Identification of the structural requirements for mutagencitiy, by incorporating molecular flexibility and metabolic activation of chemicals. II. General Ames mutagenicity model

The tissue metabolic simulator (TIMES) modeling approach is a hybrid expert system that couples a metabolic simulator together with structure toxicity rules, underpinned by structural alerts, to predict interaction of chemicals or their metabolites with target macromolecules. Some of the structural alerts representing the reactivity pattern-causing effect could interact directly with the target whereas others necessitated a combination with two- or three-dimensional quantitative structure-activity relationship models describing the firing of the alerts from the rest of the molecules. Recently, TIMES has been used to model bacterial mutagenicity [Mekenyan, O., Dimitrov, S., Serafimova, R., Thompson, E., Kotov, S., Dimitrova, N., and Walker, J. (2004) Identification of the structural requirements for mutagenicity by incorporating molecular flexibility and metabolic activation of chemicals I: TA100 model. Chem. Res. Toxicol. 17 (6), 753-766]. The original model was derived for a single tester strain, Salmonella typhimurium (TA100), using the Ames test by the National Toxicology Program (NTP). The model correctly identified 82% of the primary acting mutagens, 94% of the nonmutagens, and 77% of the metabolically activated chemicals in a training set. The identified high correlation between activities across different strains changed the initial strategic direction to look at the other strains in the next modeling developments. In this respect, the focus of the present work was to build a general mutagenicity model predicting mutagenicity with respect to any of the Ames tester strains. The use of all reactivity alerts in the model was justified by their interaction mechanisms with DNA, found in the literature. The alerts identified for the current model were analyzed by comparison with other established alerts derived from human experts. In the new model, the original NTP training set with 1341 structures was expanded by 1626 proprietary chemicals provided by BASF AG. Eventually, the training set consisted of 435 chemicals, which are mutagenic as parents, 397 chemicals that are mutagenic after S9 metabolic activation, and 2012 nonmutagenic chemicals. The general mutagenicity model was found to have 82% sensitivity, 89% specificity, and 88% concordance for training set chemicals. The model applicability domain was introduced accounting for similarity (structural, mechanistic, etc.) between predicted chemicals and training set chemicals for which the model performs correctly.

Metabolic activation of chemicals: in-silico simulation

The role of metabolism in prioritising chemicals according to their potential adverse health effects is extremely important given the fact that innocuous parents can be transformed into toxic metabolites. Our recent efforts in simulating metabolic activation of chemicals are reviewed in this work. The application of metabolic simulators to predict biodegradation (microbial degradation pathways), bioaccumulation (fish liver metabolism), skin sensitisation (skin metabolism), mutagenicity (rat liver S-9 metabolism) are discussed. The ability of OASIS approach to predict metabolism (toxicokinetics) and toxicity (toxicodynamics) of chemicals resulting from their metabolic activation in a single modelling platform is an important advantage of the method. It allows prioritisation of chemicals due to predicted toxicity of their metabolites.

Base-line model for identifying the bioaccumulation potential of chemicals

The base-line modeling concept presented in this work is based on the assumption of a maximum bioconcentration factor (BCF) with mitigating factors that reduce the BCF. The maximum bioconcentration potential was described by the multi-compartment partitioning model for passive diffusion. The significance of different mitigating factors associated either with interactions with an organism or bioavailability were investigated. The most important mitigating factor was found to be metabolism. Accordingly, a simulator for fish liver was used in the model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other significant mitigating factors, depending on the chemical structure, e.g. molecular size and ionization were also taken into account in the model. The results (r(2)=0.84) obtained for a training set of 511 chemicals demonstrate the usefulness of the BCF base line concept. The predictability of the model was evaluated on the basis of 176 chemicals not used in the model building. The correctness of predictions (abs(logBSF(Obs)-logBCF(Calc))=0.75)) for 59 chemicals included within the model applicability domain was 80%.

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.

In silico modelling of hazard endpoints: current problems and perspectives

Major scientific hurdles in the acceptance of quantitative structure-activity relationships (QSAR) for regulatory purposes have been identified. First, when quantifying important features of chemical structure complexities of molecular structure have often been ignored. More mechanistic modelling of chemical structure should proceed on two fronts: by developing a more in-depth understanding and representation of the multiple states possible for a single chemical by achieving greater rigor in understanding of conformational flexibility of chemicals; and, by considering families of activated metabolites that are derived in biological systems from an initial chemical substrate. Second, QSAR research is severely limited by the lack of systematic databases for important risk assessment endpoints, and despite many decades of research, the ability to cluster reactive chemicals by common toxicity pathways is in its infancy. Finally, computational tools are lacking for defining where a specific QSAR is applicable within the domain (universe) of chemical structures that are to be regulated. This paper describes some of the approaches being taken to address these needs. Applications of some of these new approaches are demonstrated for the prediction of chemical mutagenicity, where considerations of both molecular flexibility and metabolic activation improved the QSAR predictability and interpretations. Lastly, the applicability domain for a specific QSAR predicting estrogen receptor binding is presented in the context of a mechanistically-defined chemical structure space for large heterogeneous chemical datasets of regulatory concern. A strategic approach is discussed to selecting chemicals for model improvement and validation until regulatory acceptance criteria for risk assessment applications are met.

Quantitative prediction of biodegradability, metabolite distribution and toxicity of stable metabolites

An evaluation of the capability of organic chemicals to mineralize is an important factor to consider when assessing their fate in the environment. Microbial degradation can convert a toxic chemical into an innocuous one, and vice versa, or alter the toxicity of a chemical. Moreover, primary biodegradation can convert chemicals into stable products that can be difficult to mineralize. In this paper, we present some new results obtained on the basis of a recently developed probabilistic approach to modeling biodegradation based on microbial transformation pathways. The metabolic transformations and their hierarchy were calibrated by making use of the ready biodegradability data from the MITI-I test and expert knowledge for the most probable transformation pathways. A model was developed and integrated into an expert software system named CATABOL that is able to predict the probability of biodegradation of organic chemicals directly from their structure. CATABOL simulates the effects of microbial enzyme systems, generates the most plausible transformation pathways, and quantitatively predicts the persistence and toxicity of the biodegradation products. A subset of 300 organic chemicals were selected from Canada's Domestic Substances List and subjected to CATABOL to compare predicted properties of the parent chemicals with their respective first stable metabolite. The results show that most of the stable metabolites have a lower acute toxicity to fish and a lower bioaccumulation potential compared to the parent chemicals. In contrast, the metabolites appear to be generally more estrogenic than the parent chemicals.

Probabilistic assessment of biodegradability based on metabolic pathways: catabol system

A novel mechanistic modeling approach has been developed that assesses chemical biodegradability in a quantitative manner. It is an expert system predicting biotransformation pathway working together with a probabilistic model that calculates probabilities of the individual transformations. The expert system contains a library of hierarchically ordered individual transformations and matching substructure engine. The hierarchy in the expert system was set according to the descending order of the individual transformation probabilities. The integrated principal catabolic steps are derived from set of metabolic pathways predicted for each chemical from the training set and encompass more than one real biodegradation step to improve the speed of predictions. In the current work, we modeled O2 yield during OECD 302 C (MITI I) test. MITI-I database of 532 chemicals was used as a training set. To make biodegradability predictions, the model only needs structure of a chemical. The output is given as percentage of theoretical biological oxygen demand (BOD). The model allows for identifying potentially persistent catabolic intermediates and their molar amounts. The data in the training set agreed well with the calculated BODs (r2 = 0.90) in the entire range i.e. a good fit was observed for readily, intermediate and difficult to degrade chemicals. After introducing 60% ThOD as a cut off value the model predicted correctly 98% ready biodegradable structures and 96% not ready biodegradable structures. Crossvalidation by four times leaving 25% of data resulted in Q2 = 0.88 between observed and predicted values. Presented approach and obtained results were used to develop computer software for biodegradability prediction CATABOL.

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global (E(HOMO)) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.

Androgen receptor binding affinity of pesticide "active" formulation ingredients. QSAR evaluation by COREPA method

The COREPA approach for identifying the COmmon REactivity PAttern of biologically similar chemicals was employed to upgrade the recently derived affinity pattern for high androgen receptor (AR) binding affinity. The training set consisted of 28 steroidal and nonsteroidal ligands whose AR binding affinity was determined in competitive binding assays (in terms of pKi). The interatomic distances between nucleophilic sites and their charges providing distinct and non-overlapping integral patterns for active and inactive chemicals were assumed that it was related with the endpoint, which was under study. These stereoelectronic characteristics were used to predict pKi values of pesticide "active" formulation ingredients in an attempt to identify chemicals with potential AR binding affinity.

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.

Molecular orbital parameters as predictors of skin sensitization potential of halo- and pseudohalobenzenes acting as SNAr electrophiles

The electrophilic reactivity of a training set of 20 halo- and pseudohalobenzenes, 10 of which are reported skin sensitizers and 10 of which are reported nonsensitizers, has been modeled by MO-calculated indices using the AM1 and PM3 Hamiltonians. The electronic structures of parent molecules and the corresponding Meisenheimer intermediates (sigma-complexes) were evaluated. The NH2 group and the H atom were both studied as model nucleophile-derived substituents in the sigma-complexes. The LUMO energy differences between the parent compounds and their Meisenheimer complexes together with the maximum acceptor superdelocalizabilities determined over the aromatic reaction sites were found to discriminate correctly the sensitizing/reactive from nonsensitizing/unreactive compounds of the training set of 20 compounds. The predictive applicability of these MO indices was confirmed with a test set of seven further compounds for which sensitization data are reported in the literature. A statistically based discriminant analysis provides a model which predicts whether or not an SNAr electrophile will be a sensitizer and estimates the degree of confidence in the prediction.

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]).

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.

Modelling the interaction of small organic molecules with biomacromolecules. II. A generalized concept for biological interactions

In the first part of this series it was shown that, for interactions between substituted pyridines and anti-3-azopyridine antibody, the maximum biological activity is observed for an optimum electronic correspondence between the reactants. This particular result, together with data in the literature which points to the necessity for geometrical and lipophilic correspondence, supports a generalization for the nature of the biological action of chemical compounds. Accordingly in this paper it is proposed that the affinity towards a given biomacromolecule will be maximum only for those chemicals within a series of compounds which are characterized by optimum values of basic factors which condition the biological activity: geometric, electronic, and/or lipophilic. The practical aspects of the hypothesis should be valuable in molecular pharmacology, drug design, and theory of chemical reactivity.

Modelling the interaction of small organic molecules with biomacromolecules. I. Interaction of substituted pyridines with anti-3-azopyridine antibody

An approach is presented for modelling the biological activity of organic molecules. This approach requires a consideration of the influence of all factors (topological, steric, hydrophobic, electronic) which determine the bioactivity. In this work, the interaction between substituted pyridines and antibodies generated by anti-3-azapyridine is studied. The stereoelectronic interactions are responsible for the reaction. Meta-positions to nitrogen are found to be the most probable positions for attack. The most likely reaction products are pi-complexes with charges transfer from the biomolecule to the pyridine derivatives followed by the formation of covalent-type bonds.