Usp chemical medicines & excipients - evolution of validation practices

Track I, Session I: Chemical

Medicines and Excipients-Evolution

of Validation Practices Wednesday, April 17, 2013 (9:00 a.m. to 11:00 a.m.)

IPC–USP Science & Standards Symposium

Partnering Globally for 21st Century Medicines

Moderator: Milind Joshi, Ph.D. Chair, USP South Asia Stakeholder Forum

Acceptable Analytical Method Variation

Setting System Suitability

Requirements

Todd L. Cecil, Ph.D.

Vice President, Chemical Medicines

USP

Sources

– Instrument Characteristics

– Sample Characteristics

– Method Parameters

– Environmental Affects

– Analyst

– Instrument Settings

– Many others

Method Variation

Systematic Error

– Determinate Error

– Discoverable source

(in theory)

Estimated using Accuracy

Measuring Variation

Random Error

– Indeterminate Error

– Experimental error

Estimated using Precision

Developed in late 1980’s for the Pharmaceutical

Industry

– PhRMA -> USP <1225> -> ICH Q2A -> USP <1225>

Defined “Analytical Performance Characteristics”

– Accuracy

– Precision

– Specificity

– Detection Limit

– Quantification Limit

– Linearity

– Range

Validation to Measure Variability

Trueness

Bias

Intermediate precision

Repeatability

Reproducibility

Ruggedness

Robustness

Depends upon two factors

Acceptable Variability

Expectation

• Analyst Experience

• Instrument knowledge

• Matrix complexity

Application

Test

Procedure

Acceptance criteria

ICH and USP do not describe acceptable limits

Therefore, Acceptable Validation/ Variation is

open to interpretation by:

– Bench Chemist

– Supervisory Chemist

– Regulatory Affairs Professional

– The Regulator

– The Pharmacopeial Professional

– And fights ensue…

Acceptable Validation

Recent publications

– Pharma’s Analytical Target Profile (ATP)

– USP’s Performance-Based Procedures

Upcoming publications

– USP Validation and Verification Expert Panel

– USP Statistics Expert Committee

– USP “Requirements for Compendial Validation

<1200>” (working title)

There is Another Way!

Critical Validation Parameters

– What are the critical features (parameters) of

an acceptable procedure?

Procedure Performance Measures

– How do we measure the critical parameters?

Procedure Performance Acceptance Criteria

– What defines “good enough” for each

performance measure?

Defining Another Way Forward

Measuring the Parameters

Precision – % RSD with sufficient degrees of freedom

Accuracy – Spike Recovery or Comparison to Primary

Standard

Specificity – Resolution or Spike Recovery

Linearity – Slope, Intercept, R2

Range – Precision and accuracy

Limit of Detection – Precision

Limit of Quantification – Precision

Collapsing the Parameters

Precision – Measure of Random Error

Accuracy – Measure of Systematic Error

Specificity – Measure of Systematic Error

Linearity – Measure of Systematic Error

Range – ?

Limit of Detection – Measure of Random Error

Limit of Quantification – Measure of Random Error

Why are we measuring things so many different ways?

Does agreement mean quality? or are we hiding behind tradition?

IF we combine critical components can we gain efficiency?

Extract from <1200>

Category I* Category II*

(Quantitative)

Category II* (Semi-

quantitative)

Analytical

Performance

Characteristics

<1225> <1200> <1225> <1200> <1225> <1200>

Accuracy Y 1 Y 4 ? N

Precision Y 1 Y 5 N 5

Specificity Y 2 Y 2 Y 2

Detection Limit N N N N Y 6

Quantification

Limit N N Y 4 N N

Linearity Y 3 Y 4 N N

Range Y 3 Y 4 ? N 1 Covered in the Precision-Accuracy Study 2 Covered in the Specificity Study 3 Covered in the Range Study 4 Covered in Accuracy Study 5 Covered in Precision Study 6 Covered in the Detectability Study

Precision and Accuracy Study

When properly combined Precision and Accuracy yield a

probability of passing.

Bias-%CV Tradefoff, 98%-102% limits, True Value = 100, Prob'y Passing 0.95

0

0.2

0.4

0.6

0.8

1

1.2

2.001.801.601.401.201.000.800.600.400.200.00

Bias

%C

V

Precision

– % RSD of 6 independent samples at 100%

Accuracy

– Δ from RS label at 100%

– The data obtained for Precision can be used for

Accuracy

Combine with Acceptance Criteria to calculate

probability

Result = NORMDIST(Upper, Mean, SD, TRUE)

-NORMDIST (Lower, Mean, SD, TRUE)

Limit: NLT 0.95

Study Detail

Specificity is a special case of Accuracy.

Interferences considered

Separation Sciences

– Resolution of NLT 1.5

Specificity Study

Non-Chromatographic Procedures are harder

Spiked samples with interferences

Measure the error caused by the addition of an

interference

Limit is linked to the acceptance criteria of the

analyte

– The error caused by all interferences cannot

exceed the allowable bias from the Precision-

Accuracy Study

Specificity Study

Retasked Range

Precision-Accuracy evaluation at 80%, 90%,

100%, 110%, and 120%

Instead of Mean in the calculation, use recovery

value

Recovery Value = [Mean]/[Known]*100%

Limit: Each concentration is NLT 0.95

Range Study

Response vs Concentration

Calibration curve

Technique dependent application

Calculated vs Known Concentration

Slope =1

Intercept =0

Accuracy evaluation

How do you measure linearity?

Slope: not correlated to error

Intercept: not correlated to error

R2: limited correlation to error

Linearity

Linearity

Slope and Intercept

– Overwhelmed by random noise

– Not correlatable to systematic noise

– Adds no additional information

Basis for Linearity is not supported

Range adequate

Limit of Detection

Only applies to “Limit” procedures

S/N of 3

– independent samples at LOD

– “adequate precision and accuracy”

What is adequate?

What is the purpose of the test?

Limit Test

Measure a Standard solution of the impurity at the limit

Measure a Sample solution

– Is the response of the impurity in the Sample < Standard

– Pass

– Is the response of the impurity in the Sample ≥ Standard

– Fail

Is the Δ between pass/fail adequate?

If the limit is 0.1%, then acceptable values are

– 0.14% to 0.05%

– LOD does not assure the measurement

–Detectability does.

Detectability

A new term included in <1200>

Replaces LOD

3 steps

–1: Standard of impurity at limit

–2: Sample spiked with impurity at limit

–3: Standard spiked with impurity at 100%-%RSD* for the impurity

–*can be estimated with Horwitz

If 1=2 and 3<2, then the difference is detectable

Otherwise, procedure is not adequate

What is Horwitz?

Limit of Quantitation

Why do we make this measurement?

–10x S/N . . .

A meaningful quantity in development?

–Yes

Necessary to validate?

–No

Validation presumes

–A known procedure

–A typical value for the analyte

–Known acceptance criteria

You already know the typical range of the analyte…

Use the Range Study

–80%-120% for Assay; 50%-150% for impurities

Summary

USP is challenging validation concepts

Including DOE and QbD through the ATP

Include measurable parameters and clear criteria

Focus on Precision and Accuracy results

Use Specificity to aid understanding of Accuracy

Retask Range

Introduce detectabiltiy

Eliminate LOD, LOQ and Linearity

But Wait, There’s More…

Setting System Suitability Requirements

Validation is measured only once

System suitability is measured on a daily basis

– Traditionally uses instrument dependent measurements

– Resolution

– Tailing

– %RSD

System suitability rarely linked to variance

Use Validation protocol to evaluate Precision and Accuracy

across the days run.

System suitability can then be linked to validation

Specificity should represent necessary minimums, but should

exceed criteria of validation

Precision, Accuracy & Linearity

Harry Yang, Ph.D.

Member, USP Statistics Expert Committee

Validation is a snapshot (at any given time) of

the assay’s performance.

It is confirmation that the assay is fit for its

intended use

Required by regulatory guidelines

Method Validation

History of ICH Guidelines on Method Validation

Other Related Guidelines

Accuracy

Precision

Repeatability


Specificity

Limit of detection

Limit of quantitation

Linearity

Range

Common Validation Characteristics

Accuracy

Precision

Repeatability


Specificity

Limit of detection

Limit of quantitation

Linearity

Range

Common Validation Characteristics

Closeness between a series of measurements

obtained from multiple sampling of the same

homogeneous sample

Precision

Repeatability: intra-assay precision. Usually

same day, operator, equipment

Intermediate Precision: same laboratory but

different operators, equipment, etc.

Reproducibility: precision between laboratories

Expressed as standard deviation (SD) or relative

standard deviation (RSD)

Precision

X

SDRSD

n

XX

SDn

X

X

n

i

i

n

i

n

,

1

)(

, 1

2

1

The closeness of agreement between the value

which is accepted either as a conventional true

value or an accepted reference value and the

value found (ICH Q2(R1))

Accuracy

]var[)(])[( 22 XXE TXT

True value Mean measurement

Precision Bias

Closeness of agreement between the average

value obtained from a large series of test results

and an accepted reference value

Bias

TXBias

µT µX

Accuracy = bias + precision

Validation of Accuracy and Precision

Model

or

or

An Example

Test result Bias Intermediate

precision

Repeatability

Burdick, LeBlond, Sandell, Yang, 2013

TX TX

An Example

Test result Bias Intermediate

precision

Repeatability


TX TX

Test the hypothesis that bias = 0

H0: µ = 0 vs. H1: µ ≠ 0

Reject H0 if (which is the same as p-value < 0.05)

where

Assessment of Bias: Traditional Approach

025.0,1/

ntns

X

025.0,1nt - cutpoint of t-distribution

025.0,12 /

ntns

Y

,1

)(

, 1

2

21

n

YY

sn

Y

Y

n

i

i

n

i

n

P-value < 0.05 is equivalent to that the 95%

confidence interval contains zero, i.e.

Assessment of Bias: Traditional Approach

p-value < 0.05

p-value ≥ 0.05

nstYnstY nn /,/0 025.0,1025.0,1

Penalize more precise assay

Award small sample size

Issue with the Traditional Approach

Huberta et al, 2004

With of the 90% CI is

proportional to assay

precision (s) and

reciprocal

of the squared root of

sample size n.

nstYnstY nn /,/0 025.0,1025.0,1

Bias is deemed acceptable if the 90%

confidence interval of bias is bounded by pre-

specified acceptance limits (e.g., ±15%)

Equivalence Method

Huberta et al, 2004

nstYnstY nn /,/ 025.0,1025.0,1

Comparison Between Significance and Equivalence

Is bias acceptable?

Significance Equivalence

Yes

Yes

Yes

No

Yes

Yes

No

No

Bias is deemed acceptable if the 90%

confidence interval at each concentration level is

contained with in pre-specified range (LAL, UAL)

Equivalence Method

0

UAL

a b c

LAL

0

UAL

a b c

LAL

Plot of Bias vs. True Value

True value

Accessing Conformance to Acceptance Criteria: Precision

Intermediate precision is considered acceptable

if the 95% confidence interval is bounded by a

pre-selected number UAL

< UAL


Total Error Approach

Bias cannot be assessed independent of

precision

Huberta et al, 2004; Hoffman & Kringle, 2007

Measured value = True value + Method Bias + Method error


Test result True value Y = -

Accuracy of a method is acceptable if it is very

likely that the difference between every

measurement of a sample and the true value is

inside pre-chosen acceptance limits


Huberta et al, 2004


Risk = 1 - Probability of meeting acceptance criterion

Huberta et al, 2004

Beta-expectation tolerance interval (Huberta et al, 2004)

With 100β% confidence that bias of a future measurement is bounded by λ

Average (expected) probability for bias of a future observation is no smaller than 100β%

Beta-content tolerance interval (Hoffman & Kringle, 2007)

With 100γ% confidence that bias of 100β% future measurements is bounded by λ

Bayesian analysis (Burdick, LeBlond, Sandell, Yang, 2013)

Conditional on validation data, probability for bias of a future observation is no smaller than 100β%

Methods for Testing H0:

.)|( dataXYP T

Accuracy Profile

Huberta et al, 2004

1 Graybill FA, Wang CM. Confidence intervals on nonnegative linear combinations of variances. J Am Stat Assoc. 1980;75:869–

873.

2. Nijhuis MB, Van den Heuvel ER. Closed-form confidence intervals on measures of precision for an interlaboratory study. J

Biopharmaceutical Stat. 2007;17:123–142.

3. Satterthwaite FE. An approximate distribution of estimates of variance components. Biometric Bull. 1946;2:110–114.

4. Huberta P, Nguyen-Huub JJ, Boulangerc B, et al. Harmonization of strategies for the validation of quantitative analytical

procedures: a SFSTP proposal—part I. J Pharm Biomed Anal. 2004;36:579–586.


procedures: a SFSTP proposal—part II. J Pharm Biomed Anal. 2007;45:70–81.


procedures: a SFSTP proposal—part III. J Pharm Biomed Anal. 2007;45:82–96.

7. Mee RW. b-expectation and b-content tolerance limits for balanced one-way ANOVA random model. Technometrics.

1984;26:251–254.

8. Hahn GJ, Meeker WQ. Statistical Intervals: A Guide for Practitioners. New York:Wiley; 1991:204.

9. Hoffman D, Kringle R. Two-sided tolerance intervals for balanced and unbalanced random effects models. J Biopharm Stat.

2005;15:283–293.

10. Montgomery D. Introduction to Statistical Quality Control. 3rd ed. New York: Wiley; 1996:441.

11. Kushler RH, Hurley P. Confidence bounds for capability indices. J Quality Technol. 1992:24(4):188–195.

12. Wolfinger RD. Tolerance intervals for variance component models using Bayesian simulation. J Quality Technol.

1998;30:18–32.

13. Ntzoufras I. Bayesian Modeling in WinBUGS. New York: Wiley; 2009:308–312.

14. Spiegelhalter D, Thomas A, Best A, and Gilks, W (1996) BUGS 0.5 Examples Volume 1(version i), Example 7, Dyes, pp 24-

26. Available from http://www.mrc-bsu.cam.ac.uk/bugs/documentation/Download/eg05vol1.pdf (accessed November 20,

2012).

15. Burdick R, LeBlond D, Sandell D, Yang H. Statistical methods for validation of method accuracy and precision.

Pharmacopeia Forum, May –June Issue, 39 (3)

.16. USP. USP 36–NF 31, Validation of Compendial Procedures <1225>. Rockville, MD: USP; 2013:983–988.

17. ICH. Validation of analytical procedures: text and methodology Q2(R1). 2005.

http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q2_R1/Step4/Q2_R1__Guideline.pdf.

Accessed 27 November 2012.

References

Linearity

Response vs concentration linear curve

This is a calibration curve

It provides a means to convert a signal to a desired measured

value

Predicted concentration vs known concentration

This is a surrogate for Accuracy

Slope should be 1 and intercept should be 0

Two Types of Linearity

Todd L. Cecil, Personal communication, 2013

We wish to measure the concentration of an

analyte in a test sample.

Standards = known concentrations of an analyte

To estimate the concentration, we create a

standard curve

Calibration Curve

Standard Curve

Novick and Yang, 2013

Standard Curve

Sample


Evaluate linearity by visual inspection

Test of Linearity - ICH Q2(R1) guideline


Test of Linearity – Pearson Correlation

r = 1 r = -1

r = 0

Determine how close the predicted values to the mean

values at each concentration level

Test of Linearity – Lack of Fit (LOF)

Evidence of lack of fit

Clinical and Laboratory Standards Institute

http://www.clsi.org/source/orders/free/ep6-a.pdf

Compare straight-line to higher-order polynomial

curve fits

Recommendation: Test higher-order coefficients.

The EP6-A Guidelines





The EP6-A Guidelines


Literature


Conduct hypothesis testing with linearity claim

as the null hypothesis

Rely on failing to reject the null hypothesis to

conclude linearity

Penalize precise assay

Award small sample size

Drawbacks of Significance Test

More Literature


Two Practical Approaches

Two one-sided tests (TOST) for calibration error

Estimate bias in concentration due to approximating either

quadratic curve or proportional model using linear line

Bias is expressed as a function of a ratio of two model

parameters. Thus the Fieller’s Theorem can be applied to

obtained 90% confidence interval of the bias

Akaike information criterion (AIC)

Based on the principle of parsimony – the smallest possible

number of parameters for adequate representation of the data

where N – total number of data points, and K – the total number

of estimated regression model parameters

LeBlond, Tan and Yang, (2013a, 2013b)

Estimating Calibration Bias: Linear vs Quadratic

Models:

Bias:

Assumption: Concentration levels used in the experiment are

symmetrically spaced.


90% Confidence Interval (CI) of Bias

Linearity is accepted if the above 90% CI is contained

Within pre-specified limits.


Fieller’s exact 90% confidence Interval

Linear Model vs Proportional Model


Models:

Bias:

90% Confidence Interval of Bias

Linearity is accepted if the above

90% CI is contained

Within pre-specified limits.


90% CI of ratio of two model parameters:

90% CI of bias in concentration:

An equally-spaced experimental design is not a

necessary condition

Linearity can be tested under general conditions

Test Linearity for More General Experiment Design Conditions

Yang, Novick and LeBlond, 2013; Novick and Yang, 2013

1. USP. USP 36–NF 31, Validation of Compendial Procedures <1225>. Rockville, MD: USP; 2013:983–988.

2. ICH. Validation of analytical procedures: text and methodology Q2(R1). 2005. http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q2_R1/Step4/Q2_R1__Guideline.pdf. Accessed 27 November 2012.

3. Clinical and Laboratory Standards Institute. EP06-A02 Evaluation of the linearity of quantitative measurement procedures: a statistical approach. 2003. http://www.techstreet.com/standards/clsi/ep06a?product_id=1277866. Accessed 27 November 2012.

4. Anscombe FJ. Graphs in statistical analysis. Am Stat. 1973;27(1):17–21.

5. Van Loco J, Elskens M, Croux C, Beernaert H. Linearity of calibration curves: use and misuse of the correlation coefficient. Accred Qual Assur. 2002;7:281–285.

6. Bruggemann L, Quapp W, Wennrich R. Test for nonlinearity concerning linear calibrated chemical measurements. Accred Qual Assur. 2006;11:625–631.

7. Mandel J. (1964) The Statistical Analysis of Experimental Data. New York: Wiley; 1964.

8. Mark H, Workman J., Chemometrics in Spectroscopy, Linearity in Calibration How to Test for Non-linearity, Spectroscopy 2005;20(9):26–35

9. Liu J, Hsieh E. Evaluation of linearity in assay validation. In: Encyclopedia of Biopharmaceutical Statistics. 2nd ed. London: Informa Healthcare; 2010:467–474

10. Finney DJ. Statistical Method in Biological Assay. 2nd ed. London: Charles Griffin; 1964:27–29.

11. Berger RL, Hsu JC. Bioequivalence trials, intersection-union tests and equivalence confidence sets. Stat Sci. 1996:11(4):283–319.

12. Burnham KP, Anderson DR. Model Selection and Multimodel Inference: A Practical Information–Theoretic Approach. 2nd ed. New York: Springer; 1998:31.

13. Burnham KP, Anderson DR. Multimodel inference: understanding AIC and BIC in model selection. Sociol Meth Res. 2004;33(2):261–304.

14. David LeBlond, Charles Y Tan, Harry Yang (2013), Confirmation of Analytical Method Calibration Linearity, Pharmacopeial Forum 39(5), pp XX – XX.

15. David LeBlond, Charles Y Tan, Harry Yang (2013), Confirmation of Analytical Method Calibration Linearity: Practical Application, Pharmacopeial Forum ??(??), pp ?? – ??.

16. Steve Novick and Harry Yang (2013), Directly Testing the Linearity Assumption for Assay Validation, Accepted for publication in Journal of Chemometrics.

17. Steve Novick and Harry Yang (2013), Directly Testing the Linearity Assumption for Assay Validation, Accepted for publication in Journal of Chemometrics, The 36th Mid-west Biopharmaceutical Statistics Workshop, Muncie, Indiana, May, 2013i

18. Harry Yang, Steve Novick and David LeBlond (2013). Testing linearity under general experimental conditions. In preparation.

References

http://www.techstreet.com/standards/clsi/ep06a?product_id=1277866

Lifecycle Management of Analytical

Procedures

Joachim Ermer, Ph.D.

Member, USP Validation and Verification Expert Panel

Adaptation of the lifecycle concept [ ICH Q8] and of modern concepts for process validation to analytical procedures

to holistically align analytical procedure variability with the requirements of the product to be tested

to demonstrate that the analytical procedure meets the predefined criteria over the whole lifecycle

to facilitate continual improvement

Proposal to revision and compile USP General Chapters <1225>, <1226> and <1224> into a single General Information Chapter on Lifecycle Management of Analytical Procedures

Stimuli article to be published in PF 39(5), Sep - Oct 2013

Objectives of Expert Panel Validation & Verification

“systematic approach that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management” [ICH Q8]

systematic approach that begins with predefined objectives and emphasizes analytical procedure understanding and analytical control, based on sound science and quality risk management”

Quality by Design – Also Relevant for Analytics

Alignement of Process and Analytical Procedure

Quality Target

Product Profile

Prospective summary of the quality

characteristics of a drug product

to ensure quality, safety, efficacy

Analytical Target

Profile

Defines the objective of the test

and quality requirements

for the reportable result

PROCESS ANALYTICAL PROCEDURE

Developed starting 2008 by EFPIA / PhRMA Working

Group “Analytical Design Space”

M. Schweitzer, M. Pohl et al.: QbD Analytics. Implications and

Opportunities of Applying QbD Principles to Analytical

Measurements, Pharmaceutical Technology, Feb. 2010, 2-8

http://pharmtech.findpharma.com/pharmtech/article/articleDetail.

jsp?id=654746

Quality (data) attributes of the reportable result

performance requirements for use

accuracy and measurement uncertainty including precision

Analytical Target Profile (ATP)

http://pharmtech.findpharma.com/pharmtech/article/articleDetail.jsp?id=654746

http://pharmtech.findpharma.com/pharmtech/article/articleDetail.jsp?id=654746

Based on the understanding of the target measurement

uncertainty

Maximum allowed uncertainty to maintain acceptable levels of

confidence

Reference point for assessing the fitness of an analytical

procedure

towards predetermined performance requirements

In development phase and during all changes within the lifecycle

linked to the purpose, not to a specific analytical technique.


Any analytical procedure that conforms to the ATP is

acceptable

USP Medicines Compendium, General Chapter <10>

May be also established for existing procedures

including compendial procedures

based on (monograph) specifications, existing knowledge


The procedure must be able to quantify [Analyte]

in presence of X, Y, Z

over a range of A% to B% of the nominal concentration

with an accuracy and uncertainty such that the reportable result falls

within ±1.0% of the true value

with at least a 90% probability

determined with 95% confidence

ATP Example Assay

Aligned with process validation terminology:

Three Stage Approach to Analytical Validation

Stage 1

Procedure Design (Development and Understanding

Stage 2

Procedure Performance Qualification (PPQ)

Stage 3

Continued Procedure Performance Verification

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Changes

According to ATP requirements

Procedure selection, development and understanding

Identification and investigation of potential analytical variables

Risk assessment

Robustness studies (Method Design Space)

Analytical Control Strategy

Knowledge gathering and preparation

Stage 1 – Procedure Design

Confirmation the analytical procedure, operated in the

routine environment is capable of delivering

reproducible data which consistently meet the ATP

Includes analytical transfer

Implementation of compendial procedures

Precision study to finalize the Analytical Control Strategy

e.g. format of the reportable result (number of determinations)

May / should be built on results generated in Stage 1

Iterative character of procedure development/optimisation

Stage 2 - Procedure Performance Qualification (PPQ)

To provide ongoing assurance that the analytical

procedure remains in a state of control throughout its

lifecycle

Routine Monitoring: Ongoing program to collect and

process data that relate to method performance, e.g.

from analysis / replication of samples or standards during batch

analysis

by trending system suitability data

by assessing precision from stability studies

[J. Ermer et al.: J. Pharm. Biomed. Anal. 38/4 (2005) 653-663]

Stage 3 – Continued Procedure Performance Verification

Risk assessment to evaluate

Impact of the respective change

Required actions to demonstrate (continued) appropriate performance

Accordingly, apply

Stage 3 (if within Method Design Space)

Stage 2 (e.g. transfer)

Stage 1 (e.g. outside Method Design Space, new procedure)

Continual Improvements (Changes)

Gregory P. Martin, (Chair) Complectors Consulting

Kimber L. Barnett, Pfizer Inc.

Christopher Burgess, Burgess Analytical Consultancy, Ltd.

Paul D. Curry, Abbvie,

Joachim Ermer, Sanofi-Aventis GmbH

Gyongyi S. Gratzl, Ben Venue Laboratories, Inc.

Elizabeth Kovacs, Apotex, Inc.

David J. LeBlond, Statistical Consultant

Rosario LoBrutto, Teva Pharmaceuticals USA

Anne K. McCasland-Keller, Eli Lilly & Company

Pauline L. McGregor, PMcG Consulting

Phil Nethercote, GlaxoSmithKline

David P. Thomas, Johnson & Johnson Pharmaceutical R&D

M. L. Jane Weitzel, Quality Analysis Consultants

Government Liaison(s): Lucinda F. Buhse, FDA

USP Scientific Liaison(s):

Todd L Cecil, Kenneth Freebern, Walter Hauck, Horacio N. Pappa, Tsion Bililign

2010-2015 V&V Expert Panel

Analytical Method Validations

Current Practices and Industry

Perspective

Rajiv A. Desai, Ph.D.

Dishman Pharmaceuticals and Chemicals Ltd.

Impurity: The procedure must be able to quantify [impurity] relative to [drug]

in the presence of components likely to present in the sample

over the range from reporting threshold to the specification limit.

The accuracy and precision of the procedure must be such that the reportable result falls

within ± X% of the true value for impurity levels from 0.05% to 0.15% with 80% probability with 95% confidence,

and within ± Y% of the true value for impurity levels >0.15%, with 90% probability determined with 95% confidence.

ATP Example Impurity

Purpose of the Analytical measurement is

to get consistent, reliable and accurate data

Origin of Impurities

Impurities in Drug Substances From Equipment and

Packaging material

Earlier stage

material

Side reactions Degradents

Residual solvents

Genotoxic Impurities

Extractables Leachables

Source of Impurities in the Drug Substance and products

General Process for the Synthesis of Drug Substance

Stage 1

Solvent W

A + B C + ( traces of A and B )

Stage 2

Solvent X

C + D E + ( traces of C and D ) + M ( reaction between A and C )

Reagent R

Stage 3

Solvent Y

E + F Crude API + ( traces of E and F ) + traces of D + degradent of E

Metal catalyst

Stage 4

Solvent Z

Crude API Final API + Traces of earlier stage material

Side reactions

Degradents

Solvents

Reagents

Analytical Method Validation Criteria ….

- Suitability of Instrument

- Status of Instrument Qualification and calibration

- Suitability of reference standard , reagent, placebo, etc

- Suitability of documentation, written analytical procedure

approved protocol with pre-established acceptance criteria

USP General Chapter <1224>

Transfer of Analytical Procedures

1. Comparative testing of same lot or standards

2. Co-validation between laboratories

3. Complete or partial validation of Analytical procedures

by receiving laboratory and hence a transfer waiver

Validation of Compendial Procedures

As per cGMP regulations 211.194(a), the test methods

with established specifications, must meet standards of

accuracy and reliability

As per 211.194(a)(2) users are not required to validate

the accuracy and reliability of these methods. But

verify their suitability under actual conditions of use.


Data Elements Required to be Validated

Analytical

Performance

Charecteristics

Category I Category II

Category III Category IV

Quantitative Limit tests

Accuracy Yes Yes * * No

Precision Yes Yes No Yes No

Specificity Yes Yes Yes * Yes

Detection limit No No Yes * No

Quantitation limit No Yes No * No

Linearity Yes Yes No * No

Range Yes Yes * * No

• May be required, depending on the nature of the specific test

Category I : Procedures for Quantitation of major component or Active substance

Category II : Procedures for determining Impurities

Category III : Procedures for determining performance characteristics ( eg., dissolution, drug release, etc )

Category IV : Identification Tests

Rationale for revisiting the compendial method

An appropriate justification for a testing procedure

Elaborating the capability of the proposed method over other

types of determinations.

For revisions, a comparison should be provided for the

limitation of the current method and advantage offered by the

new method.


Verification of Compendial Procedures

Verification for a compendial test procedure is an

assessment of whether the procedure can be used for its

intended purpose, under actual conditions of use for a

specific drug substance or drug product.

User should have the appropriate experience, knowledge

and training to understand and be able to perform the

compendial procedure.



If the verification of the compendial procedure is not

successful and the USP staff is unable to resolve the

problem, it may be concluded that the procedure may not

be suitable for use

It may be necessary to develop and validate an alternate

procedure. This alternate method can be submitted to

USP , along with appropriate data to support the inclusion

or replacement of the current compendial procedure.



Method verification should be based on an assessment of

the complexity of both the procedure and the material to

which the procedure is applied

Verification should assess whether the compendial method is

suitable for the drug substance and the drug product matrix.

Taking into account the drug substance synthetic route, the

method of manufacture for the drug product or both.

US General Chapter <1226>


Drug substance from different suppliers may have different

impurity profile that may not necessarily be addressed by the

compendial method

Excepients in the drug products can vary widely among

manufacturers and may interfere directly or cause formation

of impurities that are not considered by the compendial

procedure.


Chromatography

System Suitability is an integral part of chromatography

methods

These are based on the concept that equipment, electronics,

analytical operations and samples analysed constitute an

integral system that can be evaluated as such.

Factors affecting chromatography


Mobile phase Composition, strength , temperature, pH, flow rate

Column Flow rate, dimention, Temperature, pressure, Stationary phase

Adjustments allowed in HPLC Compendial methods

pH of Mobile phase : ± 0.2 units

Concentration of salts in buffer : within ± 10%

Ratio of components in mobile phase : ± 10%

Wavelength : ± 3 nm Column length : ± 70 %

Flow rate : ± 50% Column Temperature : ± 10 deg C

Injection volume : Can be reduced, but not increased


Adjustments allowed in GC Compendial methods

Gas carrier flow rate : ± 50 %

Oven temperature : ± 10%

Temperature program : ± 20 %

Column length : ± 70 %

Injection volume and split volume : Can be adjusted, if detection

and repeatability are satisfactory


Techniques Used for Analysis

Additional testing parameters are now considered along with the

conventional methods

Analytical Instruments moving from Research to Quality Control

NMR ICP XRD

LC-MS GC-MS NIR

Used mainly for low level detections of impurities

Method validation parameters to be selected appropriately along

with sampling and sample preparations

QbD and PAT

Quality by Design (QbD ) is being encouraged by the Regulatory

guidelines, the analysis conducted at every step of the process

needs to be reliable.

Testing methods adopted under the Process Analytical technology

(PAT) should be able to provide real time analysis in the shortest

possible time.

Validation should be definitely done for analytical methods used

under the QbD and PAT environment. No matter what the stage

of the process and not just restricted to final product.

A validated method gives assurance of process control at each

stage, concept of QbD will be further reinforced.

Compendial Method and Non-compendial Method

Compendial Method Verification / Validation

Non-compendial methods Validation

Alternate to Compendial method Validation + Equivalence

Genotoxic Compounds have a potential to damage DNA at

any level of exposure. Its scientifically proved that there

are certain chemical structures which damage the DNA.

The accepted levels of such chemicals is required to be

maintained at a very low to avoid any cause of concern.

Potential Genotoxic Impurity (PGI)

When can a specification of a drug substance exclude a limit of Potential

Genotoxic Impurity ?

1. Is just a theoretical impurity, but not found during manufacturing.

2. Is formed or introduced in intermediate steps and is controlled in the

intermediate stage and does not exceed 30% of the limit derived by TTC or

any defined acceptable limits

3. Is formed or introduced in final synthesis step, it should be included.

4. However, it is possible to apply skip test if the level does not exceed 30% of

the limit. Data of atleast 6 consecutive pilot scale batches or 3 consecutive

production batches would support the justification

Method validation becomes a very important aspect

which ever stage the analysis is performed

Guideline on the limits of genotoxic impurities' (EMEA/CHMP/QWP/251344/2006),


Threshold of toxicological concern (TTC) values for genotoxic impurities

above 1.5 μg /day will be treated on a case-by-case basis. For short-duration

treatments, the acceptability of higher levels will be in line with the principles

outlined below

For more than one PGI in a drug substance, the TTC limits will be individually

applied, if the impurities are structurally different.

For more than on PGI, but structurally similar, it is expected that the mode of

action would be same, hence a sum of the limits will be accepted.

Duration of

Exposure

Single

dose

≤1 month ≤3

months

≤6

months

≤12

months

Allowable

daily intake

120 μg 60 μg 20 μg 10 μg 5 μg


Your firm did not validate analytical methods used to test APIs.

The inspection revealed that your firm had not validated the HPLC

method for assay and related substances for finished API for human

use..

Your response states that XX of the APIs manufactured at your

facility, are compendial products. The remaining YY % are non-

compendial APIs had no method validation. You committed to complete

these method validations by (Date) . However, this does not address

product currently on the market, or product that will enter the market

tested with an unvalidated method. Your proposal to verify “key

parameters” for the first API batch produced does not provide the same

level of assurance as method validation.

Regulatory Audit Warning Letter

Inadequate Instrument Qualification and Analytical Method Validation

Improvements to analytical techniques and transfer of methods to at-

or on-line applications emerged as important opportunities to reduce

risk and increase efficiency in today’s modern manufacturing facility.

A pharmaceutical company was cited for not adequately performing

the required steps to support the transition to a new testing approach.

There was no method comparison or equivalency study performed to

show that the “changes were superior to the original approved

method.

The data was used for OOS closure and lot release.

Regulatory Audit Warning Letter

Four Level Control on Analysis and Results

Health & Medicine

Usp chemical medicines & excipients - evolution of validation practices