Biomedical subjects

Y Vander Heyden

Publications and source records attributed to Y Vander Heyden.

At least 19 recordsLinked to original sources

PubMed2006

Influence of particle size on the quantitative determination of salicylic acid in a pharmaceutical ointment using FT-Raman spectroscopy.

A second order polynomial calibration model was developed and statistically validated for the direct and non-destructive quantitative analysis - without sample preparation - of the active pharmaceutical ingredient (API) salicylic acid in a pharmaceutical ointment using FT-Raman spectroscopy. The calibration curve was modeled by plotting the peak intensity of the vector normalized spectral band between 757 and 784cm(-1) against the known salicylic acid concentrations in standards. At this band, no spectral interferences from the ointment vehiculum (white vaseline) are observed. For the validation of the polynomial model, its fit and its predictive properties were evaluated. The validated model was used for the quantification of 25 ointments, compounded by different retail pharmacists. The same standards and samples were used, both for development and validation of a regression model and for quantitative determination by HPLC - with sample preparation - as described for the related substances of salicylic acid in the Ph. Eur. IV. The quantification results obtained by the FT-Raman method corresponded with the HPLC results (p=0.22), provided that the particle size of salicylic acid in the standards is the same as in the analyzed samples. The non-destructive FT-Raman method is a reliable alternative for the destructive HPLC method, as it is faster and does not require sample pre-treatment procedures.

Calibration↗

PubMed2006

Evaluation of chromatographic descriptors for the prediction of gastro-intestinal absorption of drugs.

The use of chromatographic descriptors in QSAR was evaluated. Therefore, retentions were measured on an immobilized artificial membrane system, 2 micellar liquid chromatography systems and 17 orthogonal or disimilar reversed-phase liquid chromatographic systems. It was investigated whether it was possible to model gastro-intestinal absorption as a function of chromatographic retentions applying two linear and one non-linear multivariate modeling technique. In a second step it was evaluated if models built with theoretical descriptors could be improved by adding the measured retention factors to the data set of descriptive variables. It was seen that gastro-intestinal absorption could be modelled in function of chromatographic retention using the non-linear modeling technique multivariate adaptive regression splines (MARS). The best models were obtained using a combination of theoretical and chromatographic descriptors with MARS as modeling technique.

Acebutolol↗

PubMed2006

Robustness testing of a chiral NACE method for R-timolol determination in S-timolol maleate and uncertainty assessment from quantitative data.

A robustness test of a capillary electrophoresis method for the chiral separation of timolol in nonaqueous acidified media was performed. A two-level Plackett-Burman design was applied in which one qualitative and six quantitative factors were examined. Resolution, migration times and relative migration times to pyridoxine (selected as internal standard) were examined as qualitative responses to evaluate electrophoretic performance. A quantitative response, the content of R-timolol in S-timolol maleate sample, was also considered. Even though some significant factor effects were observed on the qualitative responses, it was still possible to quantify the R-timolol in the S-timolol maleate samples properly. The quantitative response was not significantly affected by the selected factors, demonstrating the robustness of the procedure. However, the use of different HDMS-beta-CD batches seemed to affect both types of responses necessitating to introduce a warning in the procedure. Since the experiments of the Plackett-Burman design can be assimilated to laboratories in an interlaboratory study, uncertainty can be evaluated using the robustness test data. The robustness test was set-up in such a way that the required variances could be estimated.

Adrenergic beta-Antagonists↗

PubMed2006

Increasing conclusiveness of metabonomic studies by chem-informatic preprocessing of capillary electrophoretic data on urinary nucleoside profiles.

Nowadays, bioinformatics offers advanced tools and procedures of data mining aimed at finding consistent patterns or systematic relationships between variables. Numerous metabolites concentrations can readily be determined in a given biological system by high-throughput analytical methods. However, such row analytical data comprise noninformative components due to many disturbances normally occurring in analysis of biological samples. To eliminate those unwanted original analytical data components advanced chemometric data preprocessing methods might be of help. Here, such methods are applied to electrophoretic nucleoside profiles in urine samples of cancer patients and healthy volunteers. The electrophoretic nucleoside profiles were obtained under following conditions: 100 mM borate, 72.5 mM phosphate, 160 mM SDS, pH 6.7; 25 kV voltage, 30 degrees C temperature; untreated fused silica capillary 70 cm effective length, 50 microm I.D. Different most advanced preprocessing tools were applied for baseline correction, denoising and alignment of electrophoretic data. That approach was compared to standard procedure of electrophoretic peak integration. The best results of preprocessing were obtained after application of the so-called correlation optimized warping (COW) to align the data. The principal component analysis (PCA) of preprocessed data provides a clearly better consistency of the nucleoside electrophoretic profiles with health status of subjects than PCA of peak areas of original data (without preprocessing).

Algorithms↗

PubMed2006

Exploration of linear modelling techniques and their combination with multivariate adaptive regression splines to predict gastro-intestinal absorption of drugs.

In general, linear modelling techniques such as multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS), are used to model QSAR data. This type of data can be very complex and linear modelling techniques often model only a limited part of the information captured in the data. In this study, it was tried to combine linear techniques with the flexible non-linear technique multivariate adaptive regression splines (MARS). Models were built using an MLR model, combined with either a stepwise procedure or a genetic algorithm for variable selection, a PCR model or a PLS model as starting points for the MARS algorithm. The descriptive and predictive power of the models was evaluated in a QSAR context and compared to the performances of the individual linear models and the single MARS model. In general, the combined methods resulted in significant improvements compared to the linear models and can be considered valuable techniques in modelling complex QSAR data. For the used data set the best model was obtained using a combination of PLS and MARS. This combination resulted in a model with a Pearson correlation coefficient of 0.90 and a cross-validation error, evaluated with 10-fold cross-validation of 9.9%, pointing at good descriptive and high predictive properties.

Algorithms↗

PubMed2006

Chemometric analysis of soil pollution data using the Tucker N-way method.

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.

Journal Article↗

PubMed2006

A comparison of three algorithms for chromatograms alignment.

In this paper the performance of three alignment algorithms, correlation optimized warping, parametric time warping and semi-parametric time warping, is compared on real chromatograms. Among these, parametric time warping is the simplest and fastest; generally less than 1s is required to align two chromatograms. It does not require the optimization of input parameters and allows the alignment of peak shifts in only one direction, or non-complex peak shifts in both directions. With correlation optimized warping and semi-parametric time warping complex peak shifts in both directions can be corrected but at the expense of the optimization of two input parameters. Semi-parametric time warping requires the selection of the proper number of B-splines in the warping function and, if necessary, the optimization of the penalty parameter. Often the default values can be used to obtain aligned signals. The optimization of the input parameters for correlation optimized warping (section length, slack) is not easy and time-consuming. Moreover, dependent on the input parameters, the computation time of the correlation optimized warping algorithm can be twice as long as for semi-parametric time warping for which computation times up to 23 s are required. However, the performance of both algorithms is equally good considering the improvement of the precision of the peak retention times and correlation coefficients between the chromatograms, after alignment. For the data aligned in this study, the average retention time precision and the lowest correlation before warping were 14 and 0.17, and were improved to three and 0.83, and six and 0.87 after warping, with correlation optimized warping and semi-parametric time warping, respectively.

Algorithms↗

PubMed2006

Improving method capability of a drug substance HPLC assay.

The assay of a drug substance (DS) is one of the tests required to confirm the active pharmaceutical ingredient (API) quality at release. In the past, usually volumetric titration methods were performed, that were precise, but often non-specific. Nowadays specific chromatographic assay procedures are preferred. However, high performance liquid chromatographic (HPLC) methods, the way they are usually executed, tend to be less precise and have a larger total method variation compared to titration methods. The capabilities of fully validated titration and HPLC assay methods were determined and compared. It was studied which factors had the largest effects on the capability of chromatographic HPLC methods in order to improve their precision and precision-to-tolerance ratio. This was done using multiple Gage R&R (repeatability & reproducibility) studies and an experimental design approach. The investigations showed that it was feasible to define an HPLC method with a similar capability as the titration method. The most important factor determining the precision was demonstrated to be higher sample and reference material weights. When low weights are to be used, increasing the number of sample preparations and the number of reference solutions may enhance the method capability.

Chromatography, High Pressure Liquid↗

PubMed2006

Stationary phases in the screening of drug/impurity profiles and in their separation method development: identification of columns with different and similar selectivities.

The classification or characterization of stationary phases based on chromatographic parameters, in general, requires different test solutes/mixtures and several mobile phases. To simplify the classification/characterization of reversed-phase liquid chromatographic columns, to be used in separating drug/impurity profiles, a new test procedure was proposed. It consists of injecting two mixtures of relatively similar active substances applying a standard gradient. The aim was to evaluate from this approach the selectivity differences and overall separation quality of newly tested columns compared to that in an earlier selected set of eight stationary phases. The selectivity differences of the columns were evaluated by correlation coefficient-based weighted-average-linkage dendrograms and color maps. Derringer's desirability functions were used to rank similar stationary phases according to their overall separation quality. Four columns of 27 examined were, for instance, considered different from the earlier selected eight and could be added to the selection. A number of tested stationary phases might be considered as alternatives for some from the initial set. For three columns the newly tested stationary phases did not contain alternatives.

Chromatography, High Pressure Liquid↗

PubMed2006

Retention prediction of peptides based on uninformative variable elimination by partial least squares.

A quantitative structure-retention relationship analysis was performed on the chromatographic retention data of 90 peptides, measured by gradient elution reversed-phase liquid chromatography, and a large set of molecular descriptors computed for each peptide. Such approach may be useful in proteomics research in order to improve the correct identification of peptides. A principal component analysis on the set of 1726 molecular descriptors reveals a high information overlap in the descriptor space. Since variable selection is advisable, the retention of the peptides is modeled with uninformative variable elimination partial least squares, besides classic partial least squares regression. The Kennard and Stone algorithm was used to select a calibration set (63 peptides) from the available samples. This set was used to build the quantitative structure-retention relationship models. The remaining 27 peptides were used as independent external test set to evaluate the predictive power of the constructed models. The UVE-PLS model consists of 5 components only (compared to 7 components in the best PLS model), and has the best predictive properties, i.e., the average error on the retention time is less than 30 s. When compared also to stepwise regression and an empirical model, the obtained UVE-PLS model leads to better and much better predictions, respectively.

Algorithms↗

PubMed2005

Chemometric treatment of vanillin fingerprint chromatograms. Effect of different signal alignments on principal component analysis plots.

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.

Algorithms↗

PubMed2005

Evaluation of chemometric techniques to select orthogonal chromatographic systems.

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.

Algorithms↗

PubMed2005

Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals part I: peak detection.

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.

Algorithms↗

PubMed2005

Selection of orthogonal reversed-phase HPLC systems by univariate and auto-associative multivariate regression trees.

In order to select chromatographic starting conditions to be optimized during further method development of the separation of a given mixture, so-called generic orthogonal chromatographic systems could be explored in parallel. In this paper the use of univariate and multivariate regression trees (MRT) was studied to define the most orthogonal subset from a given set of chromatographic systems. Two data sets were considered, which contain the retention data of 68 structurally diversive drugs on sets of 32 and 38 chromatographic systems, respectively. For both the univariate and multivariate approaches no other data but the measured retention factors are needed to build the decision trees. Since multivariate regression trees are used in an unsupervised way, they are called auto-associative multivariate regression trees (AAMRT). For all decision trees used, a variable importance list of the predictor variables can be derived. It was concluded that based on these ranked lists, both for univariate and multivariate regression trees, a selection of the most orthogonal systems from a given set of systems can be obtained in a user-friendly and fast way.

Chromatography, High Pressure Liquid↗

PubMed2005

Development of a ginkgo biloba fingerprint chromatogram with UV and evaporative light scattering detection and optimization of the evaporative light scattering detector operating conditions.

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.

Chromatography, High Pressure Liquid↗

PubMed2005

Classification of drugs in absorption classes using the classification and regression trees (CART) methodology.

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.

Intestinal Mucosa↗

PubMed2005

Prediction of gastro-intestinal absorption using multivariate adaptive regression splines.

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.

Algorithms↗

PubMed2005

Orthogonality and similarity within silica-based reversed-phased chromatographic systems.

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.

Chromatography, High Pressure Liquid↗