Analytical Methods: A Statistical Perspective on the ICH Q2A and Q2B Guidelines for Validation of Analytical Methods

Dec 01, 2006
Volume 19, Issue 12


Vagueness in the ICH Q2A and Q2B guidelines necessitates effective protocol design and data analysis. For specificity (detection in the presence of interfering substances), the goal is statistical differences with meaningful implications on assay performance. Linearity (results directly proportional to concentration of analyte in the sample) is typically demonstrated via least squares regression. Accuracy (difference between measured and true values) usually is presented as a percent of nominal. Precision analysis is vital because it supports claims of accuracy and linearity. A well-designed experiment and statistically relevant methods will facilitate method validation in accordance with ICH guidelines.

Several articles have been published on the requirements of method validation for analytical methods.1,2 Most of these articles do not, however, concentrate on the protocol design and analysis of data from these studies. The International Conference on Harmonization (ICH) guidelines on Validation of Analytical Procedures (Q2A and Q2B) delineate the guidance and methodology for validation characteristics of an analytical procedure, but as in many guidelines, the terminology is vague enough to allow for several acceptable approaches and analyses. Appropriate statistical methods should be used; in addition, all relevant data collected during validation and all formulae used for calculating validation characteristics should be submitted and discussed as appropriate.

The following excerpt from the ICH Q2B guideline is an example of the vagueness that can trouble many scientists:

Approaches other than those set forth in this guideline may be applicable and acceptable. It is the responsibility of the applicant to choose the validation procedure and protocol most suitable for their product. However, it is important to remember that the main objective of validating an analytical procedure is to demonstrate that the procedure is suitable for its intended purpose. Due to their complex nature, analytical procedures for biological and biotechnological products in some cases may be approached differently than in this document.3

The ICH guidelines suggest combining individual validation characteristics to minimize total testing. A statistical approach to validation of analytical methods can minimize the amount of testing while meeting the requirements of the guidelines. This assertion is based on the following comment from the ICH Q2B document:

In practice, it is usually possible to design the experimental work such that the appropriate validation characteristics can be considered simultaneously to provide a sound, overall knowledge of the capabilities of the analytical procedure, for instance: specificity, linearity, range, accuracy and precision.3


Table 1. Assuming one needed to show acceptable results for all characteristics, a testing scheme could be developed to meet the minimum test requirements of the guidance.
There are four common types of analytical methods, each with its own set of validation requirements. The level of stringency is proportional to the criticality of the method in testing drug product. The four most common types of analytical procedures are:
  • 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, drug product, or other selected component(s) in the drug product.

The elements of the analytical method requiring proof through validation as contained in the ICH Q2A guideline are summarized here in Table 1.4

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