Chemometrical exploration of the wet precipitation chemistry from the Austrian Monitoring Network (1988-1999).

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.

Determining orthogonal chromatographic systems prior to the development of methods to characterise impurities in drug substances.

To define starting conditions for the development of methods to separate impurities from the active substance and from each other in drugs with an unknown impurity profile, the parallel application of generic orthogonal chromatographic systems could be useful. The possibilities to define orthogonal chromatographic systems were examined by calculation of the correlation coefficients between retention factors k for a set of 68 drugs on 11 systems, by visual evaluation of the selectivity differences, by using principal component analysis, by drawing color maps and evaluating dendrograms. A zirconia-based stationary phase coated with a polybutadiene (PBD) polymer and three silica-based phases (base-deactivated, polar-embedded and monolithic) were used. Besides the stationary phase, the influence of pH and of organic modifier, on the selectivity of a system were evaluated. The dendrograms of hierarchical clusters were found good aids to assess orthogonality of chromatographic systems. The PBD-zirconia phase/methanol/pH 2.5 system is found most orthogonal towards several silica-based systems, e.g. a base-deactivated C16 -amide silica/methanol/pH 2.5 system. The orthogonality was validated using cross-validation, and two other validation sets, i.e. a set of non-ionizable solutes and a mixture of a drug and its impurities.

Classification and regression tree analysis for molecular descriptor selection and retention prediction in chromatographic quantitative structure-retention relationship studies.

The use of the classification and regression tree (CART) methodology was studied in a quantitative structure-retention relationship (QSRR) context on a data set consisting of the retentions of 83 structurally diverse drugs on a Unisphere PBD column, using isocratic elutions at pH 11.7. The response (dependent variable) in the tree models consisted of the predicted rention factor (log kw) of the solutes, while a set of 266 molecular descriptors was used as explanatory variables in the tree building. Molecular descriptors related to the hydrophobicity (log P and Hy) and the size (TPC) of the molecules were selected out of these 266 descriptors in order to describe and predict retention. Besides the above mentioned, CART was also able to select hydrogen-bonding and molecular complexity descriptors. Since these variables are expected from QSRR knowledge, it demonstrates the potential of CART as a methodology to understand retention in chromatographic systems. The potential of CART to predict retention and thus occasionally to select an appropriate system for a given mixture was also evaluated. Reasonably good prediction, i.e. only 9% serious misclassification, was observed. Moreover, some of the misclassifications probably are inherent to the data set applied.

Assessing molecular similarity/diversity of chemical structures by FT-IR spectroscopy.

FT-IR spectra have been investigated for their ability to distinguish compounds which are chemically diverse and to produce clusters of compounds which makes sense chemically. Principal component analysis (PCA) was applied to the analysis of a small database of FT-IR spectra. The effect of the data pretreatment step of log transformation on spectral data pattern was also visualized by using PCA plots. The method of sequential projection pursuit (SPP) was applied to detect inhomogeneities in the data. Finally, cluster analysis of these spectra, depending on unweighted pair-group average linkage, was carried out.

Exploratory chemometric analysis of the classification of pharmaceutical substances based on chromatographic data.

A chemometric study has been conducted on a published data set consisting of the retention times of 83 substances, from five pharmacological families, on eight HPLC systems. Principal component analysis, clustering and sequential projection pursuit were applied. In this way it was investigated to what extent the combination of chromatography and chemometrics allows one to make conclusions about pharmacological activities of (candidate) drugs and what the contribution is of the different HPLC systems considered.

Sequential projection pursuit using genetic algorithms for data mining of analytical data.

Sequential projection pursuit (SPP) is proposed to detect inhomogeneities (clusters) in high-dimensional analytical data. Such inhomogeneities indicate that there are groups of objects (samples) with different chemical characteristics. The method is compared with principal component analysis (PCA). PCA is generally applied to visually explore structure in high-dimensional data, but is not specifically used to find clustering tendency. Projection pursuit (PP) is specifically designed to find inhomogeneities, but the original method is computationally very intensive. SPP combines the advantages of both methods and overcomes most of their weak points. In this method, latent variables are obtained sequentially according to their importance measured by the entropy index. This involves an optimization step, which is achieved by using a genetic algorithm. The performance of the method is demonstrated and evaluated, first on simulated data sets, and then on near-infrared and gas chromatography data sets. It is shown that SPP indeed reveals more easily information about inhomogeneities than PCA.

Use of mass spectrometry for assessing similarity/diversity of natural products with unknown chemical structures.

An evaluation whether mass spectral data contain useful information for assessing similarity/diversity of drug compounds is presented. A comparative study was carried out between Ward's hierarchical agglomerative clustering, based on the 2D Daylight fingerprints or on the mass spectra, of a small database of 66 synthetic substances. The influence of normalization of the mass spectral data on the clustering result has also been studied. The results were subsequently compared with an expert's classification of the same small dataset, based on own evaluation according to known structure and pharmacological activity.

Ruggedness testing of chromatographic methods: selection of factors and levels.

The first step in a ruggedness test is the selection of factors to be examined and their levels. In this paper, both topics are discussed, thereby completing a strategy described earlier. It is demonstrated, by means of some examples, that depending on the formulation (definition) of a factor, information that is physically more or less meaningful is extracted from the experimental design results. Among others, the inclusion of the compounds of a buffer and of the components of a mixture in a screening design were examined. A general guideline to select the levels of the factors in a ruggedness test was proposed. Some special cases, i.e. asymmetric intervals around the nominal level, were also discussed.

A ruggedness test strategy for procedure related factors: experimental set-up and interpretation.

A strategy to perform ruggedness tests for mainly procedure related factors is described. The different steps in the set-up of the experiments and in the interpretation of the results are given. The described strategy is based on a number of case studies and allows a statistical interpretation of the significance of the effects. It was implemented in a software tool. This original strategy was completed with a number of minimal screening designs which reduce the number of experiments to perform, but in consequence only allow a limited or no statistical interpretation of the effects. Some of the minimal designs are expandable to designs with characteristics similar to those of the original strategy.

RTS, a computer program for the experimental set-up and interpretation of ruggedness tests.

A computer program is described for the experimental set-up and interpretation of ruggedness tests. The implemented strategy was based on a number of case studies and contains both recommended designs and minimal designs. The minimal designs reduce the number of experiments, but they cannot be statistically interpreted based on the interaction or dummy factor effects. The use of randomization tests as an alternative statistical interpretation method for the significance of the effects was examined. Some of the minimal designs are expandable to designs with characteristics similar to those of the recommended designs. The program is designed to facilitate the selection of the designs and the interpretation of the results and to prevent or detect problems such as drifting of responses.