ANALYTICAL METHOD VALIDATION
Validation of an analytical procedure is the process by which it is established, by laboratory studies, that the performance characteristics of the procedure meet the requirements for the intended analytical applications
Analytical Method Validation is to be performed for new analysis methods or for current methods when any changes are made to the procedure, composition of the drug product and synthesis of the drugs substances.
Common types of analytical procedure that can be validated.
- Identification tests;
- Quantitative tests for impurities content;
- Limit tests for the control of impurities;
- Quantitative tests of the active moiety in samples of drug substance or drug product or other selected component(s) in the drug product.
Typical validation characteristics which should be considered are listed below:
- Accuracy
- Precision
- Specificity
- Detection Limit
- Quantitation Limit
- Linearity
- Range
- Robustness
- Stability of solution
The validation characteristics are to be evaluated on the basis of the type of analytical procedures.
Parameter |
Type of
Analytical Procedures |
|||
Identification |
Impurities |
Quantitative
Tests |
||
Quantitative |
Limit |
|||
Accuracy |
Not Required |
Required |
Not Required |
Required |
Precision |
Not Required |
Required |
Not Required |
Required |
Specificity |
Required |
Required |
Required |
Required |
Detection Limit |
Not Required |
Not Required |
Required |
Not Required |
Quantitation Limit |
Not Required |
Required |
Not Required |
Not Required |
Linearity |
Not Required |
Required |
Not Required |
Required |
Range |
Not Required |
Required |
Not Required |
Required |
Methods and Terminology
The recovery should be in the range of Control limit.
The following method can be applied for calculating the Upper Control Limit (UCL) and Lower Control Limit (LCL). The method involves the moving range, which is defined as the absolute difference between two consecutive measurements (|xi-xi-1|).
Where xi is an individual measurement in a set of n measurement and is the arithmetic mean of the set. Generally, the RSD should not be more than 2%.
The standard deviation, relative standard deviation (coefficient of variation) and confidence interval should be reported for each type of precision investigated.
In case of identification tests, the method should be able to discriminate between compounds of closely related structures which are likely to be present. Similarly, in case of assay and impurity tests by chromatographic procedures, specificity can be demonstrated by the resolution of the two components which elute closest to each other.[9]
It is not always possible to demonstrate that an analytical procedure is specific for a particular analyte (complete discrimination). In this case a combination of two or more analytical procedures is recommended to achieve the necessary level of discrimination.
It is recommended to have a minimum of five concentration levels, along with certain minimum specified ranges. For assay, the minimum specified range is from 80% -120% of the target concentration.
Regression line, y = ax + b
Where, a is the slope of regression line and b is the y- intercept.
Here, x may represent analyte concentration and y may represent the signal responses.
![](https://2.bp.blogspot.com/-wy37IfMXRuU/VGmHSjmTQMI/AAAAAAAAIh8/oTZJ4-vb-TA/s1600/pharmatutor-art-2304-5.jpg)
Where xi is an individual measurement in a set of n measurement and is the arithmetic mean of the set, yi is an individual measurement in a set of n measurement and
is the arithmetic mean of the set.
c. Standard Deviation of the response and the Slope.
The Detection Limit may be expressed as:
DL = 3.3σ/ s
The Quantitation Limit may be expressed as:
QL = 10σ/ s
Where, σ is standard deviation of the response and s is slope of the linearity curve.
The method used for determining the detection limit and the quantitation limit should be presented. If DL and QL are determined based on visual evaluation or based on signal to noise ratio, the presentation of the relevant chromatograms is considered acceptable for justification.
6. Range
The range of an analytical procedure is the interval between the upper and lower levels of analyte (including these levels) that have been demonstrated to be determined with a suitable level of precision, accuracy, and linearity using the procedure as written. The range is normally expressed in the same units as test results (e.g., percent) obtained by the analytical procedure.
The following minimum specified ranges should ne considered:
- For Assay of a Drug Substance (or a drug product) the range should be from 80% to 120% of the test concentration.
- For Determination of an Impurity: from 50% to 120% of the acceptance criterion.
- For Content Uniformity: a minimum of 70% to 130% of the test concentration, unless a wider or more appropriate range based on the nature of the dosage form (e.g., metered-dose inhalers) is justified.
- For Dissolution Testing: ±20% over the specified range
(e.g., if the acceptance criteria for a controlled-release product cover a region from 20%, after 1 hour, and up to 90%, after 24 hours, the validated range would be 0% to 110% of the label claim).
If measurements are susceptible to variations in analytical conditions, the analytical conditions should be suitably controlled or a precautionary statement should be included in the procedure. One consequence of the evaluation of robustness should be that a series of system suitability parameters (e.g., resolution test) is established to ensure that the validity of the analytical procedure is maintained whenever used.[16]
Examples of typical variations are:
- stability of analytical solutions;
- extraction time.
In the case of liquid chromatography, examples of typical variations are:
- influence of variations of pH in a mobile phase;
- influence of variations in mobile phase composition;
- different columns (different lots and/or suppliers);
- temperature;
- flow rate.
In the case of gas-chromatography, examples of typical variations are:
- different columns (different lots and/or suppliers);
- temperature;
- flow rate.
![](https://2.bp.blogspot.com/-WxisMbvIMoE/VGmHRObHoCI/AAAAAAAAIh0/o8N9hQLEDjw/s1600/pharmatutor-art-2304-1.jpg)
in which tα/2,n-1 is a statistical number dependent upon the sample size (n), the number of degrees of freedom (n-1), and the desired confidence level (1-α).
Its values are obtained from published tables of the Student t-distribution. The confidence interval provides an estimate of the range within which the “true” population mean (µ) falls, and it also evaluates the reliability of the sample mean as an estimate of the true mean. If the same experimental set-up were to be replicated over and over and a 95% (for example) confidence interval for the true mean is calculated each time, then 95% of such intervals would be expected to contain the true mean, µ. One cannot say with certainty whether or not the confidence interval derived from a specific set of data actually collected contains µ. However, assuming the data represent mutually independent measurements randomly generated from a normally distributed population the procedure used to construct the confidence interval guarantees that 95% of such confidence intervals contain µ.
When used appropriately, outlier tests are valuable tools for pharmaceutical laboratories. Several tests exist for detecting outliers such as the Extreme Studentized Deviate (ESD) Test, Dixon's Test, and Hampel's Rule.
Choosing the appropriate outlier test will depend on the sample size and distributional assumptions. Many of these tests (e.g., the ESD Test) require the assumption that the data generated by the laboratory on the test results can be thought of as a random sample from a population that is normally distributed, possibly after transformation.
Normalize each result by subtracting the mean from each value and dividing this difference by the standard deviation.
Take the absolute value of these results, select the maximum value (|R1|), and compare it to a previously specified tabled critical value λ1 based on the selected significance level (for example, 5%). If the the maximum value is larger than the tabled critical value, it is identified as being inconsistent with the remaining data. If the maximum value is less than the tabled critical value, there is not an outlier. Sources for -values are included in many statistical textbooks.
1 Comments
I think it would be helpful to cite the relevant ICH guidance documents and Pharmacopeia chapters that you used in preparing this summary.
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