In silico prediction of drug toxicity

It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative toxicity is advisable even before synthesis. Thus the use of predictive toxicology is called for. A number of in silico approaches to toxicity prediction are discussed. Quantitative structure-activity relationships (QSARs), relating mostly to specific chemical classes, have long been used for this purpose, and exist for a wide range of toxicity endpoints. However, QSARs also exist for the prediction of toxicity of very diverse libraries, although often such QSARs are of the classification type; that is, they predict simply whether or not a compound is toxic, and do not give an indication of the level of toxicity. Examples are given of all of these. A number of expert systems are available for toxicity prediction, most of them covering a range of toxicity endpoints. Those discussed include TOPKAT, CASE, DEREK, HazardExpert, OncoLogic and COMPACT. Comparative tests of the ability of these systems to predict carcinogenicity show that improvement is still needed. The consensus approach is recommended, whereby the results from several prediction systems are pooled. It is simply amazing that we can formulate any kind of QSAR. The (desired activity) is only the starting point. The truly formidable problem is that of toxicity, especially the difficult long-term toxicities resulting from chronic usage'. (Hansch & Leo [1])     

Development of docking-based 3D QSAR models for the design of substituted quinoline derivatives as human dihydroorotate dehydrogenase (hDHODH) inhibitors

Abstract

This study has investigated docking-based 3D quantitative structure–activity relationships (QSARs) for a range of quinoline carboxylic acid derivatives by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). A docking study has shown that most of the compounds formed H-bonds with Arg136 and Gln47, which have already been shown to be essential for the binding of ligands at the active site of the hydroorotate dehydrogenase adenovirus (hDHODH). Bioactive conformations of all the molecules obtained from the docking study were used for the 3D QSAR study. The best CoMFA and CoMSIA models were obtained for the training set and were found to be statistically significant, with cross-validated coefficients (q² ) of 0.672 and 0.613, r² _cv of 0.635 and 0.598 and coefficients of determination (r² ) of 0.963 and 0.896, respectively. Both models were validated by a test set of 15 compounds, giving satisfactory predicted correlation coefficients (r² _pred) of 0.824 and 0.793 for the CoMFA and CoMSIA models, respectively. From the docking-based 3D QSAR study we designed 34 novel quinoline-based compounds and performed structure-based virtual screening. Finally, in silico pharmacokinetics and toxicities were predicted for 24 of the best docked molecules. The study provides valuable information for the understanding of interactions between hDHODH and the novel compounds.     

A QSAR for Measuring Sublethal Responses in the Marine Macroalga Enteromorpha intestinalis

Abstract

Two methods for measuring the effect of organic toxicants on the marine macroalga Enteromorpha intestinalis were compared. EC₅₀ data for the effect of C₁?C₈ n?alcohols on neutral red dye retention and ion leakage were used to construct QSARs with log K_ow as the only physicochemical parameter. The two QSARs were statistically indistinguishable suggesting that both procedures reflect the same non-specific narcotic effect on membrane integrity. The effect on ion leakage of compounds with more specific modes of action was also investigated. The herbicides diuron and diquat had no effect at concentrations which inhibit photosynthesis, the uncoupler 2,4-dichlorophenol was no more toxic than a narcotic of similar polarity, but the known high toxicity of the antifouling compound tributyltin was clearly reflected by the ion leakage response.     

Geographical Characterization of Greek Olive Oils Using Rare Earth Elements Content and Supervised Chemometric Techniques

Different ANNs models [Multi-layer Perceptrons (MLPs) and Radial Basis Function (RBF)] were developed and evaluated for the discrimination of olive oils produced in four Greek regions according to their geographical origin. For this purpose, ninety-seven samples were analyzed for 10 rare earth elements (REE) by ICP-MS. Moreover, two additional supervised techniques, discriminant analysis (DA) and classification trees (CTs), were applied to the same set for the data pre-treatment and for comparison purposes. In addition, two approaches were used for models' training and evaluation: the classical random choice of samples for the learning data set and an innovative one, which used the two linear discriminant functions (LDFs) of the preceding DA to choose the most representative learning sample set. The results were very satisfactory for the new ANNs classifiers. Over-fitting phenomena were overcome and the prediction ability was 73%, as evaluated by an independent test sample set. The results are encouraging for the ANNs efficiency even in demanding data bases, as the one under consideration.

[Supplementary materials are available for this article. Go to the publisher's online edition of Analytical Letters for the following free supplemental resources: Additional figures and tables.]     

Modelling of the Biological Activity for a Set of Ceramic Fibre Materials: A QSAR Study

Abstract

The objective was to develop quantitative structure-activity relationships (QSARs) for a set of nine ceramic raw materials. The samples were characterized by a chemical analysis (both X-ray fluorescence and neutron activation analysis) and the morphology was determined by electron microscopy in combination with automated image analysis. Further, the fibre samples were subjected to two biological activity assays, measuring cytotoxicity and hydroxyl radical production. To investigate the produced data structures, principal component analysis (PCA) and partial least squares (PLS) were applied together with rigorous validation techniques. Significant QSARs were found for both biological activity assays. The morphology of the fibres plays an important role for the cytotoxicity and their trace element background is related to the hydroxyl radical production.     

Evaluation of the Use of QSARS for Priority Setting and Risk Assessment

Abstract

Impending changes in EEC legislation have accelerated the need to define the principles and practical considerations of the use of QSARs in priority setting and risk assessment. It is important to delineate the limitations of this approach and to review whether and how this information should be used in the risk assessment. The value and limitations of QSARs for use in priority setting and risk assessment will not be discussed in detail since the European Chemical Industry Ecology and Toxicology Centre (ECETOC) has only recently established a Task Force to tackle this issue. The terms of reference of the Task Force are: (1) compare the predictions obtained with QSARs to measured data using ECETOC databases and other sources of data and comment on the validity and applicability of such QSARs; (2) identify and review software packages which are available for accessing and using appropriate QSARs; (3) identify those aspects of environmental distribution, fate and effects where the further development of QSARs is desirable and feasible; and (4) provide a scientific basis for ECETOC's contribution to the activities of the European Chemicals Bureau (ECB) in this area. In this short paper, only an initial and personal evaluation is made of when and where to use QSARs in the priority setting and risk assessment process within the regulatory framework. Some critical remarks and suggestions are provided to guide future developments and integration of QSARs in the risk assessment process.     

Metal–organic supramolecular architectures derived from a new zwitterionic dicarboxylate ligand

A series of new coordination compounds with 1-carboxymethylpyridinium-4-benzoate (L) and transition metal ions have been synthesized. They are formulated as [M(L)₂(H₂O)₄]?·?4H₂O M=Mn (1) and Co (2), {[M(L)₂]?·?xH₂O} _n M=Mn, x?=?1 (3); M=Co, x?=?2 (4), and M=Cu, x?=?3 (5). In 1 and 2, the zwitterionic dicarboxylate ligand is monodentate through only one carboxylate to generate mononuclear molecules. The molecules are assembled through O–H?···?O interactions to give 3-D pillared layer-like architectures, in which interesting 1-D tape-like hydrogen bonding motifs are connected into 2-D layers via carboxylate-mediated hydrogen bonds. In 3–5, the organic ligands serve as bridges with one carboxylate monodentate and the other chelating, and the metal ions are linked by double bridges to give 1-D polymeric chains, which are zigzag (3) or stair-like (4 and 5) due to the cis or trans coordination geometry around metal ions. The chains are further stabilized and associated into 3-D architectures through intra- and interchain hydrogen bonding and/or π–π stacking interactions.     

Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.