Predicting the biodegradation products of perfluorinated chemicals using CATABOL.

Perfluorinated chemicals (PFCs) form a special category of organofluorine compounds with particularly useful and unique properties. Their large use over the past decades increased the interest in the study of their environmental fate. Fluorocarbons may have direct or indirect environmental impact through the products of their decomposition in the environment. It is a common knowledge that biodegradation is restricted within non-perfluorinated part of molecules: however, a number of studies showed that defluorination can readily occur during biotransformation. To evaluate the fate of PFCs in the environment a set of principal transformations was developed and implemented in the simulator of microbial degradation using the catabolite software engine (CATABOL). The simulator was used to generate metabolic pathways for 171 perfluorinated substances on Canada's domestic substances list. It was found that although the extent of biodegradation of parent compounds could reach 60%, persistent metabolites could be formed in significant quantities. During the microbial degradation a trend was observed where PFCs are transformed to more bioaccumulative and more toxic products. Perfluorooctanoic acid and perfluorooctanesulfonate were predicted to be the persistent biodegradation products of 17 and 27% of the perfluorinated sulphonic acid and carboxylic acid containing compounds, respectively.

Development and validation of an average mammalian estrogen receptor-based QSAR model.

Development and evaluation of quantitative structure activity relationships (QSARs) for predicting estrogen receptor binding from chemical structure requires reliable algorithms for three-dimensional (3D) QSAR analysis and establishment of structurally diverse training sets of chemicals whose modes of action and measures of potency are well defined. One approach to selecting an appropriate training set is to minimize the biological variability in the model development, by using structurally restricted data sets. A second approach is to extend the structural diversity of chemicals at the cost of increased variability of biological assays. In this study, the second approach was used by organizing a training set of 151 chemicals with measured human alpha Estrogen Receptor (ERalpha), mouse uterine, rat uterine, and MCF7 cell Relative Binding Affinities (RBAs). The structurally augmented training set was submitted to a 3D pattern recognition analysis to derive a model for average mammalian ER binding affinity by employing the COmmon REactivity PAttern (COREPA) approach. Elucidation of this pattern required examination of the conformational flexibility of the compounds in an attempt to reveal areas in the multidimensional descriptor space, which are most populated by the conformers of the biologically active molecules and least populated by the inactive ones. The approach is not dependent upon a predetermined and specified toxicophore or an alignment of conformers to a lead compound. Reactivity patterns associated with mammalian ER binding affinity were obtained in terms of global nucleophilicity (E(HOMO)), interatomic distances between nucleophilic sites, and local nucleophilicity (charges or delocalizabilities) of those sites. Based on derived patterns, descriptor profiles were established for identifying and ranking compounds with RBA of > 150, 150-10, 10-1 and 1-0.1% relative to 17beta-estradiol. Specificity of reactivity profiles was found to increase gradually with increasing affinities associated with RBAs ranges under study. Using the results of this analysis, an exploratory expert system was developed for use in ranking relative mammalian ER binding affinity potential for large chemical data sets. The validity of the RBA predictions were confirmed by independent development and comparison with measured RBA values.

Reactivity profiles of ligands of mammalian retinoic acid receptors: a preliminary COREPA analysis.

Retinoic acid and associated derivatives comprise a class of endogenous hormones that bind to and activate different families of retinoic acid receptors (RARs, RXRs), and control many aspects of vertebrate development. Identification of potential RAR and RXR ligands is of interest both from a pharmaceutical and toxicological perspective. The recently developed COREPA (COmmon REactivity PAttern) algorithm was used to establish reactivity profiles for a limited data set of retinoid receptor ligands in terms of activation of three RARs (alpha, beta, gamma) and an RXR (alpha). Conformational analysis of a training set of retinoids and related analogues in terms of thermodynamic stability of conformers and rotational barriers showed that these chemicals tend to be quite flexible. This flexibility, and the observation that relatively small energy differences between conformers can result in significant variations in electronic structure, highlighted the necessity of considering all energetically reasonable conformers in defining common reactivity profiles. The derived reactivity patterns for three different subclasses of the RAR (alpha, beta, gamma) were similar in terms of their global electrophilicity (nucleophilicity) and steric parameters. However, the profile of active chemicals with respect to interaction with the RXR-alpha differed qualitatively from that of the RARs. Variations in reactivity profiles for the RAR versus RXR families would be consistent with established differences in their affinity for endogenous retinoids, likely reflecting functional differences in the receptors.

A computationally based identification algorithm for estrogen receptor ligands: part 1. Predicting hERalpha binding affinity.

The common reactivity pattern (COREPA) approach is a 3-dimensional, quantitative structure activity relationship (3-D QSAR) technique that permits identification and quantification of specific global and local stereoelectronic characteristics associated with a chemical's biological activity. It goes beyond conventional 3-D QSAR approaches by incorporating dynamic chemical conformational flexibility in ligand-receptor interactions. The approach provides flexibility in screening chemical data sets in that it helps establish criteria for identifying false positives and false negatives, and is not dependent upon a predetermined and specified toxicophore or an alignment of conformers to a lead compound. The algorithm was recently used to screen chemical data sets for rat androgen receptor binding affinity. To further explore the potential application of the algorithm in establishing reactivity patterns for human estrogen receptor alpha (hERalpha) binding affinity, the stereoelectronic requirements associated with the binding affinity of 45 steroidal and nonsteroidal ligands to the receptor were defined. Reactivity patterns for relative hERalpha binding affinity (RBA; 17ss-estradiol = 100%) were established based on global nucleophilicity, interatomic distances between electronegative heteroatoms, and electron donor capability of heteroatoms. These reactivity patterns were used to establish descriptor profiles for identifying and ranking compounds with RBA of > 150%, 100-10%, 10-1%, and 1-0.1%. Increasing specificity of reactivity patterns was detected for ligand data sets with RBAs above 10%. Using the results of this analysis, an exploratory expert system was developed for use in ranking relative ER binding affinity potential for large chemical data sets.

A computationally based identification algorithm for estrogen receptor ligands: part 2. Evaluation of a hERalpha binding affinity model.

The objective of this study was to evaluate the capability of an expert system described in the previous paper (S. Bradbury et al., Toxicol. Sci. 58, 253-269) to identify the potential for chemicals to act as ligands of mammalian estrogen receptors (ERs). The basis of the expert system was a structure activity relationship (SAR) model, based on relative binding affinity (RBA) values for steroidal and nonsteroidal chemicals derived from human ERalpha (hERalpha) competitive binding assays. The expert system enables categorization of chemicals into (RBA ranges of < 0.1, 0.1 to 1, 1 to 10, 10 to 100, and >150% relative to 17ss-estradiol. In the current analysis, the algorithm was evaluated with respect to predicting RBAs of chemicals assayed with ERs from MCF7 cells, and mouse and rat uterine preparations. The best correspondence between predicted and observed RBA ranges was obtained with MCF7 cells. The agreement between predictions from the expert system and data from binding assays with mouse and rat ER(s) were less reliable, especially for chemicals with RBAs less than 10%. Prediction errors often were false positives, i.e., predictions of greater than observed RBA values. While discrepancies were likely due, in part, to species-specific variations in ER structure and ligand binding affinity, a systematic bias in structural characteristics of chemicals in the hERalpha training set, compared to the rodent evaluation data sets, also contributed to prediction errors. False-positive predictions were typically associated with ligands that had shielded electronegative sites. Ligands with these structural characteristics were not well represented in the training set used to derive the expert system. Inclusion of a shielding criterion into the original expert system significantly increased the accuracy of RBA predictions. With this additional structural requirement, 38 of 46 compounds with measured RBA values greater than 10% in hERalpha, MCF7, and rodent uterine preparations were correctly categorized. Of the remaining 129 compounds in the combined data sets, RBA values for 65 compounds were correctly predicted, with 47 of the incorrect predictions being false positives. Based upon this exploratory analysis, the modeling approach, combined with a high-quality training set of RBA values derived from a diverse set of chemical structures, could provide a credible tool for prioritizing chemicals with moderate to high ER binding affinity for subsequent in vitro or in vivo assessments.

Computer design and syntheses of antiulcer compounds. 2nd Communication: N-substituted N'-[3-[3-(1-piperidinomethyl)phenoxy]propyl]ureas.

The in vitro and in vivo antiulcer effect of a series of N-substituted N'3-[3-(1-piperidinomethyl)phenoxy]propyl]ureas was modeled by making use of the OASIS computer system for QSAR analysis. Various research schemes were employed depending on structural representation of chemicals under investigation, such as non-protonated (neutral), protonated at the piperidine and urea fragmental nitrogens, and with intramolecular hydrogen binding. According to the modeling results, it is likely a variety of structural forms of antagonist molecules to take part in the receptor interaction. The QSAR study showed that the larger the electron acceptor properties of the nitrogen and oxygen atoms of the urea fragment, the higher is in vitro and in vivo activity of the antagonists.

Computer design and syntheses of antiulcer compounds. 1st communication: N-[3-[3-(1-piperidinomethyl)phenoxy]propyl]amines and benzamides.

Aiming to develop new antiulcer agents, a quantitative structure-activity relationship (QSAR) study on in vitro (pA2) and in vivo histamine H2-receptor antagonistic activity of a series of N-[3-[3-(1-piperidinomethyl)phenoxy]propyl]amines was carried out using the OASIS computer system. The results showed that pA2 increases with the decrease (increase) of electron donor (acceptor) properties of molecules, particularly at the NH-reaction site. The finding is consistent with the assumption for an increase of histamine H2-receptor activity of the antagonists with their ability to form H-bonds with the receptor through NH groups. The correlations with hydrophobicity and related topological indices are consistent with the hypothesis that logP should indirectly reflect receptor interactions. In addition a series of N-[3-[3-(1-piperidinomethyl)phenoxy]propyl]benzamides are synthesized. The theoretically predicted in vitro activities of these compounds were found to be in accordance with in vivo tests (percent of inhibition of gastric juice and acid output [mEq/H+/3 h]).

'Dynamic' QSAR for semicarbazide-induced mortality in frog embryos.

The conventional quantitative-structure-activity relationship (QSAR) provides only a single three-dimensional (3D) model for a given two-dimensional (2D) item in the modeling process. However, in complex reaction environments with solvents of different polarity, especially biological systems, the molecules can take the form of different conformers depending on the particular interaction. Therefore, chemical behavior, e.g. toxicity, may be considered the integral effect of a set of conformers rather than the property of a single 3D isomer. The 'dynamic' QSAR method is unique in that it provided for the calculation of a set of conformers for 2D representation of each chemical of the series under investigation. Moreover, these conformers can be selected interactively according to the hypothesized mechanism of toxic action. The acute lethality of 36 semicarbazides and thiosemicarbazides, evaluated using the Frog Embryo Teratogenesis Assay: Xenopus (FETAX), was modeled by using the 'dynamic' QSAR method. The assumed mode of action, osteolathyrism, was defined by the failure of connective tissue to polymerize properly due to interference with lysyl oxidase. Conformer screenings were based on parameter distribution according to the frontier orbital energies and volume polarizability, conditioning their reactivity and hydrophobicity, respectively. The best results were obtained by the selection of conformers providing prevailing values of electron acceptor properties. Moreover, the best two-parameter QSARs encompassing all the evaluated compounds incorporate a geometric parameter, the geometric analog of the Wiener topological index, and the local electronic characteristics of the C=O or C=S group, superdelocalizabilities and charges.

Application of the dynamic quantitative structure-activity relationship method for modeling antibacterial activity of quinolone derivatives.

The dynamic approach to quantitative structure-activity relationship (QSAR) was recently introduced to mimic the multiplicity of 3D-molecular shapes taken from the chemical at the different stages of the processes conditioning the endpoint under investigation. In difference with the conventional QSAR methods, where the structure of each compound is described by a single conformation (usually the one with the lowest calculated energy), the dynamic QSPR is aiming to account for the effects of the different solvent environments at the various reaction steps under which different conformations should be active. The core of the new methodology is the 3DGEN algorithm for an exhaustive 3D molecular design and the related system for an interactive conformation screening, based on the: chemical expertise, stereoelectronic parameter ranges and parameter distributions, depending on hypothesis on interaction mechanism The new methodology is incorporated in the OASIS (optimized approach based on structural indices set) computer system for QSAR/QSPR (quantitative structure activity/property relationship). In the present work it was applied to model in vitro (inhibition of Escherichia coli DNA gyrase) and in vivo (MICs against gram-negative as well as gram positive bacteria) antimicrobial activity (AMA) of quinolone derivatives. It was found that AMA is conditioned by molecular geometry as described by pair of topological indices and electron-acceptor properties, as assessed by the energies of LUMO (Lowest Unoccupied Molecular Orbital) orbitals, charges, bond orders and polarizability of the specific molecular sites. Interaction hypothesis is created, according to which polar-polar intermolecular interactions and bond breaking (cycle "opening", analogous to that of beta-lactam moiety in cephalosporins) condition biological activity. The derived QSAR models are significant according to the conventional statistical criteria as well as to the structure-activity causality requirements stated in literature. The best QSARs are obtained for in vitro AMA (r2 = 0.93 and s2 = 0.003), whereas for in-vivo activity correlations found are with lower statistics (0.54 < r2 < 0.74 and 0.005 < s2 < 0.03). The results are statistically better than those obtained by Computer automated Structure Evaluation (CASE) method.

Bronchospasmolytic activity and toxicity modelling of theophylline derivatives by a microcomputer based method.

The OASIS (Optimized Approach based on Structural Index Sets) microcomputer system was applied to model the bronchospasmolytic activity and toxicity of theophylline derivatives. The geometric and electronic factors responsible for biological activity of these compounds were determined. The molecular topology rather than compound metrics is the factor conditioning the theophylline activity. The opposite influence of topology on bronchospasmolytic activity and toxicity was established. Although the acceptor properties (acceptor superdelocalizability indices) determine both the activity and toxicity of the studied compounds the different positions of these effects is of decisive importance in both cases.