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.

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.

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.

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.

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.

Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals

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.

Prediction of total green tea antioxidant capacity from chromatograms by multivariate modeling

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.

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.

Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals

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.

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.

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.

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.

Inclusion complexation of diazepam with different cyclodextrins in formulations for parenteral use

A parenteral formulation for the water-insoluble benzodiazepine diazepam was developed. Different cyclodextrins (CDs) suitable for parenteral injection: hydroxypropyl-beta-cyclodextrin (HP-beta-CD), hydroxy-propyl-gamma-cyclodextrin (HP-gamma-CD), sulfobutylether-7-beta-cyclodextrin (SBE-7-beta-CD) and maltosyl-beta-cyclodextrin (malt-beta-CD) were used as alternatives to cosolvents to increase solubility. The increase in solubility displayed a concentration dependency for the four CDs used. Diazepam's solubility is enhanced linearly as a function of each CD concentration. The highest improvements in solubility (dissolved concentration circa 3.5 mg/ml in 40% CD) were found by adding HP-beta-CD or SBE-7-beta-CD. The additional use of polyvinylpyrrolidone (PVP) did not further increase the solubility of diazepam with HP-beta-CD. A parenteral aqueous diazepam solution was prepared containing 10 mg diazepam/5 ml 30% HP-beta-CD or SBE-7-beta-CD solution. The preparations are in agreement with the requirements for parenteralia. Sterilisation by filtration is required since autoclaving degrades the active compound. The stability of the preparations, with and without pH adjustment to pH 5, was investigated during 18 months and during this period no noticeable degradation was observed.

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.

Enantioselective capillary electrophoretic separation of tryptophane- and tyrosine-methylesters in a dual system with a tetra-oxadiaza-crown-ether derivative and a cyclodextrin

Different dual selector systems containing a cyclodextrin derivative (methyl-beta-cyclodextrin and dimethyl-beta-cyclodextrin) and a new diaza-crown-ether derivative (N-[2-(1,4,10,13-tetraoxa-7,16-diazacyclooctadecan-7-yl)propanoyl]glycine) were studied in the enantioselective separation of tryptophan-methylester and tyrosine-methylester enantiomers. This paper deals with the systematic study of the effects of changing the composition of the background electrolyte on the resolution of the d- and l- forms using an experimental design approach. It was found that the dual systems allowed a better chiral separation of the amino acid derivatives. The experimental design approach also allowed improving the separation compared to the starting conditions (center point of the design), which were adopted from a previous study.

The effect of preprocessing methods in reducing interfering variability from near-infrared measurements of creams

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.

Fast Monolithic Micellar Liquid Chromatography: An Alternative Drug Permeability Assessing Method for High-Throughput Screening

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.