Method validation Flashcards
Analytical Method Validation Considerations :-
When we validate :-
Development of new methodology for manufacturing support
Continuous Improvement
To support changes to analytical methodology
Manage obsolescence changes
To show equivalence with pharmacopoeia methods
Raise Change Control
Follow principles outlined in ICHQ2
Purpose of validation is to show that a method is accurate, precise & specific.
Accuracy is
the degree to which the result of a measurement, calculation, or specification conforms to the correct value or a standard
Precision is
the closeness of two or more measurements to each other is known as the precision of a substance.
Specificity is
Defined as the ability of an assay to distinguish target from nontarget analytes, including matrix (i.e., specimen) components.
Limit of detection is
The minimum value that can be reproducibly detectable in an analytical method
Limit of quantification is
The minimum level of an analyte that can be safely and reproducibly reported
Linearity is
The capability of a method to accurately and precisely determine the amount of an analyte over a range of concentrations around a nominal value - the new ICH Q2 and Q14 guidelines - the Q2 gives some thought on the recommended data eg linearity = min 5 concentrations across the range, accuracy = 3 concentrations/ 3 replicates
Range is
the working range of analytical method, usually representative of the accuracy, precision and linearity
Robustness is
validation to include method parameters that could vary and still demonstrate control
Method validation documentation required
Raise Validation Protocol – Objective / Background, Acceptance Criteria, Methodology, Validation Parameters.
This will be followed by a Validation Report - Summary of Results Vs Acceptance, Criteria, Individual Results and Discussion, Conclusions & Recommendations.
What stats are applied during analytical method validation?
Two Sided T-Test can be used to demonstrate equivalence
Linearity – R value should be 0.998
Range – assay 80 – 120%
Intermediate Precision – F-Test
LOD and LOQ
Analytical method transfer considerations
For multiple strength if formulation is step up and step down where placebo contrast is same any strength can be used.
If formulation is look a like which means all strength is having same average weight just active ingredient concentration is changing in such - choose lower strength where placebo is higher.
For assay and dissolution - specificity and precision to be checked
For Related substances- RS we do specificity , precision , LOQ confirmation .
If impurity is below LOQ or not detected same needs to be spiked at limit concentration and needs to prove method is capable to deter if impurity is present in sample.
Perform identification test and any TLC to avoid future challenges.
How would you go about the method validation and tech transfer for an assay test?
I would raise a change control to assess the change. I would raise a tech transfer protocol and follow the guidance outlined in ICH Q2 for analytical method validation. This includes testing for the following:-
Accuracy
Precision
Specificity
LOQ /LOD
Linearity
Range
How would you compare the data from the accuracy and precision tests? What is t value and p value, what parameters would you measure?
Double sided t- test
T value measures the difference between the relative variation of your sample data
The closer the T value is to 0 the less variation there is in your data
P value is the calculated probability of obtaining a t value – the larger the t value the smaller the p value
How would you validate the method robustness?
Robustness typically only performed in development of a new analytical method. A number of small deliberate changes are made to method parameters such as flow rate, temperature, pH, injection volume etc. The impact on the method can
How would you validate an analytical method transfer for an assay and what values would you expect to see?
Use significance tests:
T-test comparison of experimental mean with a known value (null hypothesis: no difference between experimental and known value): determine t, compare with t from tables for P = 0.05 (significance level), if tfound < tcritical null hypothesis stands, if tfound > tcritical null hypothesis is rejected
T-test comparison of means of two samples, e.g. the means from two analytical methods (null hypothesis: the two methods give the same results): determine t (using pooled std deviation), compare with t from tables for P = 0.05 (significance level), if tfound < tcritical null hypothesis stands), if tfound > tcritical null hypothesis is rejected
NOTE: comparison of means detects systematic errors
F-test for comparison of std deviations. A significance test for comparing random errors of two sets of data, e.g. whether method A is more precise than method B (one-tailed test)or whether two methods differ in their precision (two-tailed t-test). Looking at variances (i.e. squares of std deviations, F = s12 / s22, null hypothesis: variances are equal, populations normal): determine F, compare determined F with critical value from tables for P = 0.05 (significance level), for two-tailed test: if Ffound < Fcritical null hypothesis stands
Adopting the null hypothesis that the method is not subject to systematic error (→ you want the probability of random error to be “high”) i.e., that there is no difference between the observed and known values (other than that which can be attributed to random variation) → probability that the observed difference between the mean and the true value arises solely because of random errors: the lower it is the less likely that the null hypothesis is true. Usually the null hypothesis is rejected if the probability of such difference occurring by chance is less than 1 in 20 (i.e. 0.05 or 5%). The significance level is indicated P = 0.05 and is the probability of rejecting the null hypothesis.
From Alex H:
Data 1 Data 2
X y
Xx yy
Xxx yyy
… …
Mean, SD Mean, SD,
RSD RSD
Compare RSDs
F-test
T-Test – P value greater than 0.05
ANOVA (analysis of variance):
comparing more than two means
Is it just an assay method or is it a Impurities method ? – this will determine if you need LOD/LOQ
ICHQ2 Analytical Method Validation, Annex 15 Qualification / Validation
Accuracy – closeness in agreement to a known standard value – assay, API and product: recovery ±2% RSD ≤ 3%; impurities: recovery ±10% RSD ≤ 10% (imp ≤ 0.5%) or ≤ 5% (imp >0.5%)
Precision - Precision may be considered at three levels: repeatability, intermediate precision and reproducibility. Expressed as variance, standard deviation or coefficient of variation of a series of measurements. API and product: RSD ≤ 2%, Impurities: RSD < 5% (imp > 0.2%), RSD < 10% (0.1% < imp < 0.2%), intermediate precision: RSD = 1.5 x RSD for repeatability
Specificity – ability to detect content or potency of the analyte in a sample accurately
LOD/LOQ – LOD – 1 s.d from baseline, 3 s.d from baseline
LOD: 2 or 3:1 S/N or 3.3σ/S RSD ≤ 15%; LOQ: 10:1 S/N or 10σ/S RSD ≤ 20%
Linearity – obtain test results which are directly proportional to the concentration (amount) of analyte in the sample.0.995 minimum AI r > 0.990 RSD ≤ 1.5%; impurities: r > 0.900, RSD ≤ 10% (imp ≤ 0.5%) or RSD ≤ 5% (imp > 0.5%)
Range - demonstrated that the analytical procedure has a suitable level of precision, accuracy and linearity. For the assay of a drug substance or a finished (drug) product: normally from 80 to 120 percent of the test concentration;
What range would you expect for an assay?
For the assay of a drug substance or a finished (drug) product: normally from 80 to 120 percent of the test concentration;
What value would you expect for LOD?
3:1 signal-to-noise ratio
Below the reporting threshold for the impurity
What value would you expect for LOQ
At or below the specified limit
10:1 signal-to-noise ratio
What is the P value?
P Value is the probability of obtaining a particular T value
You are carrying out a method transfer of a HPLC assay to a contract laboratory; what are your protocol expectations? What would you expect to see?
ONLY REPEATABILITY
Objective
Reference Standards
Test Items
Reagents
Analytical Method – Assay
Equipment
Evaluation of Analytical Data
Reporting of Results
Quality Management
Why / when use Analytical method validation
- All methods should be validated
- Pharmacopeia methods excluded but should be verified
- Lab equipment must be qualified prior to method validation
- Modification to methods should initiate revalidation
- Validated to ICH Q2 standards
