Method validation concept

Two landmark articles published by H. Mark et al. and G. E. Ritchie et al. [30, 31] demonstrate how ICH Q2 principles can be applied when developing a near-infrared method. The first part of the series describes the method validation concepts as described in this section and proposes additional validation protocols. The second part focuses on the implementation of assay and content uniformity methods for solid dosage forms. Authors present at length how each criterion was validated and how they meet ICH and FDA requirements. For instance, repeatability was tested with the predictions of 13 repeats of tablets at three different concentration levels (80,100, and 120% w/w). In addition to the stated guidelines, authors used ASTM E1655-97 [12] to provide statistics analysis that is not directly provided in ICH Q2(R1). 

Method Validation The signal path from detector up to the hard copy output of the final results must be perceived as a chain of error-prone components there are errors due to conception, construction, installation, calibration, and (mis-)use. Method validation checks into these aspects. 

Reproducibility in the context of Directive 96/46/EC is defined as a validation of the repeatability of recovery, from representative matrices at representative levels, by at least one laboratory, which is independent of the laboratory which initially validated the study. This independent laboratory may be within the same company, but may not be involved in the development of the method. This concept of independent laboratory validation (ILV) substitutes the conduct of interlaboratory trials (e.g., according to ISO 5725) because the resources are not available taking into consideration the high number of a.i., matrix types and concentration levels which must be validated in the registration procedure. 

In Europe, very different concepts of method validation are in use. The extent of validation depends upon legal requirements (e.g., for enforcement methods provided by the applicant), upon the required level of acceptance (e.g., for CEN methods) and upon national resources. Undoubtedly, the best method validation is performed with the help of inter-laboratory studies of performance, but such studies can be uneconomic, too slow to reach completion or restricted in scope. 

An indication of the minimum size of a subsample can be obtained by using the concept of a sampling constant. For example, in the laboratory, the sampling constant can be used to estimate the minimum size of the test portion. However, the suitability of the chosen test portion size must be confirmed as part of method validation. The sampling constant Ks has units of mass. This is the mass of the test portion necessary to ensure a relative subsampling error of 1% (at the 68% confidence level) in a single determination. The value of /Ks is numerically equal to the coefficient of variation, CV (see Chapter 6, Section 6.1.3) for results obtained on 1 g subsamples in a procedure with insignificant analytical error. 

This chapter deals with handling the data generated by analytical methods. The first section describes the key statistical parameters used to summarize and describe data sets. These parameters are important, as they are essential for many of the quality assurance activities described in this book. It is impossible to carry out effective method validation, evaluate measurement uncertainty, construct and interpret control charts or evaluate the data from proficiency testing schemes without some knowledge of basic statistics. This chapter also describes the use of control charts in monitoring the performance of measurements over a period of time. Finally, the concept of measurement uncertainty is introduced. The importance of evaluating uncertainty is explained and a systematic approach to evaluating uncertainty is described. 

In Section 4.2, we treated the performance of regression models. For classification methods, these concepts remain valid and can be directly used. However, there is an important difference concerning the performance measures to be used. While for regression the basic information for the evaluation measures are the residuals, i.e., the difference between observed and predicted v-values, this would... 

Bouabidi, A., Rozet, E., Fillet, M., Ziemons, E., Chapuzet, E., Mertens, B., Klinkenberg, R., Ceccato, A., Talbi, M., Streel, B., Bouklouze, A., Boulanger, B., Hubert, P. Critical analysis of several analytical method validation strategies in the framework of the fit for purpose concept. J. Chromatogr. A., 1217, 2010, 3180-3192. 

Method validation is discussed in Chapter 7 as it relates to the HPLC methods of analysis. Validation is a process required by law, and the concept is described by regulatory agencies in the guidance documents. The analyst performing method validation is responsible for interpreting the... 

However, certain fundamental concepts of cGMPs must be applied regardless of the details of the phased appropriate method validation strategy used. Examples are (1) proper documentation, (2) change control, (3) deviations, (4) equipment and utilities qualification, and (5) proper training. 

Introduction Quality of Analytical Results Role of Method Validation in Traceability and MU Guidelines on Traceability and Uncertainty of Results Concept of Traceability Concept of MU... 

This update on analytical quahty issues a common understanding on the topics of method validation, traceabihty, and MU of measurements. The interrelationships between method validation and traceability and MU of results have been elucidated. Throughout the landscape of guidelines and standards, the most relevant information was selected, compiled, and summarized. Different approaches are discussed for establishing traceability and assessing MU of analytical methods in general. The importance of both concepts and the link with method validation and analytical quality assurance are highlighted. 

In practice, data from method validation and collaborative studies form the basis for but are only a part of MU estimation. MU is thus more than just a method performance parameter, as described extensively in Section 8.2.2. Over the years, the concept of MU has won attention in all analytical areas and this has led to two different approaches currently accepted and used for analytical method validation. 

Method validation is a term used for the suite of procedures to which an analytical method is subjected to provide objective evidence that the method, if used in the manner specified, will produce results that conform to the statement of the method validation parameters. Like many aspects quality assurance, method validation is of a relative nature. As with the concept of fitness for purpose, a method is validated for a particular use under particular circumstances. If those circumstances vary, then the method would need to be re-validated at least for the differences. Common sense should be used, and the analysts should use his or her skill and experience to decide what aspects of a method require validation and to what extent. The goal of satisfying client requirements is prominent in most published definitions of method validation, some of which are listed below ... 

METHOD VALIDATION FOR FOOD ANALYSIS CONCEPTS AND USE OF STATISTICAL TECHNIQUES... 

B. Jiilicher, P. Gowik, S. Uhlig, Assesment of detection methods in trace analysis by means of a statistically based in-house validation concept, Analyst, 123 (1998), 173-179. 

Jenke, J.R. Chromatographic method validation A review of current practices and procedures. Part I. General concepts and guidelines. J. Liq. Chromatogr. 1996, 19 (4), 719-736. 

There are very few papers in the open literature dealing with the general principles of test method validation. On the other hand, a lot of detailed descriptions of the validation of specific test methods are available. A brief overview of the concepts, aims and procedures in validation is given in this document. 

The presented concept of analytical procedure offers a clear perspective on the problem of method validation which is an issue of great concern in quality matters. Validation is generally taken to mean a process of demonstration that a methodology is suitable for its intended application. The question is how should suitability be assessed, based on customer needs ... 

Reliability and relevance are two core concepts of in vitro method validation. The experimental design employed in validation projects at EURL ECVAM is based on all or most modules of the modular approach [17]. It involves seven independent modules where three modules are related to reliability and two to relevance... 

In addition to PAT methods validation, the industry has expressed some anxiety regarding how real-time in-process data would be viewed by regulatory agencies with respect to process validation. The problem stems from another institutional paradigm around what process validation means. Ironically, this concept of process validation may prove critical in finally integrating PAT into mainstream API manufacturing. 

There have been various attempts to place the concept of detection limit on a more firm statistical ground. The International Conference on Harmonization (ICH see Chapter 4) of Technical Requirements for Registration of Pharmaceuticals for Human Use has proposed guidelines for analytical method validation (Ref. 18). The ICH Q2B guideline on validation methodology suggests calculation based on the standard deviation, s, of the response and the slope or sensitivity, S, of the calibration curve at levels approaching the limit. For the limit of detection (LOD),... 

Review Figure 4.1, which places in context most of the validation concepts and steps for a candidate method that we have discussed. We will discuss quality control in the context of quality assurance below. 

Validation concepts, first noted in the worldwide industry as a quality related essential in the late 1970s, are now included and referenced as the norm for all GMP codes applicable in all countries. Validation, not even defined in the original 1976 US GMPs, has grown to cover analytical methods, computer systems and controls, cleaning methods, utilities, and processes a detailed definition appeared in the May 1996 appendix to the US GMPs. 

Although the steps in solving analytical problems usually follow the order listed above, knowledge of basic statistics is useful not just for handling the data and method validation but is required for proper sampling and selection of an analytical method. The statistics and definitions needed to understand what is meant by accuracy, precision, error, and so on are covered in Section 1.3. Students not familiar with these terms and concepts may want to read Section 1.3 at this point. Steps (1) and (2) are covered in this section, while steps (3) through (5) are discussed in the sections following Section 1.3. 

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