Daniela Bonelli

Drug-induced phospholipidosis (PLD) is characterized by accumulation of phospholipids, the inducing drugs and lamellar inclusion bodies in the lysosomes of affected tissues. These side effects must be considered as early as possible during drug discovery, and, in fact, numerous in silico models designed to predict PLD have been published. However, the quality of any in silico model cannot be better than the quality of the experimental data set used to build it. The present paper reports an overview of the difficulties and errors encountered in the generation of databases used for the published PLD models. A new database of 466 compounds was constructed from seven literature sources, containing only publicly available compounds. A comparison of the PLD assignations in selected databases proved useful in revealing some inconsistencies and raised doubts about the previously assigned PLD+ and PLD– classifications for some chemicals. Finally, a Partial Least Squares Discriminant Analysis (PLS-DA) approach was also applied, revealing further anomalies and clearly showing that metabolism as well as data quality must be taken into account when generating accurate methods for predicting the likelihood that a compound will induce PLD. A new curated database of 331 compounds is proposed.

The discovery that proinflammatory prostaglandins are produced by cyclooxygenase-2 (COX-2), an inducible isoform of the constitutive cyclooxygenase-1 (COX-1), opened a new frontier in the treatment...

The paper illustrates the chemometric strategies appropriate for extracting information from a large amount of biological data regarding the antibiotic activity of 6-aminoquinolones. The unique framework based on principal component analysis, projection onto latent structures, and response surface methodologies permits the structure−activity correlations to be shown and to suggest new compounds for further testing. The low activity of the suggested molecules points out the limitations of quantitative structure−activity relationship models when the training set is not properly designed in order to balance all the structural variations taken into account.

A chemometric approach based on multivariate calibration and design in latent variables permits one to rationalize the antibacterial activity of quinolones against gram negative bacteria, ranking the relative importance of individual structural features, and suggesting a new active structure.

In order to establish quantitative relationships between the antibacterial activities of a number of thienyl- and furyl-benzimidazoles and benzoxazoles, the biological data were measured homogeneously for a selected set of 16 representative compounds of the available set of 103. The data were analyzed by the PLS method. The results of linear PLS modelling and PLS response surface modelling permitted a straightforward interpretation of the structural features relevant to the activities and the prediction of a possible optimal structure.

Traditional descriptors for Qsar models are compared with two alternative blocks obtained by computer chemistry tools: electronic densities and principal properties. All three sets show similar prediction abilities by PLS modelling.

Three literature data sets are modelled by PLS response surfaces in terms of the principal properties of organic substituents by means of the CARSO procedure. The models permit one to rationalize the biological data, to predict new activities, and to detect optimal structures.

A set of a hundred aromatic substituents were multivariately characterized by nine descriptor variables taken from the literature. From the 9*100 data set were calculated four principal properties for the aromatic substituents as the four first dimensions in a principal components analysis, PCA. The first three principal properties were used to develop a strategy for selecting substituents from eight subgroups according to a factorial design.