laboratory Guide to Method
Validation and Related Topics”, EURACHEM / CITAC Working Group, Middlesex, United
Kingdom, 1998
5.5.
Carol DeSain and Charmaine Vercimark Sutton, “Validation for medical devices and
diagnostic manufacturers”, Interpharm Press, Buffalo Grove, 1994.
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