12th USP Science & Standards Symposium - New Delhi

1. 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

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

3. Acceptable Analytical Method Variation
Setting System Suitability
Requirements
Todd L. Cecil, Ph.D.
Vice President, Chemical Medicines
USP

4. Method Variation

Sources
– Instrument Characteristics
– Sample Characteristics
– Method Parameters
– Environmental Affects
– Analyst
– Instrument Settings
– Many others

5. Measuring Variation

Random Error

Systematic Error
Indeterminate Error
Experimental error
– Determinate Error
– Discoverable source
(in theory)
Estimated using Precision
Estimated using Accuracy
–
–

6. Validation to Measure Variability


Developed in late 1980’s for the Pharmaceutical
Industry
– PhRMA -> USP <1225> -> ICH Q2A -> USP <1225>
Defined “Analytical Performance Characteristics”
Intermediate precision
– Accuracy
Repeatability
– Precision
Reproducibility
– Specificity
Ruggedness
Robustness
– Detection Limit
– Quantification Limit
Trueness
Bias
– Linearity
– Range

7. Acceptable Variability
Depends upon two factors
Application
Expectation

Test
•
Analyst Experience

Procedure
•
Instrument knowledge

Acceptance criteria
•
Matrix complexity

8. Acceptable Validation

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…

9. There is Another Way!

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)

10. Defining Another Way Forward

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?

11. Measuring the Parameters
 Precision
– % RSD with sufficient degrees of freedom
 Accuracy
– Spike Recovery or Comparison to Primary
Standard
 Specificity
 Linearity
 Range
– Resolution or Spike Recovery
– Slope, Intercept, R2
– Precision and accuracy
 Limit
of Detection – Precision
 Limit
of Quantification – Precision

12. Collapsing the Parameters
 Precision
– Measure of Random Error
 Accuracy
– Measure of Systematic Error
 Specificity
 Linearity
 Range
– Measure of Systematic Error
– Measure of Systematic Error
–?
 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?

13. Extract from <1200>
Category I*
Analytical
Performance
Characteristics
Accuracy
Precision
Specificity
Detection Limit
Quantification
Limit
Linearity
Range
1
Category II*
(Quantitative)
Category II* (Semiquantitative)
<1225>
<1200>
<1225>
<1200>
<1225>
<1200>
Y
Y
Y
1
1
2
Y
Y
Y
4
5
2
?
N
Y
N
5
2
N
N
N
N
Y
6
N
N
Y
4
N
N
Y
Y
3
3
Y
Y
4
4
N
?
N
N
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

14. 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
1.2
1
%CV
0.8
0.6
0.4
0.2
0
0.00
0.20
0.40
0.60
0.80
1.00
Bias
1.20
1.40
1.60
1.80
2.00

15. Study Detail

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

16. Specificity Study

Specificity is a special case of Accuracy.
 Interferences considered
 Separation Sciences
– Resolution of NLT 1.5

17. 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 PrecisionAccuracy Study

18. Range 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

19. Linearity



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

21. 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

22. Limit of Detection
 Only
 S/N
applies to “Limit” procedures
of 3
– independent samples at LOD
– “adequate precision and accuracy”
 What
is adequate?
 What
is the purpose of the test?

23. 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.

24. Detectability
 A new
term included in <1200>
 Replaces
3
LOD
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

25. What is Horwitz?

26. 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

27. Summary
 USP
is challenging validation concepts
 Including
 Include
 Focus
 Use
DOE and QbD through the ATP
measurable parameters and clear criteria
on Precision and Accuracy results
Specificity to aid understanding of Accuracy
 Retask
Range
 Introduce
detectabiltiy
 Eliminate
LOD, LOQ and Linearity

28. 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

30. Precision, Accuracy & Linearity
Harry Yang, Ph.D.
Member, USP Statistics Expert Committee

31. Method Validation

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

32. History of ICH Guidelines on Method Validation

33. Other Related Guidelines

34. Common Validation Characteristics

Accuracy
 Precision







Repeatability
Intermediate precision
Specificity
Limit of detection
Limit of quantitation
Linearity
Range

35. Common Validation Characteristics

Accuracy
 Precision







Repeatability
Intermediate precision
Specificity
Limit of detection
Limit of quantitation
Linearity
Range

36. Precision

Closeness between a series of measurements
obtained from multiple sampling of the same
homogeneous sample

37. 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)
n
X
 Xn
i 1
n
n
, SD 
(X
i 1
i
 X )2
n 1
, RSD 
SD
X

38. Accuracy

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))
Bias
Precision
E[( X  T ) 2 ]  (  X  T ) 2  var[ X ]
True value
Mean measurement

39. Bias

Closeness of agreement between the average
value obtained from a large series of test results
and an accepted reference value
Bias   X  T
µT
µX

40. Accuracy = bias + precision

41. Validation of Accuracy and Precision

Model
or
or

42. An Example
Test result
X  T
Bias
 X  T
Burdick, LeBlond, Sandell, Yang, 2013
Intermediate
precision
Repeatability

43. An Example
Test result
X  T
Bias
 X  T
Burdick, LeBlond, Sandell, Yang, 2013
Intermediate
precision
Repeatability

44. Assessment of Bias: Traditional Approach
X
s/n
 t n 1, 0.025

Test the hypothesis that bias = 0
H0: µ = 0 vs. H1: µ ≠ 0
Y
Reject H0 if
s2 / n
n
where
Y 
Yn
i 1
n
 t n 1,0.025
(which is the same as p-value < 0.05)
n
, s2 
 (Y  Y )
i 1
2
i
n 1
,
t n 1,0.025
- cutpoint of t-distribution

45. Assessment of Bias: Traditional Approach

P-value < 0.05 is equivalent to that the 95%
confidence interval contains zero, i.e.


0   Y  t n 1, 0.025s / n , Y  t n 1, 0.025s / n 




p-value < 0.05
p-value ≥ 0.05

46. Issue with the Traditional Approach

Penalize more precise assay

Award small sample size


0   Y  t n 1, 0.025s / n , Y  t n 1, 0.025s / n 




With of the 90% CI is
proportional to assay
precision (s) and
reciprocal
of the squared root of
sample size n.
Huberta et al, 2004

47. Equivalence Method

Bias is deemed acceptable if the 90%
confidence interval of bias is bounded by prespecified acceptance limits (e.g., ±15%)
Huberta et al, 2004


 Y  t n 1, 0.025s / n , Y  t n 1, 0.025s / n 





48. Comparison Between Significance and Equivalence
Is bias acceptable?
Significance
Equivalence
Yes
No
Yes
No
Yes
Yes
No
Yes

49. Equivalence Method

Bias is deemed acceptable if the 90%
confidence interval at each concentration level is
contained with in pre-specified range (LAL, UAL)
Plot of Bias vs. True Value
UAL
0
LAL
a
True value
b
c

50. 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
Burdick, LeBlond, Sandell, Yang, 2013

51. Total Error Approach

Bias cannot be assessed independent of
precision
Huberta et al, 2004; Hoffman & Kringle, 2007

52. Total Error Approach

Measured value = True value + Method Bias + Method error
Y = Test result
- True value

53. Total Error Approach

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

54. Total Error Approach

Risk = 1 - Probability of meeting acceptance criterion
Huberta et al, 2004

55. Methods for Testing H0:

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% P(  Y  X     | data)   .
T

56. Accuracy Profile
Huberta et al, 2004

57. References
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.
5. Huberta P, Nguyen-Huub JJ, Boulangerc B, et al. Harmonization of strategies for the validation of quantitative analytical
procedures: a SFSTP proposal—part II. J Pharm Biomed Anal. 2007;45:70–81.
6. Huberta P, Nguyen-Huub JJ, Boulangerc B, et al. Harmonization of strategies for the validation of quantitative analytical
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 2426. 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.

58. Linearity

59. Two Types of 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
Todd L. Cecil, Personal communication, 2013

60. Calibration Curve

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

61. Standard Curve
Novick and Yang, 2013

62. Standard Curve
Sample
Novick and Yang, 2013

63. Test of Linearity - ICH Q2(R1) guideline

Evaluate linearity by visual inspection
Novick and Yang, 2013

64. Test of Linearity – Pearson Correlation
r=1
r = -1
r=0

65. Test of Linearity – Lack of Fit (LOF)

Determine how close the predicted values to the mean
values at each concentration level
Evidence of lack of fit

66. The EP6-A Guidelines

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.
Novick and Yang, 2013

67. The EP6-A Guidelines
Novick and Yang, 2013

68. Literature
Novick and Yang, 2013

69. Drawbacks of Significance Test

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

70. More Literature
Novick and Yang, 2013

71. 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)

72. Estimating Calibration Bias: Linear vs Quadratic
Models:
Bias:
Assumption:
Concentration levels used in the experiment are
symmetrically spaced.
LeBlond, Tan and Yang, (2013a, 2013b)

73. 90% Confidence Interval (CI) of Bias
Fieller’s exact 90% confidence Interval
Linearity is accepted if the above 90% CI is contained
Within pre-specified limits.
LeBlond, Tan and Yang, (2013a, 2013b)

74. Linear Model vs Proportional Model
Models:
Bias:
LeBlond, Tan and Yang, (2013a, 2013b)

75. 90% Confidence Interval of Bias
90% CI of ratio of two model parameters:
90% CI of bias in concentration:
Linearity is accepted if the above
90% CI is contained
Within pre-specified limits.
LeBlond, Tan and Yang, (2013a, 2013b)

76. Test Linearity for More General Experiment Design Conditions

An equally-spaced experimental design is not a
necessary condition

Linearity can be tested under general conditions
Yang, Novick and LeBlond, 2013; Novick and Yang, 2013

77. References
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.

79. Lifecycle Management of Analytical
Procedures
Joachim Ermer, Ph.D.
Member, USP Validation and Verification Expert Panel

80. Objectives of Expert Panel Validation & Verification

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

81. Quality by Design – Also Relevant for Analytics

“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”

82. Alignement of Process and Analytical Procedure
PROCESS
Quality Target
Product Profile
Prospective summary of the quality
characteristics of a drug product
to ensure quality, safety, efficacy
ANALYTICAL PROCEDURE
Analytical Target
Profile
Defines the objective of the test
and quality requirements
for the reportable result

83. Analytical Target Profile (ATP)

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

84. Analytical Target Profile (ATP)

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.

85. Analytical Target Profile (ATP)

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

86. ATP Example Assay

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

87. Three Stage Approach to Analytical Validation

Aligned with process validation terminology:
Stage 2
Procedure Performance Qualification (PPQ)
Stage 3
Continued Procedure Performance Verification
Changes
Risk assessment
Knowledge management
Analytical Control Strategy
Stage 1
Procedure Design (Development and Understanding

88. Stage 1 – Procedure Design

According to ATP requirements

Procedure selection, development and
understanding

Identification and investigation of potential
analytical variables


Robustness studies (Method Design Space)


Risk assessment
Analytical Control Strategy
Knowledge gathering and preparation

89. Stage 2 - Procedure Performance Qualification (PPQ)

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

90. Stage 3 – Continued Procedure Performance Verification

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]

91. Continual Improvements (Changes)

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)

92. 2010-2015 V&V Expert Panel

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

94. Analytical Method Validations
Current Practices and Industry
Perspective
Rajiv A. Desai, Ph.D.
Dishman Pharmaceuticals and Chemicals Ltd.

95. ATP Example Impurity

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.

96. Purpose of the Analytical measurement is
to get consistent, reliable and accurate data

97. Source of Impurities in the Drug Substance and products
Origin of Impurities
Impurities in Drug Substances
Earlier stage
material
From Equipment and
Packaging material
Residual solvents
Side reactions
Degradents
Genotoxic Impurities
Extractables
Leachables

98. 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

99. 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

100. 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

101. USP General Chapter <1225>
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.

102. Data Elements Required to be Validated
Analytical
Performance
Charecteristics
Category I
Category II
Quantitative
Category III
Category IV
Limit tests
Accuracy
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
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

103. USP General Chapter <1225>
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.

104. USP General Chapter <1226>
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.

105. USP General Chapter <1226>
Verification of Compendial Procedures
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.

106. US General Chapter <1226>
Verification of Compendial Procedures
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.

107. US General Chapter <1226>
Verification of Compendial Procedures
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.

108. US General Chapter <621>
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

109. US General Chapter <621>
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

110. US General Chapter <621>
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

111. 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

112. 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.

113. Compendial Method and Non-compendial Method
Compendial Method

Verification / Validation
Non-compendial methods

Validation
Alternate to Compendial method 
Validation + Equivalence

114. Potential Genotoxic Impurity (PGI)
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.

115. 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),

116. Potential Genotoxic Impurity (PGI)
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
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
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.

117. Regulatory Audit Warning Letter
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 noncompendial 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.

118. Regulatory Audit Warning Letter
Inadequate Instrument Qualification and Analytical Method Validation
Improvements to analytical techniques and transfer of methods to ator 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.

119. Four Level Control on Analysis and Results