Quick Reference Guide - Waters Corporation€¦ · • Detection & Quantitation Limits •...

Elsa32 for Empower SoftwareQuick Reference Guide

34 Maple StreetMilford, MA 01757

71500059105, Revision A

NOTICE

The information in this document is subject to change without notice and should not be construed as a commitment by Waters Corporation. Waters Corporation assumes no responsibility for any errors that may appear in this document. This document is believed to be complete and accurate at the time of publication. In no event shall Waters Corporation be liable for incidental or consequential damages in connection with, or arising from, the use of this document.

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All other trademarks or registered trademarks are the sole property of their respective owners.

Table of Contents

Preface ......................................................................................... 6

Chapter 1 About Elsa32 for Empower ............................................................... 11

1.1 Overview ............................................................................... 11

1.2 Method Validation Protocol ................................................... 11

1.3 Experimental Plan................................................................. 12

1.3.1 Linearity, Accuracy, and Precision ............................. 12

1.3.2 LOD, LOQ, Sensitivity, and Robustness .................... 13

1.3.3 Experimental Analysis ............................................... 14

Chapter 2 Using Elsa32 for Empower ............................................................... 15

2.1 Data Access and Security..................................................... 15

2.2 Using the Elsa32 Wizard ....................................................... 15

2.2.1 Linearity Test.............................................................. 16

2.2.2 Accuracy Test ............................................................ 17

2.2.3 Precision Test ............................................................ 18

2.2.4 Limit of Quantitation Test ........................................... 19

2.2.5 Robustness Test ........................................................ 20

2.3 Understanding the Elsa32 Analytical Method Validation Window................................................................................. 21

2.4 Empower Result Selection .................................................... 22

2.5 Elsa32 Method Validation Calculations.................................. 24

2.6 Elsa32 Method Validation Report .......................................... 26

Table of Contents 3

Table of Contents 4

Appendix A Statistical Tests ................................................................................. 27

A.1 Skew Test Student Test and Confidence Limits .................. 27

A.2 Cochran Test ....................................................................... 29

A.3 Dixon Test ........................................................................... 30

A.4 Fisher Test ........................................................................... 31

Appendix B Parameter Calculations .................................................................... 33

B.1 Linearity Calculations .......................................................... 33

B.1.1 System Response .................................................... 33

B.1.2 Regression Calculations............................................ 33

B.1.3 Slope Existence......................................................... 34

B.1.4 Linearity Adjustment.................................................. 34

B.1.5 Slope and Intercept Comparison............................... 34

B.1.6 Intercept Through Zero Test...................................... 35

B.1.7 Weighting Factors ..................................................... 35

B.2 Accuracy .............................................................................. 37

B.2.1 % Recovery .............................................................. 37

B.2.2 Average % Recovery................................................. 38

B.3 Precision .............................................................................. 39

B.3.1 Repeatability and Intermediate Precision ................. 39

B.3.2 % Recovery ............................................................... 39

B.3.3 Average % Recovery................................................. 40

B.3.4 Repeatability and Intermediate Precision Variances .................................................................. 40

B.3.5 Repeatability and Intermediate Precision Calculation from Response .......................................................... 41

B.3.6 Repeatability and Intermediate Precision % RSD Calculation ........................................................ 41

B.3.7 Intermediate Precision and Repeatability Variances Comparison ............................................................... 42

B.3.8 Intermediate Precision and Linearity Variances Comparison ............................................................... 42

B.4 Limit of Detection and Limit of Quantitation ......................... 43

B.4.1 Experimental Determination Based on Signal to Noise ..................................................................... 43

B.4.2 Statistical Determination on Standard Deviation of the Response ........................................................ 43

B.5 Sensitivity ............................................................................ 44

B.5.1 Sensitivity Calculation .............................................. 45

B.5.2 Discriminator Capacity .............................................. 45

B.6 Robustness .......................................................................... 45

B.6.1 Determination of Significant Factors ........................ 46

Appendix C Bibliography and References............................................................ 48

Index ....................................................................................... 49

Table of Contents 5

Preface

The Elsa32 for Empower Software Quick Reference Guide is intended for users wanting to perform method validation with Empower™ software. This guide describes the basics of how to use the Elsa32 software for various method validation processes with the Empower software.

Related Documentation

Waters Licenses, Warranties, and Support: Provides software license and warranty information, describes training and extended support, and tells how Waters handles shipments, damages, claims, and returns.

Online Documentation Available with Elsa32 for Empower

Elsa32 for Empower Help: Describes all Elsa32 windows, menus, menu selections, and dialog boxes. Also includes some reference and overview information. Included as part of the Elsa32 for Empower software.

Online Documentation Available with Empower

Empower Help: Describes all Empower windows, menus, menu selections, and dialog boxes for the base software and software options. Also includes reference information and procedures for performing all tasks required to use Empower software. Included as part of the Empower software.

Empower Read Me File: Describes product features and enhancements, helpful tips, installation and/or configuration considerations, and changes since the previous version.

Empower LIMS Help: Describes how to use the Empower LIMS Interface to export results and import worklists.

Empower Toolkit Professional Help: Describes how to use the common-object- model, message-based protocol to communicate with the Empower software from a third-party application.

Printed Documentation Available with Empower

Empower Software Getting Started Guide: Provides an introduction to the Empower software. Describes the basics of how to use Empower software to acquire data, develop a processing method, review results, and print a report. Also covers basic information for managing projects and configuring systems.

6

Empower Software Data Acquisition and Processing Theory Guide: Provides theories pertaining to data acquisition, peak detection and integration, and quantitation of sample components.

Empower System Installation and Configuration Guide: Describes Empower software installation, including the stand-alone Personal workstation, Workgroup configuration, and the Enterprise client/server system. Discusses how to configure the computer and chromatographic instruments as part of the Empower System. Also covers the installation, configuration, and use of acquisition servers such as the LAC/E32 module, the busLAC/E™ card, and interface cards used to communicate with serial instruments.

Empower System Upgrade and Configuration Guide: Describes how to add hardware and upgrade the Empower software using an import-and-export upgrade method.

Empower Software System Administrator’s Guide: Describes how to administer the Empower Enterprise client/server system and Workgroup configuration.

Empower Software Release Notes: Contains last-minute information about the product. Also provides supplementary information about specific Empower software releases.

Printed Documentation for Software Options

Empower System Suitability Quick Reference Guide: Describes the basics of the Empower System Suitability option and describes the equations used by the System Suitability software.

Empower PDA Software Getting Started Guide: Describes the basics of how to use the Empower PDA option to develop a PDA processing method and to review PDA results.

Empower GC Software Getting Started Guide: Describes how to use the Empower GC option to develop a GC processing method and to review GC results.

Empower GPC Software Getting Started Guide: Describes how to use the Empower GPC option to develop a GPC processing method and to review GPC results.

Empower GPCV Software Getting Started Guide: Describes how to use the Empower GPCV option to develop a GPCV processing method and to review GPCV results.

Empower Light Scattering Software Getting Started Guide: Describes how to use the Empower Light Scattering option to develop a light scattering processing method and to review light scattering results.

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Empower ZQ Mass Detector Software Getting Started Guide: Describes installation, configuration, calibration, and tuning methods, as well as how to operate the ZQ Mass Detector with Empower software.

Empower Chromatographic Pattern Matching Software Getting Started Guide: Describes how to use the Chromatographic Pattern Matching option to develop a pattern matching processing method and to review pattern matching results.

Empower Dissolution System Software Quick Start Guide: Describes how to operate the Alliance® Dissolution System using Empower software.

Empower Toolkit Programmer’s Reference Guide: Describes how to use the common-object-model, message-based protocol to communicate with Empower software from a third-party application.

Waters Integrity System Getting Started Guide: Describes features of the Waters Integrity® System and provides step-by-step tutorials that guide a user through the use of the Empower Mass Spectrometry (MS) option.

Empower AutoArchive Software Installation and Configuration Guide: Describes how to install and configure the Empower AutoArchive option.

Documentation on the Web

Related product information and documentation can be found on the World Wide Web. Our address is http://www.waters.com.

Related Adobe Acrobat Reader Documentation

For detailed information about using Adobe® Acrobat® Reader, see the Adobe Acrobat Reader Online Guide. This guide covers procedures such as viewing, navigating, and printing electronic documentation from Adobe Acrobat Reader.

Printing This Electronic Document

Adobe Acrobat Reader lets you easily print pages, page ranges, or the entire document by selecting File > Print. For optimum print quantity, Waters recommends that you specify a PostScript® printer driver for your printer. Ideally, use a printer that supports 600 dpi print resolution.

8

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Documentation Conventions

The following conventions can be used in this guide:

Notes

Notes call out information that is helpful to the operator. For example:

Note: Record your result before you proceed to the next step.

Convention Usage

Purple Purple text indicates user action such as keys to press, menu selec-tions, and commands. For example, “Click Next to go to the next page.”

Italic Italic indicates information that you supply such as variables. It also indicates emphasis and document titles. For example, “Replace file_name with the actual name of your file.”

Courier Courier indicates examples of source code and system output. For example, “The SVRMGR> prompt appears.”

Courier Bold Courier bold indicates characters that you type or keys you press in examples of source code. For example, “At the LSNRCTL> prompt, enter set password oracle to access Oracle.”

Underlined Blue Indicates hypertext cross-references to a specific chapter, section, subsection, or sidehead. Clicking this topic using the hand symbol brings you to this topic within the document. Right-clicking and selecting Go Back from the shortcut menu returns you to the origi-nating topic. For example, “Linearity, Accuracy, and Precision are discussed in Section 1.3, Experimental Plan”

Keys The word key refers to a computer key on the keypad or keyboard. Screen keys refer to the keys on the instrument located immediately below the screen. For example, “The A/B screen key on the 2414 Detector displays the selected channel.”

… Three periods indicate that more of the same type of item can optionally follow. For example, “You can store filename1, filename2, … in each folder.”

> A right arrow between menu options indicates you should choose each option in sequence. For example, “Select File > Exit” means you should select File from the menu bar, then select Exit from the File menu.

9

Attentions

Attentions provide information about preventing damage to the system or equipment. For example:

Cautions

Cautions provide information essential to the safety of the operator. For example:

STOPAttention: To avoid damaging the detector flow cell, do not touch the flow cell window.

Caution: To avoid burns, turn off the lamp at least 30 minutes before removing it for replacement or adjustment.

Caution: To avoid electrical shock and injury, unplug the power cord before performing maintenance procedures.

Caution: To avoid chemical or electrical hazards, observe safe laboratory practices when operating the system.

10

Overview 11

Chapter 1About Elsa32 for Empower

1.1 Overview

The Elsa32 for Empower™ software performs extensive method validation consistent with the method validation guidelines recommended by the ICH4.

A method validation protocol calculates theses parameters:

• Linearity & Range

• Accuracy

• Repeatability & Intermediate Precision

• Detection & Quantitation Limits

• Sensitivity

• Robustness

Working interactively with Empower software, Elsa32 performs the direct transfer of chromatography data with no manual data entry to ensure a rapid and efficient validation of chromatography methods.

As Elsa32 calculates each phase of method validation, the flexibility of view filters allows you to quickly retrieve and sort all results stored in the Empower database.

Elsa32 interacts with Empower, transferring chromatography data without manual intervention, then verifying the data. Elsa32 generates an Excel workbook that includes a complete set of validation parameters and an instant summary report of all validation processes, including a series of Excel charts for fast visual review.

Elsa32 is based on Windows®. It works with Empower to establish a link to an Excel workbook. Elsa32 works on a stand-alone workstation or in a client/server configuration.

1.2 Method Validation Protocol

Elsa32 is based on a method validation protocol that follows the ICH4 guidelines and optimizes the number of assays necessary to prove the reliability of the results.

The number of assays are limited to values that fit the validity range of statistical tests. The proposed experimental plan suggests a minimum number of assays providing enough results to answer the different criteria qualifying the analytical method.

32

In the design of the experimental work, the first parameter to consider is the specificity.

As dedicated analytical tools, such as the mass spectrometer or photodiode array detector, investigate the purity and identity of the component, the Empower PDA or Mass Spectrometry option processes this validation information. However, Elsa32 software computes a comprehensive statistical test on the retention time distribution to ensure the identity of the component throughout the results.

The experimental plan is optimized to simultaneously perform the Linearity, Accuracy, and Precision tests using a common set of assays analyzed during 3 or more consecutive days. The other LOD, LOQ, and Robustness criteria can be evaluated separately.

1.3 Experimental Plan

1.3.1 Linearity, Accuracy, and Precision

Linearity

The experimental protocol is based on the generation of two calibration curves every day: one for the pharmaceutical form, and one for the active form.

The active form (standard) contains only the analyte of interest; the pharmaceutical form is spiked with the excipients and/or impurities.

Each curve should have at least five concentration levels over the range (minimum 80 to 120%).

Table 1-1 Experimental Plan Linearity and Precision

Linearity Day 1 Linearity Day 2 Linearity Day 3

Level 1: 80% Pharmaceutical + Standard









Precision Day 1 Precision Day 2 Precision Day 3

6 x 100%Pharmaceutical









About Elsa for Empower 12

At least three replicates of each level should be carried out on different days under different conditions (such as a different analyst and instrument).

Accuracy

Accuracy is calculated from the same set of assays analyzed for the linearity (no extra experiment). The active form calibration curves generated every day are used to calculate the % Recovery on the pharmaceutical form.

Precision

Precision investigates repeatability on six repetitions carried out on the same day under the same operating conditions (such as one analyst, same laboratory, instrument, and reagent).

Intermediate Precision on three replicates should be carried out on different days under different conditions (such as a different analyst, instrument, and reagents).

1.3.2 LOD, LOQ, Sensitivity, and Robustness

LOD

Limit of Detection is generally required for impurities or breakdown products and can be obtained by six blanks of the sample containing all constituents except the component to be analyzed. If the chromatogram shows no interference, Empower System Suitability can calculate the baseline noise and evaluate the LOD. If there is interference, LOD is calculated from the blank standard deviation.

Table 1-2 Experimental Plan LOQ and Robustness

Limit of Detection Limit of Quantitation

Six Blanksbaseline noise

Level 1Pharmaceutical at LOQ

RobustnessLevel 2

Pharmaceutical at LOQ

Factor 1 4 x (Min. + Max)







Sensitivity/Precision

Six ReplicatesPharmaceutical LOQ

Experimental Plan 13

LOQ

Limit of Quantitation evaluates the lowest limit for precise quantitative measurements. It is calculated from LOD for impurities or from the linearity study for the main component. The Linearity is verified on five levels down to the LOQ and the Precision is controlled by six replicates at the LOQ.

Sensitivity

Sensitivity is the ability to register small changes in concentration and depends on the slope of the calibration curve. It is deduced from the Linearity and Precision analysis either at the LOQ or at the nominal 100% concentration.

Robustness

Regarding the number of critical method parameters, which can influence the results, an 8-analysis factorial plan (4 factors) or 16-analysis factorial plan (up to 9 factors) can test the robustness of the method. It implies systematic, controlled variations of the different parameters around the normal value. The minimum and maximum values are assumed likely to be encountered for different operators or instruments.

These factorial plans take into consideration most interactions between the factors.

This study has to be achieved in the development phase before undertaking the complete method validation process.

1.3.3 Experimental Analysis

All the different analyses have to be performed independently. For each individual trial, a separate amount of the component is weighed and registered in the Empower custom field Amount_0.

The response of the chromatographic system is the signal generated by the detector and integrated by the Empower data system. This response reflects the state of the system. It assumes that the instruments have been qualified and the performances of the complete system has been checked at the moment of method validation.

The entire instrument conditions, system suitability parameters, and integration parameters are linked to the results and permanently saved by the Empower database in the instrument methods and processing methods.

You must create custom fields describing the key criteria of the method in your Empower project. Custom fields facilitate the sorting and ordering for all results and make information management easier to use and more quickly obtainable with the Result view filters command.

About Elsa32 for Empower 14

Chapter 2Using Elsa32 for Empower

2.1 Data Access and Security

Upon startup, the Elsa32 for Empower Login dialog box appears. Use your Empower user information and password to get access to the appropriate project information you want to use in Elsa32.

Note: This software is password-protected and all Validation workbooks are saved as read-only and are also password-protected.

2.2 Using the Elsa32 Wizard

Using the wizard you can easily configure the method validation software:

• Organization: The name of your organization.

• Analyst: User name that is placed on every method validation report with every Validation workbook.

• Password: Your access password.

• Project: Change the project and choose the specific result field information imported from Empower.

• Field 1: Retention Time identifies the peak of interest.

• Field 2: Response of the system can be the Area, Height, or the Response field for the internal standard.

• Field 3: The custom field. Amount_0 registers the amount entered for the sample during analysis.

• Field 4: Optional.

• Alpha: Define the statistical risk level Alpha (α) that you want to work with for method validation. This parameter is related to the confidence level p (α = 1 – p): α = 0.05 (5%) or α = 0.01 (1%) are the accepted values.

• Component: Select a component from the list.

Data Access and Security 15

32

Figure 2-1 Elsa32 Wizard

The smaller that α is, the wider the confidence interval will be and the more likely the measurements will be accepted.

Note: As you navigate through the wizard, also keep in mind that the method validation parameters can be set individually by clicking the appropriate wizard tab(s).

2.2.1 Linearity Test

Following the suggested validation protocol, at least five concentration levels over three days are necessary. The linearity is computed by the least-squares curve of best-fit procedure. Very often for bio-analysis, weighted least-squares curves yield useful improvements compared to conventional calibration. A wide range of 16 weighting factors is available (from 1/X … to 1/Variance).

The limitations to 10 concentration levels and 10 days provide a sufficient number of experiments and fit the validity range of statistical tests.

Using Elsa for Empower 16

The software controls the number of results selected in Empower and corresponds to the number of points expected. In each result, it checks to see if the component is present.

Figure 2-2 Linearity Page

2.2.2 Accuracy Test

The accuracy is evaluated on the Linearity Range using the same set of data points as the Linearity test. The % Recovery is the relative percentage of recovery of sample amount:

% Recovery = Calculated Amount / True Amount

This ratio can be calculated either with the 100% standard (active form) if the curve is through zero or with the whole calibration curve.

A calibration curve or the 100% standard is evaluated for each day.

The Average % recovery is computed from each point of the pharmaceutical form calibration curve.

Using the Elsa32 Wizard 17

Figure 2-3 Accuracy Page

2.2.3 Precision Test

The precision is evaluated by independent assays on homogenous samples of the pharmaceutical form; each initial amount is weighed separately and should correspond to the 100% amount of the linearity calibration curve.

The repeatability requires at least six replicates. As the 100% standard of the calibration curve is analyzed under the same conditions, it can be added to these replicates.

To evaluate the intermediate precision, these replicates are grouped by the number of days, with a minimum of 3 days.

Using Elsa32 for Empower 18

Figure 2-4 Precision Page

2.2.4 Limit of Quantitation Test

A calibration curve is established at the Limit of Quantitation with at least five calibration levels. It is used to calculate the % Recovery on replicates.

By default the number of days is one, except if the replicates are performed on different days or at a different concentration level; then they can be grouped by adjusting the number of days.

Replicates are analyzed to evaluate the accuracy and the precision at the LOQ.

Using the Elsa32 Wizard 19

Figure 2-5 LOQ Page

The validation process qualifies the method at the LOQ for a predefined amount or concentration level. The amount of the LOQ can be entered (optional). The value entered here will be printed in the report.

2.2.5 Robustness Test

The lower and upper limits of each Robustness parameter define the controlled variations of the analytical conditions applied during the experimental plan.

Up to 9 parameters can be tested with a 16-assay experimental plan; an 8-assay plan is sufficient for 4 parameters.


Figure 2-6 Robustness Wizard Window

2.3 Understanding the Elsa32 Analytical Method Validation Window

You can access Elsa32 method validation functions from the menu bar or by using the command buttons.

The Auto-Validation command successively calculates the method validation parameters. It automates the method validation process generating reports and charts. Microsoft Excel allows you to navigate through the validation workbook to display the contents of each validation sheet or chart.

Understanding the Elsa32 Analytical Method Validation Window 21

Figure 2-7 Elsa32 Analytical Method Validation Window

2.4 Empower Result Selection

The view filters interactively select and order the Empower results during data transfer. A specific view filter is applied at each step when navigating through the wizard.


Note: The sort order is important so that your data fits precisely into the Excel workbook.

In the Robustness test, custom fields are particularly useful to register the experimental conditions. They should be ordered to correspond to the matrix of the factorial plan.

Table 2-1 View Filter Configuration

Process \ Field

Sample Name

Sample Type

Concentration Level

Date Acquired

Processing Method

Linearity

=Mysample* Standard (Ascend) (Ascend) =day1=day2=day3

=Mymethod*

Precision

=Mysample* N/A N/A (Ascend)=day1=day2=day3

=Mymethod*

LOQ Standard

=Mysample* Standard (Ascend) (Ascend)=day

=Mymethod*

LOQ Replicate

=Mysample* Unknown N/A (Ascend)=day

=Mymethod*

Table 2-2 Robustness View Filters Example

Process \ Field

Sample Name

pHMobile Phase

QuantityDissol Time

Processing Method

Robust-ness

=form* (Ascend) (Ascend) (Ascend) (Ascend) =Rug*

Empower Result Selection 23

Figure 2-8 Empower Robustness View Filter Editor

2.5 Elsa32 Method Validation Calculations

Method validation processing generates a worksheet dedicated to each parameter. It contains the original data, the intermediate calculation with formulas, and the finished results.

A visual inspection on graphs or colored cells verifies the distribution and homogeneity of original data and corroborates the calculations.

Calculations are detailed in Appendix A for the Statistical tests and in Appendix B for the method validation parameters.


Figure 2-9 Elsa32 Robustness Worksheet

Elsa32 Method Validation Calculations 25

Figure 2-10 Elsa32 Robustness Chart

2.6 Elsa32 Method Validation Report

Elsa32 produces a summary report that displays results for the statistical tests and specifies the calculations needed to confirm if the method is stable enough to be validated.


B

Appendix AStatistical Tests

Elsa32 uses recommended statistical tests to evaluate the method: Student test and confidence limits, Cochran test, Dixon test, and Fisher test.

A.1 Skew Test Student Test and Confidence Limits

The first statistical hypothesis is to work with a normal distribution of the experimental values. The distortion or asymmetry of the distribution is tested by a skew test for n ≥15.

n

Skew = n /[(n–1)(n–2) S3] ∑ (xi – µ)3

1

This factor is compared to a statistical table to qualify the distribution.

A Student test identifies the values outside the confidence limits.

In a normal distribution, the confidence level p describes the probability to find the true value x in defined confidence limits. These limits establish the confidence interval of the mean value µ; they are evaluated with the standard deviation σ.

The following colors flag the cells containing values above or below the statistical limits:

• 1 Sigma (green)

• L 0.95 Sigma (yellow)

• L 0.99 Sigma (red)

These limits are set by a Student test based on α (Alpha) and the standard deviation σ (Sigma). t (α; n – 1) = Student coefficient for n – 1 degrees of freedom.

Statistical Tests 27

B

Figure A-1 Normal Distribution

For an unknown standard deviation calculated from the distribution and S being an estimate of standard deviation: σ = S/ n ½

L 0.95 σ = µ ± t (0.05; n–1) S/ n ½

L 0.99 σ = µ ± t (0.01; n–1) S/ n ½

The 1 σ, 2 σ, and 3 σ values set up the axis units on charts plotting the Retention Time or any other Empower fields. Therefore a quick visual check on these limits verifies the reliability of data.


B

Figure A-2 Retention Time Plot with σ Limits

During the validation plan, replicate measurements are grouped by day or by calibration level, then these values are processed into the statistical test calculation. The number of values determine the degree of freedom for the tests.

The different tests are dependent on:

• α = the risk level (α = 1– p ; 0.05 or 0.01)

• k = the number of groups

• n = the number of points per group

• N = the total number of points, N = n x k

A.2 Cochran Test

The Cochran test is the largest estimate of the variance in a group divided by the sum of the variances of all the groups.


B

C test = max ( Variance ) / sum ( Variance )

The C test depends on α, k, and n C theor. = C (α;k;n–1).

If C test calc < C test theor. ⇒ test is True: the Variances are homogeneous.

If C test calc ≥ C test theor. ⇒ test is False: the Variances are nonhomogeneous.

When the C test is False, the group of the highest variance is classified as suspect and a Dixon test is applied to detect outliers values.

A.3 Dixon Test

The Dixon test is applicable only to small series of samples ( n ≤ 10 ) and depends on a Q(α;n).

This test is activated only when the Cochran test is False to flag outliers.

The Dixon test orders the suspect measurements in ascending values and compares the minimum and maximum values with the median. The farthest value from the median is selected.

Y1 ≤ Y2 ≤ Y3 ≤ ...Ym ≤ ...Yn–1 ≤ Yn

If Ym–Y1 ≥ Yn–Ym : Y1 is selected, Q calc = Y2 – Y1 / Yn – Y1.

If Ym–Y1 < Yn–Ym : Yn is selected, Q calc = Yn – Yn-1 / Yn – Y1.

The Dixon coefficients are compared with the values found in statistical tables for the two confidence levels. There are three possibilities:

• Q calc ≤ Q(0.05;n)

No suspect measurement. Green and * mark the sample for nonhomogeneous variance; the measurement must be kept.

• Q(0.05;n) < Q calc ≤ Q(0.01;n)

Suspect measurement. Yellow and ** mark the sample as suspect; the measurement can be eliminated.

• Q(0.01;n) < Q calc

Erroneous measurement. Red and *** mark the sample as erroneous measurement; the measurement should be eliminated.

If too many outlying values should be eliminated (>10%), it reveals a deviation from a normal distribution. The distribution should be examined carefully.


B

A.4 Fisher Test

The Fisher test (F test) is mainly used for variance comparison in ANOVA calculations.

The null hypothesis S1² = S2² is tested against S1² > S2².

The ratio of two variances is compared with the F(α;ν1;ν2) theoretical value in tables (also obtained by the FINV(α;ν1;ν2) function in Excel).

F test = S1² / S2²

ν1 = n1 and ν2 = n2 are the degrees of freedom of variances S1² and S2² with S1² > S2².

If F test ≤ F(α;ν1;ν2), the null hypothesis is accepted, the difference is not significant, and the two variances are coherent.

This test is performed to control the least-square regression (slope significantly ≠ 0 and validity of the linearity adjustments). It is frequently applied to compare the method errors (calculation) with the experimental errors.

This is the comparison of the variance of method Sc² with the experimental variance Se²:

• Sc²: Intergroup variance between the groups: k–1 degrees of freedom

• Se²: Intragroup variance within the groups: N–k degrees of freedom

• By evaluating with a Fisher test the ratio Intergroup variances / Intragroup variances

These variances are calculated from the n measurements in the k groups using: the total sum of squares of ( x –X ). X = general mean of the x values in all the groups.

The total sum of squares:

k n

∑ T² = ∑ ∑ ( xij – X )²

j=1 i=1

(N –1 degrees of freedom)

Total Variance: St² = ∑ T² / ( N – 1 )

Intragroup sum of squares of ( x – Xj ): Xj = mean of the x values in each group.

k n

∑ E² = ∑ [ ∑ ( xij – Xj )² ]

j=1 i=1

(N – k degrees of freedom)


B

Intragroup variance: Se² = ∑ E²/ ( N –k )

Intergroup sum of squares: ∑ C² = ∑ T² – ∑ E²

(k – 1 degrees of freedom)

Intergroup variance: Sc² = ∑ C² / ( k –1 )

The ratio F = Sc² / Se² with ν1 = k–1 and ν2 = N – k evaluates the validity of the variance calculations.

F test ≤ F(α;ν1;ν2) ⇒ F test is True: the difference is not significant, the two variances are coherent.

The experimental errors and the errors relative to the method are similar.


B

Appendix BParameter Calculations

B.1 Linearity Calculations

B.1.1 System Response

The purpose of method validation is to evaluate the system response as a function of the true concentration or the injected amount. This function must describe both precisely and accurately the system response. This function is designed to be as simple as possible (linear).

The linear model is estimated by the least-squares curve of the best-fit procedure. The linear regression of the sample (Pharmaceutical form) and the standard (Active form) are calculated, then the slopes and intercepts are compared.

The response function deduced from the standard must be valid for the sample.

B.1.2 Regression Calculations

The method of the least-squares for determining slope b and intercept a are based on the following formulas:

slope b = [N ( ∑ xy ) – ( ∑ x ) ( ∑ y )] / [N ( ∑ (x²)) – ( ∑ x )²]

intercept a = [( ∑ y ) ( ∑ (x²)) – ( ∑ x ) ( ∑ xy )] / [N( ∑ (x²)) – ( ∑ x )²]

The Regression function also provides:

• The standard error on the slope and intercept

• The determination coefficient r²

• The standard error for the y estimate

• SSreg: the regression sum of squares and SI² the variance of the regression:

SI² = SSreg / 1 (1 degree of freedom)

• SSres: the residual sum of squares and the variance:

SR² = SSres / (N-2) (N-2 degrees of freedom)

Parameter Calculations 33

B

A variable change is applied for the ANOVA calculations:

Y = y + b (X – x)

X is the mean value of x in each group and Y = bX + a ⇒ (Y – y) = b (X – x).

Statistical tests are applied on the Y variable.

B.1.3 Slope Existence

The intragroup sum of squares and experimental errors can be calculated from the sum of variances on Y in each group (n – 1 degrees of freedom).

∑ E² = ( n–1 ) ∑ ( Var(Y) ) ⇒ Se² = ∑ E² / ( N –k ) (N – k degrees of freedom)

The intergroup sum of squares and linear regression errors are deduced:

∑ L² = ∑ T² – ∑ E² ⇒ SL² = ∑ L² / ( k –2 ) (k – 2 degrees of freedom)

The first Fisher test observes whether a relationship between x and y exist. It must be highly significant.

F1 test = SI² / SR² > F (α;1;N–2) ⇒ the slope is significantly < >0

B.1.4 Linearity Adjustment

The second Fisher test compares the linear fitting errors with the experimental errors. To accept the linearity model (good adjustments) it must be nonsignificant:

F2 test = SL² / Se² < F (α;k-2;N-k) ⇒ the system response is linear

This ratio must be close to 1. If this ratio is too small (<0.1), the test is passed, but the experimental errors are too important compared to the regression errors. The experimental method must be checked carefully.

B.1.5 Slope and Intercept Comparison

To quantitate the sample (pharmaceutical form) with the standard (active form) linear curve, the two response functions must be equivalent, the two slopes and the two intercepts have to be comparable.

| b1–b2 | / ( S²b1 + S²b2 )½ < t (α;2N–4) ⇒ the two slopes are comparable

| a1–a2 | / ( S²a1 + S²a2 )½ < t (α;2N–4) ⇒ the two intercepts are comparable

If the two curves differ, it means the sample matrix (excipients and/or impurities) influences the result. The method should then be modified.


B

B.1.6 Intercept Through Zero Test

A Student test verifies if the curve is linear through zero. The absolute value of the intercept is compared to its standard error Sa:

| a | / Sa < t (α;N–2) ⇒ the intercept is through 0

If the two calibration curves are comparable and the intercepts are through zero, then the 100% standard (active form) can be used to quantitate the sample (proportionality calculation).

When the test is false and zero is outside the confidence limits, there is a bias which reveals a systematic error. The linear calibration curve of the standard (active form) should be considered as the reference.

B.1.7 Weighting Factors

For weighted least-square regression, select the desired weighting factor from the list:

(X, 1/X, X², 1/ X², Y, 1/Y, Y², 1/ Y², Ln(X), 1/Ln(X), Log(X), 1/Log(X), K*exp(X), 1/K*exp(X), S², 1/ S²)

The least-square regression minimizes the sum of squares of residuals:

Residual = wi = Observed value – Calculated value

For every factor, the weights are normalized so that the sum of the weights is equal to the number of data points N:

W’i = N.wi/ sum (wi)

Weighted least-square regression tries to correct the influence of the concentration on residuals, particularly at the higher levels. The plotting of residuals against concentration facilitates the choice of the weighting factor.


B

Figure B-1 %Residuals Chart

The Relative % Residuals of Concentration:

% Residuals = 100 [predicted concentration – true concentration] / true concentration

X0 concentration measurement and Xc the predicted value:

% RRC = 100 [ (Xc–X0) /X0 ] = 100 [ Xc / X0 –1 ]

For each concentration level, this graph visualizes the % Residual. In most cases it is reasonable to expect these percentages will be constant.

The comparison of the standard deviation on % Residuals calculated for each linear curve is a good criterion to determine the curve showing the best linearity fitting. The lower the % Residual standard deviation, the better the fitting. When the % Residuals show values as a function of the concentration, a weighted least-squares procedure could be applied to reduce the highest concentration influences.

%Residuals

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

0 100 200 300 400 500 600 700

Amount_0

Per

cent

%

%Residuals(X1)

%Residuals(X2)


B

B.2 Accuracy

Every analytical method carried out under identical conditions gives nonidentical results; it exhibits random and systematic errors. The purpose of the Accuracy study is to evaluate how far the result is from the theoretical value.

The accuracy calculations use the same set of data previously processed for Linearity. They compute the Accuracy over the Linearity Range. These calculations are based on the % Recovery determination.

B.2.1 % Recovery

The % Recovery is evaluated for each point of the sample calibration curve (Pharm. form). It is the relative percentage of recovery of sample amount.

The ratio Calculated Amount / True Amount can be calculated with the 100% standard or with the whole calibration curve.

The predicted amount or concentration X calc. is calculated from the 100% Standard point (Active form):

b = Y100 / X100

X calc = Y / b = Y [ X100 / Y100 ]

or from the equation of the whole standard calibration curve (Active form):

Y = b X + a

X calc = (Y – a ) / b

% Recovery: r = 100 [ X calc / X true ] %

Each ratio is evaluated with the response factor b or the calibration curve Y = b X + a generated on the same day.

These factors or these curves are calculated from the standard (Active form) linearity calibration points on day1, day2, day3, …, dayn.

The n replicated calibration levels are grouped by level: k groups.

N = k x n


B

B.2.2 Average % Recovery

After the verification of variance homogeneity by the Cochran test, the average validity of the % Recovery is tested by a Fisher test.

C test = max ( Var. ) / sum ( Var. ) < C(α;k;N–1)

The test on the validity of the % Recovery average is estimated with a Fisher test using the ratio:

F test = Sc²/ Se²

Sc² is the intergroup variance over Se² in the intragroup variance.

The two variances, Sc² (method error) and Se² (experimental error) are coherent if the null hypothesis H0: ScC = Se² compares to Sc² > Se² and is accepted with a certain predetermined risk α.

F test is not significant:

F test < F (α;k-1;N–k) ⇒ Sc² = Se² F test is True.

F test is significant:

F test > F (α;k-1;N–k) ⇒ Sc² > Se² F test is False

When the F test is false, the difference between the two variances is statistically significant, Sc² differs from Se² the method errors are too important

When the F test is true and Sc²/ Se² <0.1, the experimental errors (day-to-day changes) are very important. The individual calibration curves should be checked.

The average % Recovery R is calculated by taking into account all the N calibration points of the sample (Pharm. form). Its confidence limit L is estimated from the total standard deviation and the student coefficient.

St² = total variance

R = 1 / N ∑ r

L = R ± t (α; N–1) St/ (N) ½

The “target” value of the average % Recovery R is 100%. This ideal value would be reached if mean X calc = mean X true.

The random and systematic variations displace the average % Recovery and its confidence limits.


B

The accuracy of the method is verified if the “target” value 100% falls into the confidence limits.

L– ≤ 100% ≤ L+

When the 100% point is outside the confidence interval, then the method is not accurate; it is biased by systematic errors. These systematic errors are sometimes produced by a recognized effect (matrix effect), which then can be corrected by an estimated correction factor.

The Accuracy chart shows the % Recovery of all calibration points. It can confirm the presence of possible outliers.

B.3 Precision

B.3.1 Repeatability and Intermediate Precision

Repeatability expresses the closeness of agreement between successive measurements of the same amount carried out under identical experimental conditions.

Intermediate precision expresses the closeness of agreement between successive measurements of the same amount carried out under changed experimental conditions.

These two parameters reflect the dispersion characteristics of measurements resulting from random effects.

The repeatability represents the minimum variability. The intermediate precision represents the maximum variability of the results.

The precision is evaluated by independent assays on homogenous sample (Pharm. form). Each initial amount is weighed separately and should correspond to the 100% amount of the linearity calibration curve.

B.3.2 % Recovery

The repeatability assays must be carried out on the same day as the linearity analysis. In fact, the % Recovery, which is used for evaluating repeatability and intermediate precision, is calculated from the linearity standard (Active form) calibration curve generated each day (see Section B.2, Accuracy).

The 100% point of the calibration can be included as an extra point into the repetitive assays for precision:

% Recovery: r = 100 [ X cal. / X true ] %


B

B.3.3 Average % Recovery

The verification of variance homogeneity is performed by the Cochran test is:

C test = max (Var.) / sum (Var.) < C (α;k;N–1)

If the C test is false, a Dixon test flags the outlier.

Average % Recovery: R = 1 / N ∑ r

The formulas used in the repeatability and intermediate precision calculation allow deleting the outlier and recalculating the new values.

B.3.4 Repeatability and Intermediate Precision Variances

The repeatability and intermediate precision variances are calculated on the % Recovery to determine the % RSD. They are also computed on the response (area, height, ...) for a complete estimation of the coherence of the linearity and precision measurements.

Variance Definitions:

Sj² variance of the j group k

The repeatability variance is Sr² = 1 / k ∑ Sj² (N – k degrees of freedom)

Sr² is the intragroup variance.

k

Sg² is the intergroup variance: Sg² = 1 / (k–1) ∑ ( mj – µ ) ² – Sr² / n

1

k

mj : mean of j group µ : mean of all the groups µ = 1/k ∑ ( mj )

1

The intermediate precision variance SR² = Sr² + Sg² (k – 1 degrees of freedom) regarding the previous formula : SR² ≥ Sr².

The formula to calculate Sg² authorizes a different number of points per group nj which permits the elimination of outlier values. Several intermediate variables are computed [3]:

kk

T_1 = ∑ ( nj.mj ) T_2 = ∑ ( nj.mj² )

ll

kk


B

T_3 = ∑ ( nj ) T_4 = ∑ ( nj² )

ll

k

T_5 = ∑ [( nj–1 ) . Sj² )]

l

Sr² = T_5/ ( T_3 – k ) SR² = Sr² + Sg²

Sg² = [[[(T_2.T_3 – (T_1)²] / T_3.(k–1)] – Sr²] . [T_3.(k–1)/[(T_3)²–T_4]]

Average % Recovery m = T_1 / T_3

B.3.5 Repeatability and Intermediate Precision Calculation from Response

In this case, the specific formulas used [3] are valid when all groups have the same number of points per group n.

kkk

T_1 = ∑ ( mj ) T_2 = ∑ ( mj² ) T_3 = ∑ Sj²

lll

Sr² = T_3 / k SR² = Sr² + Sg²

Sg² = [[(k.T_2)–(T_1)²] / k.(k–1)] – Sr² / n

Average Response m = T_1 / k

The confidence limits of the average % Recovery are calculated for:

Repeatability L = R ± t (α; N–1) Sr/ (N) ½

Intermediate Precision L = R ± t (α; N–1) SR/ (N) ½

B.3.6 Repeatability and Intermediate Precision % RSD Calculation

% RSD values are deduced from the previous variance results calculated on % Recovery:

Repeatability % RSD = 100. ( Sr / µ )

Intermediate precision % RSD = 100. ( SR / µ )

Because the % Recovery reduces the variations related to the initial response, the % RSD of the response (area) is generally greater than the % RSD calculated from % Recovery.

The ratio % RSD (% recovery) / % RSD (response) ≤ 1


B

B.3.7 Intermediate Precision and Repeatability Variances Comparison

Normally, the intermediate precision variance is greater or equal to the repeatability variance.

SR² = Sr² + Sg²

The variance ratio Intermediate precision / repeatability is evaluated by a Fisher test to check if it is statistically significant.

Intermediate precision Var. (k –1) degrees of freedom

Repeatability Var. (N – k) degrees of freedom

F test = Intermediate precision Var. / repeatability Var. < F (α;k–1;N–k) test true the variances are not different.

If the ratio I. Prec / repe is statistically significant F test ≥ F (α;k–1;N–k), then the time factor influences on the precision of the method.

As intermediate precision analysis are performed on different days, some variable factors (instrument calibration, reagents, moisture, etc.) influence the method from one day to another.

B.3.8 Intermediate Precision and Linearity Variances Comparison

The experimental plan necessitates the analyst to do the precision analysis following the linearity on the same day. In this way the two set of measurements must have coherent variances.

To verify the variance coherence, an F test is applied:

F test = SR²/ Se²

The Linearity Variance is the experimental variance Se² on the response:

Se² is calculated from the Linearity 1 study (Nl – kl degrees of freedom).

The Intermediate precision Variance on the response is SR² (kr –1 degrees of freedom).

As it is impossible to determine which variance will be greater, two F limits values are calculated:

if SR²/ Se² > 1, then F test > F(α;kr–1;Nl–kl)

if SR²/ Se² < 1, then F test < F(1–α;kr–1;Nl–kl)

as F (1–α;kr–1;Nl–kl) = 1 / F (α;Nl–kl;kr–1)


B

If the difference is not significant, then the two variances are coherent when:

1 / F (α;Nl-kl;kr–1) < F test < F (α;kr–1;Nl–kl) F test is true.

If the difference between the two variances is statistically significant, the two sets of measurements must be carefully investigated to determine the source of the highest variance.

B.4 Limit of Detection and Limit of Quantitation

In the USP directions, the method to analyze the product and its impurities includes quantitative assays and limit tests.

There are different ways to define these limits related to their evaluation approach.

The instrumental or experimental approach relies on one or more blank analysis. The noninstrumental or calculated approach is based on statistical calculations deduced from known concentration analysis (linearity study).

B.4.1 Experimental Determination Based on Signal to Noise

A visual evaluation of the maximum height of the baseline noise or the baseline noise evaluation in Empower System Suitability:

The LOD is determined from a blank analysis using the response factor R ( amount / height ) and the max Height

LOD = 3. max Height . R

LOQ = 10. max Height . R

This approach can easily be influenced by potential interferences and the background subtraction is often very critical (auto-zero).

Only a statistical determination gets rid of potential interferences.

B.4.2 Statistical Determination on Standard Deviation of the Response

This approach is the appropriate description of the detection and quantitation limit expected for the component based on the response function (calibration curve).

The y value of the response measurement corresponding to the LOD or LO,

y = Yblank + 3.3 Sblank

y = Yblank + 10 Sblank


B

can be evaluated based on the standard deviation of 6 Blanks or from the regression equation. The intercept of the curve is the best estimate of the blank signal, and the standard error of the y value Sxy is the best estimate of the blank standard deviation.

Yblank = a and Sblank = Sxy

LOD: y = a + bx = Yblank + 3.3 Sblank

LOQ: y = a + bx = Yblank + 10 Sblank

The x values corresponding to LOD or LOQ are:

LOD = 3.3 Sxy / b LOQ = 10 Sxy / b

S²xy can be estimated by the residual variance of the regression calculated from the residual sum of squares:

S²xy = SSres /(N–2) (N–2 degrees of freedom)

Sxy = [SSres /(N–2)]½

(Refer to Linearity and reference for Sxy estimation.

This Limit of Quantitation must be validated by the analysis of a suitable number of samples (≥6) prepared at the limit of quantitation.

The precision and accuracy of the method near the limit of quantitation is controlled by the % Recovery.

The precision is verified by the % RSD calculation, the average % recovery, and its confidence interval specifies whether or not the method is accurate.

Charts display the linear curve and % recovery at the LOQ.

If the precision and accuracy are not acceptable, the LOQ must be increased (double) and the accuracy and precision must be checked again.

B.5 Sensitivity

The sensitivity concept is a specific validation criteria required by the EEC Analytical Validation.

The sensitivity is the ability of the method to correlate a significant variation in the system response to a small concentration change.

The discriminator capacity is the minimum Concentration or Amount difference that can be recorded by the function response for a given probability.


B

B.5.1 Sensitivity Calculation

In the case of a linear response, the sensitivity is obtained by dividing the slope of the calibration curve by the standard deviation of the response at the considered concentration level.

Sensitivity = b /Se

Sensitivity at the Limit of Quantitation: Se² = Sr²

Sr² is the repeatability variance on the % recovery from r replicates, and b is the slope of the calibration curve at LOQ ⇒ Sensitivity = b /Sr

Sensitivity at the 100% concentration level

At the 100% concentration level of the sample (Pharm. form), Se² = Sr².

Sr² is the repeatability variance on the response (area or height) from ANOVA of Precision, and b is the slope of the calibration curve of the sample (Pharm. form) at 100% ⇒ Sensitivity = b /Sr.

B.5.2 Discriminator Capacity

The discriminator capacity ∆x is the inverse of the sensitivity multiplied by a statistical coefficient calculated from the formula published by the SFSTP. The discriminator capacity is:

At the LOQ ∆x = [ t (α;r–1) + t (2α;r–1) ].√2 .Sr/ b (r – 1 degrees of freedom)

At the 100% ∆x = [ t (α;N–k) + t (2α;N–k) ].√2 .Sr/ b (N – k degrees of freedom)

These values of the discriminator capacity have the same units as Concentration or Amount and can be also expressed in percentage of the considered Concentration or Amount.

B.6 Robustness

The aim of the robustness study is to verify that under realistic limits, variations of each method parameter do not alter the validity of results. The controlled variations in experimental conditions are those which could occur when the operators, instruments, or laboratories are changed.

Each critical factor is varied around the normal value; the upper and lower parameter values are likely to be encountered in different laboratories with different operators.


B

The use of a factorial plan to register the simultaneous variations of several critical factors considerably reduces the number of assays necessary to evaluate the possible influences of each critical factor.

In Empower, a custom field is created for each factor. A Result view filter is used to select the results and to order them in ascending order so that they automatically fit the matrix of the experimental plan.

When the Amount is included in the list of tested critical factors, if the linearity has been processed, the Amount effect is automatically suppressed by the computation of the corrected response (Area).

B.6.1 Determination of Significant Factors

The influence of each parameter is evaluated using the mean effect that is compared with the confidence interval.

The value at the bottom of each parameter column is the mean effect of the parameter. It is the sum of the analytical response (area, height, etc.) multiplied by the coefficient +1 or –1 (Min or Max) and averaged by the number of assays (8 or 16). In the example in Figure 2-9:

A parameter effect = Sum (-Y1-Y2-Y3….+Y14+Y15+Y16) / 16 = 17778.38

B parameter effect = Sum (-Y1-Y2-Y3….+Y14+Y15+Y16) / 16 = -136112

C parameter effect = Sum (-Y1-Y2+Y3….-Y14+Y15+Y16) / 16 = 15484.34

The Confidence Limits = ± t Sy/ (N) ½ = ±77482.14

t: Student coefficient =

t (α,N–1) = 2.131451

Sy: Standard deviation = 145407.3

N: Number of assays = 16

α: risk level = 0.05

If the mean effect falls outside the confidence limits, the parameter has a significant influence on the results.

In the example: B parameter effect (%Solvent): | –136112 | > 77482.14

| %Solvent effect | > | Confidence Interval | ⇒ %Solvent influences the results.

Every parameter or interaction which significantly influences the result is flagged by " * S * " and red.


B

A Fisher test based on variance comparison confirms the Student test.

A factor influences the results significantly if its variance is higher than the average of the variances.

Significance of the j factor can be represented simply by the Rf ratio as a percentage of the total variance:

Rf = Sj² / ∑ Sj

² = Sj² / ∑ Τ² > F(α;1;Ν−1) / ( Ν−1 )

The Fisher test flags the significant factor with " * S * " and red.

Based on the additive property of factor variance, the effect of the significant factor can be subtracted.

Next, the same iterative process (Fisher test) can reveal secondary factors that influence the results at a lower variation level. While the Fisher test is significant, the secondary factors are colored in yellow. When it is no longer significant, the iterative process stops and the flag color is green.

The robustness errors can also be expressed in terms of robustness variation coefficients:

CVo% = 100.Sy/Ymean

(CVo% = 2.25 %). Thus, it can be compared with the coefficient of variation of repeatability CVr% calculated during the method validation process.

Thus, take into account only the x first secondary factors which are related to the repeatability coefficient of variation of the method, verifying:

CV-x% / CVr% > ( F(α;Ν−1;Νr))½

or more simply,

CV-x% > 1.5 CVr%

The optimize function based on the coefficient of variation determines the acceptable factor limits to achieve operation within a robust method.


Bibliography and References 48

C

Appendix CBibliography and References

ICH Harmonised Tripartite Guideline: Validation of Analytical Methods: Methodology, ICH Topic Q2B - Pharm. Ind. 59, Nr. 2 1997, 107-110

ICH Harmonised Tripartite Guideline: Validation of Analytical Methods: Definition and Terminology, ICH Topic Q2A

J. Caporal-Gautier, J.M. Nivet, P. Algranti, M. Guilloteau, M. Histe, M. Lallier, J.J N'Guyen-Huu et R.Russotto - Guide de validation analytique Rapport d'une commission SFSTP 1. Methodologie. - S.T.P. Pharma Pratiques 2 (4) 205-226 1992

USP XXII (1990, pp. 1710-1712)

Miller JC and Miller JN (1989). Statistics for Analytical Chemistry. Ellis Horwood, Chichester, 2nd ed.

J. Fleury – Introduction à l’usage des méthodes statistiques en pharmacie. – Edition Médecine et Hygiène, Genève, 1987.

EEC Documentation: Analytical Method Validation (ref. III/847/87)

J.L. Virlichie and A. Ayache - A ruggedness test model and its application for HPLC method validation. - STP Pharm. Prat., 5 (1995) 49-60

Index

Symbols% Recovery

accuracy 37precision 39

AAccuracy calculations

% recovery 37average % recovery 38

Accuracy parameterscalculations 37–39overview 13setup 17

Average % recoveryaccuracy 38precision 40

CCalculations

overview 24parameters 33–47statistical tests 27–32

Cochran test 29Confidence limits 27Configuring software 15Conventions, documentation 9

DData access security 15Determination of significant factors 46Discriminator capacity 45Dixon test 30Documentation

conventions 9related 6

EElsa32 Wizard

accuracy 17linearity 16LOQ 19precision 18robustness 20setting parameters 15software configuration 15

Experimental analysis, overview 14Experimental determination based on signal

to noise 43Experimental plan

accuracy 13linearity 12overview 12precision 13

FFisher test 31

IIntercept through zero test 35Intermediate precision and linearity

variances comparison 42

LLimit of Detection (LOD) parameters

calculations 43–44overview 13

Limit of Quantitation (LOQ) parameterscalculations 43–44overview 14setup 19

Linearity adjustment 34

Index 49

Linearity calculationsintercept through zero 35linearity adjustment 34regression 33slope and intercept comparison 34slope existence 34system response 33weighting factors 35

Linearity parameterscalculations 33–36overview 12setup 16

LOD and LOQ calculationsexperimental determination based on

signal to noise 43statistical determination on standard

deviation of the response 43

MMethod validation

calculations 24reports 26worksheets 24

Method validation parameterscalculations 33–47overview 12–14setup 15

Method validation protocol, overview 11

OOverview

experimental analysis 14experimental plan 12method validation parameters 12–14method validation protocol 11repeatability and intermediate precision

39software design 11

I

PPrecision calculations

% recovery 39average % recovery 40intermediate precision and linearity

variances comparison 42repeatability and intermediate precision

% RSD 41repeatability and intermediate precision

from response 41repeatability and intermediate precision

variances 40repeatability and intermediate precision

variances comparison 42Precision parameters

calculations 39–43overview 13setup 18

RRegression calculations 33Related documentation 6Repeatability and intermediate precision

calculations% RSD 41from response 41variances 40variances comparison 42

Repeatability and intermediate precision, overview 39

Reports, method validation 26Robustness calculations,determination of

significant factors 46Robustness parameters

calculations 45overview 14setup 20

Index 50

SSecurity, data 15Sensitivity calculations, discriminator

capacity 45Sensitivity parameters

calculations 44–45overview 14

Setting parametersaccuracy 17linearity 16LOQ 19precision 18robustness 20

Skew test 27Slope and intercept comparison 34Slope existence calculations 34Software design 11Statistical determination on standard

deviation of the response 43Statistical tests

Cochran 29confidence limits 27Dixon 30Fisher 31Skew 27Student 27

Student test 27system response evaluation 33

VViewing

Empower results 22validation workbook 21

WWeighting factors 35

I

Index 51

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