Validation, method elements

Identification of sources of analytical bias in method development and method validation is another very important application of reference materials in geochemical laboratories. USGS applied simplex optimization in establishing the best measurement conditions when the ICP-AES method was introduced as a substitute for AAS in the rapid rock procedure for major oxide determinations (Leary et al. 1982). The optimized measurement parameters were then validated by analyzing a number of USGS rock reference samples for which reference values had been established first by classical analyses. Similar optimization of an ICP-AES procedure for a number of trace elements was validated by the analysis of U S G S manganese nodule P-i (Montaser et al. 1984). 

Table 1. Data elements required for analytical method validation [1, 8]...

While validating a production process, several steps were listed as they pertained to each of the components of manufacturing equipment, process conditions, personnel, and so forth. These key elements multiply rapidly when it comes to analytical methods validation. Take, for example, HPLC — the most commonly used method of analysis. A typical analytical method would involve use of columns, pumps, heaters, detectors, controllers, samplers, sensors, recorders, computers, reagents, standards, and operators — put together as a system. Each of these components and systems needs independent validation, followed by a validation of the system. Note that when this equipment is used to manufacture a product such a therapeutic proteins wherein HPLC techniques are used for the purification purpose, then all additional requirements of a manufacturing system also apply, including, but not limited to, the requirement that the equipment be of a sanitary kind. This limits the choice for manufacturers, and these considerations should be taken into account in the first selection of equipment. 

One useful approach to visualising these relationships is to list bullet points for each of the pairs in the manner shown below. In this way key areas are identified although there are not corresponding relationships between individual bullet points. Individual elements of the model are covered more fully in Chapter 7 where method validation is considered as a whole. 

Method validation is important to ensure that the analytical method is in statistical control. A method may be validated by the so-called method evaluation function (MEF) (Christensen et al., 1993), which is obtained by linear regression analysis of the measured concentrations versus the true concentrations. A true concentration in a solution can be obtained by use of a high purity standard obtained from another manufacturer or batch than the one used for calibration. Both the high purity standard and the solvent are weighed using a traceable calibrated balance. If certified reference material is available this is preferred. The method evaluation includes the most important characteristics of the method as the following elements (see Figure 2.7) ... 

The analysis of polymer processing is reduced to the balance equations, mass or continuity, energy, momentum and species and to some constitutive equations such as viscosity models, thermal conductivity models, etc. Our main interest is to solve this coupled nonlinear system of equations as accurately as possible with the least amount of computational effort. In order to do this, we simplify the geometry, we apply boundary and initial conditions, we make some physical simplifications and finally we chose an appropriate constitutive equations for the problem. At the end, we will arrive at a mathematical formulation for the problem represented by a certain function, say / (x, T, p, u,...), valid for a domain V. Due to the fact that it is impossible to obtain an exact solution over the entire domain, we must introduce discretization, for example, a grid. The grid is just a domain partition, such as points for finite difference methods, or elements for finite elements. Independent of whether the domain is divided into elements or points, the solution of the problem is always reduced to a discreet solution of the problem variables at the points or nodal pointsinxxnodes. The choice of grid, i.e., type of element, number of points or nodes, directly affects the solution of the problem. 

The interpretation and implementation of published methods invariably differ at different laboratories due to diversity of utilized instruments, their incidental elements and supplies, and the differences in method interpretation. Each analytical method must be validated at the laboratory before it is used for sample analysis in order to demonstrate the laboratory s ability to consistently produce data of known accuracy and precision. Method validation includes the construction of a calibration curve that meets the acceptance criteria the determination of the method s accuracy and precision and the MDL study. A method SOPs must be prepared and approved for use. Method validation documentation is kept on file and should be always available to the client upon request. 

An important objective of this project was to provide elements of method validation by estimating the bias, that is to say the difference between the measured value and the true value of measurands in samples. This can be underlined through a proficiency testing analysing either a spiked pure water or a matrix sample (drinking water), according to the diagram represented in Fig. 3. 

Fig. 4 Elements in the proficiency testing organised to evaluate matrix bias and method validation...

Method validation must be performed in a regulatory-compliant environment. In particular, the organization must have a QA unit (QAU), adequate laboratory equipment and facilities, written procedures, and qualified personnel. Since a successful validation requires the cooperative efforts of each of these organizational elements, successful fulfillment of the regulatory and technical objectives of validation requires senior management support. Additionally, it is essential that the organization have a well-defined validation master plan (YMP) for analytical methods, which defines the steps necessary to effectively validate methods. 

The issues of method validation and assessment of measurement uncertainty in the determination of potentially toxic trace elements in rice are of permanent interest for the scientific community. In this context, the sources of uncertainty associated with the determination of Cd, Cu, Pb, and Zn have been recently estimated in rice through an interlaboratory comparison [30]. Four Brazilian laboratories participated in the proficiency test. The analytical technique used were FAAS, ET-AAS, and ICP-AES. The rice samples were supplied by the Institute for Reference Materials and Measurements (IRMM), Joint Research Center of the European Commission, within the scope of the interlaboratory comparison International Measurement Evaluation Programme (IMEP) 19 Trace Elements in Rice (see also Chapter 7 in this book). Three out of the four laboratories reported values close to the reference values. It was emphasized that, in order to establish a reliable uncertainty budget, all significant sources of uncertainty should be identified. 

As noted above, the CMC subsection is only one of three elements comprising the BLA s chemistry section. The other two subsections address samples and the methods validation package. 

Abstract In an effort to assess the method validation done using ICP-AES in our laboratory for potable water, an Environmental Laboratory Approval Program organized by New York State Department of Health, Wadsworth Center providing the reference material has been undertaken for 14 trace elements and seven other chemical constituents. The certified means for the reference material and the results obtained in our laboratory are compared. The comparisons helped us assess the quality of our work. All the data from the inductively coupled plasma atomic emission spectrometer (ICP-AES) fall into the ranges specified. 

The purpose of this section is to provide a brief review of the methods and techniques commonly used in the elemental analysis of humic substances with special emphasis placed on areas that may cause difficulties in their analysis. There are few specific references to methods of elemental analysis of humic substances. A computerized search of Chemical Abstracts since 1966 revealed no references to techniques of elemental analysis when elemental analysis was cross-referenced with humic or fulvic acid materials. In general, the methods of analysis have been developed to be applicable to a wide range of organic materials. However, it should be pointed out that most methods have been validated on the basis of the analysis of stable, nonhy-groscopic, nonvolatile, pure compounds and not heterogeneous mixtures. 

Various types of validation generally required in biopharmaceutical manufacturing include process validation, facility and equipment validation, analytical method validation, software validation, cleaning validation and expression system characterization. Combined with other elements of cGMP, including lot release testing, raw material testing, vendor quality certifications, and vendor audits, the quality of product can be consistently assured. 

It is possible to produce artificial matrix materials [12]. Such materials can be prepared on a mass basis by weighing all components both to mimic the matrix composition and the content of trace elements or trace organic substances. The materials could help to have matrix materials available for which the exact contents and composition are known. As a consequence it would be, in theory, possible to certify them on a mass basis and validate methods with highly traceable materials. In organic trace analysis this would circumvent the unknown extraction step. In reality, this is much more difficult to achieve than can be expected. The real matrix composition of many materials is unknown — in particular for environment samples. The physico-chemical status of the various substances depends on the history of the material. Therefore, various natural samples of expected similar composition are different in behaviour. In addition, when preparing mixtures of solid components, losses cannot be excluded and unfortunately are not quantifiable. Attempts have been made where losses were demonstrated but not quantified [12]. Therefore, materials certified for matrix composition and analyte content on a mass basis do not yet exist or are not of real use for method validation by routine laboratories. They may be of interest for laboratories active in the field of fundamental research in chemical metrology where smaller quantities of material are handled. 

Many working laboratories will have performed a similar analysis many times over, whether it be the analysis of active ingredients for a pharmaceutical production line, or the analysis of an element in an ore for a mining company. When the analytical method was first established, method validation will have determined repeatability and reproducibility standard deviations, and these will have been verified for use in the particular laboratory. 

Kelley, M., and DeSilva, B. (2007) Key elements of bioanalytical method validation for macromolecules. The AAPS Journal, 9, E156 E163. 

Examples for reference materials useful for instrument calibration, method validation and development in the field of human materials for which certified or other kinds of concentration values are reported for the 13 trace elements considered in this book (Al, As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Se, Tl, V and Zn) are given in Table 3. The data are taken from the survey prepared by Cortes Toro et al. (1990) and from other sources (BCR, 1992, Trahey, 1992, Chai Chifang, 1993) which the reader should consult for further details. Most of the columns are self explanatory. Column T contains a code (C = certified, N = noncertified or information value) for the type of reference value specified by the issuing authority. The uncertainty in the concentration value is expressed as a percentage error, but the meaning of this may differ somewhat from one material to another. In most cases it expresses the 95% confidence interval of the mean, but in a few other cases a tolerance interval, or some other definition (sometimes unspecified), may have been used by the producer. 

APPROPRIATE BIOLOGICAL REFERENCE MATERIALS USEFUL FOR INSTRUMENT CALIBRATION, METHOD VALIDATION AND DEVELOPMENT FOR HUMAN MATERIALS AND THE 13 ELEMENTS TREATED IN THIS BOOK... 

The placement of a new element in the Periodic Table requires knowledge of its atomic number and electronic configuration. Even though the atomic number can be positively assigned by a-decay chains, no knowledge is obtained about the electronic configuration or chemical properties of a new element fi om these physical methods. The elements are just placed in the Periodic Table by atomic number in various groups or series based on simple extrapolation of known Periodic Table trends or firom theoretical calculations and predictions of the electronic structures. It remains to the experimental chemist to attempt to validate or contradict these predictions. 

Conostan S-21 Blended Standards (100 ppm organic multi element standard in oil) was used in this work (ConocoPhillips, Houston, TX) for the samples calibration. Method validation was completed with three Standard Reference Materials (SRM) from NIST (National Institute for Standards and Technology, Gaithersburg, MD) 1085b, 1084a lubricating oils, and 1634c (residual fuel oil). 

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