Data reference deconvolution

Table 5 Prediction Errors Associated with ISMN GEOMATRIX IVIVC Developed Using the Study-Specific Reference Data for Deconvolution or the Reference Data from Study 194.573 for Deconvolution of All Study Data. Prediction Errors Outside of the FDA Acceptance Criteria Are Indicated in Bold...

Reference deconvolution is unusual among data processing methods in using internal evidence to repair the damage done to the spectral data by instrumental imperfections parenthetically, it may be noted that the form of the correction function R t) itself can be a useful guide to instrumental fault-finding. The principal uses of reference deconvolution are in the pro-... 

One method of reducing the effects of the line-broadening due to the z-gradient present during data acquisition is to use reference deconvolution ... 

DOSY NMR experiments are popular for analysing diffusional properties of mixtures. Quality of the results is affected by experimental factors such as baseline drift, peak shift and phase shift. Huo et al proposed a series of pre-processing operations to reduce the experimental distortions, including baseline correction and reference deconvolution to remove frequency and phase shifts. The corrected data can be successfully analysed with a combination of multivariate curve resolution with non-linear least square regression. 

Because online separations provide such a wealth of information about target proteins, interpretation becomes of critical importance in order to make full use of the data. The first step in any analysis of LC-MS data involves integration and deconvolution of sample spectra to determine protein mass and intensity. In manual analysis (Hamler et al., 2004), users identify protein umbrellas, create a total ion chromatogram (TIC), integrate the protein peak, and deconvolute the resulting spectrum. Deconvolution of ESI spectra employs a maximum entropy deconvolution algorithm often referred to as MaxEnt (Ferrige et al., 1991). MaxEnt calculates... 

The reference formulation is used to correct for differences in drug clearance between study populations when data from more than one study are combined. The reference formulation is chosen so that when it is used in deconvolution with the ER formulation, the in vivo drug release or absorption from the ER formulation is obtained. Appropriate reference formula-... 

Figure 9 Observed data (amount absorbed in vivo vs. amount released in vitro) for the five ISMN test formulations included in IVIVC development and internal validation. The fitted IVIVC equations are shown as well as the corresponding predicted lines. Panel a shows the analysis where the study-specific reference was used for deconvolution and panel b where the reference for Study 194.573 was used for the deconvolution analysis of all study data.

IVIVC studies normally involve two to four ER formulations and a reference formulation (e.g., IV solution, immediate release, or oral solution). Data analysis involves deconvolution of each ER formulation, using the reference data for each subject. Thus, if a subject drops out of the study prior to the IR arm, none of that subject s data... 

Principal component analysis (PCA) [61] was first used to determine the number of independently varying chemical species present and to provide initial estimates of the spectral shapes resulting from these species and of their concentration profiles. Reference ATR FTIR spectra for several components (the solvent acetonitrile and water, the reagent cyclopentyltrichlorosilane and the product o7h3) were measured to assist in the deconvolution of the data. Frequency windows were selected that allowed the best discrimination between the reference compounds (725-775 cm-1 for acetonitrile, 850-900 cm-1 for water and the silsesquioxane). Finally, the MCR technique was applied to the data in the selected frequency windows to find the component spectra and relative concentration profiles that best fit the observed spectra. 

For MTDSC, it is also essential to calibrate for the reversing heat capacity to allow quantification of the deconvoluted results. There are several approaches by which this may be achieved, with more accurate methods requiring greater sophistication and more time hence, a decision needs to be made with regard to how important accurate heat capacity data are to the objectives of the study. For most pharmaceutical applications, fairly simple calibration procedures such as those about to be outlined are usually sufficient. However, for more accurate work it is essential to use more detailed approaches such as that described in Reference 11. In this summary, we outline only the simple approaches, but readers should be aware of the availability of more complex methods that yield more reliable results. 

---