Cluster-based Design in Environmental QSAR

A QCAR model for predicting antioxidant activity of wild mushrooms

Wild mushrooms have been described as sources of natural antioxidants, particularly phenolic compounds. However, many other compounds present in wild mushrooms can also act as antioxidants (reducers), so whole extracts from a wide range of species need to be examined. To gain further knowledge in this area, the relationship between the antioxidant potential (scavenging effect and reducing power) and chemical composition of twenty three samples from seventeen Portuguese wild mushroom species was investigated. A wide range of analytical parameters reported by our research group (including ash, carbohydrates, proteins, fat, monounsaturated fatty acids, polyunsaturated fatty acids, saturated fatty acids, phenolics, flavonoids, ascorbic acid and beta-carotene) were studied and the data were analysed by partial least squares (PLS) regression analysis to allow correlation of all the parameters. Antioxidant activity correlated well with phenolic and flavonoid contents. A QCAR (Quantitative Composition-Activity Relationships) model was constructed, using the PLS method, and its robustness and predictability were verified by internal and external cross-validation methods. Finally, this model proved to be a useful tool in the prediction of mushrooms' reducing power.

ST-scale as a novel amino acid descriptor and its application in QSAM of peptides and analogues

In this study, structural topology scale (ST-scale) was recruited as a novel structural topological descriptor derived from principal component analysis on 827 structural variables of 167 amino acids. By using partial least squares (PLS), we applied ST-scale for the study of quantitative sequence-activity models (QSAMs) on three peptide datasets (58 angiotensin-converting enzyme (ACE) inhibitors, 34 antimicrobial peptides (AMPs) and 89 elastase substrates (ES)). The results of QSAMs were superior to that of the earlier studies, with determination coefficient (r(2)) and cross-validated (q(2)) equal to 0.855, 0.774; 0.79, 0.371 (OSC-PLS: 0.995, 0.848) and 0.846, 0.747, respectively. Therefore, ST-scale descriptors were considered to be competent to extract information from 827 structural variables and relate with their bioactivities.

Determination of selectivity differences for basic compounds in gradient reverse phase high performance liquid chromatography under high pH conditions by partial least squares modelling

The retention behaviour of compounds in a chromatographic system is believed to be multivariate by nature, i.e. many physico-chemical properties of an analyte can influence its retention. Principal component analysis (PCA) and partial least squares (PLS) can therefore be particularly useful tools for visualising, exploring and modelling the complex interactions between solutes and the mobile and stationary phase. PCA allows the relationships between compounds (the observations) and their retention parameters (the variables) to be visualised in usually just two or three dimensions. PLS can be used to model quantitative structure-retention relationships (QSRRs) and may lead to better understanding of retention and selectivity changes in chromatographic systems. The objective of the study was to investigate the chromatographic behaviour of basic compounds under optimised gradient conditions using octadecyl high performance liquid chromatography (HPLC) columns designed for high pH separations. Three pharmaceutical mixtures were analysed by linear gradient reverse phase HPLC (RP-HPLC) at high pH using ammonia as a pH modifier, and methanol and/or acetonitrile as the organic modifier. The separations were carried out on three octadecyl columns: Waters XTerra MS C18, Agilent Zorbax Extend C18 and Thermo Hypersil-Keystone BetaBasic-18. Multivariate PCA and PLS modelling were employed to explore and explain the differences in selectivity between the chromatographic systems studied when the basic compounds were analysed under the high pH conditions. The interactions between the analytes and the mobile-stationary phases were described by relating the compound molecular descriptors with the selectivity of each chromatographic system. The selectivity differences between the chromatographic systems were identified.

In silico tools to aid risk assessment of endocrine disrupting chemicals

In silico or computational tools could be used more effectively in endocrine disruptor risk assessment for prescreening potential endocrine disruptors, improving experimental in vitro screening assay design and facilitating more thorough data analyses. The in silico tools reviewed here are three-fold and include the use of: (1) nuclear receptor (NR) crystal structures and homology models to examine potential modes of ligand binding by different representative compounds; (2) multivariate principal component analyses (PCA) techniques to select best predicted cell lines for endocrine disrupting chemicals (EDC) risk assessment purposes; (3) NR quantitative structure-activity relationships (QSARs) that can be constructed from varied biological data sources, using multivariate partial least squares (PLS) techniques and specific descriptors. The cytosolic and NR examples discussed here include the Ah receptor, (AhR), the human oestrogen receptor alpha (hERalpha) and the human pregnane X receptor (PXR). The varied biological data sets can be compared to give a more integrated dimension to receptor cross talk mechanisms, with further support from molecular modelling studies.

Simultaneous spectrophotometric determination of phenobarbital, phenytoin and methylphenobarbital in pharmaceutical preparations by using partial least-squares and principal component regression multivariate calibration

Two multivariate calibration methods, partial least squares (PLS-2) and principal component regression (PCR) have been applied to the simultaneous spectrophotometric analysis of ternary mixtures of phenytoin (DPH), phenobarbital (PBT) and methylphenobarbital (MPBT) in the Comital-L pharmaceutical formulation. The PLS-2 and PCR procedures were employed to evaluate the data of a variable number of calibration solutions measured over the wavelength range 400-700 nm. The concentration ranges used to construct the calibration matrix were varied between 5 and 30 microg ml(-1). The proposed methods were validated by applying them to the analysis of the Comital-L pharmaceutical formulation and the average relative errors were less than 6% for each one of the analyzed compounds. The results obtained by both proposed methods have been compared with the results obtained by application of a RPLC reference method.

Multivariate data analysis using D-optimal designs, partial least squares, and response surface modeling: A directional approach for the analysis of farnesyltransferase inhibitors

We have investigated the combined use of partial least squares (PLS) and statistical design principles in principal property space (PP-space), derived from principal component analysis (PCA), to analyze farnesyltransferase inhibitors in order to identify "activity trends" (an approach we call a "directional" approach) and quantitative structure-activity relationships (QSAR) for a congeneric series of inhibitors: the benzo[f]perhydroisoindole (BPHI) series. Trends observed in the PCA showed that the descriptors used were relevant to describe our structural data set by clearly identifying two well-defined structural subclasses of inhibitors. D-Optimal design techniques allowed us to define a training set for PLS study in PP-space. Models were derived for each biological assay under evaluation: the in vitro Ki-Ras and cellular HCT116 tests. Each of these assay-based sets was subdivided once more into two subsets according to two structural classes in this BPHI series as revealed by the PCA model. The response surface modeling (RSM) methodology was used for each subset, and the corresponding RSM plots helped us identify "activity trends" exploited to guide further analogue design. For more precise activity predictions more refined PLS models on constrained PP-spaces were developed for each subset. This approach was validated with predicted sets and demonstrates that useful information can be extracted from just a few very informative and representative compounds. Finally, we also showed the potential use of such a strategy at an early stage of an optimization process to extract the first "activity trends" that might support decision making and guide medicinal chemists in the initial design of new analogues and/or lead followup libraries.

Statistical molecular design of building blocks for combinatorial chemistry

The reduction of the size of a combinatorial library can be made in two ways, either base the selection on the building blocks (BB's) or base it on the full set of virtually constructed products. In this paper we have investigated the effects of applying statistical designs to BB sets compared to selections based on the final products. The two sets of BB's and the virtually constructed library were described by structural parameters, and the correlation between the two characterizations was investigated. Three different selection approaches were used both for the BB sets and for the products. In the first two the selection algorithms were applied directly to the data sets (D-optimal design and space-filling design), while for the third a cluster analysis preceded the selection (cluster-based design). The selections were compared using visual inspection, the Tanimoto coefficient, the Euclidean distance, the condition number, and the determinant of the resulting data matrix. No difference in efficiency was found between selections made in the BB space and in the product space. However, it is of critical importance to investigate the BB space carefully and to select an appropriate number of BB's to result in an adequate diversity. An example from the pharmaceutical industry is then presented, where selection via BB's was made using a cluster-based design.