Search

Background: The combined influence of age-associated factors such as general health, degree of dependency, diminished odor perception, and poor oral health on the risk for malnutrition was explored.Methods: A total of 81 persons living in retirement homes took part in the study (mean age 83.4 years, SD = 6.6, range 61-98). The Mini-Nutritional Assessment (MNA) was used to evaluate the risk of malnutrition. Odor perception was measured by the detection threshold for isoamylacetate. The number of drugs taken by each person was counted. General health status was determined by the Medical Outcome Study (MOS) scores. Oral examinations were carried out to count the number of natural teeth and type of dentures.Results: On average, women had slightly, but significantly, lower MNA scores than men (respectively, 23.4, SD = 2.8; and 24.6, SD = 2.6; p = .048). The correlations between age and MNA score and between odor perception and MNA score were not significant. Significant correlations were found between age and number of natural teeth (r = -.26, p = .001) and between MNA score and number of natural teeth (r = .27, p = .001). The mean MNA score of complete denture wearers (22.8, SD = 2.9) was significantly lower than that of partial denture wearers (25.8, SD = 2.9; p = .0005). The total MOS and MNA scores were not correlated, but a significant correlation was found with the subscales mental functioning (r = .29, p = .003), social functioning (r = . 19, p = .045), and perceived health (r = .19, p = .047). No relation was found between the activities of daily living (ADL) and MNA scores. A significant negative correlation was observed between number of drugs taken and the MNA score (r = -.34, p = .001). When participants without risk of malnutrition (MNA > or = 24) were compared with those at risk (MNA = 17-23.5), again, the number of drugs taken was significantly different (on average, respectively, 4.5, SD = 2.9; and 7.0, SD = 2.6; p < .0005). Using multiple regression to test the separate effects of the different independent variables, the number of drugs taken showed a significant negative regression coefficient (beta = -.31, p = .008), as did the mental health score (beta = .27, p =.02), giving a total R2 = .32. The other parameters did not contribute significantly.Conclusion: Among the elderly in retirement homes, the health state (as measured by the MOS subscale mental health and by the medication use) appears to be the most clinically relevant parameter to explain the risk for malnutrition. Loss of natural teeth and perceived health are less independently contributing, whereas no contribution derives from decline of odor perception, degree of dependency, and age itself. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) spectroscopy is a fast and non-destructive analytical technique that offers many advantages for a broad range of industrial applications. In this work, we reviewed recent developments in the pharmaceutical domain where it can be applied from raw material identification to final product release. The characteristics of NIR allow the technique to be implemented as a process analytical technology (PAT). Moreover, recent instrumental developments open the perspectives of numerous applications in the NIR imaging area. After "Introduction", according to their subject, the applications are discussed in the parts "Identification", "Water content", "Assay" and "Other applications".

Direct orthogonal signal correction (DOSC) is applied to correct for major variance sources such as temperature effects, time influences and instrumental differences in near infrared (NIR) data. The samples analysed are creams containing different concentrations of an active drug. The final aim is to classify the samples according to their concentration of active compound. Having performed DOSC on the data, it is not necessary anymore to apply sophisticated chemometric techniques to correct for temperature or time effects and to attribute the samples to their respective concentration classes. Moreover, the application of DOSC on the NIR spectra recorded on two different instruments shows that this method can be considered as a valuable alternative for the standardisation in classification applications. Since the applied algorithm tends to overfit, in a second part of this paper, a comparison is made with an algorithm designed by Westerhuis, which should overcome this problem. Although the calibration set results show that the overfitting has been partially corrected for by the latter algorithm, the test set results did not improve significantly.

The analysis of spectral measurement data sets using local factor analysis (LFA) requires the rank of the sub-matrix under study to be equal to the number of absorbing species present in the associated sub-system. However, because of mass balance or kinetic constraints, LFA will fail if local rank deficiency occurs. A local rank deficiency sub-system may be present in a global full-rank reaction system or a rank-deficient one. In this paper, the problems occurring when using window target-testing factor analysis (WTTFA), one type of the LFA methods, in a local rank-deficient situation are shown. A new augmented WTTFA (AWTTFA) is then proposed for the correct use of WTTFA when rank deficiency occurs. Principles of this new method have been demonstrated by a simulated kinetic system and an industrial batch data set.

An exploratory analysis was performed in order to evaluate the feasibility of building of neural network (NN) systems automating the identification of amphetamines necessary in the investigation of drugs of abuse for epidemiological, clinical and forensic purposes. A first neural network system was built to distinguish between amphetamines and nonamphetamines. A second, more refined system, aimed to the recognition of amphetamines according to their toxicological activity (stimulant amphetamines, hallucinogenic amphetamines, nonamphetamines). Both systems proved that discrimination between amphetamines and nonamphetamines, as well as between stimulants, hallucinogens and nonamphetamines is possible (83.44% and 85.71% correct classification rate, respectively). The spectroscopic interpretation of the 40 most important input variables (GC-FTIR absorption intensities) shows that the modeling power of an input variable seems to be correlated with the stability and not with the intensity of the spectral interaction. Thus, discarding variables only because they correspond to spectral windows with weak absorptions does not seem be not advisable.

This study describes the chemometric treatment of vanillin fingerprint chromatograms to distinguish vanillin from different sources. Prior to principal component analysis, which is used to discriminate vanillin from different origins, the fingerprints are aligned. Three alignment algorithms are tested, correlation optimized warping (COW), target peak alignment (TPA) and semi-parametric time warping (STW). The performance of the three algorithms is evaluated and the effect of the different alignments on the PCA score plots is investigated. The alignment obtained with STW differs somewhat from that with COW and TPA. However, equivalent score plots were obtained regarding the different vanillin groups.

N-way methods, particularly the Tucker method, are often the methods of choice when analyzing data sets arranged in three- (or higher) way arrays, which is the case for most environmental data sets. In the future, applying N-way methods will become an increasingly popular way to uncover hidden information in complex data sets. The reason for this is that classical two-way approaches such as principal component analysis are not as good at revealing the complex relationships present in data sets. This study describes in detail the application of a chemometric N-way approach, namely the Tucker method, in order to evaluate the level of pollution in soil from a contaminated site. The analyzed soil data set was five-way in nature. The samples were collected at different depths (way 1) from two locations (way 2) and the levels of thirteen metals (way 3) were analyzed using a four-step-sequential extraction procedure (way 4), allowing detailed information to be obtained about the bioavailability and activity of the different binding forms of the metals. Furthermore, the measurements were performed under two conditions (way 5), inert and non-inert. The preferred Tucker model of definite complexity showed that there was no significant difference in measurements analyzed under inert or non-inert conditions. It also allowed two depth horizons, characterized by different accumulation pathways, to be distinguished, and it allowed the relationships between chemical elements and their biological activities and mobilities in the soil to be described in detail.

Several chemometric techniques were compared for their performance to determine the orthogonality and similarity between chromatographic systems. Pearson's correlation coefficient (r) based color maps earlier were used to indicate selectivity differences between systems. These maps, in which the systems were ranked according to decreasing or increasing dissimilarities observed in the weighted-average-linkage dendrogram, were now applied as reference method. A number of chemometric techniques were evaluated as potential alternative (visualization) methods for the same purpose. They include hierarchical clustering techniques (single, complete, unweighted-average-linkage, centroid and Ward's method), the Kennard and Stone algorithm, auto-associative multivariate regression trees (AAMRT), and the generalized pairwise correlation method (GPCM) with McNemar's statistical test. After all, the reference method remained our preferred technique to select orthogonal and identify similar systems.

A series of two papers describing a procedure for automated peak deconvolution is presented. The goal is to develop a package of routines that can be used by non-experienced users. Part I (this paper) concerns peak detection, whereas Part II is dedicated to the deconvolution itself. In this first part, the most interesting features of the peak detection algorithms, which precede the deconvolution step, are outlined. High-order derivatives provide valuable information to assess the number of underlying compounds under a given peak cluster. A smoothing technique was found essential to compute properly the derivatives, since the noise is amplified when differences are calculated. The Savitsky-Golay smoother was applied in combination with the Durbin-Watson criterion to automate the window size selection. This strategy removed the noise without loosing valuable information. In some cases, it was found preferable to split the chromatogram in different elution regions, and apply the Durbin-Watson test and the Savitsky-Golay smoother to each region, separately. The derivatives allowed obtaining estimates of both peak parameters and the corresponding ranges for each eluting compound to be used in the deconvolution. An algorithm oriented to compare peaks from different chromatograms is also presented to perform deconvolution, using information from several related chromatograms.

Several interlinked algorithms for peak deconvolution by non-linear regression are presented. These procedures, together with the peak detection methods outlined in Part I, have allowed the implementation of an automatic method able to process multi-overlapped signals, requiring little user interaction. A criterion based on the evaluation of the multivariate selectivity of the chromatographic signal is used to auto-select the most efficient deconvolution procedure for each chromatographic situation. In this way, non-optimal local solutions are avoided in cases of high overlap, and short computation times are obtained in situations of high resolution. A new algorithm, fitting both the original signal and the second derivatives is proved to avoid local optima in intermediate coelution situations. This allows achieving the global optimum without the need of background knowledge by the user. A previously reported peak model, a Gaussian with a polynomial standard deviation whose complexity can be modulated to enhance the fitting quality, was applied. However, the original formulation was modified to account baseline outside the peak region. Also, the optimal model complexity was auto-selected via error propagation theory. The method is able to process simultaneously several related chromatograms. The software was tested with both simulated and experimental chromatograms obtained with monolithic silica columns.

In this paper, a fast strategy for determining the total antioxidant capacity of Chinese green tea extracts is developed. This strategy includes the use of experimental techniques, such as fast high-performance liquid chromatography (HPLC) on monolithic columns and a spectrophotometric approach to determine the total antioxidant capacity of the extracts. To extract the chemically relevant information from the obtained data, chemometrical approaches are used. Among them there are correlation optimized warping (COW) to align the chromatograms, robust principal component analysis (robust PCA) to detect outliers, and partial least squares (PLS) and uninformative variable elimination partial least squares (UVE-PLS) to construct a reliable multivariate regression model to predict the total antioxidant capacity from the fast chromatograms.

The goal of this study is to derive a methodology for modeling the biological activity of non-nucleoside HIV Reverse Transcriptase (RT) inhibitors. The difficulties that were encountered during the modeling attempts are discussed, together with their origin and solutions. With the selected multivariate techniques: robust principal component analysis, partial least squares, robust partial least squares and uninformative variable elimination partial least squares, it is possible to explore and to model the contaminated data satisfactory. It is shown that these techniques are versatile and valuable tools in modeling and exploring biochemical data.

Multivariate adaptive regression splines (MARS) and a derived method two-step MARS (TMARS) were used for modelling the gastro-intestinal absorption of 140 drug-like molecules. The published absorption values for these molecules were used as response variable and calculated molecular descriptors as potential explanatory variables. Both methods were compared and their potential use in quantitative structure-activity relationship (QSAR) context evaluated. The predictive abilities of the models were studied using different sequences of Monte Carlo cross validation (MCCV). It was shown that both types of models had good predictive abilities and that for the data used, MARS gave better results than TMARS. It could be concluded that both methods could be valuable for QSAR modelling.

In completion of an earlier defined generic chiral screening approach, a generic separation strategy for basic, bifunctional, and neutral compounds was proposed and evaluated. This strategy adds to a previously defined strategy for acidic compounds. The screening experiment of the actual strategy used a mobile phase of 5 mM phosphate buffer pH 11.5/ACN (30/70 v/v), a temperature of 25 degrees C, and a voltage of 15 kV. The selected chiral stationary phases were Chiralpak AD-RH, Chiralcel OD-RH, Chiralcel OJ-RH, and Chiralpak AS-RH, all based on polysaccharide selectors. It was seen that 31 out of 48 test compounds were partially or baseline-resolved under screening conditions. After execution of the optimization steps of the strategy, this number increased to 41, with a total of 21 baseline-separated compounds. Combined with the results obtained from the acidic test set examined in the earlier defined strategy, of all tested compounds 82.5% showed enantioselectivity and 49.2% could be baseline-separated.

A fingerprint chromatogram of a standardized Ginkgo biloba extract is developed on a monolithic silica column using a ternary gradient containing water, iso-propanol and tetrahydrofuran. For the detection, UV and evaporative light scattering (ELS) detectors are used, the latter allowing detection of the poor UV absorbing compounds as ginkgolides (A-C and J) and bilobalide in the extract. The complementary information between the UV and ELS fingerprint is evaluated. The ELS detector used in this study can operate in an impactor 'on' or 'off' mode. For each mode, the operating conditions such as the nebulizing gas flow rate, the drift tube temperature and the gain are optimized by use of three-level screening designs to obtain the best signal-to-noise (S/N) ratio in the final ELS fingerprint chromatogram. In both impactor modes, very similar S/N ratios are obtained for the nominal levels of the design. However, optimization of the operating conditions resulted, for both impactor modes, in a significant increase in S/N ratios compared to the initial evaluated conditions, obtained from the detector software.

Classification and regression trees (CART) were evaluated for their potential use in a quantitative structure-activity relationship (QSAR) context. Models were build using the published absorption values for 141 drug-like molecules as response variable and over 1400 molecular descriptors as potential explanatory variables. Both the role of two- and three-dimensional descriptors and their relative importance were evaluated. For the used dataset, CART models showed high descriptive and predictive abilities. The predictive abilities were evaluated based on both cross-validation and an external test set. Application of the variable ranking method to the models showed high importances for the n-octanol/water partition coefficient (logP) and polar surface area (PSA). This shows that CART is capable of selecting the most important descriptors, as known from the literature, for the absorption process in the intestinal tract.

Multivariate calibrations must be updated when new samples show different spectral characteristics. In this paper, we discuss how to do this when the calibration is performed with a topological multivariate calibration method based on Delaunay triangulation (DT). The updating leads either to the expansion of the original calibration set or to the creation of a new local model. Outliers in the new samples with respect to the original calibration set are first detected and divided in two groups, namely, marginal outliers, which are considered to be extensions of the calibration set and are used for updating the calibration set, and true outliers. If a sufficient number of true outliers are found to be situated close enough to each other, they can form the basis for a new local model. Several updating simulations performed on a real data set show that the updating procedure performs well. The results for prediction with the DT method after updating are comparable to or better than those after updating with partial least squares (PLS) and it is concluded that, in many cases, the DT method is a valuable alternative for multivariate calibration.

The starting point of this study was a current set of 32 chromatographic systems used to select initial conditions for method development to determine the impurity profile of a drug. The system exhibiting the best selectivity is then selected for further method development. In this current set eight silica-based phases are applied in conjunction with four mobile phases at different pH. In order to save time and resources, the possibilities for a meaningful subset selection were investigated. The most differing systems in terms of selectivity, in other words only the most orthogonal systems, need to be selected. Since the stationary phases are all silica-based, the selectivity differences are examined within a more homogeneous group than if, for instance, also zirconia- or polymer-based columns would be involved. To select the subset of systems also the best overall separation performances are taken into account. The selection is based both on the HPLC-DAD data of a generic set of 68 drugs, and on the LC-MS-DAD results for a mixture of 15 drugs, less different in structure. The orthogonality is evaluated using weighted-average-linkage dendrograms and color maps, both created from the Pearson-correlation coefficients r between normalized retention times r. The Derringer's desirability functions are applied to define the systems with the best overall separation performances. Proposals for different representative subsets of the initial 32 systems are made.

The present paper deals with the application of different chemometric methods to an environmental data set derived from the monitoring of wet precipitation in Austria (1988-1999). These methods are: principal component analysis (PCA); projection pursuit (PP); density-based spatial clustering of application with noise (DBSCAN); ordering points to identify the clustering structures (OPTICS); self-organizing maps (SOM), also called the Kohonen network; and the neural gas (NG) network. The aim of the study is to introduce some new approaches into environmetrics and to compare their usefulness with already existing techniques for the classification and interpretation of environmental data. The density-based approaches give information about the occurrence of natural clusters in the studied data set, which, however, do not occur in the case presented here; information about high-density zones (very similar samples) and extreme samples is also obtained. The partitioning techniques (clustering, but also neural gas and Kohonen networks) offer an opportunity to classify the objects of interest into several defined groups, the patterns of ionic concentration of which can be studied in detail. The visual aids, such as the color map and the Kohonen map, for each site are very helpful in understanding the relationships between samples and between samples and variables. All methods, and in particular projection pursuit, give information about samples with extreme characteristics.

A difficulty when applying partial least squares (PLS) in multivariate calibration is that overfitting may occur. This study proposes a novel approach by combining PLS and boosting. The latter is said to be resistant to overfitting. The proposed method, called boosting PLS (BPLS), combines a set of shrunken PLS models, each with only one PLS component. The method is iterative: the models are constructed on the basis of the residuals of the responses that are not explained by previous models. Unlike classical PLS, BPLS does not need to select an adequate number of PLS components to be included in the model. On the other hand, two parameters must be determined: the shrinkage value and the iteration number. Criteria are proposed for these two purposes. BPLS was applied to seven real data sets, and the results demonstrate that it is more resistant than classical PLS to overfitting without loosing accuracy.

The effect of five factors on the capillary electrochromatographic enantioseparation of acidic compounds was studied using an experimental design. The studied factors were pH, acetonitrile content in the mobile phase, temperature, buffer concentration, and applied voltage. These experiments allowed defining a generic separation strategy applicable on acidic compounds with chemical and structural diversity. The starting screening conditions consist of a 45 mM ammonium formate electrolyte at pH 2.9 mixed with 65% acetonitrile, an applied voltage of 15 kV, and a temperature of 25 degrees C. The screening phase occasionally can be followed by an optimization procedure. Evaluation of the proposed strategy pointed out that it allows achieving baseline resolution within a relatively short time when a beginning of separation is obtained at the starting conditions. This strategy revealed enantioselectivity for 11 compounds out of 15, of which 10 could be baseline-separated after the proposed optimization steps.

Several methods estimating the partitioning over biological membranes and thus the biological activity of potential oral drug molecules have been developed and are described in the literature. A previous study suggested that fast micellar liquid chromatography on a monolithic column could be one of them. For a set of diverse pharmaceuticals, retention by this fast chromatographic method was determined, besides other parameters also thought or established to describe oral permeability or absorption, e.g., from the Caco-2 permeability method. In view of a high-throughput determination of membrane permeability, a study was made of which information fast micellar liquid chromatography is providing and to what degree this system can replace other methods, i.e., deliver similar information. The retention with this fast method, which is mainly based on hydrophobic interactions, proved useful to sort substances into classes of Caco-2 and percent intestinal absorption.

This work is part of a study in which the possibility of NIR combined with some chemometrical methods is investigated as a suitable technique to classify clinical study samples of a cream. In this study, the influence of different preprocessing methods on the removal of spectral variations due to some variance sources has been investigated. The applied preprocessing methods are standard normal variate (SNV), detrend correction, offset correction, and first and second derivation. The investigated variance sources are different batches of ingredients, different samples of the same batch, different days and different positions of the sample cup in the sample drawer of the instrument. A nested ANOVA design has been applied in order to quantify the variances introduced by these variance sources. Since ANOVA is a univariate technique, the necessary variable (wavelength) selection has been performed by the Fisher criterion. The best results, i.e. largest reduction of interfering variability and clearest distinction between different clinical study samples, are obtained with the second derivative spectra.

The multivariate adaptive regression splines (MARS) methodology was applied to build quantitative structure-retention relationships (QSRRs). The response (dependent variable) in the MARS models consisted of the logarithms of the extrapolated retention factors (log k(w)) of 83 structurally diverse drugs on a Unisphere PBD column, using isocratic elutions at pH 11.7. A set of 266 molecular descriptors was used as predictor (independent) variables in the MARS model building. The optimal MARS model uses 34 basis functions to describe the retention and has acceptable predictive properties for new objects. The molecular descriptors included in the model describe hydrophobicity, molecular size, complexity, shape and polarisability. Some additional MARS models were created using alternative strategies. These include models with log P as the single predictor and models obtained with only the three most important molecular descriptors. The use of classification and regression trees (CART) as feature selection technique for predictor variables used in the MARS model was also investigated. Further, it is also studied whether allowing quadratic terms instead of interaction terms might lead to better MARS models.

The influence of using normal-phase and reversed-phase versions of four commercial polysaccharide stationary phases on chiral separations was investigated with capillary electrochromatography (CEC). Both versions of the stationary phases, Chiralcel OD, OJ, and Chiralpak AD, AS were tested for the separation of two basic, two acidic, a bifunctional, and a neutral compound. Different background electrolytes were used, two at low pH for the acid, bifunctional and neutral substances, and three at high pH for the basic, bifunctional and neutral ones. This setup allowed evaluating differences between both stationary-phase versions and between mobile-phase compositions on a chiral separation. Duplicate CEC columns of each stationary phase were in-house prepared and tested, giving information about the intercolumn reproducibility. In general, reversed-phase versions of the current commercial polysaccharide stationary phases are found to be best for reversed-phase CEC, even though at high pH no significant differences were seen between both versions. Most differences were observed at low pH. For acidic compounds, it was seen that an ammonium formate electrolyte performed best, which is also an excellent electrolyte if coupling with mass spectrometry is desired. For basic, bifunctional and neutral compounds, no significant differences between the three tested electrolytes were observed at high pH. Here, a phosphate buffer is preferred as electrolyte because of its buffering capacities. However, if coupling to mass spectrometry is wanted, the more volatile ammonium bicarbonate electrolyte can be used as an alternative.

To select appropriate stationary phases from the continuously expanding supply of potentially suitable HPLC columns, the properties of 28 frequently applied stationary phases were determined by measuring several chromatographic parameters. From these results, based on chromatographic expertise, eight stationary phases with different properties and selectivities were selected. The aim of this study is to apply chemometric tools to evaluate the initially selected set of columns, i.e. a more systematic approach for making such a selection is examined. Starting from the information obtained on the 28 stationary phases, the re-evaluation was performed independently based on the chemometric techniques Pareto-optimality, principal component analysis (PCA), and Derringer's desirability functions. The aim was to select a set of efficient columns exhibiting large selectivity differences. The chemometrically selected stationary phases were divided in groups based on hydrophobicity, a critical retention-determining property in reversed-phase chromatography. This allowed to further reducing the selection to three columns. It is demonstrated that the selection by the chemometric approaches in general is fairly comparable with the initial selection.

This paper proposes strategies in normal- and reversed-phase liquid chromatography (NP-HPLC or NPLC and RP-HPLC or RPLC), which were developed using three polysaccharide-based stationary phases. Those strategies are implemented in a knowledge-based system for the chiral separation of drug enantiomers. Each strategy includes a screening and an optimisation stage. The screening stage allows a fast evaluation of separation possibilities and enantioselectivity for many drugs in a short period of time, while the optimisation stage gives the opportunity to enhance, if needed, the initially obtained separation. Different examples demonstrate the effectiveness of the strategies for fast method development.

The aim of this study was to compare the miniaturised techniques, capillary electrochromatography (CEC) and capillary liquid chromatography (CLC), for the chiral separation of chlorthalidone. In both cases, hydroxypropyl-beta-cyclodextrin was used as a chiral selector in the mobile phase, while an achiral stationary phase was used. Earlier, this separation was already optimised in CEC. Now, the separation was optimised in CLC. The influence of the organic modifier content and the cyclodextrin concentration on the separation was studied by means of a central composite design. Optimal separation conditions were determined, after response modelling, from the response surface contour plots. When these conditions were compared with those of the CEC optimisation, we can see the potential of using CLC as a chiral separation technique since less chiral selector was used, faster separations were obtained and better repeatability was observed in comparison with its electrical-driven counterpart., (Copyright 2004 Elsevier B.V.)

The recently introduced monolithic silica columns were tested for their use in micellar liquid chromatography. Micellar methods are utilized in high-throughput quantitative structure-retention relationships to estimate an indicator of the membrane permeability of drugs, namely, the octanol-water partition coefficient, log P. The monolithic column's ability to function at higher flow rates might be useful to speed up these chromatographic methods estimating the log P. Therefore, the elution behavior of diverse basic pharmaceutical substances was determined on a classical particle-based and a monolithic column, both with and without a micellar medium in the mobile phase. Utilizing among others principal component analysis, the extent to which these methods differ in retention characteristics was examined in the context of high-throughput determination of log P. Results indicate that combining monolithic columns with micellar media leads to faster log P and possibly even better permeability predictions.

Two new diaza-crown ether derivatives (R-1, RS-1) have been synthesized from 1,4,10,13-tetraoxa-7,16-diazacyclooctadecane and tested as potential chiral selectors in capillary electrophoresis (CE) for the chiral separation of five amino acid derivatives. The individual use of the selectors did not lead to chiral differentiation. However, they enhanced the enantioselective effect of different cyclodextrins in dual selector systems. In this paper, we report the effect of different substituted diaza-crown ether derivatives on the separation results obtained in dual systems with cyclodextrins.

In this paper, the application of Classification And Regression Trees (CART) is presented for the analysis of biological activity of Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs). The data consist of the biological activities, expressed as pIC50, of 208 NNRTIs against wild-type HIV virus (HIV-1) and four mutant strains (181C, 103N, 100I, 188L) and the computed interaction energies with the Reverse Transcriptase (RT) binding pocket. CART explains the observed biological activity of NNRTIs in terms of interactions with individual amino acids in the RT binding pocket, i.e., the original data variables.

Generic orthogonal chromatographic systems might be helpful tools as potential starting points in the development of methods to separate impurities and the active substance in drugs with unknown impurity profiles. The orthogonality of 38 chromatographic systems was evaluated from weighted-average-linkage dendrograms and color maps, both based on the correlation coefficients between the retention factors on the different systems. On each chromatographic system, 68 drug substances were injected as mixtures of three or four components to increase the throughput. The (overlapping) peaks were identified and resolved with a peak purity algorithm, orthogonal projection approach (OPA). The visualization techniques applied allowed a simple evaluation of orthogonal and (groups of) similar systems.

Selection of RP-LC columns with suitable selectivity for a given analysis is difficult. For example, the European Pharmacopoeia (Ph. Eur.) and other official compendia for drug analysis only give a general description of the stationary phase in the operating procedure of a liquid chromatographic method. The need for a general test method to characterise RP-LC columns has been rising since the 1970s. A project to define a chromatographic procedure characterising RP-LC columns was started earlier. A procedure to measure test parameters was introduced and a classification of the columns, based on a minimal number of parameters, was obtained. This paper focuses on correlating the column classification with the selectivity obtained for a real separation. The separation of acetylsalicylic acid (aspirin) and related compounds was performed according to the Ph. Eur. monograph on the stationary phases previously characterised chromatographically. It was examined whether the classes of columns, determined using test parameter results, contain either suitable or unsuitable supports for the aspirin separation. The system suitability test prescribed by the Ph. Eur. in order to distinguish between suitable or unsuitable columns for this separation was also evaluated.

A principal component regression (PCR) model is built for prediction of total antioxidant capacity in green tea using near-infrared (NIR) spectroscopy. The modelling procedures are systematically studied with the focus on outlier detection. Different outlier detection methods are used and compared. The root mean square error of prediction (RMSEP) of the final model is comparable to the precision of the reference method.

The European Pharmacopoeia (Ph. Eur.) and other official compendia give only a general description of the stationary phase in the description of a liquid chromatographic method. Therefore the selection of a column giving suitable selectivity presents difficulties. Earlier, a test procedure was proposed that allows to measure 36 chromatographic parameters which have been described for characterising stationary phases. This procedure was carried out on 69 reversed-phase liquid chromatography (RP-LC) columns. This paper focuses on the classification of RP-LC stationary phases based on chromatographic parameters. A chemometric study was conducted using 24 parameters that could be measured in a repeatable and reproducible way. Principal component analysis was used to classify the columns and to estimate the minimal number of parameters necessary for a rational classification. It is shown that after reducing the number of parameters from 24 to four or three, similar classifications were obtained. The column classifications were compared to the European Pharmacopoeia stationary phase description and to the column properties obtained from the manufacturers.

Erythromycin is a mixture of macrolide antibiotics produced by Saccharopolyspora erythreas during fermentation. A new method for the analysis of erythromycin by liquid chromatography has previously been developed. It makes use of an Astec C18 polymeric column. After validation in one laboratory, the method was now validated in an interlaboratory study. Validation studies are commonly used to test the fitness of the analytical method prior to its use for routine quality testing. The data derived in the interlaboratory study can be used to make an uncertainty statement as well. The relationship between validation and uncertainty statement is not clear for many analysts and there is a need to show how the existing data, derived during validation, can be used in practice. Eight laboratories participated in this interlaboratory study. The set-up allowed the determination of the repeatability variance, s(2)r and the between-laboratory variance, s(2)L. Combination of s(2)r and s(2)L results in the reproducibility variance s(2)R. It has been shown how these data can be used in future by a single laboratory that wants to make an uncertainty statement concerning the same analysis.

A fast screening strategy was developed in capillary electrochromatography (CEC) for the chiral separation of basic and bifunctional compounds. The screening conditions were determined on polysaccharide chiral stationary phases using 15 pharmaceutical compounds. The content and type of organic modifier, as well as the pH of the mobile phase appeared to have the largest influence on the chiral resolution. It was seen that for acidic compounds, our approach was not suitable. A generic mobile phase for basic and bifunctional compounds was determined. The testing on 20 additional compounds showed that the proposed mobile phase performed well since enantioselectivity was observed for 86% of the investigated compounds. A comparison of CEC and reversed-phase liquid chromatography (RPLC) results was attempted to demonstrate the potential of the used technique for chiral method development.

The effect of nine peak measurement/analysis parameters on chiral capillary electrophoresis (CE), followed by UV detection, was studied. The parameters pertained UV-detection (detection wavelength, reference wavelength, and wavelength bandwidths), signal processing (data acquisition rate, type and amount of filtering) and peak detection (detection threshold and peak width). The influence of these factors on the chiral separation of dimethindene enantiomers was studied at two different concentrations (i.e., at high and low signal-to-noise (S/N) ratio) by the means of experimental designs. The electropherogram characteristics considered were the resolution between the two enantiomers, the peak areas, and the S/N ratio. A D-optimal design was first used as screening design to identify the most critical parameters. Afterwards, a modelling of the different responses as a function of these critical parameters was performed based on the results of a face-centered central composite design. The results showed that the signal-processing parameters should be carefully selected when developing a CE separation since very important variations in the separation, the S/N ratio and the peak area of the substances can occur by setting these parameters at different levels. The detection wavelength should also be carefully chosen for optimal peak area measurement. The role of these parameters becomes more important with decreasing concentration of the analytes (i.e., low S/N ratio). This study showed that the peak measurement/analysis parameters should be optimized as the chemical and physical parameters of a method. They also should always be well specified in order to allow a good transfer of a method from one instrument to another.

The robustness of a generic method for chiral separation in capillary electrophoresis using highly-sulfated cyclodextrins in a low pH phosphate buffer and the "short-end injection technique" was studied. In this study, we focused on the robustness of the separations and not of the quantitative analysis of the enantiomers. The robustness was evaluated for the enantiomeric separation of a basic (propranolol), a neutral (praziquantel) and an acidic (warfarin) compound. The influence of eight factors which were believed to affect significantly the separations was studied using a 11-factor, 12-experiment Plackett-design. Statistical interpretation of the factor effects on different analytical responses (selectivity and resolution) was performed. The separations of the three compounds could be considered as rather robust as the factor effects were generally not significant (alpha = 0.05) and small.

Itraconazole is a poorly water soluble compound. One method to increase the aqueous solubility of itraconazole is through formation of a solid dispersion. The purpose of this study is to develop a 40% w/w itraconazole formulation through solid dispersion formation, using hydroxypropyl-beta-cyclodextrin (HP-beta-CD) and hydroxypropylmethyl-cellulose (HPMC) as mixture components. The solid dispersion was obtained by melt-extrusion using a twin-screw corotating melt extruder. A D-optimal mixture design was applied for the development of the optimal itraconazole formulation. The itraconazole fraction varied between 20% w/w and 50% w/w in the mixture design and the HPMC and HP-beta-CD fractions varied between 10% w/w and 60% w/w. The itraconazole formulation was optimized by producing clear extrudates, minimizing the torque, and maximizing the glass transition temperature and the apparent itraconazole solubility in 0.1 N HCl. Regression models were developed for the torque, glass transition temperature, and apparent solubility of itraconazole. High itraconazole fraction in the mixture promoted a better melt processing (minimizes torque). High HPMC fraction (>33% w/w) resulted in clear extrudates, indicating a solid dispersion and resulted in high glass transition temperature of the melt. High HP-beta-CD fraction resulted in increased apparent itraconazole solubility in 0.1 N HCl. The optimal itraconazole formulation consisted of 45% w/w HPMC and 15% HP-beta-CD w/w.