Critical value method validation parameter

The approaches described above give approximate values for the LoD and LoQ. This is sufficient if the analyte levels in test samples are well above the LoD and LoQ. If the detection limits are critical, they should be evaluated by using a more rigorous approach [1, 2, 14]. In addition, the LoD and LoQ sometimes vary with the type of sample and minor variations in measurement conditions. When these parameters are of importance, it is necessary to assess the expected level of change during method validation and build a protocol for checking the parameters, at appropriate intervals, when the method is in routine use. 

Vn.30. In general, all of these sonrces of uncertainty should be integrally observed in the variability of the calculated results obtained for the critical experiments. The variability should include the Monte Carlo standard deviation in each calculated critical experiment value as well as any change in the calculated value caused by the consideration of experimental uncertainties. Thus these uncertainties will be intrinsically included in the bias and uncertainty in the bias. This variation or nncertainty in the bias should be established by a valid statistical treatment of the calculated k y values for the critical experiments. Methods exist [VII. 10] that enable the bias and uncertainty in the bias to be evaluated as a function of changes in a selected characteristic parameter. 

The results of the ruggedness testing and bias evaluation should be published in full. This report should identify the critical parameters, including the materials within the scope of the method, and detail the effect of variations in these on the final result. It should also include the values and relevant uncertainties associated with bias estimations, including both statistical and reference material uncertainties. Since it is a requirement of the validation procedure that this information should be available before carrying out the collaborative study, publishing it would add little to the cost of validating the method and would provide valuable information for future users of the method. 

Computational modeling is a powerful tool to predict toxicity of drugs and environmental toxins. However, all the in silico models, from the chemical structure-related QSAR method to the systemic PBPK models, would beneht from a second system to improve and validate their predictions. The accuracy of PBPK modeling, for example, depends on precise description of physiological mechanisms and kinetic parameters applied to the model. The PBPK method has primary limitations that it can only predict responses based on assumed mechanisms, without considerations on secondary and unexpected effects. Incomplete understanding of the biological mechanism and inappropriate simplification of the model can easily introduce errors into the PBPK predictions. In addition values of parameters required for the model are often unavailable, especially those for new drugs and environmental toxins. Thus a second validation system is critical to complement computational simulations and to provide a rational basis to improve mathematical models. 

In addition to the tables and figures from ANSI/ANS-8.1, there are several handbooks that contain similar tables and figures that cover other fissile isotopes in other relatively simple parametric combinations. These values are also based on experimental results of critical experiments (although most also utilize validated calculational methods as well). The key to the use of these handbook values is the simplicity of the parametric combinations which, in real situations, translates into the most reactive credible values of the remaining parameters. Use of the ANSI/ANS-8.1 or handbook values usually result in more restrictive limits than might otherwise be achievable from a more detailed analysis of the proposed process. 

The simple metals, whose conduction bands correspond to s and p shells in isolated atoms, include the alkali metals, the divalent metals Be, Mg, Zn, Cd, and Hg, the trivalent metals Al, Ga, In, and Tl, and the tetravalent metals (white) Sn and Pb. Almost all of their properties which are related to electronic band structure are explicable by nearly-free-electron theory using pseudopotentials (Sections 3.5 and 3.6). The extent to which they conform in detail to this generalization varies from one case to another. For all the metals cited simple pseudopotential theory is fairly successful in predicting or fitting Fermi surface properties. This will be evident from a consideration of the comparisons of theoretical and fitted pseudopotential parameters already shown in Figure 12. However, the use of perturbation theory is not very critical in this context [i.e., the contribution of screening to the values of v q) which are of interest is not large]. In other contexts the validity of perturbation theory is more critical, and indeed the use of pseudopotential-perturbation theory is then not always so successful. An example is the calculation of phonon dispersion relations by such methods, which has enjoyed remarkable success for Na, Mg, and j(i2i,i22) jjjjQ difficulties for the heavier metals and those... 

Clearly the criticisms above of the use of eqn. (2) for obtaining single parameter estimates are also applicable to its use in obtaining pore size distributions. In particular, if the DR equation is not homogeneous, then it should not be used as the kernel of the GDR equation, eqn. (7), since Eq does not correspond to a single-valued adsorption energy in pores of uniform size and therefore the relationship Eq = h(z) is not valid. The probable bias in micropore size distributions estimated using methods which involve this inconsistency needs to be explored. 

---