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In the present study, we investigated structure-permeability relationships for the blood-brain barrier (BBB) of 16 imipramine and phenothiazine derivatives. The compounds belong to structurally related chemical classes of catamphiphiles, representatives of which have previously been investigated for membrane activity and ability to overcome multidrug resistance (MDR) in tumour cells. These studies show that phenothiazines and structurally related drugs (imipramines, thioxanthenes, acridines) interact with membrane phospholipids, and additionally inhibit the MDR transport P-glycoprotein. This study aimed to identify common 3D structural characteristics of these compounds related to their mechanism of transport across the BBB. For this purpose Genetic Algorithm Similarity Programme (GASP), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) were applied. The results demonstrate the importance of the spatial distribution of molecular hydrophobicity for the BBB penetration of the investigated compounds. It suggests that the compounds should follow a specific profile of two hydrophobic and one hydrophilic centres in a particular space configuration, for optimal BBB penetration. [ABSTRACT FROM AUTHOR]

A large data set containing values for fish, algae and Daphnia toxicity for more than 2000 chemicals and mixtures was investigated. The data set was taken from the New Chemicals Data Base of the European Union [hosted by the European Chemicals Bureau, Joint Research Centre. European Commission (http://ecb.jrc.it)]. The data are submitted by industry, according to the requirements of EU Council Directive 67/548/EEC as amended for the seventh time by EU Council Directive 92/32/EEC. The toxicities of neutral chemicals, salts, metal complexes, as well as chemical mixtures were extracted. A baseline effect was demonstrated by chemicals known to act by a narcotic mechanism of action, i.e., a relationship was observed between the toxicity and the logarithm of the octanol-water partition coefficient (log P). However, the prediction of the toxicity of more reactive chemicals was found to require the use of additional descriptors. [ABSTRACT FROM AUTHOR]

In the present study, structure-activity relationship (QSAR) models for the prediction of the toxicity to the bacterium Sinorhizobium meliloti have been developed, based on a data set of 140 compounds. The data set is highly heterogeneous both in terms of chemistry and mechanisms of toxic action. For deriving QSARs, chemicals were divided into groups according to mechanism of action and chemical structure. The QSARs derived are considered to be of moderate statistical quality. A baseline effect (relationship between the toxicity and log P ), which can be related to non-polar narcosis, was observed. To explain toxicity greater than the baseline toxicity, other structural descriptors were used. The development of models for non-polar and polar narcosis had some success. It appeared that the toxicity of compounds acting by more specific mechanisms of toxic action is difficult to predict. A global QSAR was also developed, which had square of the correlation coefficient r 2 =0.53. A QSAR with reasonable statistical parameters was developed for the aliphatic compounds in the data set ( r 2 =0.83). QSARs could not be obtained for the aromatic compounds as a group. [ABSTRACT FROM AUTHOR]

The conventional treatment of neurodegenerative diseases (NDDs) is based on the "one molecule-one target" paradigm. To combat the multifactorial nature of NDDs, the focus is now shifted toward the development of small-molecule-based compounds that can modulate more than one protein target, known as "multi-target-directed ligands" (MTDLs), while having low affinity for proteins that are irrelevant for the therapy. The in silico approaches have demonstrated a potential to be a suitable tool for the identification of MTDLs as promising drug candidates with reduction in cost and time for research and development. In this study more than 650,000 compounds were screened by a series of in silico approaches to identify drug-like compounds with predicted activity simultaneously towards three important proteins in the NDDs symptomatic treatment: acetylcholinesterase (AChE), histone deacetylase 2 (HDAC2), and monoamine oxidase B (MAO-B). The compounds with affinities below 5.0 µM for all studied targets were additionally filtered to remove known non-specifically binding or unstable compounds. The selected four hits underwent subsequent refinement through in silico blood-brain barrier penetration estimation, safety evaluation, and molecular dynamics simulations resulting in two hit compounds that constitute a rational basis for further development of multi-target active compounds against NDDs.

The aim of this study was to investigate the chemical space and interactions of natural compounds with sulfotransferases (SULTs) using ligand- and structure-based in silico methods. An in-house library of natural ligands (hormones, neurotransmitters, plant-derived compounds and their metabolites) reported to interact with SULTs was created. Their chemical structures and properties were compared to those of compounds of non-natural (synthetic) origin, known to interact with SULTs. The natural ligands interacting with SULTs were further compared to other natural products for which interactions with SULTs were not known. Various descriptors of the molecular structures were calculated and analyzed. Statistical methods (ANOVA, PCA, and clustering) were used to explore the chemical space of the studied compounds. Similarity search between the compounds in the different groups was performed with the ROCS software. The interactions with SULTs were additionally analyzed by docking into different experimental and modeled conformations of SULT1A1. Natural products with potentially strong interactions with SULTs were outlined. Our results contribute to a better understanding of chemical space and interactions of natural compounds with SULT enzymes and help to outline new potential ligands of these enzymes.

Xanthates (alkyl or aryl derivatives of dithiocarbonic acid) have been shown to be selective mechanism-based inactivators of cytochromes P450 2B1/2B6 and 2E1 due to covalent binding of a reactive intermediate to apoprotein after double hydrogen abstraction at α-carbon atom, suggesting interaction of the xanthate dithiocarbonic head with the enzyme heme. The structures of xanthates with a long alkyl chain are similar to the fatty acids. Saturated fatty acids (FA) such as lauric acid (LA), are metabolized by different cytochrome P450 isoforms to ω- and (ω-1)-hydroxy products, in humans done by CYP4A11 and CYP2E1, respectively. In the present study we aimed at elucidating the possible interactions of xanthates with two cytochrome P450 isoforms CYP4A11 and CYP2E1 involved in the metabolism of the FA. Our experiments showed that LA-ω-hydroxylation by CYP4A11 is inhibited in a competitive manner by xanthates with long alkyl chain (C12-xanthate being the most potent inhibitor). On the other hand LA-(ω-1)-hydroxylation reaction by purified CYP2E1 is inactivated by a mechanism-based type. The suggested differences in the interactions of C12-xanthate with the two cytochrome P450 isoforms were investigated by molecular modeling using docking approach. The results suggested that in CYP2E1 active site C12-xanthate coordinates to the heme with its most vulnerable dithiocarbonic head leading to a mechanism-based inactivation. In CYP4A11 xanthate alkyl chain is exposed to the heme, thus, a potenial ω-hydroxylated xanthate product could be formed, which could inhibit in a competitive manner the hydroxylation of LA. The observed differences of xanthates interactions with the active sites of the two similar cytochrome P450 isoforms (CYP4A11 and CYP2E1) involved in the metabolism of FA, which lead to different changes in the enzyme activity, suggest that xanthates can be used as probing tools for analyzing enzyme active sites when exploring useful and selective compounds influencing FA homeostasis.

Preliminary investigations of our research team have shown that some pyrrole hydrazones posses strong inhibitory activity against the tuberculosis bacilli, and thus represent a new perspective for development of anti-tuberculosis agents. In this work the anti-tuberculosis activity of an in-house series of pyrrole hydrazones was investigated by quantitative structure-activity relationships (QSAR) analysis and by pharmacophore modelling. Different constitutional, topological, physicochemical, and quantum-mechanical descriptors of the chemical structure were calculated. The QSAR models included the number of chlorine, fluorine and nitrogen atoms, molecular flexibility and shape indexes, and magnitudes of charged molecular surfaces areas and hydrophobic volumes, suggesting importance of these structural characteristics for the activity. Next, a pharmacophore analysis was applied. A possible pharmacophore responsible for the compound interactions with their biological target in the 3D space consisted of five features, including hydrophobic centres, a potential H-bond acceptor and a potential metal ligator.

Acute toxicity in different biological systems, including humans and rodents in vivo, and human and rodent cell lines in vitro, was investigated. The data were taken from the MEIC (Multicentre Evaluation of In Vitro Cytotoxicity) programme. Quantitative structure-activity-activity relationship (QSAAR) models were developed for the in vivo human and rodent toxicity including a combination of toxicity endpoint and structural descriptors as predictor variables. The human peak blood/serum LC(50) concentrations were most strongly related to human liver cell toxicity, while the in vivo oral human lethal doses were most closely related to the in vivo rodent LD(50) values. The QSAARs included structural descriptors encoding electronic/reactivity properties, presence of H-bond donors, compound aromaticity, and size/shape properties. Quantitative structure-activity relationships (QSARs) were derived by using structural descriptors accounting for molecular hydrophobicity, size and shape, and electronic properties. These models have the potential to provide useful insights in the development of non-animal (in vitro and in silico) methods for predicting human and mammalian toxicity.

The present study focuses on development of new potential inhibitors of cyclooxygenase-2 (COX-2): series of N-pyrrolylcarboxylic acids. 3D-QSAR (Quantitative Structure-Activity Relationship) CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Index Analysis) models for predicting inhibitory activities against COX-1 and COX-2 as well as for evaluating in vivo anti-inflammatory activity were obtained and used for preliminary screening of new anti-inflammatory N-pyrrolylcarboxylic acids. Nine compounds were selected for in vivo testing and evaluated for their potency to decrease carrageenin-induced edema in rats. The compounds were applied i.p. at doses 20 mg/kg and 40 mg/kg and p.o. at doses 10 mg/kg and 40 mg/kg. Six compounds showed more than 70% protection of the edema. Indomethacin (2 mg/kg i.p.), used as a reference drug, possessed 54% anti-inflammatory activity under similar experimental conditions.

The quality of quantitative structure-activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol-water partition coefficient (log K(ow)). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided.