Method validation traditional

Even though the G2 neutral test set is very valuable, it is biased towards small molecules and does not cover all bonding situations that may arise for a given element. The validation of semiempirical methods has traditionally been done using larger test sets which, however, have the drawback that the experimental reference data are often less accurate than those in the G2 set. 

Partial or complete re-validation is another precedented approach to method transfer. Those variables described in method validation guidance documents (ICH Q2B, 1996 USP, 2012c) that are likely to be impacted by method transfer, should be assessed and documented (transfer or validation protocol). Agut et al. (2011) indicated that, in the changing industry model with the increased outsourcing of R and D activities (alliances, outsourcing, etc.), method re-validation may constitute, in some cases, an efficient approach when the transfer is performed from the Analytical Development Laboratory of an external partner who does not share exactly the same environment (validation standards, analytical culture or traditions , equipment, etc.). ... 

In traditional method validation, assessment of the calibration has been discussed in terms of linear calibration models for univariate systems, with an emphasis on the range of concentrations that conform to a linear model (linearity and the linear range). With modern methods of analysis that may use nonlinear models or may be multivariate, it is better to look at the wider picture of calibration and decide what needs to be validated. Of course, if the analysis uses a method that does conform to a linear calibration model and is univariate, then describing the linearity and linear range is entirely appropriate. Below I describe the linear case, as this is still the most prevalent mode of calibration, but where different approaches are required this is indicated. 

There are two approaches to single-laboratory method validation The traditional one that identifies and then evaluates the set of analytical parameters, and a more recent one that is based on the evaluation of uncertainty. 

It is important to first note that many classical clinical assays have traditionally measured one metabolite to detect one disease. Consequently, many of the rules of method validation were designed around this premise. MS/MS, as originally designed, detected two classes of compounds, amino acids and acylcamitines, in four to five different MS methods (known as scan modes such as neutral loss, precursor ion, or selected reaction monitoring), for approximately 500 distinct masses, more than 70 known compounds, and 20-30 stable isotope internal standards. How then did one approach such a complicated validation to gain acceptance as a reliable, useful method The answer is quite simple - start simply and compare to what was already established. 

An important fact inherent in the purity analysis of a recombinant pharmaceutical is that the absolute purity of any protein is an elusive, if not an unobtainable, measurement. For biopharmaceuticals, purity is a relative term. Protein purity is method-dependent and is defined by the shortcomings of the analytical procedure. Also, unlike small traditional drugs, proteins are highly complex molecules. For these two reasons, more than one method must be utilized to define a protein s purity. The greater the number of methods used in the purity analysis, the greater the assurance is that the product is pure. Furthermore, the purity determined by an analytical method can only be properly interpreted based on the method s validation. Analytical methods validation is critical to and inseparable from purity determinations. A detailed discussion on analytical methods validation is beyond the scope of this chapter but other sources of information are available for the interested reader.11 13... 

Spores are not killed by the antimicrobials listed in 333.412 of the TFM under the testing conditions specified. Spore challenge, therefore, can be applied as a tool to validate a chosen method [11,12]. In a time-kill test, for example, by comparing for retrieval efficiency of the spores added to the test product and to saline, one could validate the efficiency of the chosen method in retrieval of surviving microorganisms. If the chosen method fails to retrieve spores efficiently, then it cannot retrieve vegetative cells either and, hence, is not a valid method to use. If the relative spore-recovery rate from the test product is less than 100%, then its potency should be discounted from the recovery rate in order to refiect the true efficacy. Table 1 shows, through method validation, how a traditional method described in the TFM effectively evaluates some formulations but is inadequate for others. 

Spectroscopic methods can provide fast, non-destructive analytical measurements that can replace conventional analytical methods in many cases. The non-destructive nature of optical measurements makes them very attractive for stability testing. In the future, spectroscopic methods will be increasingly used for pharmaceutical stability analysis. This chapter will focus on quantitative analysis of pharmaceutical products. The second section of the chapter will provide an overview of basic vibrational spectroscopy and modern spectroscopic technology. The third section of this chapter is an introduction to multivariate analysis (MVA) and chemometrics. MVA is essential for the quantitative analysis of NIR and in many cases Raman spectral data. Growth in MVA has been aided by the availability of high quality software and powerful personal computers. Section 11.4 is a review of the qualification of NIR and Raman spectrometers. The criteria for NIR and Raman equipment qualification are described in USP chapters <1119> and < 1120>. The relevant highlights of the new USP chapter on analytical instrument qualification <1058> are also covered. Section 11.5 is a discussion of method validation for quantitative analytical methods based on multivariate statistics. Based on the USP chapter for NIR <1119>, the discussion of method validation for chemometric-based methods is also appropriate for Raman spectroscopy. The criteria for these MVA-based methods are the same as traditional analytical methods accuracy, precision, linearity, specificity, and robustness however, the ways they are described and evaluated can be different. 

Method validation for NIR or Raman spectroscopic methods using chemometrics is outlined in USP chapter <1119>. The criteria for method validation are the same as other quantitative analytical methods accuracy, precision, intermediate precision, linearity, specificity, robustness. However, because these methods are statistical in nature and are based on a previously validated analytical method, the validation of MVA methods is somewhat different than traditional analytical methods. 

This chapter reviews the use of spectroscopic methods for the quantitative analysis of pharmaceutical products. In recent years, there has been great progress made in the use of techniques such as NIR and Raman for real world pharmaceutical problems. USP chapters for NIR and Raman spectroscopy outline the requirements for equipment qualification and method validation. Because spectroscopic methods for quantitative analysis often involve the use of MVA and chemometrics, the approaches for method validation are somewhat different than that for traditional chromatographic methods. 

The book begins with fundamentals, both of hydrogen exchange itself, in Chapter 1, and how a traditional peptide-based experiment is conducted, in Chapter 2. HX-MS is a data-mtensive method data analysis and visualization tools play an essential role. The genraal requirements for a software platform and several examples of data analysis software are reviewed in Chapter 3. The requirements for method validation and standards for hydrogen exchange measurements are presented in Chapter 4. 

The focus of this chapter is validation of liquid chromatographic methods. Numerous articles describe details and provide examples for LC method validation [6,7] therefore, a step-by-step guide to method validation is not provided here. This chapter provides an overview of LC method validation as well as a discussion about some of the pitfalls associated with traditional validation and some alternative practices that are currently being discussed in the literature. 

It is not imcommon for a method that has been shown to be valid based on traditional guidelines to fail when transferred to a testing lab for routine use. While the traditional method validation guidelines just discussed provide a level of assiuance that an analytical method will perform adequately, they do not directly address the piupose of a quantitative method, that is, that the method provides a "reasonable" estimate of the true value of the sample tested. In addition, traditional validation guidelines recommend evaluation of method robustness by... 

The risk of making an incorrect inference by accepting a subpotent lot (as illustrated by this example) is controlled by the criteria in an ATP. This concept of risk is not currently included in traditional method validation guidelines however, it has been incorporated in other applications [23]. 

Traditional method-validation experiments described by ICH and the harmonized lUPAC guidelines provide a reasonable assurance that the method performs as needed. However, these guidelines do not recommend establishing criteria for total method variability nor do they directly answer the question of risk and agreement of a sample measurement with... 

Method validation has traditionally focused on quantitative aspects, and with the exception of the last few years, less attention has been paid to reliable identification. Confirmation of potential positives is a matter of concern due to the imdesirable effects associated with erroneous... 

Under these circumstances it is both tempting and common practice for designers to treat plastics as though they were traditional materials and to apply familiar design methods with what seem appropriate materials constants. It must be admitted that this pragmatic approach does often yield acceptable results. However, it should also be recognized that the mechanical characteristics of plastics are different from those of metals, and the validity of this pragmatic approach is often fortuitous and usually uncertain. 

Robust and resistant isochrons can have very different characteristics than traditional least-squares or error-weighted least squares regressions. Some methods ignore analytical errors entirely, others infer them from the observed scatter of the data, and still others make use of analytical errors only to the extent that they are validated by their observed scatter. 

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