1. Laboratory Validation of Analytical Methods
Prepared by Hock Eng, Khoo

2. Definitions
Validation is the process of demonstrating or confirming the performance characteristics of a method of analysis.
A process of evaluating method performance and demonstrating that it meets a particular requirement.
Validation applies to a specific operator, laboratory, and equipment utilizing the method over a reasonable concentration range and period of time.

3. Why Method Validation? To minimize analytical and instrumental errors
To give reliable and reproducible results in accordance with the given specifications of the test method
To ensure the quality of the test results
To meet accreditation requirement
Objective evidence for defense against challenges
To be assured of the correctness of results

4. When Method Validation Not Required?
Standard methods on condition that
– used within their scope of applicability (e.g. matrices, ranges, etc)
– without modifications (including QA plan and reporting)
Otherwise, required

5. Before start to validate a method
Ask yourself
What analytes should be detected?
What are the expected concentration levels?
What are the sample matrices?
Are there interfering substances expected, and, if so, should they be detected and quantified?
Are there any specific legislative or regulatory requirements?
Should information be qualitative or quantitative?
What are the required detection and quantitation limits?
What is the expected concentration range?
What precision and accuracy is expected?
How robust should the method be?
Which type of equipment should be used? Is the method for one specific instrument, or should it be used by all instruments of the same type?
Will the method be used in one specific laboratory or should it be applicable in all laboratories at one side or around the globe?
What skills do the anticipated users of the method have?
Before start to validate a method

6. Parameters for Method Validation
Accuracy
Precision
Specificity
Limit of detection
Limit of quantitation
Linearity and range
Ruggedness
Robustness

7. Strategy for Method Validation
Develop a validation protocol, an operating procedure or a validation master plan for the validation.
For a specific validation project define owners and responsibilities.
Develop a validation project plan.
Define the application, purpose and scope of the method.
Define the performance parameters and acceptance criteria.
Define validation experiments.
Source: LabCompliance (2007). Validation of Analytical Methods and Procedures: Tutorial.

8. Strategy for Method Validation
Verify relevant performance characteristics of equipment.
Qualify materials, e.g. standards and reagents for purity, accurate amounts and sufficient stability.
Perform pre-validation experiments.
Adjust method parameters or/and acceptance criteria if necessary.
Perform full internal (and external) validation experiments.
 Develop standard operational protocols (SOPs) for executing the method in the routine.
 Define criteria for revalidation.
 Define type and frequency of system suitability tests and/or analytical quality control (AQC) checks for the routine.
 Document validation experiments and results in the validation report.

9. Strategy for Method Validation
Major compounds
Traces
quantitative
qualitative
Limit of detection × √
Limit of quantitation
Linearity
Range
Precision
Accuracy
Specificity
Ruggedness

10. Method validation tools
Linear-data plotter
can be used with data from a linearity experiment to assess the reportable range of a method.
provides a plot of the average of a group of replicate test results on the y-axis versus the assigned value (in % or concentration units) on the x-axis.
SD Calculator
can be with data from a replication experiment to calculate the mean, standard deviation (SD or smeas), and coefficient of variation (CV).
a histogram display of the data is also available.

11. Method validation tools
Paired-data Calculator
can be used with data from a comparison of methods experiment to calculate linear regression statistics (slope, y-intercept, and standard deviation about the regression line, sy/x), and the correlation coefficient (r, Pearson product moment correlation coefficient); t-test statistics (average difference between two methods or biasmeas; SDdiff, standard deviation of the differences between the two methods).
can also be used to provide a "comparison plot" that shows the test method results on the y-axis versus the comparative method results on the x-axis, as well as a "difference plot" that displays the difference between the test minus comparative results on the y-axis versus the comparative method result on the x-axis.

12. Sampling
Sample preparation
Analysis
Calibration
Data evaluation
Reporting

13. Sampling Analysis starts with sampling
For trace analysis: sampling becomes a major source of error
Differs from matrix to matrix

14. Sampling Method When, where and how to collect samples
Sample transportation to laboratory
Sampling equipment
Sample containers
Sample-treatment procedures (drying, mixing, etc. prior to measurements)
Sub-sampling procedures
Storage during sampling

15. To obtain a representative sample Types of sampling
Sampling Method
To obtain a representative sample
Types of sampling
car or bin sampling
stratified sampling
random sampling
2 steps sampling
interval sampling

16. Problems during sampling and storage of samples
Losses from volatilization
Decomposition by means of:
temperature
UV irradiation
microbial activity
chemical reactions with oxygen, sample container, etc.

17. ISTA Validation Process
ISTA method validation is a five-step process:
Test method selection and/or development.
Validation through either multi-laboratory characterization of the test method performance, peer verification of the test method, or verification of performance claims for the test method.
Review of data.
Approval of the test method by the relevant ISTA Technical Committee, publication in ISTA Method Validation Reports and preparation of a Rules proposal for the test method.
Final acceptance by the ISTA voting members and publication of the test method in the ISTA Rules.
Source: Hampton, J. (2005), ISTA Method Validation, Issues of Technical Common Interest,
Seed Testing International No. 130, p. 22, October 2005

18. Accuracy
To determine the closeness of the test results obtained to the true value of the standard used
To measure the systemic error of the analysis

19. Working standard can be used as a standard addition (spiked analysis).
Accuracy
Method used:
Purchase standard from the supplier with a known tolerance given or standard recommended by a particular reference method.
Working standard can be used as a standard addition (spiked analysis).
Perform the method with more than 2 determinations at low, middle and high concentrations.

20. Accuracy
Method used:
For methods using an instrument with a known standard calibration curve, selection of the range can be made.
For methods without the above, the range can be selected from a knowledge of the concentration of samples analyzed.
Based on the results obtained, calculate the average of the standard values obtained and compare them with recommended values (Table 1) or
Compare the result obtained with certified reference material (CRM).

21. Table 1: Acceptable Recovery Percentages
Accuracy
Table 1: Acceptable Recovery Percentages
Analyte (%)
Unit
Mean Recovery (%)
100
100%
98-102 10
10% 1 1%
97-103
0.1
0.1%
95-105
0.01
100 ppm
90-107
0.001
10 ppm
80-110
0.0001
1 ppm
100 ppb
10 ppb
60-115
1 ppb
40-120
Source: AOAC (2002). AOAC Requirements for Single Laboratory Validation of Chemical Methods. DRAFT , \AOACI\eCam\Single-Lab_Validation_47.doc.

22. Precision
To determine the degree of agreement within the test results for a particular sample.
This is a measurement of the random errors of an analysis.
3 types of precision measurement
(1) Repeatability (one single operator, single laboratory, short time span)
(2) Intermediate precision (internal reproducibility: between operators, single laboratory)
(3) Reproducibility (proficiency testing/collaborative studies between laboratories)

23. Compare the precision results with the RSD% in Table 2.
Repeatability (one single operator, single laboratory, short time span).
Refers to the degree of agreement of results when conditions are maintained as constant as possible.
Select one sample for every matrix and perform replication for each matrix within the same day.
If resources permit, run the same sample for 3 different days (within 7 days).
Calculate the standard deviation (SD) and relative standard deviation (RSD) for each matrix.
Compare the precision results with the RSD% in Table 2.

24. Precision
Intermediate precision (internal reproducibility: between operators, single laboratory).
To evaluate the degree of agreement between different operators for a particular sample.
Measure the random errors inherent when different analysts perform the same analysis with the same sample.
Use 2-tailed F-test to determine whether there is any significant difference in the results between the precisions of two operators.
Calculate the sample variance for each operator.
Calculate the precision for each operator according to the types of samples and compare the RSD(%).

25. Only applicable when resources are available.
Precision
Reproducibility (proficiency testing/collaborative studies between laboratories).
Only applicable when resources are available.
The blind sample(s) will be analyzed at least three times by using the routine method and/or other standard methods.
Calculate the average value of the replicates and SD if more than three replicates are performed.
Compare the results obtained from other laboratories in terms of the z-score, mean valeu and median once these results are made available.

26. Table 1: Acceptable Recovery Percentages
Precision
Table 1: Acceptable Recovery Percentages
Analyte (%)
Unit
RSDr (%)
RSDR (%)
100
100% 1 2 10
10%
1.5 3 1% 4
0.1
0.1% 6
0.01
100 ppm 8
0.001
10 ppm (μg/g) 11
0.0001
1 ppm 16
10 ppb (μg/kg) 15 32
AOAC (2002). AOAC Requirements for Single Laboratory Validation of Chemical Methods. DRAFT , \AOACI\eCam\Single-Lab_Validation_47.doc.

27. The value of RSD% can be calculated from HORRAT formula:
Precision
The value of RSD% can be calculated from HORRAT formula:
RSDr = C–0.15 (Repeatability)
RSDR = 2C–0.15 (Reproducibility)
HORRAT = RSD (found)/RSD (calculated)
Acceptable values for this ratio are typically 0.5 to 2.

28. Limit of Detection
It is the smallest amount or concentration of an analyte that can be estimated with acceptable reliability.
An alternative definition of the limit of detection and limit of determination is based upon the variability of the blank.
The blank value plus three times the standard deviation of the blank is taken as the detection limit and the blank value plus 10 times the standard deviation of the blank is taken as the determination limit.
The detection limit is only useful for control of undesirable impurities that are specified as “not more than” a specified low level and for low-level contaminants.
Limits of detection and determination are unnecessary for composition specifications although the statistical problem of whether or not a limit is violated is the same near zero as it is at a finite value.

29. Instrument Detection Limit
To determine the lowest concentration of analyte in a sample that can be detected.
It is important for trace analysis.
Perform several determinations and calculate the SD of the blank
LOD = x + 3 SD (x = average blank reading) or
Perform several determinations at the lowest acceptable concentration using a standard sample (fortified blank sample) and calculate the SD.
LOD = x + 3 SD (x = average sample blank reading)

30. Method Detection Limit
It is based on a sample, which have gone through the entire sample preparation prior to analysis.
Method detection limit is approximately 4 × instrument detection limit.
Perform several determinations each at low, middle and high range concentrations using a sample.
Calculate SD at each limit.
Plot SD against concentration of the analytes.
Extrapolate the graph until the y-axis is reached.
At zero concentration, record the SD0.
Method detection limit = 3 SD0
It is unique for a particle sample matrix.

31. Signal to Noise Ratio
Source: LabCompliance (2007). Validation of Analytical Methods and Procedures: Tutorial.

32. Source: International Conference on Harmonization (ICH) of Technical Requirements for the Registration of Pharmaceuticals for Human Use, Validation of analytical procedures: Methodology, adopted in 1996, Geneva

33. Specificity/Selectivity
The terms selectivity and specificity are often used interchangeably.
To determine the purity of the peak area in chromatogram.
Difficult to ascertain whether the peaks within a sample chromatogram are pure or consist of more than one compound.
The analyst should know how many compounds are in the sample or whether procedures for detecting impure peaks should be used.

34. Examples of pure and impure HPLC peaks
Source: LabCompliance (2007). Validation of Analytical Methods and Procedures: Tutorial.

35. Linearity
To determine the degree of variance about the calibration curve
Perform several determinations using a standard sample at low, middle and high range.
Calculate the SD of each concentration group.
Calculate the best-fit line of the calibration curve and the correlation coefficient (r2) of the curve.
Use the F-test to determine the significant difference in variance of the curve for each concentration group.

36. Ruggedness
Ruggedness is defined by U.S. Pharmacoepia as the degree of reproducibility of results obtained under a variety of conditions, such as different laboratories, analysts, instruments, environmental conditions, operators and materials.
Ruggedness is a measure of reproducibility of test results under normal, expected operational conditions from laboratory to laboratory and from analyst to analyst.
Ruggedness is determined by the analysis of aliquots from homogeneous lots in different laboratories.
Refer to the examples of “Ruggedness Trial” in the AOAC Requirements for Single Laboratory Validation of Chemical Methods (DRAFT ).

37. Robustness
Robustness examine the effect that operational parameters have on the analysis results.
These parameters are pH, flow rate, column temperature, injection volume, detection wavelength or mobile phase composition.
If the influence of the parameter is within a previously specified tolerance, the parameter is said to be within the method’s robustness range.
Obtaining data on these effects helps to assess whether a method needs to be revalidated when one or more parameters are changed, for example, to compensate for column performance over time.
It is recommended to consider the evaluation of a method’s robustness during the development phase, and any results that are critical for the method should be documented.

38. Sources
ISTA guide:
EURACHEM guide:
AOAC guide:
Other guide:

39. Thank You