The validation individuality should be investigated based on the nature of the analytical method. outcome for each applicable validation characteristic are compared against the selected approval criteria and are summarized in the analytical method validation report. ICH also provides recommendations on statistical analysis required to demonstrate method appropriateness.

Reference Standards:^2,4,7.

Investigation of drugs and their metabolites in a biological matrix is performed using calibration standards and quality control samples (QCs) spiked with reference standards. The purity of the reference standard used to prepare spiked samples can affect study data. For this reason, legitimated analytical reference standards of known identity and purity should be used to prepare solutions of known concentrations. If possible, the reference standard should be identical to the analyte. When this is not possible, an established chemical form (free base or acid, salt or ester) of known purity can be used. Three types of reference standards are usually used:

(1) Licensed reference standards (e.g., USP compendial standards),

(2) Commercially-supplied reference standards obtained from a trustworthy commercial source, and/or

(3) Other materials of predictable purity custom-synthesized by an analytical laboratory or other noncommercial establishment.

The source and lot number, ending date, certificates of analyses when available, and/or internally or externally generated evidence of identity and purity should be furnished for each reference and internal standard (IS) used. If the reference or internal standard expires, stock solutions made with this lot of standard should not be used unless purity is re-established.

Selectivity:

Selectivity is the ability of an analytical method to separate and measure the analyte in the crowd of other components in the sample. facts should be provided that the substance quantified is the intended analyte. Analyses of blank samples of the appropriate biological matrix (plasma, urine, or other matrix) should be obtained from at least six sources. Each blank sample should be tested for hindrance, and selectivity should be ensured at the lower limit of quantification (LLOQ). Potential interfering substances in a biological matrix include endogenous matrix components; metabolites; breakdown products; and, in the actual study, concomitant medication and other xenobiotics. If the method is planned to quantify more than one analyte, each analyte should be tested to ensure that there is no obstruction.

Accuracy, Precision, and Recovery:

The accuracy of an analytical method describes the closeness of mean test results obtained by the method to the actual value (concentration) of the analyte. Accuracy is determined by repeat analysis of samples containing known amounts of the analyte (i.e., QCs). Accuracy should be measured using a minimum of five determinations per concentration. A minimum of three concentrations in the range of expected study sample concentrations is recommended. The mean value should be within 15% of the nominal value except at LLOQ, where it should not deviate by more than 20%. The deviation of the mean from the nominal value serves as the measure of accuracy.

The precision of an analytical method describes the closeness of individual measures of an analyte when the procedure is applied repeatedly to multiple aliquots of a single homogeneous volume of biological matrix. Precision should be measured using a minimum of five determinations per concentration. A minimum of three concentrations in the range of expected study sample concentrations is recommended. The precision determined at each concentration level should not exceed 15% of the coefficient of variation (CV) except for the LLOQ, where it should not exceed 20% of the CV. The recovery of an analyte in an assay is the detector response obtained from an amount of the analyte added to and extracted from the biological matrix, compared to the detector response obtained for the true concentration of the analyte in solvent. Recovery pertains to the extraction efficiency of an analytical method within the limits of variability. Recovery of the analyte need not be 100%, but the extent of recovery of an analyte and of the internal standard should be consistent, precise, and reproducible.

Calibration Curve:

A calibration (standard) curve is the relationship between response and known concentrations of the analyte. The relationship between response and concentration should be continuous and reproducible. A calibration curve should be generated for each analyte in the sample. The calibration standards can contain more than one analyte. A calibration curve should be prepared in the same biological matrix as the samples in the intended study by spiking the matrix with known concentrations of the analyte. In rare cases, matrices may be difficult to obtain (e.g., cerebrospinal fluid). In such cases, calibration curves constructed in stand in matrices should be reliable. Concentrations of standards should be chosen on the basis of the concentration range expected in a particular study. A calibration curve should consist of a blank sample (matrix sample processed without analyte or internal standard), a zero sample (matrix sample processed without analyte but with internal standard), and at least six non-zero samples (matrix samples processed with analyte and internal standard) covering the expected range, including LLOQ. Method validation experiments should include a minimum of six runs conducted over several days, with at least four concentrations (including LLOQ, low, medium, and high) analyzed in replica in each run.

a. Lower Limit of Quantification (LLOQ)

Lower Limit of Quantification (LLOQ) Contains The lowest standard on the calibration curve, should be accepted as the LLOQ if the following conditions are met:

· The analyte response at the LLOQ should be at least five times the response compared to blank response.

· Analyte peak (response) should be expressable, discrete, and reproducible, and the back-calculated concentration should have precision that does not exceed 20% of the CV and accuracy within 20% of the nominal concentration. The LLOQ should not be confused with the limit of detection (LOD) and/or the low QC sample.

· The LLOQ should be recognized using at least five samples and determining the CV and suitable confidence interval should be determined.

b. Upper Limit of Quantification (ULOQ):

The highest standard will define the ULOQ of an analytical method.

· Analyte peak (response) should be reproducible and the back-calculated concentration should have precision that does not exceed 15% of the CV and accuracy within 15% of the nominal concentration

c. Calibration Curve/Standard Curve/Concentration-Response:

· The simplest model that adequately describes the concentration-response relationship should be used. Selection of weighting and use of a complex regression equation should be justified. Standards/calibrators should not deviate by more than 15% of nominal concentrations, except at LLOQ where the standard/calibrator should not deviate by more than 20%.

· The acceptance criterion for the standard curve is that at least 75% of non-zero standards should meet the above criteria, including the LLOQ. Excluding an individual standard should not change the model used. Exclusion of calibrators for reasons other than failing to meet acceptance criteria and assignable causes is discouraged.

d. Quality Control Samples (QCs):

· At least three concentrations of QCs in duplicate should be incorporated into each run as follows: one within three times the LLOQ (low QC), one in the midrange (middle QC), and one approaching the high end (high QC) of the range of the expected study concentrations.

· The QCs provide the basis of accepting or rejecting the run. At least 67% (e.g., at least four out of six) of the QCs concentration results should be within 15% of their theoretical values. At least 50% of QCs at each level should be within 15% of their nominal concentrations. A confidence interval approach yielding comparable accuracy and precision in the run is an appropriate alternative.

· The minimum number of QCs should be at least 5% of the number of unknown samples or six total QCs, whichever is greater.

· It is recommended that calibration standards and QCs be prepared from separate stock solutions. However, standards and QCs can be prepared from the same spiking stock solution, provided the stability and accuracy of the stock solution have been verified. A single source of blank matrix may also be used, provided absence of matrix effects on extraction recovery and detection has been verified. At least one demonstration of precision and accuracy of calibrators and QCs prepared from separate stock solutions is expected. Acceptance/rejection criteria for spiked, matrix-based calibration standards and QCs should be based on the nominal (theoretical) concentration of analytes.

4. Sensitivity:

Sensitivity is defined as the lowest analyte concentration that can be measured with acceptable accuracy and precision (i.e., LLOQ).

5. Reproducibility:

Reproducibility of the method is assessed by replicate measurements using the assay, including quality controls and possibly incurred samples. Reinjection reproducibility should be evaluated to determine if an analytical run could be reanalyzed in the case of instrument interruptions.

6. Stability:

The chemical stability of an analyte in a given matrix under specific conditions for given time intervals is assessed in several ways. Pre-study stability evaluations should cover the expected sample handling and storeroom conditions during the conduct of the study, including conditions at the experimental site, during delivery, and at all other secondary sites. Drug constancy in a biological fluid is a function of the storage situation, the physicochemical properties of the drug, the matrix, and the container system. The stability of an analyte in a particular matrix and container system is relevant only to that matrix and container system and should not be extrapolated to other matrices and container systems. constancy testing should evaluate the stability of the analytes during sample collection and handling, after long-term (frozen at the intended storage temperature) and short-term (bench top, room temperature) storage, and after freeze and melt cycles and the analytical process. Conditions used in stability experiments should reflect situations likely to be encountered during actual sample handling and analysis. If, during sample analysis for a study, storage conditions changed and exceeded the sample storage conditions evaluated during method validation, stability should be recognized under these new conditions.

The procedure should also include an evaluation of analyte stability in stock solution. All stability determinations should use a set of samples prepared from a freshly made stock solution of the analyte in the appropriate analyte-free, interference-free biological matrix. Stock solutions of the analyte for stability evaluation should be prepared in an appropriate solvent at known concentrations. Stability samples should be compared to freshly made calibrators. At least three replicates at each of the low and high concentrations should be assessed. Stability sample results should be within 15% of nominal concentrations.

a. Freeze and Thaw(melt) Stability:

During freeze/thaw stability evaluations, the freezing and thawing of stability samples should mimic the intended sample handling conditions to be used during sample analysis. Stability should be assessed for a minimum of three freeze-thaw cycles.

b. Bench-Top Stability:

Bench top stability experiments should be designed and conducted to cover the laboratory managing conditions that are expected for study samples.

c. Long-Term Stability:

The storage time in a long-term stability evaluation should equal or exceed the time between the date of first sample collection and the date of last sample analysis.

d. Stock Solution Stability:

The stability of stock solutions of drug and internal standard should be evaluated. When the stock solution exists in a different state (solution vs. solid) or in a different buffer composition (generally the case for macromolecules) from the licensed reference standard, the stability data on this stock solution should be generated to rationalize the duration of stock solution storage stability.

e. Processed Sample Stability:

The stability of processed samples, including the resident time in the auto sampler, should be determined

· New technologies or advancements:^2,7

The Dried Blood Spot (DBS) methodology has been successful in individual cases, the method has not yet been widely accepted. Benefits of DBS include reduced blood sample volumes collected for drug analysis and ease of collection, storage, and transportation. A complete validation will be essential prior to using DBS in synchronized studies. This validation should address, at a minimum, the effects of the following issues: storage and handling temperature, homogeneity of sample spotting, hematocrit, stability, and carryover. Correlative studies with conventional sampling should be conducted during drug development.

Statistics^3,5,6,7 in Analytical Method Validation:

Statistical investigation of data obtained during a method validation should be performed to reveal validity of the analytical method. The statistics required for the interpretation of analytical method validation results are the computation of the following-

1. mean,

2. Standard deviation,

3. Relative standard deviation,

4.confidence intervals, and

5. Regression analysis.

These calculations are classically performed using statistical software packages such as Excel, Minitab, etc. The purpose of statistical analysis is to review a collection of data that provides an understanding of the examined method characteristic. The approval criterion for each validation characteristic are typically around the individual values as well as the mean and relative standard deviation.

Mean:

Mean or average of an numbers set is the essential and the most frequent statistics used. The mean is calculated by adding all data points and dividing the sum by the number of samples. It is typically denoted by x̄ (x bar) and is computed using the following formula:

where X_i are individual values and n is the number of individual data points.

Standard Deviation:

The standard divergence or the deviation of a data set is the evaluation of the spread of the values in the sample set and is computed by measuring the variation or difference between the mean and the individual values in a set. It is computed using the following formula:

Where, X_i is individual value, X̄ is the sample mean, and n is the number of individual data points.

Relative Standard Deviation:

The relative standard deviation is computed by taking the standard deviation of the sample set multiplied by 100% and dividing it by the sample set average. The relative standard deviation is expressed as percent. Typically, the acceptance criterion for accuracy, precision, and repeatability of data is expressed in % RSD:

Confidence Interval:

Confidence intervals are used to designate the trustworthiness of an estimation. Confidence intervals gives us limits around the sample mean to predict the range of the true population of the mean. The prediction is usually based on possibility or probability of 95%. The confidence interval depends on the sample standard deviation and the sample mean. Confidence interval for:

Where, s is the sample deviation, X̄ is the sample mean, n is the number of individual data points, and z is constant obtained from statistical tables for z. The value of z depends on the confidence level listed in statistical tables for z. For 95%, z is 1.96. For small samples, z can be replaced by t-value obtained from the Student’s t-distribution tables. The value of t corresponds to n-1. Following Table provides an example of a classic data analysis summary for the estimation of a system correctness or precision for a high-powered liquid chromatography (HPLC) analysis.

Table no. 2:An Example of a System Precision Determination for a HPLC Analysis.

Injection Number	Area Response for analyte peak in standard
1	451662
2	450752
3	447638
4	452541
5	449321
6	448747
Mean	450110
Standard Deviation	1861
%RSD	0.41
95% confidence interval	448621 to 451599

The calculated confidence interval in Table indicates that the range of the true population of the mean is between 448621 and 451599.

Regression Analysis:

Regression analysis is used to evaluate a linear relationship between test results. A linear relationship is usually evaluated across the range of the analytical practice. The data obtained from analysis of the solutions prepared at a range of different concentration levels is usually investigated by plotting on a graph. Linear regression evaluates the relationship between two variables by fitting a linear equation to experimental data. A linear regression line has an equation of the form-

Y = b_o + b₁X,

Where,

X- Independent or self-determining variable and Y - Dependent variable.

The slope of the line is b₁, and b_o is the intercept (the value of y when x = 0). The statistical procedure of ruling the “best-fitting” straight line is to obtain a line through the points to reduce the deviations of the points from the approaching line. The best-fit criterion of integrity of the robustness or fitness is known as the principle of least squares. Following Table provides an example of data that is evaluated for linearity.

Table no. 3:Solution Concentration versus Measurements:

Concentration X	Response Y
Concentration X	Measurement
10	213
20	378
30	629
40	848
50	994
60	1227

In this example, measurement values (Response Y) are plotted against corresponding concentration (X), refer to Figure

Figure no.-1

In this case, the data obviously shows a linear relationship. The fitted or estimated regression line equation is computed using the subsequent formula:

Y = b₀ + b₁X + e_i

Summary or Outline of work:

Measurement of drug concentrations in biological matrices (such as serum, plasma, blood, urine, and saliva) is an vital attribute of medicinal product development.

From this review we came to know regarding these validation parameters, and off course how important those are. It is therefore vital that the applied Bioanalytical methods used are well characterized, completely validated and documented to an adequate standard in order to yield consistent or reliable results.

REFERENCES:

1. Rama Rao Kalakuntla and K. Santosh Kumar, Bioanalytical Method Validation: A Quality Assurance Auditor View point, Journal of Pharmaceutical Sciences and Research, Vol.1(3), 2009,pg. no.1-10.

2. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) Center for Veterinary Medicine (CVM)September 2013 Biopharmaceutics Revision 1.

3. Eugenie Webster (Khlebnikova) ,Statistical Analysis in Analytical Method Validation, IVT Dec 16, 2013.

4. Vinod P. Shah,1 Kamal K. Midha, Bioanalytical Method Validation—A Revisit with a Decade of Progress, Pharmaceutical Research, Vol. 17, No. 12, 2000.

5. http:www.authorstream.com.

6. http://en.wikipedia.org/wiki/bioanalysis.

7. www.fda.gov/downloads/drugs/Guidelinescompliance Regulatory information /Guidance/ucm368107.pdf.

Received on 16.11.2015 Accepted on 28.12.2015

Asian J. Pharm. Ana. 5(4): October- December, 2015; Page 219-225

DOI: 10.5958/2231-5675.2015.00035.6

Validation characteristics	ICH definition
Accuracy	The accuracy of an analytical procedure expresses 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. This is sometimes termed trueness.
Specificity	Specificity is the ability to assess unequivocally the analyte in the presence of components which may be expected to be present. Typically these might include impurities, degradants, matrix, etc.
Precision	The precision of an analytical procedure expresses the closeness of agreement (degree of scatter) between a series of measurements obtained from multiple sampling of the same homogeneous sample under the prescribed conditions. Precision may be considered at three levels: repeatability, intermediate precision and reproducibility.
Repeatability	Repeatability expresses the precision under the same operating conditions over a short interval of time. Repeatability is also termed intra-assay precision .
Intermediate precision	Intermediate precision expresses within-laboratories variations: different days, different analysts, different equipment, etc.
Reproducibility	Reproducibility expresses the precision between laboratories (collaborative studies, usually applied to standardization of methodology).
Limit of detection	The detection limit of an individual analytical procedure is the lowest amount of analyte in a sample which can be detected but not necessarily quantitated as an exact value.
Limit of quantitation	The quantitation limit of an individual analytical procedure is the lowest amount of analyte in a sample which can be quantitatively determined with suitable precision and accuracy. The quantitation limit is a parameter of quantitative assays for low levels of compounds in sample matrices, and is used particularly for the determination of impurities and/or degradation products.
Linearity	The linearity of an analytical procedure is its ability (within a given range) to obtain test results which are directly proportional to the concentration (amount) of analyte in the sample.
Range	The range of an analytical procedure is the interval between the upper and lower concentration (amounts) of analyte in the sample (including these concentrations) for which it has been demonstrated that the analytical procedure has a suitable level of precision, accuracy and linearity.
Robustness	The robustness of an analytical procedure is a measure of its capacity to remain unaffected by small, but deliberate variations in method parameters and provides an indication of its reliability during normal usage.