TABLE OF CONTENTS

INTRODUCTION.................................................................................................................................4

1. Specificity.......................................................................................................................4

1.1. Identification ........................................................................................................5

1.2. Assay and Impurity Test(s) ....................................................................................5

2. LINEARITY ......................................................................................................................6

3. RANGE ..........................................................................................................................6

4. ACCURACY.....................................................................................................................7

4.1. Assay ..................................................................................................................7

4.2. Impurities (Quantitation).........................................................................................8

4.3. Recommended Data..............................................................................................8

5. PRECISION.....................................................................................................................8

5.1. Repeatability.........................................................................................................9

5.2. Intermediate Precision...........................................................................................9

5.3. Reproducibility ......................................................................................................9

5.4. Recommended Data..............................................................................................9

6. DETECTION LIMIT............................................................................................................9

6.1. Based on Visual Evaluation....................................................................................9

6.2. Based on Signal-to-Noise .................................................................................... 10

6.3 Based on the Standard Deviation of the Response and the Slope............................. 10

6.4 Recommended Data............................................................................................ 10

7. QUANTITATION LIMIT..................................................................................................... 11

7.1. Based on Visual Evaluation.................................................................................. 11

7.2. Based on Signal-to-Noise Approach...................................................................... 11

7.3. Based on the Standard Deviation of the Response and the Slope............................. 11

7.4 Recommended Data............................................................................................ 12

8. ROBUSTNESS .............................................................................................................. 12

9. System Suitability Testing............................................................................................... 13

Page 5

VALIDATION OF ANALYTICAL PROCEDURES:

METHODOLOGY

INTRODUCTION

This document is complementary to the parent document which presents a discussion of the characteristics

that should be considered during the validation of analytical procedures. Its purpose is to provide some

guidance and recommendations on how to consider the various validation characteristics for each analytical

procedure included as part of a registration application for approval of veterinary medicinal products

submitted to the European Union, Japan, and the United States. In some cases (for example,

demonstration of specificity), the overall capabilities of a number of analytical procedures in combination

may be investigated in order to ensure the quality of the drug substance or drug product. In addition, the

document provides an indication of the data which should be presented in a registration application.

All relevant data collected during validation and formulae used for calculating validation characteristics

should be submitted and discussed as appropriate.

Approaches other than those set forth in this guideline may be applicable and acceptable. It is the

responsibility of the applicant to choose the validation procedure and protocol most suitable for their product.

However it is important to remember that the main objective of validation of an analytical procedure is to

demonstrate that the procedure is suitable for its intended purpose. Due to their complex nature, analytical

procedures for biological and biotechnological products in some cases may be approached differently than

in this document.

Well-characterized reference materials, with documented purity, should be used throughout the validation

study. The degree of purity necessary depends on the intended use.

In accordance with the parent document, and for the sake of clarity, this document considers the various

validation characteristics in distinct sections. The arrangement of these sections reflects the process by

which an analytical procedure may be developed and evaluated.

In practice, it is usually possible to design the experimental work such that the appropriate validation

characteristics can be considered simultaneously to provide a sound, overall knowledge of the capabilities of

the analytical procedure, for instance: specificity, linearity, range, accuracy and precision.

1. SPECIFICITY

An investigation of specificity should be conducted during the validation of identification tests, the

determination of impurities and the assay. The procedures used to demonstrate specificity will depend

on the intended objective of the analytical procedure.

It is not always possible to demonstrate that an analytical procedure is specific for a particular analyte

(complete discrimination). In this case a combination of two or more analytical procedures is

recommended to achieve the necessary level of discrimination.

1.1. Identification

Page 6

Suitable identification tests should be able to discriminate between compounds of closely

related structures which are likely to be present. The discrimination of a procedure may be

confirmed by obtaining positive results (perhaps by comparison with a known reference

material) from samples containing the analyte, coupled with negative results from samples

which do not contain the analyte. In addition, the identification test may be applied to materials

structurally similar to or closely related to the analyte to confirm that a positive response is not

obtained. The choice of such potentially interfering materials should be based on sound

scientific judgement with a consideration of the interferences that could occur.

1.2. Assay and Impurity Test(s)

For chromatographic procedures, representative chromatograms should be used to

demonstrate specificity and individual components should be appropriately labelled. Similar

considerations should be given to other separation techniques.

Critical separations in chromatography should be investigated at an appropriate level. For

critical separations, specificity can be demonstrated by the resolution of the two components

which elute closest to each other.

In cases where a non-specific assay is used, other supporting analytical procedures should be

used to demonstrate overall specificity. For example, where a titration is adopted to assay the

drug substance for release, the combination of the assay and a suitable test for impurities can

be used.

The approach is similar for both assay and impurity tests:

1.2.1 Impurities are available

For the assay, this should involve demonstration of the discrimination of the analyte in the

presence of impurities and/or excipients; practically, this can be done by spiking pure

substances (drug substance or drug product) with appropriate levels of impurities and/or

excipients and demonstrating that the assay result is unaffected by the presence of these

materials (by comparison with the assay result obtained on unspiked samples).

For the impurity test, the discrimination may be established by spiking drug substance or drug

product with appropriate levels of impurities and demonstrating the separation of these

impurities individually and/or from other components in the sample matrix.

1.2.2 Impurities are not available

If impurity or degradation product standards are unavailable, specificity may be demonstrated

by comparing the test results of samples containing impurities or degradation products to a

second well-characterized procedure, e.g., pharmacopoeial method or other validated analytical

Page 7

procedure (independent procedure). As appropriate, this should include samples stored under

relevant stress conditions: light, heat, humidity, acid/base hydrolysis and oxidation.

- for the assay, the two results should be compared.

- for the impurity tests, the impurity profiles should be compared.

Peak purity tests may be useful to show that the analyte chromatographic peak is not

attributable to more than one component (e.g., diode array, mass spectrometry).

2. LINEARITY

A linear relationship should be evaluated across the range (see section 3) of the analytical procedure. It

may be demonstrated directly on the drug substance (by dilution of a standard stock solution) and/or

separate weighings of synthetic mixtures of the drug product components, using the proposed

procedure. The latter aspect can be studied during investigation of the range.

Linearity should be evaluated by visual inspection of a plot of signals as a function of analyte

concentration or content. If there is a linear relationship, test results should be evaluated by appropriate

statistical methods, for example, by calculation of a regression line by the method of least squares. In

some cases, to obtain linearity between assays and sample concentrations, the test data may need to

be subjected to a mathematical transformation prior to the regression analysis. Data from the

regression line itself may be helpful to provide mathematical estimates of the degree of linearity.

The correlation coefficient, y-intercept, slope of the regression line and residual sum of squares should

be submitted. A plot of the data should be included. In addition, an analysis of the deviation of the

actual data points from the regression line may also be helpful for evaluating linearity.

Some analytical procedures, such as immunoassays, do not demonstrate linearity after any

transformation. In this case, the analytical response should be described by an appropriate function of

the concentration (amount) of an analyte in a sample.

For the establishment of linearity, a minimum of 5 concentrations is recommended. Other approaches

should be justified.

3. RANGE

The specified range is normally derived from linearity studies and depends on the intended application

of the procedure. It is established by confirming that the analytical procedure provides an acceptable

degree of linearity, accuracy and precision when applied to samples containing amounts of analyte

within or at the extremes of the specified range of the analytical procedure.

The following minimum specified ranges should be considered:

- for the assay of a drug substance or a finished (drug) product: normally from 80 to 120 percent of

the test concentration;

- for content uniformity, covering a minimum of 70 to 130 percent of the test concentration, unless a

wider more appropriate range, based on the nature of the dosage form, is justified;

Page 8

- for dissolution testing: +/-20 % over the specified range; e.g., if the specifications for a controlled

released product cover a region from 20%, after 1 hour, up to 90%, after 24 hours, the validated

range would be 0-110% of the label claim.

- for the determination of an impurity: from the reporting level of an impurity1 to 120% of the

specification;

for impurities known to be unusually potent or to produce toxic or unexpected pharmacological

effects, the detection/quantitation limit should be commensurate with the level at which the

impurities must be controlled.

Note: for validation of impurity test procedures carried out during development, it may be necessary

to consider the range around a suggested (probable) limit;

- if assay and purity are performed together as one test and only a 100% standard is used, linearity

should cover the range from the reporting level of the impurities1 to 120% of the assay specification;

4. ACCURACY

Accuracy should be established across the specified range of the analytical procedure.

4.1. Assay

4.1.1 Drug Substance

Several methods of determining accuracy are available:

a) application of an analytical procedure to an analyte of known purity (e.g., reference material);

b) comparison of the results of the proposed analytical procedure with those of a second wellcharacterized

procedure, the accuracy of which is stated and/or defined (independent

procedure, see 1.2.);

c) accuracy may be inferred once precision, linearity and specificity have been established.

4.1.2 Drug Product

Several methods for determining accuracy are available:

a) application of the analytical procedure to synthetic mixtures of the drug product components

to which known quantities of the drug substance to be analysed have been added;

b) in cases where it is impossible to obtain samples of all drug product components, it may be

acceptable either to add known quantities of the analyte to the drug product or to compare

the results obtained from a second well-characterized procedure, the accuracy of which is

stated and/or defined (independent procedure, see 1.2.).

c) accuracy may be inferred once precision, linearity and specificity have been established.

1 see chapters AReporting Impurity Content of Batches@ of the corresponding VICH-Guidelines: AImpurities in

New Drug Substances@ and AImpurities in New Drug Products@

Page 9

4.2. Impurities (Quantitation)

Accuracy should be assessed on samples (drug substance/drug product) spiked with known

amounts of impurities.

In cases where it is impossible to obtain samples of certain impurities and/or degradation

products, it is considered acceptable to compare results obtained by an independent

procedure (see 1.2.). The response factor of the drug substance can be used.

It should be clear how the individual or total impurities are to be determined, e.g., weight/weight

or area percent, in all cases with respect to the major analyte.

4.3. Recommended Data

Accuracy should be assessed using a minimum of 9 determinations over a minimum of 3

concentration levels covering the specified range (e.g., 3 concentrations/3 replicates each of

the total analytical procedure).

Accuracy should be reported as percent recovery by the assay of known added amount of

analyte in the sample or as the difference between the mean and the accepted true value

together with the confidence intervals.

5. PRECISION

Validation of tests for assay and for quantitative determination of impurities includes an investigation of

precision.

5.1. Repeatability

Repeatability should be assessed using:

a) a minimum of 9 determinations covering the specified range for the procedure (e.g., 3

concentrations/3 replicates each)

or

b) a minimum of 6 determinations at 100% of the test concentration.

5.2. Intermediate Precision

The extent to which intermediate precision should be established depends on the

circumstances under which the procedure is intended to be used. The applicant should

establish the effects of random events on the precision of the analytical procedure. Typical

Page 10

variations to be studied include days, analysts, equipment, etc. It is not considered necessary

to study these effects individually. The use of an experimental design (matrix) is encouraged.

5.3. Reproducibility

Reproducibility is assessed by means of an inter-laboratory trial. Reproducibility should be

considered in case of the standardization of an analytical procedure, for instance, for inclusion

of procedures in pharmacopoeias. These data are not part of the marketing authorization

dossier.

5.4. Recommended Data

The standard deviation, relative standard deviation (coefficient of variation) and confidence

interval should be reported for each type of precision investigated.

6. DETECTION LIMIT

Several approaches for determining the detection limit are possible, depending on whether the

procedure is non-instrumental or instrumental. Approaches other than those listed below may be

acceptable.

6.1. Based on Visual Evaluation

Visual evaluation may be used for non-instrumental methods but may also be used with

instrumental methods.

The detection limit is determined by the analysis of samples with known concentrations of

analyte and by establishing the minimum level at which the analyte can be reliably detected.

6.2. Based on Signal-to-Noise

This approach can only be applied to analytical procedures which exhibit baseline noise.

Determination of the signal-to-noise ratio is performed by comparing measured signals from

samples with known low concentrations of analyte with those of blank samples and

establishing the minimum concentration at which the analyte can be reliably detected. A

signal-to-noise ratio between 3 or 2:1 is generally considered acceptable for estimating the

detection limit.

6.3 Based on the Standard Deviation of the Response and the Slope

The detection limit (DL) may be expressed as:

Page 11

DL = 3.3 s

S

where s = the standard deviation of the response

S = the slope of the calibration curve

The slope S may be estimated from the calibration curve of the analyte. The estimate of s may

be carried out in a variety of ways, for example:

6.3.1 Based on the Standard Deviation of the Blank

Measurement of the magnitude of analytical background response is performed by analyzing

an appropriate number of blank samples and calculating the standard deviation of these

responses.

6.3.2 Based on the Calibration Curve

A specific calibration curve should be studied using samples containing an analyte in the range

of DL. The residual standard deviation of a regression line or the standard deviation of yintercepts

of regression lines may be used as the standard deviation.

6.4 Recommended Data

The detection limit and the method used for determining the detection limit should be

presented. If DL is determined based on visual evaluation or based on signal-to-noise ratio, the

presentation of the relevant chromatograms is considered acceptable for justification.

In cases where an estimated value for the detection limit is obtained by calculation or

extrapolation, this estimate may subsequently be validated by the independent analysis of a

suitable number of samples known to be near or prepared at the detection limit.

7. QUANTITATION LIMIT

Several approaches for determining the quantitation limit are possible, depending on whether the

procedure is non-instrumental or instrumental. Approaches other than those listed below may be

acceptable.

7.1. Based on Visual Evaluation

Visual evaluation may be used for non-instrumental methods but may also be used with

instrumental methods.

The quantitation limit is generally determined by the analysis of samples with known

concentrations of analyte and by establishing the minimum level at which the analyte can be

quantified with acceptable accuracy and precision.

7.2. Based on Signal-to-Noise Approach

Page 12

This approach can only be applied to analytical procedures that exhibit baseline noise.

Determination of the signal-to-noise ratio is performed by comparing measured signals from

samples with known low concentrations of analyte with those of blank samples and by

establishing the minimum concentration at which the analyte can be reliably quantified. A

typical signal-to-noise ratio is 10:1.

7.3. Based on the Standard Deviation of the Response and the Slope

The quantitation limit (QL) may be expressed as:

QL = 10 s

S

where s = the standard deviation of the response

S = the slope of the calibration curve

The slope S may be estimated from the calibration curve of the analyte. The estimate of s may

be carried out in a variety of ways for example:

7.3.1 Based on Standard Deviation of the Blank

Measurement of the magnitude of analytical background response is performed by analyzing

an appropriate number of blank samples and calculating the standard deviation of these

responses.

7.3.2 Based on the Calibration Curve

A specific calibration curve should be studied using samples, containing an analyte in the

range of QL. The residual standard deviation of a regression line or the standard deviation of yintercepts

of regression lines may be used as the standard deviation.

7.4 Recommended Data

The quantitation limit and the method used for determining the quantitation limit should be

presented.

The limit should be subsequently validated by the analysis of a suitable number of samples known to be

near or prepared at the quantitation limit.

8. ROBUSTNESS

The evaluation of robustness should be considered during the development phase and depends on the

type of procedure under study. It should show the reliability of an analysis with respect to deliberate

variations in method parameters.

Page 13

If measurements are susceptible to variations in analytical conditions, the analytical conditions should

be suitably controlled or a precautionary statement should be included in the procedure. One

consequence of the evaluation of robustness should be that a series of system suitability parameters

(e.g., resolution test) is established to ensure that the validity of the analytical procedure is maintained

whenever used.

Examples of typical variations are:

- stability of analytical solutions,

- extraction time

In the case of liquid chromatography, examples of typical variations are:

- influence of variations of pH in a mobile phase,

- influence of variations in mobile phase composition,

- different columns (different lots and/or suppliers),

- temperature,

- flow rate.

In the case of gas-chromatography, examples of typical variations are:

- different columns (different lots and/or suppliers),

- temperature,

- flow rate.

9. SYSTEM SUITABILITY TESTING

System suitability testing is an integral part of many analytical procedures. The tests are based on the

concept that the equipment, electronics, analytical operations and samples to be analyzed constitute

an integral system that can be evaluated as such. System suitability test parameters to be established

for a particular procedure depend on the type of procedure being validated. See Pharmacopoeias for

additional information.

## No comments:

Post a Comment